In industrial settings, gears play a crucial role by assisting various machinery functions such as speed control, torque manipulation, and altering motion direction.The malfunction or failure of these gear components can have serious repercussions, resulting in production halts and financial losses. As a result, there is an increasing requirement to monitor the state of these components in order to avoid such issues from occurring. To address this need, research efforts have focused on early defect detection in gears in order to reduce the impact of possible failures. This study focused on analyzing vibration and thermal datasets from two extruder machine gearboxes using an autoencoder Long Short-Term Memory (LSTM) model. The major goal is to implement an outlier detection approach to detect and classify defects. The results of this study highlighted the extraordinary performance of the Autoencoder LSTM model, which achieved an impressive accuracy rate of 94.42% in recognizing malfunctioning gearboxes within the extruder machine system. Furthermore, the study used a thorough global metrics evaluation methodology to further test the model’s dependability and efficacy, consequently substantiating the proposed approach’s validity.

Leucokinins (LKs) constitute a family of neuropeptides identified in numerous insects and many other invertebrates. The LKs act on G-protein coupled receptors that display only distant relations to other known receptors. In adult Drosophila, 26 neurons/neurosecretory cells of three main types express LK. The four brain interneurons are of two types, and these are implicated in several important functions in the fly’s behavior and physiology, including feeding, sleep-metabolism interactions, state-dependent memory formation, as well as modulation of gustatory sensitivity and nociception. The 22 neurosecretory cells (ABLKs) of the abdominal neuromeres coexpress LK and a diuretic hormone (DH44), and together these regulate water and ion homeostasis and associated stress, as well as food intake. In Drosophila larvae, LK neurons modulate locomotion, escape responses, and aspects of ecdysis behavior. A set of lateral neurosecretory cells, ALKs, in the brain express LK in larvae, but inconsistently so in adults. These ALKs coexpress three other neuropeptides and regulate water and ion homeostasis, feeding and drinking, but the specific role of LK is not yet known. This review summarizes Drosophila data on embryonic lineages of LK neurons, functional roles of individual LK neuron types, interactions with other peptidergic systems, and orchestrating functions of LK.

With the latest advances in information and communication technologies, greater amounts of sensitive user and corporate information are constantly shared across the network making it susceptible to an attack that can compromise data confidentiality, integrity and availability. Intrusion Detection Systems (IDS) are important security mechanisms that can perform a timely detection of malicious events through the inspection of network traffic or host-based logs. Throughout the years, many machine learning techniques have proven to be successful at conducting anomaly detection but only a few considered the sequential nature of data. This work proposes a sequential approach and evaluates the performance of a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a Long-Short Term Memory (LSTM) on the CIDDS-001 dataset. The resulting performance measures of this particular approach are compared with the ones obtained from a more traditional one, that only considers individual flow information, in order to determine which methodology best suits the concerned scenario. The experimental outcomes lead to believe that anomaly detection can be better addressed from a sequential perspective and that the LSTM is a very reliable model for acquiring sequential patterns in network traffic data, achieving an accuracy of 99.94% and a f1-score of 91.66%.

Hand and arm gesture recognition using radio frequency (RF) sensing modality proves valuable in man-machine interface and smart environment. In this paper, we use time series analysis method for accurately measuring the similarity of the micro-Doppler (MD) signatures between the training and test data, thus providing improved gesture classification. We characterize the MD signatures by the maximum instantaneous Doppler frequencies depicted in the spectrograms. In particular, we apply the dynamic time warping (DTW) method and compare its performance with that of the long short-term memory (LSTM) network. Both methods take into account the values as well as the temporal evolution and trends of time series data. It is shown that the DTW method achieves high gesture classification rates and is robust to time misalignment.

Predicting heart disease is crucial for early diagnosis and intervention, significantly improving patient outcomes and reducing mortality rates. This study compares various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and Long Short-Term Memory (LSTM) networks, using the Cleveland Heart Disease dataset. Comprehensive preprocessing steps were undertaken, such as handling missing values, converting categorical variables to numeric forms, and binarizing the target variable for binary classification. Each model was rigorously evaluated using performance metrics, including accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). SHapley Additive exPlanations (SHAP) values were employed to provide insights into feature importance, ensuring model transparency and interpretability. The results indicate that XGBoost outperformed all other models, achieving an accuracy of 90% and an AUC-ROC of 0.94, demonstrating its superior ability to capture complex patterns in the data through advanced optimization techniques and regularization. This study highlights the significant potential of advanced machine learning techniques, particularly ensemble methods like Gradient Boosting and XGBoost, in enhancing heart disease prediction. These models offer higher accuracy and valuable interpretability, making them practical tools for early diagnosis in clinical settings. Future research should focus on integrating these models into healthcare systems and exploring hybrid approaches to further improve predictive performance and clinical applicability.

A contemporary technique for calculating seismic risk is earthquake nowcasting (EN), which analyzes the progression of the earthquake (EQ) cycle in fault systems. 'Natural time' is a novel idea of time that serves as the foundation for EN assessment. The earthquake potential score (EPS), which has been discovered to have useful uses regionally and worldwide, is a unique tool EN uses to predict seismic risk. For the estimation of the EPS for the occurrences with the highest magnitude among these applications, we have since 2021 concentrated on Greece territory, applying sophisticated Artificial Intelligence (AI) algorithms, both wise (semi)supervised and unsupervised models, along with a customized dynamic sliding window technique that performs as a stochastic filter able to fine-tune the geo seismic occurrences. Long short-term memory (LSTM) neural networks, random forests, and clustering (geospatial) models are three machine learning techniques that are particularly good at finding patterns in vast databases and may be used to enhance earthquake prediction performance. In this study, we attempt to forecast whether practical Machine-learning and AI/Game-theoretic-based approaches can help predict big earthquakes and the normal future seismic cycle for 6-12 months. Specifically, we focus on answering two questions for a given region: (1) Is there a chance that a significant earthquake—say, one with a magnitude of M ≥ 6.0 will happen in the upcoming year? (2) What is the largest earthquake magnitude predicted in the upcoming year, and with which exact geographic coordinates (GCS)? Our results are quite promising and project a high precision accuracy score (≥ 98%) for seismic nowcasting in terms of four predictive parameters: the approximate (a) latitude, (b) longitude, (c) focal depth, and (d) magnitude of the phenomena.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

This study introduces session-aware recommendation models, leveraging GRU (Gated Recurrent Unit) and attention mechanisms for advanced latent interaction data integration. A primary ad-vancement is enhancing latent context, a critical factor for boosting recommendation accuracy. We address existing models' rigidity by dynamically blending short-term (most recent) and long-term (historical) preferences, moving beyond static period definitions. Our approaches, pre-combination (LCII-Pre) and post-combination (LCII-Post) with fixed (Fix) and flexible learning (LP) weight configurations, are thoroughly evaluated. We conducted extensive experiments to as-sess our models' performance on public datasets such as Amazon and MovieLens 1M. Notably, on the MovieLens 1M dataset, LCII-PreFix achieved a 1.85% and 2.54% higher Recall@20 than II-RNN and BERT4Rec+st+TSA, respectively. On the Steam dataset, LCII-PostLP outperformed these models by 18.66% and 5.5%. Furthermore, on the Amazon dataset, LCII showed a 2.59% and 1.89% improvement in Recall@20 over II-RNN and CAII. These results affirm the significant enhancement our models bring to session-aware recommendation systems, showcasing their po-tential for both academic and practical applications in the field.

Natural Fibre Composites (NFC) are eco-friendly, sustainable alternatives to synthetic polymers. However, some intrinsic natural fillers´ properties hinder their widespread implementation as re-inforcement in polymeric matrices and require further investigation. In the scope of this study, the thermal, rheologic, mechanical (tension and flexural modes), morphological properties, water ab-sorption and dimensional stability of the NF polypropylene (PP) based injection moulded compo-sites with the rice husk (rh) and olive pits (op) of 20 wt% and 30% wt%, were investigated. The results suggest that the higher content of the rice husk and olive pits led to a similar reduction of the Melt Flow Index (MFI), independent of the additive type compared to virgin polypropylene (PPv). The melting and crystallization temperatures of the PPrh and PPop composites did not change with statistical significance. The composites are stiffer than the PP matrix by up to 49% and possess higher mechanical strength in what concerns the tension mode at the expense of de-creased ductility. PPrh and PPop have superior flexural modulus in the bending mode, while the flexural strength improvement was accomplished for the PP30%rh. The dimensional stability of the composites was improved as the linear shrinkage in the flow direction was decreased by 49% for PPrh and 30% for PPop, positively correlating with an increase in the filler content and stiff-ness. PPop was less susceptible to water sorption than PPrh due to fibres' composition and larger surface-to-area volume ratios.

In order to obtain the good match between yield strength and low-temperature toughness, the short-time partial austenitization (SPA) process was employed for V-microalloying 0.22C-5.24Mn steel. The initial microstructure after intercritical tempering was dual-phase ferrite and reversed austenite (RA), while the final microstructure was consisted of ferrite, RA and secondary martensite (SM) after subjected to SPA process. (V, Mo)C with disc-like morphology mainly precipitated during intercritial tempering, and the aspect ratio of particle decreased, leading to the appearance of near-spherical morphology. After subjected to SPA process, the resultant multiphase hierarchical microstructure (three layers: out layer of ferrite, interlayer of SM and inner layer RA) enabled high yield strength of 1097 MPa, total elongation of 14%, and impressive impact energy of 33.3 J at -20 °C. The strengthening contribution of (V, Mo)C precipitation was estimated about 108 MPa.

Recently, various applications of indoor location-based services using Wi-Fi fingerprint data have been studied. However, since fingerprint data collected at a specific location does not have continuous information between signals and correlation information between signals, path data cannot exist in principle. Therefore, most indoor positioning technologies are based on predicting the position of a stationary positioner at a specific location based on signal data collected at a specific location in the indoor space. Due to the discontinuous nature of these signals, they are unable to account for user movement. Therefore, there is a need for techniques to improve the accuracy of indoor positioning in moving situations. This paper proposes a method for enriching all data points with relevant information to obtain improved indoor positioning results and an improved technique for generating movement path data. The proposed technique generates relevant information with continuity by expressing data points as Bounding Boxes (BB) and subsequently clustering them using grid cells. Through the proposed method, we represented and expanded the indoor location data, which consists of data points, as BB. However, data points represented by BB do not have any adjacency information with each other, and as a result, it is not possible to create a movement path between neighboring BB. To solve the problem, we used clustering, a machine learning technique that categorizes data points into groups, to group BBs. This was done by constructing a YOLO (You Only Look Once) grid cell that included all BBs and clustered them into grid cells that were divided into specific regions through the suggestion phase. The clustering results align with the indoor structure of the building, indicating that the data points have been appropriately clustered. BBs inside a grid cell are contained within a moveable area and have related information. We also generated association information between neighboring grid cells through the proposal technique, and then generated path information that can be traveled between grid cells. In this paper, we use the movement path information generated from a dataset as training data for machine learning, and through this, we propose an enhanced indoor positioning technology.

The time-resolved CIDNP method can provide information about degenerate exchange reactions (DEE) involving short-lived radicals. In the temperature range from 8 to 65°C, the DEE reactions of the guanosine-5’-monophosphate anion GMP(-H)- with the neutral radical GMP(-H)•, of the N-acetyl tyrosine anion NacTyrO- with a neutral radical NacTyrO• and of the tyrosine anion TyrO- with a neutral radical TyrO• were studied. In all the studied cases, the radicals were formed in the reaction of quenching triplet 2,2’-dipyridyl. The reorganization energies were obtained from Arrhenius plots. The rate constant of the reductive electron-transfer reaction in pair GMP(-H)•/TyrO- was determined at T = 25°C. Rate constants of GMP(-H)• radical reduction reactions with TyrO- and NacTyrO- anions calculated by the Marcus cross relation differ from the experimental ones by two orders of magnitude. The rate constants of several other electron transfer reactions involving GMP(-H)-/•, NacTyrO-/•, and TyrO-/• pairs calculated by cross relation agree well with the experimental values. The rate of nuclear paramagnetic relaxation was found for the 3,5 and β-protons TyrO• and NacTyrO•, the 8-proton GMP(-H)• the 3,4-protons DPH• at each temperature. In all cases, the dependences of the rate of nuclear paramagnetic relaxation on temperature are described by the Arrhenius dependence.

Advancements in technology have revolutionized communication on social media platforms, where reviews and comments greatly influence product selection. Current opinion mining methods predominantly focus on textual content, overlooking the rich information within customer-posted images, termed here as Multus-Medium. This research introduces an innovative deep learning approach, Multus Medium Opinion Mining (MMOM), capitalizing on both text and image data for comprehensive product review analysis. MMOM employs an integrated model of Bidirectional Long Short-Term Memory (BiLSTM) and embedded Convolutional Neural Network (CNN), incorporating architectures of GoogleNet and VGGNet, thus enabling efficient extraction and fusion of textual and visual features. This synergistic approach enables data collection, preprocessing, feature extraction, fusion strategy-based feature vector generation, and subsequent product recommendation. Performance evaluation on two diverse real-world datasets name as “flicker8k” and “t4sa”. These datasets show substantial improvement over existing methods. MMOM outperforms standard benchmark models, achieving an accuracy, F1 score and ROC over fliker8k are 90.38%, 88.75% and 93.08%, whereas for twitter dataset are 88.54%, 86.34%, and 92.26% respectively, the accuracy of purposed model is 7.34% and 9.54% higher than the other two mentioned techniques. These statistics highlight the robustness and applicability of MMOM across various domains. The compelling results underscore the potential of MMOM, providing a more holistic and precise approach to opinion mining in the era of social media product reviews. This research, product recommendation helps the customer to make purchasing decision. Last but not the least, the purposed scheme can further be expanded in any other sentiment task like hospital recommendation system, crop farming recommendation and medical diagnostic system etc.

The deformation monitoring information of concrete dams contains some high-frequency com-ponents, and the high-frequency components are strongly nonlinear, which reduces the accuracy of dam deformation prediction. In order to solve such problems, this paper proposes a concrete dam deformation monitoring model based on empirical mode decomposition (EMD) combined with wavelet threshold noise reduction and sparrow search algorithm (SSA) optimization of long short-term memory network (LSTM). The model uses EMD combined with wavelet threshold to decompose and denoise the measured deformation data. On this basis, the LSTM model based on SSA optimization is used to mine the nonlinear function relationship between the reconstructed monitoring data and various influencing factors. The example analysis shows that the model has good calculation speed, fitting and prediction accuracy and it can effectively mine the date char-acteristics inherent in the measured deformation, and reduce the influence of noise components on the modeling accuracy.

While thunderstorms can pose severe risks to property and life, forecasting remains challenging, even at short lead times, as these often arise in meta-stable atmospheric conditions. In this paper, we examine the question of how well we could perform short-term (up to 180min) forecasts using exclusively multi-spectral satellite images and past lighting events as data. We employ representation learning based on deep convolutional neural networks in an “end-to-end” fashion. Here, a crucial problem is handling the imbalance of the positive and negative classes appropriately in order to be able to obtain predictive results (which is not addressed by many previous machine-learning-based approaches). The resulting network outperforms previous methods based on physically-based features and optical flow methods (similar to operational prediction models) and generalizes across different years. A closer examination of the classifier performance over time and under masking of input data indicates that the learned model actually draws most information from structures in the visible spectrum, with infrared imaging sustaining some classification performance during the night.

Turner syndrome (TS) is a chromosomal disorder that is caused by a missing or structurally ab-normal second sex chromosome. Subjects with TS are at an increased risk of developing intrauterine growth retardation, low birth weight, short stature, congenital heart diseases, infertility, obesity, dyslipidemia, hypertension, insulin resistance, type 2 diabetes mellitus, metabolic syndrome, and cardiovascular diseases (stroke and myocardial infarction). The underlying pathogenetic mechanism of TS is unknown. The assumption that X chromosome-linked gene haploinsufficiency is associated with the TS phenotype is questioned since such genes have not been identified. Thus, other pathogenic mechanisms have been suggested to explain this phenotype. Morphogenesis encompasses a series of events that includes cell division, the production of migratory precursors and their progeny, differentiation, programmed cell death, and integration into organs and systems. The precise control of the growth and differentiation of cells is essential for normal development. The cell cycle frequency and the number of proliferating cells are essential in cell growth. 45,X cells have a failure to proliferate at a normal rate, leading to a decreased cell number in a given tissue during organogenesis. A convergence of data indicates an association between a prolonged cell cycle and the phenotypical features in Turner syndrome. This review aims to examine old and new findings concerning the relationship between a prolonged cell cycle and TS phenotype. These studies reveal a diversity of phenotypic features in TS that could be explained by reduced cell proliferation. The implications of this hypothesis for our understanding of the TS phenotype and its pathogenesis are discussed. It is not surprising that 45,X monosomy leads to cellular growth pathway dysregulation with profound deleterious effects on both embryonic and later stages of development. The prolonged cell cycle could represent the beginning of the pathogenesis of TS, leading to a series of phenotypic consequences in embryonic/fetal, neonatal, pediatric, adolescence, and adulthood life.

The power-generation capacity of grid-connected photovoltaic (PV) power systems is increasing. As output power forecasting is required by electricity market participants and utility operators for the stable operation of power systems, several methods have been proposed using physical and statistical approaches for various time ranges. A short-term (30 min ahead) forecasting method has been previously proposed by our laboratory for geographically distributed PV systems using motion estimation. This study focuses on an important parameter for estimating the proposed motion and optimizing the parameter. This parameter is important because it is associated with the smoothness of the vector field, which is the result of motion estimation and influences the forecasting accuracy. In the periods with drastic power output changes, the evaluation was conducted on 101 PV systems located within a circle of 15-km radius in the Kanto region of Japan. The results indicate that the absolute mean error of the proposed method with the optimized parameter is 10.3%, whereas that of the persistent prediction method is 23.7%. Therefore, the proposed method is effective in forecasting for periods when PV output changes drastically in a short time.

We investigate the power of time series analysis based on a variety of information-theoretic approaches from statistics (AIC, BIC) and machine learning (Minimum Message Length) - and we then compare their efficacy with traditional time series model and with hybrids involving deep learning. More specifically, we develop AIC, BIC and Minimum Message Length (MML) ARMA (autoregressive moving average) time series models - with this Bayesian information-theoretic MML ARMA modelling already being new work. We then study deep learning based algorithms in time series forecasting, using Long Short Term Memory (LSTM), and we then combine this with the ARMA modelling to produce a hybrid ARMA-LSTM prediction. Part of the purpose of the use of LSTM is to seek capture any hidden information in the residuals left from the traditional ARMA model. We show that MML not only outperforms earlier statistical approaches to ARMA modelling, but we further show that the hybrid MML ARMA-LSTM models outperform both ARMA models and LSTM models.

In the present trend of constructing taller and longer structures, the application of lightweight aggregate concrete is becoming an increasing important advanced solution in the modern construction industry. In engineering practice, the analysis of lightweight concrete elements is performed using the same algorithms used for normal concrete elements. As an alternative to traditional engineering methods, nonlinear numerical algorithms based on constitutive material models may be used. The paper presents a comparative analysis of curvature calculations for flexural lightweight concrete elements, incorporating analytical code methods EN 1992-1 and ACI 318-14, as well as a numerical analysis using the constitutive model of cracked tensile lightweight concrete recently proposed by the authors. To evaluate the adequacy of the theoretical predictions, experimental data of 51 lightweight concrete beams tested during five different programmes were collected. A comparison of theoretical and experimental results showed that the most accurate predictions are obtained using numerical analysis and the constitutive model proposed by the authors. In the future, the latter algorithm can be used as a reliable tool for improving the design standard methods or numerical modelling of lightweight concrete elements subjected to short-term loading.

Statistics of languages are calculated by counting characters, words, sentences, word rankings. Some of these random variables are also the main “ingredients” of classical readability formulae. Revisiting the readability formula of Italian, known as GULPEASE, shows that of the two terms that determine the readability index G – the semantic index G_C, proportional to the number of characters per word, and the syntactic index G_F, proportional to the reciprocal of the number of words per sentence −, G_F is dominant because G_C is, in practice, constant for any author throughout seven centuries of Italian Literature. Each author can modulate the length of sentences more freely than he can do with the length of words, and in different ways from author to author. For any author, any couple of text variables can be modelled by a linear relationship y=mx, but with different slope m from author to author, except for the relationship between characters and words, which is unique for all. The most important relationship found in the paper is, in author’s opinion, that between the short−term memory capacity, described by Miller’s “7∓2 law”, and the word interval, a new random variable defined as the average number of words between two successive punctuation marks. The word interval can be converted into a time interval through the average reading speed. The word interval is spread in the same of Miller’s law, and the time interval is spread in the same range of short−term memory response times. The connection between the word interval (and time interval) and short−term memory appears, at least empirically, justified and natural, and should further investigated. Technical and scientific writings (papers, essays etc.) ask more to their readers. A preliminary investigation of these texts shows clear differences: words are on the average longer, the readability index G is lower, word and time intervals are longer. Future work done on ancient languages, such as Greek or Latin, could bring us a flavor of the short term−memory features of these ancient readers.

This qualitative study explores the pivotal role of Supplier Relationship Management (SRM) in enhancing resilience within e-commerce supply chains. In an era marked by rapid digital transformation and global interconnectedness, the resilience of supply chains has become a strategic imperative for organizations striving to maintain operational efficiency and meet customer expectations. Through semi-structured interviews with supply chain managers, procurement officers, and strategic sourcing specialists across diverse industries, this research investigates the strategies, challenges, and outcomes associated with SRM practices. Findings reveal that effective communication and collaboration are fundamental to successful SRM, fostering trust, transparency, and responsiveness in supplier relationships. Strategic risk management through proactive identification and mitigation strategies enables organizations to navigate uncertainties such as economic fluctuations and geopolitical tensions. SRM implementation yields significant organizational benefits, including improved supply chain efficiencies, enhanced product quality, and increased customer satisfaction. However, challenges such as technological complexities, cultural differences among global suppliers, and regulatory compliance issues underscore the need for adaptive strategies and digital investments. The integration of digital technologies such as AI and blockchain enhances SRM effectiveness by enabling real-time data exchange, predictive insights, and decision-making capabilities. Ultimately, SRM emerges as a cornerstone of sustainable growth and competitive advantage in e-commerce, empowering organizations to optimize supply chain performance and resilience amidst dynamic market conditions.

This study explores the strategic importance of leveraging supplier relationships for competitive advantage in the e-commerce industry. Strong supplier relationships are crucial for enhancing operational efficiencies, cost management, quality assurance, innovation, sustainability, and overall supply chain resilience. Through collaborative partnerships, e-commerce companies can streamline logistics, reduce costs, ensure consistent product quality, and foster innovation through joint development initiatives. Ethical sourcing and sustainable practices play a pivotal role in supplier relationship management (SRM), aligning with consumer expectations and enhancing brand reputation. Advanced technologies such as AI, blockchain, and IoT further transform SRM practices by improving transparency, efficiency, and decision-making capabilities across the supply chain. The study also discusses challenges in managing global supplier relationships, emphasizing the importance of effective communication, cultural sensitivity, and strategic risk management. By addressing these challenges and nurturing trust-based relationships with suppliers, e-commerce firms can navigate market complexities and achieve sustainable growth. This research provides insights for e-commerce companies seeking to optimize their supplier relationships and maintain competitive advantage in a dynamic market environment.

Supplier diversity initiatives in procurement have emerged as strategic imperatives for organizations aiming to enhance competitiveness and foster socio-economic equity. This research investigates the impact of supplier diversity initiatives across various industries, analyzing implementation strategies, challenges, and outcomes through a mixed-methods approach. Qualitative data, including interviews with procurement professionals and case studies of exemplary organizations, reveal diverse approaches to implementing supplier diversity—from formalized programs with dedicated resources to ad hoc initiatives driven by regulatory compliance or social responsibility goals. Challenges identified include the identification and qualification of diverse suppliers, scalability issues, and internal resistance within procurement teams. Quantitative analysis of survey data highlights positive impacts on organizational performance metrics, such as procurement spend allocation towards diverse suppliers, supplier-driven innovation, and enhanced supply chain resilience. Best practices in successful supplier diversity programs underscore strategic alignment with overall procurement strategies, effective supplier relationship management, and strong leadership commitment. Socio-economic impacts encompass economic inclusion, community engagement, and skills development among diverse supplier networks, contributing to local economic growth and broader social benefits. Despite these benefits, challenges remain in measuring qualitative outcomes and overcoming systemic barriers to implementation. Cultivating an inclusive organizational culture and leveraging leadership support are crucial for sustaining supplier diversity efforts. Continued collaboration and innovation in supplier diversity practices are recommended to maximize benefits and drive meaningful socio-economic impact globally.

Agile procurement has emerged as a transformative strategy for organizations striving to maintain competitiveness in a rapidly evolving marketplace. This qualitative research investigates the implementation, benefits, challenges, and outcomes of agile procurement practices in supply chain management. Through semi-structured interviews and case studies across diverse industries, this study explores how agile procurement enhances organizational adaptability and responsiveness. Key findings reveal that agile procurement enables organizations to swiftly adjust strategies in response to market fluctuations and customer demands, fostering increased customer satisfaction and loyalty. The integration of advanced technologies, including AI and blockchain, facilitates real-time data analysis, optimizing procurement processes and enhancing supply chain efficiency. Organizational culture emerges as a critical factor in successful agile procurement, with leadership support and a collaborative environment driving cultural change towards agility and innovation. Despite its benefits, challenges such as resistance to change and the need for significant technology investments are identified, requiring careful change management and strategic alignment. Case studies illustrate the versatility of agile procurement across sectors, showcasing its role in enhancing operational efficiencies, innovation capabilities, and overall supply chain performance. In conclusion, agile procurement offers organizations a strategic framework to navigate complexities and uncertainties effectively, capitalizing on market opportunities while mitigating risks. Embracing agile principles fosters a resilient organizational culture capable of adapting to dynamic market conditions. Future research should focus on further refining agile procurement strategies to address emerging challenges and sustain competitive advantage in the global marketplace.

This qualitative study explores the transformative role of Supplier Relationship Management (SRM) in the context of e-commerce, focusing on the integration of digital technologies and evolving supplier interactions. As digital advancements such as artificial intelligence, blockchain, data analytics, cloud computing, and the Internet of Things (IoT) reshape traditional SRM practices, e-commerce companies are experiencing significant improvements in efficiency, transparency, and collaboration. This research employs semi-structured interviews, document analysis, and case studies to examine the impact of these technologies on SRM, identify key collaborative practices, and assess risk management strategies. Findings indicate that digital integration enhances decision-making, streamlines operations, and fosters deeper, trust-based supplier relationships. AI facilitates predictive analytics and automates processes, while blockchain provides transparency and reduces fraud. Data analytics supports informed sourcing decisions and risk mitigation, and cloud platforms improve real-time collaboration. IoT enhances supply chain visibility, allowing for better inventory management. Despite these benefits, challenges such as high implementation costs, integration difficulties, data privacy concerns, and security risks remain significant barriers. Collaboration practices, including joint development, shared resources, and coordinated marketing, contribute to innovation and mutual growth, emphasizing the need for effective communication and trust. Risk management strategies, such as scenario planning and supplier diversification, are crucial for maintaining supply chain resilience. Strategic alignment between e-commerce companies and suppliers, including goal congruence and capability matching, is essential for seamless coordination and competitive advantage. The study underscores the importance of a holistic approach to SRM, integrating technology, collaboration, risk management, and strategic alignment to navigate the complexities of supplier relationships in the digital age. These insights provide a framework for e-commerce companies to optimize their SRM practices and achieve sustainable growth in a dynamic marketplace.

This research aims to explore the adoption and impact of green supply chain practices (GSCP) on sustainable development in Bangladesh. The study seeks to understand the current state of GSCP adoption across various industries, identify key drivers and barriers, assess the perceived benefits and challenges, and evaluate the overall impact on environmental sustainability, economic performance, and social well-being. The research employs a qualitative approach, utilizing interviews with key stakeholders, document analysis, and literature review to gather data and insights. Interviews were conducted with representatives from government agencies, businesses, civil society organizations, and academia to obtain diverse perspectives on GSCP adoption and its implications for sustainable development in Bangladesh. The findings reveal a mixed scenario of GSCP adoption across different industries in Bangladesh, with sectors such as textiles leading the way. Key drivers of GSCP adoption include regulatory pressures, market forces, corporate social responsibility initiatives, and financial incentives. However, organizations face significant barriers such as high upfront costs, lack of awareness and expertise, regulatory uncertainty, and supply chain complexity. Despite these challenges, GSCP offer various benefits, including environmental sustainability, economic performance, and social well-being. Successful initiatives demonstrate tangible impacts, highlighting the potential of GSCP to contribute to sustainable development. The research is limited by its qualitative nature and focus on selected industries and stakeholders. Future studies could employ quantitative methods and broader sampling to provide a more comprehensive understanding of GSCP adoption and its implications in Bangladesh. The findings provide actionable insights for policymakers, industry leaders, and other stakeholders to promote GSCP adoption and enhance sustainable development in Bangladesh. Strategies for overcoming barriers and maximizing benefits are outlined, emphasizing the importance of collaboration and capacity-building initiatives. GSCP adoption can have positive social implications by promoting ethical labor practices, improving working conditions, and fostering community engagement. By prioritizing sustainability, businesses can contribute to social well-being and inclusive growth. This research contributes to the literature on GSCP adoption and sustainable development in Bangladesh by providing empirical insights and practical recommendations. The study adds to the understanding of the drivers, barriers, benefits, and challenges associated with GSCP adoption, highlighting the importance of integrating environmental, economic, and social considerations in supply chain management.

The integration of supply chain analytics and Customer Relationship Management (CRM) systems represents a critical strategy for organizations seeking to enhance operational efficiency and customer-centricity in today's competitive business environment. This qualitative study investigates the motivations, strategies, challenges, and outcomes associated with integrating supply chain analytics with CRM systems across diverse industries. Motivations for integration include gaining a unified view of customer data, improving decision-making capabilities, enhancing operational efficiency, and ensuring strategic alignment across organizational functions. Strategies employed by organizations include cross-functional collaboration, investment in scalable technology platforms, and leadership commitment to drive successful integration initiatives. Despite the potential benefits, the integration process presents challenges such as data integration complexities, technical interoperability issues, and organizational resistance to change. Addressing these challenges requires robust technological solutions, effective change management strategies, and a proactive approach to data governance and security. The study identifies significant outcomes from integration efforts, including improved operational efficiency through streamlined processes and optimized resource allocation, enhanced customer insights leading to personalized experiences, and strengthened strategic decision-making supported by real-time analytics.

This study explores the dynamic interplay between supply chain integration and marketing strategies in achieving customer satisfaction. Through a qualitative approach involving semi-structured interviews with supply chain and marketing professionals, the research delves into how these traditionally distinct functions can be aligned to enhance operational efficiency and deliver superior customer experiences. Findings reveal that strategic alignment between supply chain management (SCM) and marketing fosters better coordination of product availability and service delivery, thereby meeting customer expectations consistently and enhancing brand loyalty. Information sharing and technological integration emerge as critical enablers, facilitating real-time data exchange and collaborative decision-making. The study also identifies challenges such as organizational silos, cultural resistance, and technological constraints that impede seamless integration, highlighting the need for proactive strategies to foster cross-functional collaboration. Additionally, the evolving roles of sustainability and customer-centricity are emphasized as key drivers for differentiating brands and achieving competitive advantage. By integrating sustainable practices and adopting a customer-centric approach, organizations can build stronger customer relationships and enhance their market position. The research underscores the importance of leadership support, investment in technology, and a collaborative organizational culture in overcoming integration barriers. The study’s findings provide actionable insights for organizations aiming to optimize their SCM and marketing strategies, drive innovation, and sustain growth in an increasingly complex and competitive business environment. This research contributes to the growing body of knowledge on supply chain and marketing integration, offering practical recommendations for achieving customer satisfaction and long-term success.

This qualitative research investigates the role of collaborative planning in synchronizing supply chain and marketing activities among multinational companies (MNCs). Collaborative planning integrates traditionally separate functions to enhance operational efficiency, customer responsiveness, and strategic alignment. The study employs semi-structured interviews with key informants from diverse MNCs across industries and geographies to explore practices, challenges, and outcomes associated with collaborative planning initiatives. Findings highlight the pivotal role of cross-functional collaboration and advanced technologies, such as artificial intelligence and big data analytics, in improving demand forecasting accuracy, optimizing inventory management, and enabling personalized marketing strategies. Challenges include organizational resistance, cultural barriers, and the complexity of aligning global strategies. Strategic outcomes encompass improved operational efficiency, cost savings, enhanced customer satisfaction, and strengthened competitive advantage. Moreover, the integration of sustainability goals underscores the importance of environmental responsibility and regulatory compliance in collaborative planning frameworks. The study also identifies emerging trends in digital transformation and agile supply chains shaping future practices. Overall, collaborative planning emerges as a strategic imperative for MNCs to navigate complexities, capitalize on opportunities, and sustain growth in a globalized marketplace.

Aligning supply chain and marketing functions is crucial for high-tech startups aiming to successfully launch new products in competitive markets. This qualitative research explores the strategies, challenges, benefits, and best practices associated with supply chain and marketing alignment in high-tech startups. Through a detailed analysis of case studies and semi-structured interviews with industry professionals, the study identifies key insights into how startups can effectively integrate these functions to enhance operational efficiency, market responsiveness, customer satisfaction, and risk management. The findings reveal that successful alignment requires the formation of cross-functional teams, leveraging integrated technologies like CRM and ERP systems, establishing regular inter-departmental communication, and developing joint performance metrics. These strategies foster collaboration and ensure that supply chain and marketing efforts are coordinated towards shared goals. However, startups face challenges such as differing departmental objectives, resource constraints, communication barriers, and technological limitations, which hinder effective alignment. Benefits observed from effective alignment include improved operational efficiency through streamlined processes, enhanced market responsiveness to adapt quickly to changing demands, increased customer satisfaction by delivering products that meet market expectations, and better risk management through proactive identification and mitigation of supply chain and market risks. Best practices identified include fostering a culture of collaboration, investing in technology for data integration and transparency, aligning strategic objectives across functions, and continuously monitoring and adjusting alignment strategies. Integration of broader elements such as sustainability, entrepreneurship, emotional intelligence, marketing, and supplier relationship management further enriches the alignment framework. This study contributes to a deeper understanding of how high-tech startups can strategically align their supply chain and marketing functions to achieve competitive advantage and sustainable growth in dynamic market environments.

This study explores the impact of supply chain visibility (SCV) on marketing strategies within the fast-moving consumer goods (FMCG) industry. In a rapidly evolving market characterized by complex supply chains and dynamic consumer demands, SCV offers a transformative capability by providing real-time data and insights into supply chain operations. Through semi-structured interviews with industry practitioners, the research identifies several key themes: enhanced demand forecasting, increased consumer trust through transparency, improved marketing agility, and the integration of advanced technologies. Findings suggest that SCV significantly improves the accuracy of demand forecasts by providing timely insights into inventory levels, production schedules, and market conditions. This enhanced forecasting aligns supply chain capabilities with marketing efforts, reducing stockouts and optimizing promotional activities. Moreover, SCV fosters consumer trust by enabling transparency regarding product sourcing and ethical practices, which can be effectively communicated in marketing campaigns to build brand loyalty. The agility provided by SCV allows companies to quickly adjust their marketing strategies in response to market disruptions and shifts in consumer preferences, making their campaigns more resilient and responsive. Advanced technologies such as IoT, blockchain, and analytics further enhance the benefits of SCV, providing deeper insights and more sophisticated capabilities for marketing. The study also highlights the importance of cross-functional collaboration between supply chain and marketing teams in leveraging SCV data effectively. Despite challenges such as the need for significant investment and organizational change, the advantages of SCV in enhancing marketing strategies and overall organizational performance are substantial. The research underscores SCV's critical role in shaping effective marketing strategies, offering FMCG companies a path to greater efficiency, agility, and consumer engagement in a competitive landscape.

This study investigates green logistics strategies adopted by businesses to enhance sustainability within their supply chains, particularly focusing on reducing carbon footprints and environmental impacts associated with transportation and distribution activities. Through qualitative analysis and interviews with supply chain managers, industry experts, and sustainability officers, this research provides insights into motivations, strategies, challenges, and benefits related to green logistics practices. Key findings highlight the critical role of aligning green logistics with corporate sustainability goals. Advanced technologies such as telematics, GPS tracking, and data analytics are instrumental in optimizing transportation routes, reducing fuel consumption, and minimizing emissions. Additionally, strategies like using eco-friendly vehicles, energy-efficient warehouse operations, and implementing reverse logistics are identified as effective means to mitigate environmental impacts. Collaboration and partnerships with suppliers, logistics providers, and stakeholders are essential for sharing best practices and fostering joint initiatives. Despite benefits such as cost savings and enhanced corporate reputation, challenges include high initial investments, the absence of standardized metrics for environmental impact, and regulatory complexities. However, regulatory compliance also acts as a driver for green logistics adoption and presents opportunities for improving sustainability performance. The integration of sustainability goals with logistics strategies underscores the interdisciplinary nature of sustainable supply chain management. This holistic approach encompasses aspects of entrepreneurship, emotional intelligence, marketing, and supplier relationship management.

This qualitative study investigates the adoption of Lean Principles in Supply Chain Management (SCM), aiming to explore the motivations, challenges, and outcomes associated with Lean implementation across diverse organizational contexts. Through semi-structured interviews and thematic analysis, insights were gleaned from stakeholders involved in SCM processes, revealing the critical role of organizational culture, leadership commitment, and strategic alignment in driving successful Lean initiatives. The findings underscore the importance of fostering a culture of continuous improvement, where employees are empowered to proactively identify and address inefficiencies. Leadership emerged as pivotal in setting the tone, providing resources, and championing Lean initiatives, while strategic alignment ensured integration with broader organizational goals and objectives. Operational practices and processes were central, highlighting the need for customization and contextual adaptation in Lean implementation. Despite the potential benefits, the outcomes and impact of Lean adoption varied depending on factors such as organizational maturity and industry context. The study also revealed broader cultural and organizational changes catalyzed by Lean adoption, fostering innovation, collaboration, and adaptability. In summary, this study contributes to our understanding of Lean adoption in SCM, offering practical insights for organizations seeking to enhance efficiency and competitiveness in today's dynamic business landscape.

Ethical sourcing practices play a pivotal role in addressing price inflation within renewable energy supply chains. This qualitative research investigates the multifaceted impacts of ethical sourcing, drawing insights from interviews with key stakeholders in the renewable energy sector. The study reveals that ethical sourcing enhances supply chain transparency, traceability, and resilience by mitigating risks associated with unethical practices such as labor exploitation and environmental degradation. Fair trade initiatives contribute to economic stability and reduce price volatility by ensuring fair compensation for producers and workers, fostering long-term relationships, and reinvesting premiums into community development projects. Sustainable sourcing practices align with consumer preferences for eco-friendly products, reduce the demand for virgin resources, and promote regulatory compliance and incentives, thereby stabilizing costs and reducing environmental impact. Socially responsible companies that prioritize fair labor practices and community support experience fewer disruptions and benefit from a more stable and productive workforce. Entrepreneurship and emotional intelligence among leaders drive the adoption and successful implementation of ethical sourcing practices, enhancing brand reputation, consumer trust, and market competitiveness. Despite challenges, such as higher implementation costs and supplier resistance, the long-term benefits of ethical sourcing outweigh the initial costs and contribute to sustainable supply chains. Collaboration, consumer education, and effective regulation are essential for promoting ethical sourcing across the renewable energy sector.

China’s leading development of a complete battery value chain for Electric Vehicles (EV) is restructuring the global automotive sector. The competitive advantage of China's EV battery industry lies on its firms’ core competency and political economic geography. Based on first-hand empirical material and data obtained from years of fieldwork in an EV battery cluster in South China, this paper identifies what the Chinese EV battery industry’s core competency is and details how it is built up from below. The current core competency of Chinese battery firms is their mass manufacturing capability to supply lithium-ion batteries vehicle manufacturers (OEMs) with stable and consistent quality as well as competitive prices. This competency is acquired by firms through technological learning at workshop level while taking use of their experiences accumulated in mass production of batteries for consumer electronics sectors. Furthermore, the rapid learning and accumulation of knowledge on battery manufacturing at large scale is also facilitated by the industrial cluster environment where firms are embedded. Supported and promoted by local government policies, Chinese EV battery clusters are composed of firms from different segments of a complete battery value chain. The findings illuminate the likely strategic strengths, weaknesses, and futures of the Chinese EV battery industry in global competition.

Procurement plays a central and defining role in the efficiency of supply chains. In recent years, the understanding and practice of procurement management have changed significantly due to increasing emphasis on and demand for agility and sustainability within supply chain management systems. Meanwhile, traditional supply chain systems, characterized by siloed processes, limited visibility, and inefficiencies due to manual interventions, can no longer cope with the current dynamic market conditions. Besides, the increasing requirements of sustainability, ethical sourcing, and environmental impact have intensified, making more responsible, transparent, and traceable procurement practices unnegotiable requirements for modern systems. In this paper, we examine the potential of emerging technologies and innovations such as blockchain, artificial intelligence, and the Internet of Things (IoT) in filling these gaps within the supply chain systems of enterprises in the US. The study employs a comprehensive review of relevant literature and case studies to discover and provide insights on how these technologies can improve procurement processes, optimize resource allocation and utilization, and improve sustainability. The findings of this study highlight the need for stakeholders to embrace technological advancements to effectively navigate the dynamic landscape of supply chain management.

This study presents a systematic review of the impact of Industry 4.0, digitalization, and information systems on the procurement function with a focus on the Portuguese reality. Despite global trends emphasizing digital transformation and innovation, the procurement practices in Portugal seem to lag. The adoption of cutting-edge technologies and the comprehensive integration of digital solutions within the procurement and supply chain management sector are yet to materialize fully in the Portuguese SME context. The benefits and obstacles of these technologies in transitioning procurement into a more strategic role are analyzed. By examining existing literature, critical factors for organizations to consider when implementing these technologies and the potential benefits are identified. The results indicate that digitalization has resulted in a data-driven approach to procurement, with the aid of advanced analytics and artificial intelligence for decision-making. Industry 4.0 has facilitated smarter factories, warehouses, and logistics operations with a focus on automation, connectivity, and real-time data. Procurement can more efficiently manage suppliers, track contracts, identify cost-saving opportunities, and increase sustainability with the aid of information systems. The study also highlights potential challenges for organizations. Overall, this study provides insightful findings on the role of digitalization in transforming procurement and the challenges and opportunities that organizations may face.

The only cases where exact distributions of estimates are known is for samples from exponential families, and then only for special functions of the parameters. So statistical inference was traditionally based on the asymptotic normality of estimates. To improve on this we need the {\it Edgeworth expansion} for the distribution of the standardized estimate. This is an expansion in $n^{-1/2}$ about the normal distribution, where $n$ is typically the sample size. The 1st few terms of this expansion were originally given for the special case of a sample mean. In earlier work we derived it for {\it any } standard estimate, hugely expanding its application. We call an estimate $\hat{w}$ of an unknown vector $w\in R^p$, a {\it standard estimate}, if $E\ \hat{w}\rightarrow w$ as $n\rightarrow \infty$, and for $r\geq 1$ the $r$th order cumulants of $\hat{w}$ have magnitude $n^{1-r}$ and can be expanded in $n^{-1}.$ Here we give another huge extension. We give the expansion of the distribution of {\it any smooth function} of $\hat{w}$, say $t(\hat{w})\in R^q,$ giving its distribution to $n^{-5/2}$. We do this by showing that $t(\hat{w})$, is a standard estimate of $t(w)$. This provides far more accurate approximations for the distribution of $t(\hat{w})$ than its asymptotic normality. %NOT USED: The building blocks of the Edgeworth expansions are the {\it cumulant coefficients } of the estimate. These cumulant coefficients are also needed for bias reduction, Bayes estimates and confidence regions. We give {\it chain rules} for the cumulant coefficients of $t(\hat{w})$ in terms of those of $\hat{w}$ and the derivatives of $t(w)$, up to those needed for 5th order Edgeworth expansions for $t(\hat{w})$ and its {\it tilted expansion}, useful for the tail of the distribution.

Keywords: Green human resource management1; Green supply chain management2; Regulation and legislation3; Sustainable development goals4.

Malaria is rare in Europe, but it is a medical emergency and programs for its control should ensure access to early diagnosis and prompt treatment within 24-48 h of the symptom’s onset. The consistent number of imported malaria cases and the risk of reappearance of autochthonous cases stimulated laboratories in non-endemic countries to evaluate diagnostic methods/algorithms: microscopy remains the gold standard, but with limitations. RDTs have greatly expanded the ability to diagnose malaria for rapid results due to simplicity and low cost, but they lack sensitivity and specificity. PCR-based assays provide more relevant information but need well-trained technicians. According to WHO global Technical Strategy for Malaria 2016-2030, the future direction for a rapid diagnosis is the development of Point-Of-Care Testing, which would be helpful for prompt/accurate treatment and for preventing the diseases spread. Despite the limitations of current diagnostic methods, they play important roles in dealing with the global malaria situation, including decreasing its incidence. Recently evidence suggested that Artificial Intelligence could be utilized for assisting pathologists in malaria diagnosis. Future studies need to be conducted to see whether this system can be used for routine malaria diagnostic procedures.

The improvement of agricultural low-carbon efficiency is an important path to promote the realization of the "double carbon" goal in the Yellow River Basin. In the context of rapid urbanization development, it is of great significance to explore whether the promotion of urbanization will affect the agricultural low-carbon efficiency. Based on the panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this paper uses super-DEA model, three-dimensional kernel density model and Markov chain model to measure and analyze the spatio-temporal evolution of agricultural low-carbon efficiency in the Yellow River Basin, on this basis, the panel Tobit model is used to analyze the relationship between urbanization and low-carbon efficiency in agriculture. The results show that: (1) The level of agricultural low-carbon efficiency in the Yellow River Basin is low and has not reached the effective state, showing a slow downward trend in general, among which, the agricultural low-carbon efficiency in the lower reaches is higher than that in the middle reaches, and the upper reaches is the lowest. (2) The agricultural low-carbon efficiency in the Yellow River Basin has a negative trend of transferring to the low level on the whole, and tends to maintain the original state level, and it is difficult to realize the leapfrog transfer between states. Agricultural low-carbon efficiency has obvious spatial spillover effect and "club convergence" phenomenon, high efficiency area has a positive driving effect on the neighborhood area, while low efficiency area has a negative impact on the neighborhood area. (3) The level of urbanization has a significant positive impact on the low-carbon efficiency of agriculture in the upper, middle and lower reaches of the Yellow River Basin, which plays an important role in promoting the green development of agriculture. Finally, based on the above research conclusions, relevant policy recommendations were put forward, in order to provide references for promoting the improvement of low-carbon agricultural efficiency and promoting high-quality agricultural development in the Yellow River Basin.

Renewable jet fuel (RJF) production has been recognized as a promising approach for reducing the aviation sector's carbon footprint. Over the last decade, commercial production of RJF has piqued the interest of airlines and governments around the world. However, RJF production can be challenging due to its dispersed supply resources. Furthermore, the production of RJF is more costly compared to producing conventional jet fuel. In this study, using a mixed integer linear program (MILP), we design an RJF supply chain network in which we obtain an optimized configuration of the supply chain and determine operational decisions required to meet RJF demand at airports. To accelerate commercialization of RJF production, we considered four different monetary incentive programs, which could cover the supply chain’s costs. This study is validated by employing the model to design an RJF supply chain in the Midwestern US. Results from this study are promising as they show the supply chain can achieve commercialization with partial financial coverage from the incentive programs. Based on the findings of this study, policymakers can devise policies to commercialize RJF production and accelerate its adoption by industry.

Grain supply is the lifeblood of a country, and the stability of the supply chain is a crucial prerequisite for ensuring national grain security. This paper draws on the definition of resilience in physics and economics. It takes supply chain fracture resilience, impact resilience, and synergy resilience as the secondary indicators. It constructs a comprehensive evaluation indicator system of the grain supply chain resilience, measures the resilience indicator of China's grain supply chain from 1996 to 2021, and analyzes the role of supply, cost, exchange rate, and other external shocks in influencing the resilience of China's grain supply chain on this basis. The study found that the overall level of China's total grain supply chain resilience has been growing year by year and can be divided into three stages: low-level stabilization stage, continuous growth stage, and high-level stabilization stage. Grain supply chain fracture resilience has been growing steadily, grain supply chain impact resilience fluctuation is more obvious, and grain supply chain synergy resilience has been changing more gently. In the inquiry of the impact of external shocks on the resilience of China's grain supply chain, it was found that world grain exports and the RMB exchange rate have a significant positive impact on China's grain supply chain resilience level, while the international oil price has a significant negative impact. Based on this, the paper puts forward suggestions for ensuring stable production and supply in the grain market, improving the structure of foreign trade in grain, and actively coping with international commodity price shocks.

Allergies are a common and increasing health problem affecting millions of people worldwide. In addition to genetic predisposition, this increase in allergic cases is attributed to air pollution, climate change, more time spent indoors, lack of physical activity, and alterations in eating habits. The Mediterranean diet (MD), which includes a lot of fruits and vegetables, whole grains, legumes, nuts, olive oil and fish has been linked to a variety of health benefits, including a lower risk of chronic and allergic diseases. The purpose of this paper is to explore the effects of the dietary components of the MD on food allergies. Electronic databases PubMed, Scopus, Science Direct, and EBSCO were used to conduct this systematic review up to March 2023. 696 studies were initially identified and nine were included (five human and four animal studies). The findings of this review showed an overall beneficial effect between the food components of the MD and food allergies. Although the results are promising, the limited number of studies highlights the need for more research.

World health is increasingly threatened by the growing number of spice-related food hazards. Further development of reliable methods for rapid, non-targeted identification of counterfeit ingredients within the supply chain is needed. ENEA has developed a portable, user-friendly photoacoustic laser system for food fraud detection, based on a quantum cascade laser and multivariate calibration. Following a study on the authenticity of saffron, the instrument was challenged with a more elusive adulterant, olive leaves in oregano. The results show that the reported method of laser sensing and chemometric analysis was able to detect adulterants at mass ratios of at least 20% in less than five minutes.

Locust is a pest that attacks human and animal food and endangers national security by threatening food security. Spraying is the easiest and fastest way to deal with them. But the easiest way is not always the best way. With chemical resistance, not only will permanent immunity from locust attacks not arise, but they will also become resistant to poisons. While we have to pay billions of dollars annually for this strategy, we will also cause water and soil pollution. Therefore, the research results of other researchers were used and modeled to measure the possibility of hunting invasive locusts to investigate the possibility and benefits of this action. So, this study was conducted in the field method. The obtained results indicate that it is possible to hunt invasive locusts. If traps are placed on time and in the right place, the possibility of preserving crops after the pest attack increases by 2.3 times more in protected fields than in unprotected fields. By hunting locusts, fields and even pastures are preserved, poisons are prevented from entering the food chain and ecosystem, part of the economic loss of farmers affected by the invasion of locusts will be compensated, and by providing animal feed through hunting locusts, water consumption will be reduced, soil erosion and energy consumption are saved.

Nowadays, frameworks and models are critical to enabling organizations to identify their current sustainability integration into business and to follow up on these initiatives over time. In this context, the maturity models offer a structured way of analyzing how a supply chain meets specific sustainability requirements and which areas demand attention to reach maturity levels. This study proposes a five-level maturity model to help supply chains identify their level of engagement with sustainability practices combining three perspectives: intra and inter-organizational sustainability practices, triple-bottom-line approach and critical areas for sustainability. All the steps followed in constructing the maturity model were based on a literature review, and case studies supported its improvement, application, and testing. The proposed model presents many advantages, such as being used as a self-assessment tool, a roadmap for sustainability behaviour improvement, and a benchmarking tool to evaluate and compare standards and best practices among organizations and supply chains.

Urban growth in various cities across the world, especially in developing countries, leads to land use change. Thus, predicting future urban growth in the most rapidly growing region of Nigeria becomes a significant endeavor. This study analyzes land use and land cover (LULC) change and predicts the future urban growth of the Lagos metropolitan region, using Cellular Automata (CA) model. To achieve this, the GlobeLand30 datasets from years 2000 and 2010 were used to obtain LULC maps, which were utilized for modeling and prediction. Change analysis and prediction for LULC scenario for 2030 were performed using LCM and CA_Markov chain modeling. The results show a substantial growth of artificial surfaces, which will cause further reductions in cultivated land, grassland, shrubland, wetland, and waterbodies. There was no appreciable impact of change for bare land, as its initial extent of cover later disappeared completely. Additionally, artificial surfaces/urban growth in Lagos expanded to the neighboring towns and localities in Ogun State during the study period, and it is expected that such growth will be higher in 2030. Lastly, the study findings will be beneficial to urban planners and land use managers in making key decisions regarding urban growth and improved land use management in Nigeria.

Water resource is considered as a significant factor in development of regional environment and society. Water consumption prediction can provide important decision basis for the regional water supply scheduling optimisations. According to the periodicity and randomness nature of the daily water consumption data, a Markov modified autoregressive moving average (ARIMA) model is proposed in this study. The proposed model, combined with the Markov chain, can correct the prediction error, reduce the continuous superposition of prediction error, and improve the prediction accuracy of future daily water consumption data. The daily water consumption data of different monitoring points are used to verify the effectiveness of the model, and the future water consumption is predicted, in the study area. The results show that the proposed algorithm can effectively reduce the prediction error compared to the ARIMA.

The out-of-equilibrium dynamics of many body systems has recently received a burst of interest, also due to experimental implementations. The dynamics of both observables, such as magnetization and susceptibilities, and quantum information related quantities, such as concurrence and entanglement entropy, have been investigated under different protocols bringing the system out of equilibrium. In this paper we focus on the entanglement entropy dynamics under a sinusoidal drive of the tranverse magnetic field in the 1D quantum Ising model. We find that the area and the volume law of the entanglement entropy coexist under periodic drive for an initial non-critical ground state. Furthermore, starting from a critical ground state, the entanglement entropy exhibits finite size scaling even under such a periodic drive. This critical-like behaviour of the out-of-equilibrium driven state can persist for arbitrarily long time, provided that the entanglement entropy is evaluated on increasingly subsytem sizes, whereas for smaller sizes a volume law holds. Finally, we give an interpretation of the simultaneous occurrence of critical and non-critical behaviour in terms of the propagation of Floquet quasi-particles.

Irish beef farms have experienced poor viability longitudinally, with officially acknowledged ‘crisis’ levels in 2013. In response, beef Producer Organisation (PO) legislation was introduced. Through the lens of international evidence in the literature about how POs function, this paper presents an analysis of Irish stakeholders’ views in the context of a public consultation process. While stakeholders indicate the need for individual POs to collaborate, little emphasis is placed on other factors necessary for success: collaboration with other chain actors; and market differentiation of products. Stakeholders identified primary threats as poor engagement on the part of both farmers and processors/purchasers.

Although the signaling function of Na/K ATPase is been studied for decades, the chasm between the pumping function and the signaling function of Na/K – ATPase is still an open issue. This article explores the relationship between ion pumping and signaling with attention to the amplification of oxidants through this signaling function. Starting with some experimental observations published by our laboratories and others, we develop some predictions regarding cellular oxidant stress.

Speech emotion recognition (SER) is a technology that can be applied in distance education to analyze speech patterns and evaluate speakers’ emotional states in real-time. It provides valuable insights and can be used to enhance the learning experience by enabling the assessment of instructors’ emotional stability, a factor that significantly impacts information delivery effectiveness. Students demonstrate different engagement levels during learning activities, and assessing this engagement is an important aspect of controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is the emotional states of their instructors. Accordingly, this research uses deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entails integrating Transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance SER. Feature extraction from audio data used Mel-frequency cepstral coefficients, chroma, Mel spectrogram, zero crossing rate, spectral contrast, centroid, bandwidth, roll-off, and root-mean square, with subsequent optimization processes adding noise to, conducting time stretching, and shifting the audio data. Notably, several Transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The pre-processing and data augmentation methodologies significantly enhanced the precision of the results in that the model achieved accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% on the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio-Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets. Furthermore, it achieved 83% accuracy on another dataset created for this research—the Saudi Higher Education Instructor Emotions dataset. The results demonstrate this model’s considerable accuracy in detecting emotions in speech data across different languages and datasets.

In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

In-wheel motor vehicles are gaining attention as a new type of electric vehicle due to their efficient power units located inside each wheel hub. However, they are more susceptible to wheel resonance due to the increase in unsprung mass caused by the motor's weight. This can result in decreased both the ride comfort and driving stability. To resolve this issue, this study aims to apply an optimal switching controller with a semi-active n actuator; magnetorheological (MR) damper. For the implementation of the optimal switching controller, a road type classification is also carried out. An acceleration sensor is used for road type classification, and control logics include a ride comfort controller; the linear quadratic regulator (LQR _Paved Road) and a wheel motion controller (LQR_Off Road) for improved driving stability. For paved roads, the LQR_Paved Road control input is applied to the MR damper. However, if a road type prone to wheel resonance is detected, the control logic switches to the LQR_Off Road. During the transition, a weighted average of both LQR_ Paved Road and LQR_Off Road control input is applied to the actuator. Computer simulations are undertaken to evaluate vibration control performance, including the ride comfort and driving stability at various road profiles.

Background Acute right heart failure (RHF) is associated with high morbidity and mortality. Temporary mechanical circulatory support (tMCS) is now part of the advanced management strategy for refractory cardiogenic shock (CS). Among such support systems, VA-ECMO remains the fastest MCS system to set up. The ProtekDuo cannula can be used as a temporary and percutaneous right ventricular assist device (RVAD) in relay with VA-ECMO in the context of prolonged weaning. Case Summary This case report describes the clinical course of a 69-year-old patient who underwent elective aortic valve replacement surgery complicated by acute RHF due to iatrogenic ligation of the right coronary artery. VA-ECMO was initiated in the context of refractory cardiogenic shock. Faced with prolonged MCS weaning, due to lack of recovery of the right ventricle associated with the absence of any other therapeutic option, a percutaneous RVAD via a ProtekDuo cannula was implanted as a bridge to recovery. The ProtekDuo was gradually weaned while the patient began rehabilitation. The patient was transferred to the medical ward after 5 weeks in the ICU, stayed 5 months in an acute medical setting, went to a rehabilitation center afterward and returned home 6 months after the initial surgery without any neurological sequalae or organ failures. In total, she remained on MCS for 39 days (21 on VA-ECMO and 18 on ProtekDuo). Discussion In cases of refractory RHF, after stabilization and organ perfusion restoration under VA-ECMO, it is feasible to switch to a percutaneous RVAD, allowing early mobilization of patients during prolonged MCS weaning until the right ventricle adapts to new loading conditions.

A recent research has identified an inverse amplitude link between obliquity damping and short eccentricity amplification during Mid-Late Pleistocene based on LR04 δ18O and equatorial Pacific Site 846 sea surface temperature records, which is associated with the Earth's long-term cooling. In the present study, new evidence of this anticorrelation is presented from Antarctic D-CO2-CH4 records, global benthic-planktic δ18O, and regional (Atlantic, Pacific, Mediterranean, and Indian) climate-related proxies. Based on a critical review of theoretical constrains (Earth's oblateness changes and ice-volume phase lag in the obliquity band <5.0 kyr), this widespread and symmetric (bipolar) obliquity response damping has been interpreted as an effect of the obliquity-oblateness feedback, which could be the latent physical mechanism at the origin of the Mid-Pleistocene Transition (MPT). Indeed, results and considerations of the present work suggest that fast and positive/negative net variation of the Earth's oblateness in the obliquity band was controlled by dominant glacio-eustatic water mass component and, assuming a rapid response of the ice-volume to surface temperature changes, the mean obliquity lag response is estimated to be <5.0 kyr over the past 800 kyr. These elements may explain the interglacial/glacial damping observed in the obliquity response. The consolidation of the Earth's long-term icy-state in the subtrend IV, culminated with the post-MPT obliquity damping, might have contributed to the strengthening of the short eccentricity response by mitigating the obliquity ‘ice-killing’ during obliquity maxima (interglacials), favouring the obliquity-cycle skipping and a feedback amplified ice-growth in the short eccentricity band (obliquity damping hypothesis). This could suggest a different impact of the climate friction than what is generally believed, which is presumably the latent physical mechanism that triggers transient ‘competitive’ interaction between obliquity and short eccentricity started early during the Piacenzian.

The correct classification of defects originating from partial discharges (PD) in medium-voltage (MV) switchgears with air insulation (AIS) remains a challenging research topic for scientists worldwide. In this article, the authors simulated four possible defects occurring in the power industry, including one that is a simultaneous combination of two commonly ones. In addition, the correctness of the algorithm was checked by adding a classification class without any fault. The measurement signals were recorded with TEV sensors. The effectiveness of various hy-brid-connected neural networks was tested and discussed: GoogleNet and SqueezeNet based on spectrograms, SAE with FNN, 2D-CNN with LSTM, and hybrid AE combined with CNN and LSTM. The highest effectiveness – approximately 97% – was demonstrated by the GoogleNet and SqueezeNet networks. The research results are expected to form the basis for the development of a universal and wireless capacitive sensor for monitoring the level of PD in switchgears.

Multilabel learning goes beyond standard supervised learning models by associating a sample with more than one class label. Among the many techniques developed in the last decade to handle multilabel learning best approaches are those harnessing the power of ensembles and deep learners. This work proposes merging both methods by combining a set of gated recurrent units, temporal convolutional neural networks, and long short-term memory networks trained with variants of the Adam optimization approach. We examine many Adam variants, each fundamentally based on the difference between present and past gradients, with step size adjusted for each parameter. We also combine Incorporating Multiple Clustering Centers and a bootstrap-aggregated decision trees ensemble, which is shown to further boost classification performance. In addition, we provide an ablation study for assessing the performance improvement that each module of our ensemble produces. Multiple experiments on a large set of datasets representing a wide variety of multilabel tasks demonstrate the robustness of our best ensemble, which is shown to outperform the state-of-the-art. The MATLAB code for generating the best ensembles in the experimental section will be made available at https://github.com/LorisNanni.

In the modern construction industry, lightweight aggregate concrete (LWAC) is often used in the production of load-bearing structural members. LWAC can be up to 40% lighter by volume in comparison to normal strength concrete. On the other hand, the lack of adequate numerical models often limits the practical application of innovative building materials, such as lightweight concrete, in real projects. This trend is due to the uncertainties in design standard methods and calculation errors, the level of which is generally unacceptable to civil engineers in terms of safety and reliability. In the present paper, a comparative numerical deformation analysis of a full-scale bridge deck slab and girder has been carried out. Using the physical model proposed by the authors and the finite element software ATENA, the deformations of full–scale lightweight and traditional reinforced concrete elements under short-term effects of permanent and variable loads was compared. Depending on the safety and serviceability limit requirements, it was found that the amount of longitudinal reinforcement in lightweight reinforced concrete elements can be reduced compared to normal reinforced concrete elements with the same parameters. The results of the numerical analysis show that the deformation analysis model proposed by the authors can be a reliable tool for the design of lightweight concrete flexural members by selecting the optimum geometrical and reinforcement parameters limited by the stiffness condition.

This paper realizes early bearing fault warning through bearing fault time series prediction, and proposes a bearing fault time series prediction model based on optimized maximum correlation kurtosis deconvolution (MCKD) and long short-term memory (LSTM) recurrent neural network to ensure bearings operation reliability. The model is based on lifecycle vibration signal of the bearing, to begin, the cuckoo search (CS) is utilized to optimize the parameter filter length L and deconvolution period T of MCKD, taking into account the influence and periodicity of the bearing time series, the fault impact component of the optimized MCKD deconvolution time series is improved. Then select the LSTM learning rate α depending on deconvolution time series. Finally, the dataset obtained through various preprocessing approaches are used to train and predict the LSTM model. The average prediction accuracy of the optimized MCKD-LSTM model is 26 percent higher than that of the original time series, proving the efficiency of this method, and the prediction results track the real fault data well, according to the XI'AN JIAOTONG University XJTU-SY bearing dataset.

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.

The capacity and the mechanism of the adsorption of aqueous barium (Ba), cobalt (Co), strontium (Sr) and zinc (Zn) by Ecuadorian (NatAllo) and synthetic (SynAllo-1 and SynAllo-2) allophanes were studied as a function of contact time, pH and metal ion concentration using kinetic and equilibrium experiments. The mineralogy, nano-structure and chemical composition of the allophanes were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and specific surface area analyses. The evolution of adsorption fitted to a pseudo-first-order reaction kinetics, where equilibrium between aqueous metal ions and allophane was reached within < 10 min. The metal ion removal efficiencies varied from 0.7 to 99.7 % at pH 4.0 to 8.5. At equilibrium, the adsorption behavior is better described by the Langmuir model than by the Dubinin-Radushkevich model, yielding sorption capacities of 10.6, 17.2 and 38.6 mg/g for Ba^(2+), 12.4, 19.3 and 29.0 mg/g for HCoO_2^-, 7.2, 15.9 and 34.4 mg/g for Sr^(2+) and 20.9, 26.9 and 36.9 mg/g for Zn^(2+), respectively, by NatAllo, SynAllo-2 and SynAllo-1. The uptake mechanism is based on a physical adsorption process. Allophane holds great potential to remove aqueous metal ions and could be used instead of zeolites, montmorillonite, carbonates and phosphates for wastewater treatment.

Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of the electricity prices, which includes high volatility, sharp price spikes and seasonality, various types of electricity price forecasting models still compete and can not outperform each other consistently. Neural Networks have been successfully used in machine learning problems and Recurrent Neural Networks (RNNs) have been proposed to address time-dependent learning problems. In particular, Long Short Term Memory and Gated Recurrent Units (GRU) are tailor-made for time series price estimation. In this paper, we propose to use Gated Recurrent Units as a new technique for electricity price forecasting. We have trained a variety of algorithms with rolling 3-year window and compared the results with the RNNs. In our experiments, 3-layered GRUs outperformed all other neural network structures and state of the art statistical techniques in a statistically significant manner in the Turkish day-ahead market.

This study investigates the critical role of Supplier Relationship Management (SRM) in enhancing the e-commerce user experience. In an increasingly competitive digital marketplace, effective SRM practices are essential for optimizing supply chain operations and meeting customer expectations. The research employs a qualitative approach, combining semi-structured interviews with industry stakeholders, extensive literature review, and case studies to explore various dimensions of SRM. Key findings reveal that strategic supplier selection criteria, including quality, cost efficiency, reliability, innovation, and sustainability, are pivotal in ensuring a robust and adaptable supply chain. Effective communication practices, such as transparency, frequent updates, and digital tools, foster strong supplier relationships and enable timely issue resolution. Performance management strategies, centered on KPI monitoring and continuous improvement, support operational excellence and customer satisfaction. Risk management practices, including diversification, contingency planning, and advanced analytics, are crucial for mitigating disruptions and ensuring supply chain resilience, as highlighted by the challenges posed during the COVID-19 pandemic. Technological integration with blockchain, AI, IoT, data analytics, and automation enhances efficiency, transparency, and decision-making capabilities in SRM. Furthermore, the study underscores the evolving integration of sustainability and ethical sourcing into SRM strategies, reflecting broader corporate responsibility goals. Ultimately, this research contributes to understanding how effective SRM strategies drive competitive advantage in e-commerce by enhancing supply chain reliability, responsiveness, and customer-centricity. As e-commerce continues to evolve, adapting and innovating SRM practices will be vital for companies aiming to sustain growth and meet the dynamic demands of global markets.

This qualitative study explores the dynamics of Supplier Relationship Management (SRM) and its impact on innovation within the e-commerce sector. Through in-depth interviews and documentary analysis of three case study firms known for their innovative practices, the study examines how collaborative SRM practices influence innovation outcomes, organizational resilience, and competitive advantage in dynamic market environments. Key themes include the pivotal role of trust, transparency, and joint problem-solving in fostering effective SRM relationships, enabling knowledge sharing and co-development of innovative products and services. Innovation outcomes observed encompass product innovation, process improvements, and technological advancements facilitated by close collaboration between e-commerce firms and their suppliers. Challenges such as managing supplier diversity, navigating regulatory complexities, and mitigating geopolitical risks highlight the complexities and strategic imperatives of SRM implementation in global supply chains. The study also emphasizes the transformative role of digital technologies, including real-time data exchange, predictive analytics, and agile supply chain management, in enhancing SRM effectiveness and innovation capabilities. Strategic alignment with organizational goals and market strategies further enhances the impact of SRM, enabling firms to prioritize innovation initiatives, allocate resources effectively, and sustain growth amidst economic uncertainties. The findings contribute to theoretical understanding and practical insights into SRM strategies that promote innovation excellence and competitive positioning within e-commerce. The implications for practice underscore the importance of fostering collaborative supplier relationships, investing in digital transformation, and adopting proactive strategies to manage risks and regulatory compliance.

This qualitative study explores the complexities of managing suppliers for quality among Italian restaurant owners, investigating their perspectives, challenges, and strategic approaches in supplier management. Through semi-structured interviews with 15 restaurant owners across diverse regions of Italy, key themes emerged regarding supplier selection criteria, challenges in supplier management, strategies for ensuring quality, and the impact of external factors on supply chain dynamics. Findings reveal that authenticity, reliability, and sustainability are paramount in supplier selection, aligning with consumer expectations for genuine Italian ingredients and culinary excellence. Economic pressures, operational disruptions, and external influences, exacerbated by events like the COVID-19 pandemic, necessitate adaptive strategies such as cost-effective sourcing, rigorous quality control measures, and technological integration to maintain supply chain continuity and uphold quality standards. Strategic initiatives, including rigorous quality assurance protocols, digital procurement platforms, and collaborative partnerships with suppliers, enhance transparency, efficiency, and responsiveness in supply chain operations. Entrepreneurial initiatives and emotional intelligence play crucial roles in fostering innovative sourcing strategies and building trust-based relationships with suppliers, enhancing supply chain resilience and responsiveness to market demands. External factors, such as market dynamics and sustainability trends, underscore the need for continuous adaptation and strategic foresight in supplier management practices. The study contributes to understanding the intricate dynamics of supplier management in Italian cuisine, offering insights for enhancing operational efficiency, maintaining quality standards, and fostering sustainable supplier relationships in the food service industry.

This qualitative study explores how businesses are adapting their Supplier Relationship Management (SRM) strategies to address the challenges and capitalize on the opportunities presented by evolving e-commerce trends. Through in-depth interviews with key industry stakeholders, the research investigates the integration of advanced technologies such as artificial intelligence (AI), blockchain, and the Internet of Things (IoT) into SRM practices. Findings reveal that these technologies enhance efficiency, transparency, and responsiveness within supply chains, enabling businesses to navigate complexities such as supply chain dynamics, fluctuating consumer demand, and regulatory compliance. The study identifies effective strategies employed by businesses, including enhancing communication channels with suppliers, diversifying supplier bases, and investing in technological advancements. These strategies enable businesses to mitigate risks, improve collaboration, and drive innovation, thereby maintaining competitive advantage in the digital marketplace. Collaborative practices such as joint product development, shared risk management, and co-investment in technology emerge as critical drivers of successful SRM. Ultimately, this research contributes to a deeper understanding of SRM in the context of e-commerce, offering insights into how businesses can optimize their supply chain operations amidst technological disruptions and market fluctuations. The findings underscore the importance of proactive adaptation and strategic alignment with suppliers to foster resilient and agile supply chains. As e-commerce continues to evolve, businesses must leverage these insights to refine their SRM strategies and sustain growth in an increasingly competitive environment.

This study investigates the adoption of emerging technologies in procurement within supply chain management, focusing on the transformative potential of artificial intelligence (AI) and blockchain. Through a qualitative approach, involving semi-structured interviews with procurement professionals and technology experts, the study explores how these technologies enhance operational efficiency, optimize inventory management, and improve supply chain transparency and trust. AI-driven predictive analytics were found to significantly enhance demand forecasting, inventory optimization, and supplier performance evaluation, fostering data-driven decision-making and proactive risk management. Blockchain technology ensures secure and transparent transactions, facilitating compliance with ethical sourcing standards and reducing risks associated with counterfeit goods. Despite these benefits, organizations face substantial challenges in adopting these technologies, including data security concerns, integration complexities with existing IT infrastructure, and skills shortages in AI and blockchain expertise. Leadership commitment and organizational readiness are identified as crucial enablers for overcoming these barriers and driving successful technology adoption. The study also highlights the strategic importance of supplier relationship management, emphasizing how emerging technologies enhance real-time collaboration, performance insights, and contractual transparency. By addressing these challenges and leveraging technological innovations strategically, organizations can achieve sustainable growth, resilience, and competitive advantage in an increasingly digital and interconnected global economy. The findings contribute to advancing knowledge on procurement innovation and offer valuable insights for organizations seeking to navigate the complexities of technology-driven procurement practices. Future research directions may explore the long-term impact of technology adoption on supply chain dynamics, sustainability initiatives, and organizational performance.

This qualitative study explores the role of supply chain partnerships in enhancing brand value through collaborative marketing efforts. By analyzing diverse cases from industries including consumer electronics, apparel, automotive, and food and beverage, the research examines how different forms of partnerships—strategic alliances, co-marketing, joint ventures, and supplier integrations—contribute to brand value. The study employed a multiple case study design, incorporating semi-structured interviews, document analysis, and participant observations to gather data. The findings reveal that these partnerships enable companies to leverage complementary strengths, resulting in enhanced innovation, product quality, sustainability, market reach, and ethical standards. Collaborative marketing strategies such as joint product launches, sustainable branding campaigns, and co-developed customer programs are identified as key drivers of brand value, effectively increasing brand awareness, customer loyalty, market share, and perceived quality. Despite challenges like coordination complexity, goal misalignment, cultural differences, and cost management issues, the benefits realized—such as improved brand image, faster time-to-market, and reduced costs—demonstrate the substantial value of effective supply chain partnerships. The study underscores the importance of strategic alignment between supply chain activities and brand objectives, highlighting that successful partnerships foster deeper consumer engagement and stronger market positions. This research provides actionable insights for companies seeking to optimize their supply chain collaborations to enhance brand value, offering a foundation for future studies and practical applications in supply chain management and marketing. The findings contribute to a deeper understanding of how supply chain partnerships can be strategically managed to drive brand excellence in a competitive market environment.

The integration of supply chain data analytics with marketing strategies has become increasingly critical for organizations seeking to enhance customer insights and competitive advantage in today's dynamic business environment. This qualitative research explores the intersection of these two disciplines, focusing on implementation strategies, benefits, challenges, and future directions. Through semi-structured interviews with industry experts and stakeholders, key themes emerged, highlighting the importance of cross-functional collaboration, technological integration, and robust data governance in achieving effective integration. The study identifies significant benefits, including enhanced customer insights, improved operational efficiencies, and competitive advantage through personalized marketing strategies and optimized supply chain management. However, challenges such as data silos, technological complexities, and regulatory compliance issues pose barriers that require strategic solutions and organizational commitment. Future directions point towards the continued evolution of predictive analytics, AI-driven solutions, and sustainability integration, shaping the future landscape of supply chain management and marketing. Proactive engagement with regulatory frameworks and ethical considerations will be crucial in navigating these challenges and building trust with consumers. Ultimately, this research contributes to a deeper understanding of how organizations can leverage data analytics to drive strategic decision-making, enhance customer relationships, and achieve sustainable growth in an increasingly data-driven economy.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative research explores the intricate relationship between cultural influences on supply chain practices and their implications for marketing strategies. Cultural dimensions such as individualism versus collectivism, uncertainty avoidance, and contextuality significantly shape decision-making processes, risk management strategies, and relationship dynamics within global supply chains. Through semi-structured interviews with supply chain managers, marketing executives, and cultural experts across diverse industries and regions, this study investigates how cultural values impact supply chain operations and consumer engagement strategies. Key findings highlight that collectivist cultures prioritize consensus-building and long-term relationships, fostering trust and collaboration among supply chain partners. In contrast, individualist cultures emphasize efficiency and accountability but may face challenges in relationship building. Risk management practices vary with cultural orientations towards uncertainty, influencing the adoption of structured versus flexible approaches to mitigate disruptions. In terms of marketing implications, the study underscores the importance of cultural sensitivity in crafting effective strategies. High-context cultures require marketing messages that resonate emotionally through implicit communication and cultural symbolism, while low-context cultures prefer clear, direct messaging focusing on product benefits. The study concludes by advocating for cultural competence as a strategic imperative for global businesses, enabling them to leverage cultural diversity for innovation and competitive advantage. It emphasizes the need for continuous adaptation to cultural nuances and market dynamics to foster inclusive collaboration and achieve sustainable growth in a globalized economy.

This qualitative investigation delves into the intricate interplay of cultural factors within supply chain integration, aiming to elucidate how diverse cultural dimensions shape communication dynamics, decision-making processes, and collaborative efforts among global supply chain practitioners. Employing a phenomenological approach, the study explores the lived experiences and subjective perceptions of supply chain managers and professionals, providing nuanced insights into the profound impact of national culture, organizational culture, digital technologies, and sustainability considerations on supply chain operations. Key findings highlight the significant influence of national culture, as conceptualized by Hofstede's cultural dimensions, on supply chain interactions. These dimensions—such as power distance, individualism versus collectivism, uncertainty avoidance, and masculinity versus femininity—play crucial roles in shaping how individuals perceive authority, manage risks, negotiate agreements, and engage in collaborative practices across diverse cultural contexts. Cultural norms and values deeply influence both intra-organizational dynamics and inter-organizational relationships within global supply chains. Organizational culture emerges as a pivotal determinant of supply chain integration success. Cultures characterized by transparency, trust, inclusivity, and a commitment to continuous improvement create environments conducive to effective cross-border collaboration. Conversely, organizations with conflicting or ambiguous cultural norms face challenges in aligning goals, fostering mutual understanding, and maintaining open communication channels with international partners. The study underscores the transformative role of digital technologies in enhancing supply chain management practices. Technologies such as blockchain, artificial intelligence, and the Internet of Things improve supply chain visibility, optimize operations, and enhance decision-making processes, strengthening resilience against disruptions. The adoption and integration of these technologies depend on organizational readiness, technological capabilities, and cultural acceptance within supply chain networks. Cultural factors influence the adoption pace, implementation strategies, and utilization patterns of digital technologies, emphasizing the need for strategic alignment between technological investments and cultural norms to maximize their potential benefits. Furthermore, sustainability considerations are critical in shaping cultural values and priorities within supply chain networks. Organizations increasingly recognize the importance of environmental sustainability, ethical sourcing practices, and corporate social responsibility in shaping their reputations, stakeholder relationships, and long-term business goals. Cultural factors significantly influence organizations' approaches to sustainability, guiding their strategic decisions and operational practices.

This qualitative research investigates the intricate relationship between market dynamics and supply chain sustainability in the renewable energy sector. Amidst the global energy transition, renewable energy has emerged as a crucial player in mitigating climate change and fostering sustainable development. However, ensuring the sustainability of renewable energy supply chains presents complex challenges shaped by various market forces. Through semi-structured interviews and thematic analysis, this study explores how policy frameworks, technological innovation, market competition, consumer preferences, and investor demands influence sustainability practices across the renewable energy supply chain. The findings highlight the critical role of supportive policy environments, technological advancements, and stakeholder collaboration in driving sustainability performance and mitigating risks within renewable energy supply chains. Furthermore, the study identifies challenges such as supply chain complexity, regulatory compliance, and stakeholder engagement, underscoring the need for collaborative efforts and multi-stakeholder partnerships. By addressing these challenges and leveraging opportunities, stakeholders can advance sustainability goals, mitigate climate change impacts, and foster a more resilient and equitable renewable energy sector. The research contributes to a deeper understanding of market dynamics and supply chain sustainability in the renewable energy sector, offering insights for policymakers, industry stakeholders, and sustainability practitioners to inform evidence-based decision-making and drive positive change.

In the past few years, cloud-based additive manufacturing has shown its application and potential in industries. For this reason, when the demand for cloud-based additive products increases, efficient scheduling of related tasks becomes more important to achieve optimized duration. This research proposes a Heuristic Scheduling Algorithm (HSA) for use in Cloud-Based Additive Manufacturing (CBAM) with an approach to Critical Chain Buffer Management (CC/BM). This research aims to schedule and optimize the time in cloud-based additive manufacturing, in addition to the parts production process, it also includes the steps of receiving online orders and informing the manufacturing unit and sending them to the customer. The HSA proposed in this research consists of three phases 1- Buffer sizing, 2- Additive manufacturing and logistics scheduling, and 3- Buffer controlling. To evaluate, the performance of the algorithm is simulated. The finding shows that the proposed HAS can effectively solve the CBAM scheduling problem.

Food production is a complex matter, affecting people’s lives, organizations’ profits, and the well-being of the whole planet and has multifaceted ethical considerations surrounding its production, distribution, and consumption. This paper addresses the pressing need to confront ethical challenges within the food system, encompassing issues such as environmental sustainability, food security, and individual food choices for better oral and systemic health of all individuals around the globe. From agricultural practices to global trade and food waste, ethical implications are addressed across various domains, highlighting the interconnectedness of ethical decision-making in the food industry. Central themes explored include the ethical dimensions of food production methods, the impact of global trade on food ethics, and the role of individuals in making ethically informed food choices. Additionally, the paper dives into the spiritual and physical significance of food, particularly through the lens of oral health as a gateway to holistic well-being. Recognizing the complexity of the food ecosystem, the paper calls for serious interventions in legislation and economics to promote ethical protocols and techniques for sustainability reasons. It emphasizes the importance of ethical considerations in food safety management systems, regulatory frameworks, and quality standards. Moreover, the paper underscores the need for a comprehensive approach to address ethical dilemmas and moral values inherent in the food industry and oral health policies, advocating for the adoption of the precautionary principle and ethical decision-making frameworks. The article aims to serve as a call to action for stakeholders across the food industry and the healthcare sector, to prioritize ethical practices, promote transparency, rearrange economic parameters and work towards a more sustainable and equitable food system for inner and outer oral and systemic health and human sustainability for all.

This review deals with the developmental origins of extraocular, jaw and laryngeal muscles, the expression, regulation and functional significance of myosin heavy chains (MyHCs) they express and changes in MyHC expression during phylogeny. Myogenic progenitors from mesoderm in the prechordal plate and branchial arches specify craniofacial muscle allotypes with different repertoires for MyHC expression. To cope with very complex eye movements, extraocular muscles express 11 MyHCs, ranging from the superfast extraocular MyHC to the slowest, non-muscle MyHC IIB. They have distinct global and orbital layers, singly- and multiply-innervated fibres, longitudinal MyHC variations, and palisade endings that mediate axon reflexes. Jaw-closing muscles express the high-force masticatory MyHC, cardiac or limb MyHCs depending on the appropriateness for the acquisition and mastication of their food. Laryngeal muscles express extraocular and limb muscle MyHCs but shift towards slower MyHCs in large animals. During postnatal development, MyHC expression of craniofacial muscles is subject to neural and hormonal modulation. The primary and secondary myotubes of developing EOMs are postulated to induce, via different retrogradely transported neurotrophins, the rich diversity of neural impulse patterns that regulate the specific MyHCs they express. Thyroid hormone shifts 2A MyHC towards 2B in jaw, laryngeal muscles and possibly extraocular muscles.

Hydrogen sulfide (H2S) is an environmental toxicant of health concern. The brain is a major target in acute H2S poisoning. This study was to test the hypothesis that acute and subchronic ambient H2S exposures alter the brain metabolome. Male 7-8 week-old C57BL/6J mice were exposed by whole-body inhalation to 1000 ppm H2S for 45 min and euthanized at 5 min or 72 h for acute exposure. For subchronic study, mice were exposed to 5 ppm H2S 2 h/day, 5 days/week for 5 weeks. The brainstem was removed for metabolomic analysis. Enrichment analysis showed the metabolomic profiles in acute and subchronic H2S exposures matched with those of cerebral spinal fluid from patients with seizures or Alzheimer’s disease. Acute H2S exposure decreased excitatory neurotransmitters, aspartate and glutamate, while the inhibitory neurotransmitter, serotonin, was increased, which may explain H2S-induced loss of consciousness. Branched-chain amino acids and glucose were increased by acute H2S exposure. Subchronic H2S exposure within OSHA guideline surprisingly decreased serotonin altering central nervous system. In subchronic H2S exposure, glucose was decreased while polyunsaturated fatty acids, inosine, and hypoxanthine were increased. Collectively, these results provide important mechanistic clues of acute and subchronic ambient H2S poisonings and show that H2S alters brainstem metabolome.

(1) Background: The stylohyoid syndrome, known as classical Eagle Syndrome (cES), is characterized by calcificatication of the stylohyoid chain with numerous nonspecific symptoms, mainly pain. This study introduces Minimally Invasive Cervical Styloidectomy (MICS). (2) Methods: MICS was performed on sixty-five patients diagnosed with classical Eagle syndrome. Patients underwent meticulous differential diagnosis. Surgical plans were established based on the findings from neck angioCT. (3) Results: The healing process was uneventful without significant complications. The overall success rate was 97.0%, with a follow-up of a minimum of six months. In one case, the surgery did not yield the desired improvement. In one case, a partial relapse of symptoms was observed. (4) Conclusions: MICS is a straightforward and efficient surgical treatment technique for stylohyoid syndrome.

The objective of this study was to apply simulation and computational intelligence techniques using artificial intelligence and genetic algorithm for economic and environmental optimization of the reverse network (manufacturers, waste managers and recyclers in São Paulo, Brazil) of waste electrical and electronic equipment (WEEE) to promote circular economy. For the economic evaluation, the reduction of: fuel, drivers, insurance, depreciation, maintenance and charges was considered and for the environmental evaluation, the environmental impact was measured in the abiotic, biotic, water, land, air and greenhouse gases compartments. It is concluded that the optimized structure of the WEEE reverse chain for São Paulo, Brazil reduced the number of collections, making the most of the cubage. It also generated economic and environmental gains, contributing to the strategic actions of the circular economy. Thus, the proposed simulation allows replication in organizational practice, mainly to meet the 2030 agenda on reducing the carbon footprint generated in transport in large cities. Thus, this study can guide companies on structuring the reverse WEEE chain in São Paulo, Brazil for economic and environmental optimization, a relevant aspect considering the exponential generation of WEEE, requiring the implementation of the national solid waste policy, and subsequently the signature of the electronics sector agreement in São Paulo.

In recent years, there has been a wave of research on complex networks. The purpose of this paper is to study the graph theoretic mathematical properties of random cyclooctatetraene chain. We obtain the explicit analytical expressions for the variances of the Gutman index, Schultz index, multiplicative degree-Kirchhoff index and additive degree-Kirchhoff index of a random cyclooctatetraene chain with $n$ octagons, which plays a crucial role in the research and application of topological indices.

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low field NMR (LF-NMR) relaxation characteristic data of various sample oils were analyzed. We also suggest a new method for prediction size of edible oil molecules using LF-NMR relaxation time distributions. According to the relative molecular mass, carbon chain length, transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid are displayed, reflecting the composition information of different oils. To study the correlation between the rotation correlation time and molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation, and molecular dynamics of complex fluids. Measurements of grease in the free fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured by low-field nuclear magnetic resonance.

We present a new analytical method to find the asymptotic stable equilibria states based on the Markov chain technique. We reveal this method on the SIR-type epidemiological model that we developed for viral diseases with long-term immunity memory pandemic. This is a large-scale model containing 15 nonlinear ODE equations, and classical methods have failed to analytically obtain its equilibria. The proposed method is used to conduct a comprehensive analysis by a stochastic representation of the dynamics of the model, followed by finding all asymptotic stable equilibrium states of the model for any values of parameters and initial conditions.

The evolution towards Industry 4.0 is driving the need for innovative solutions in the area of network management, considering the complex, dynamic and heterogeneous nature of ICT supply chains. To this end, Intent-Based networking (IBN) which is already proven to evolve how network management is driven today, can be implemented as a solution to facilitate the management of large ICT supply chains. In this paper, we first present a comparison of the main architectural components of typical IBN systems and, then, we study the key engineering requirements when integrating IBN with ICT supply chain network systems while considering AI methods. We also propose a general architecture design that enables intent translation of ICT supply chain specifications into lower level policies, to finally show an example of how the access control is performed in a modelled ICT supply chain system.

The early diagnosis of mastitis represents an essential factor for a prompt detection of the animal for further actions. In fact, if not culled, infected cows must be segregated from the milking herd and milked last, or milked with separate milking units. Besides microbiological analysis, the somatic cell count (SCC) commonly used as predictor of intramammary infection, frequently lead to a misclassification of milk samples. To overcome these limitations, more specific biomarkers are continuously evaluated. Total amino acid content increases significantly in mastitic milk compared to normal one. Bovine mastitis can arise as a result of infection of the mammary gland by Staphylococcus aureus. Multiplication of this bacterium within the mammary gland is required for infection to persist. S. aureus requires branched-chain amino acids (BCAAs: isoleucine, leucine, valine) for protein synthesis, branched-chain fatty acids synthesis and environmental adaptation by responding to their availability via transcriptional regulators. The importance of BCAAs for S. aureus physiology necessitates that it either synthesize them or scavenge them from the environment. Increase of BCAAs in composite milk has been postulated to be linked to mammary infection by S. aureus. In the present work, we demonstrated, by a direct ion-pairing reversed-phase method based on the use of the evaporative light-scattering detector (IP-RP-HPLC-ELSD), applied to 65 composite cow milk samples, a correlation between the concentration of isoleucine and leucine and S. aureus load.

Bayesian analysis for masked data under competing risk frameworks is studied for the purpose of assessing the impact of covariates on the hazard functions when the failure time is exactly observed for some subjects but only known to lie in an interval of time for the remaining subjects. Such data, known as partly interval-censored data, usually result from periodic inspection. Dirichlet and Gamma processes are assumed as priors for masking probabilities and baseline hazards. The Markov Chain Monte Carlo (MCMC) technique is employed for the implementation of the Bayesian approach. The effectiveness of the proposed model is tested through numerical studies, including simulated and real data sets.

Digital technologies have changed the way supply chain operations are structured. In this article, we conduct systematic syntheses of literature on the impact of new technologies on supply chains and the related cyber risks. A taxonomic/cladistic approach is used for the evaluations of progress in the area of supply chain integration in the Industrial Internet of Things and Industry 4.0, with a specific focus on the mitigation of cyber risks. An analytical framework is presented, based on a critical assessment with respect to issues related to new types of cyber risk and the integration of supply chains with new technologies. This paper identifies a dynamic and self-adapting supply chain system supported with Artificial Intelligence and Machine Learning (AI/ML) and real-time intelligence for predictive cyber risk analytics. The system is integrated into a cognition engine that enables predictive cyber risk analytics with real-time intelligence from IoT networks at the edge. This enhances capacities and assist in the creation of a comprehensive understanding of the opportunities and threats that arise when edge computing nodes are deployed, and when AI/ML technologies are migrated to the periphery of IoT networks.

This paper proposed emotion recognition methods for consecutive facial images, and implements the inference of neural network model on a field-programmable gate array (FPGA). The proposed emotion recognition methods are based on a neural network model architecture that combines convolution neural networks (CNNs), long short-term memory (LSTM), and fully connected neural networks (FCNNs), called CLDNN or ConvLSTM-FCN. This type of neural network mod-el can analyze the local feature sequences obtained through convolution of data, making it suita-ble for processing time-series data such as consecutive facial images. In this paper, sequences of facial images are sampled from videos corresponding to various emotional state of subjects. The sampled images are then pre-processed with the processes includes facial detection, greyscale conversion, resize, and data augmentation if necessary. The 2-D CNN in ConvLSTM-FCN is used for feature extraction for these pre-processed facial images. These sequences of facial im-age features are time sequences with dependent properties between the elements within them. The LSTM is then used to model these time sequences followed by fully connected neural net-works (FCNNs) for classification. The proposed consecutive facial emotion recognition method achieves an average recognition rate of 99.51% on RAVDESS dataset, 87.80% on BAUM-1s dataset and 96.82% on eNTERFACE’05 data set, using 10-fold cross-validation on the PC. Some com-parisons of recognition accuracies between the proposed method and the other existing related works are conducted in this paper. According to the comparisons, the proposed emotion recog-nition methods outperform the existing related researches. The proposed emotion recognition methods are then implemented on an FPGA chip using the neural network model inference algo-rithms in this paper, and the accuracies of the experiments conducted on the FPGA chip are iden-tical to those obtained on the PC. This verifies that the proposed neural network model imple-mented on the FPGA chip performs well.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.

Human violence recognition is an area of great interest in the scientific community, given its broad spectrum of applications, especially in video surveillance systems, since detecting violence in real-time could prevent criminal acts and save lives. Despite the number of existing proposals and research, most focus on the precision of results, leaving aside efficiency and its practical implementation. Thus, this work proposes a model that is effective and efficient in recognizing human violence in real-time. The proposed model consists of three modules: a first module called Spatial Motion Extractor (SME), in charge of extracting regions of interest from a frame; a second module called Short Temporal Extractor (STC), whose function is to extract temporal characteristics of rapid movements, finally the Global Temporal Extractor (GET) module, responsible for identifying long-lasting temporal features and fine-tuning the model. The proposal was evaluated regarding efficiency, effectiveness, and ability to operate in real-time. The results obtained on Hockey, Movies, and RWF-2000 datasets demonstrated that this approach is highly efficient compared to other alternatives. A VioPeru dataset was created to validate real-time applicability with violent and non-violent videos captured by real video surveillance cameras in Peru. The effectiveness results in this dataset outperformed the best existing proposal. Therefore, our proposal has contributions in efficiency, effectiveness, and real-time.

Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control and proposes a novel algorithm called Switched Auto-regressive Neural Control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC while enabling continuous learning. Results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions with minimal deviations from actual values.

The land subsidence occurring in goafs after coal mining is a protracted process. The accurate prediction of long-term land subsidence in goafs relies heavily on the availability of long-term monitoring data. However, the scarcity of continuous long-term land subsidence monitoring data subsequent to the cessation of mining significantly hinders the accurate prediction of long-term land subsidence in goafs. To address this challenge, this study proposes an innovative method based on Interferometric Synthetic Aperture Radar (InSAR) for predicting long-term land subsidence of goafs following coal mining. The proposed method employs a concatenation approach that integrates multiple short-term monitoring data from different coal faces, each with distinct cessation times, into a cohesive and uniform long-term sequence by normalizing the subsidence rates. The method was verified using actual monitoring data from the Yangquan No.2 mine in Shanxi Province, China. Initially, coal faces with same shapes but varying cessation times were selected for analysis. Using InSAR monitoring data collected between June and December of 2016, the average subsidence rate corresponding to the duration after coal mining cessation of each coal face was back-calculated. Subsequently, a function relating subsidence rate to the duration after coal mining cessation was fitted to the data. Finally, the relationship between cumulative subsidence and the duration after coal mining cessation was derived by integrating the function. The results indicated that the relationship between subsidence rate and duration after coal mining cessation followed an exponential function for a given coal face, whereas the relationship between cumulative subsidence and duration after coal mining cessation conformed to the Knothe time function. Notably, after the cessation of coal mining, significant land subsidence persisted in the goaf of the Yangquan No.2 mine for a duration ranging from 5 to 10 years. The cumulative subsidence curve along the long axis of the coal face ultimately exhibited an inclined W-shape. The proposed method enables the quantitative prediction of residual land subsidence in goafs, even in cases where continuous long-term monitoring data are insufficient, thus providing valuable guidance for construction decisions above the goaf.

Emotions play a vital role in understanding human behavior and interpersonal relationships. The ability to recognize emotions through Electroencephalogram (EEG) signals offers an alternative to traditional methods, such as questionnaires, enabling the identification of emotional states in a non-intrusive manner. Automatic emotion recognition holds great potential, eliminating the need for clinical examinations or physical visits, thereby contributing significantly to the advancement of Brain-Computer Interface (BCI) technology. However, one of the key challenges lies in effectively selecting and extracting relevant features from the EEG signal to establish meaningful distinctions between different emotional states. The process of feature selection is often time-consuming and demanding. In this research, we propose a groundbreaking approach for automatically identifying three emotional states (positive, negative, and neutral) by leveraging auditory stimulation of EEG signals. Our novel method directly applies the raw EEG signal to a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) architecture, bypassing the conventional feature extraction and selection steps. This unconventional approach offers a significant departure from existing literature. Our proposed network architecture comprises ten convolutional layers, followed by three LSTM layers and two fully connected layers. Through extensive simulations and evaluations on 12 active channels, our algorithm demonstrates exceptional performance, achieving an accuracy of 97.42\% and 95.23\% for the binary classification of negative and positive emotions, as well as a Cohen's Kappa coefficient of 0.96 and 0.93 for the three-class classification (negative, neutral, and positive), respectively. These promising results highlight the efficacy of our novel methodology and its potential implications in advancing emotion recognition using EEG signals.

Laser energy per unit surface, necessary to trigger the material removal, decreases with the pulse shortening becoming the pulse-time independent in the sub-picosecond range. These pulses are shorter the electron-to-ion energy transfer time and electronic heat conduction time minimizing the energy losses. The electrons receiving the energy larger than the threshold, drag the ions off the surface in the mode of electrostatic ablation. We show that the pulse shorter than the ion period (Shorter-the-Limit (StL)) ejects conduction electrons with the energy larger than the work function (from a metal) leaving the bare ions immobile in a few atomic layers. The electrons emission is followed by the bare ion’s explosion, ablation, and THz radiation from expanding plasma. We compare this phenomenon to the classic photo effect, nanocluster Coulomb explosions, show differences and consider possibilities for detecting the new mode of ablation experimentally by emitted THz radiation and consider applications of high-precision nano-machining with this low intensity irradiation.

: This study was aimed at assessing adaptation and bonding of discontinuous (short) glass fiber-reinforced composite to intraradicular dentin EverX Flow (GC Corporation, Tokyo, Japan), when used as intracanal composite filling and anchorage instead of traditional fiber posts. (2) Methods: Seventy intact extracted human teeth were endodontically treated and randomly divided into 6 groups (n=10), depending on the materials used in the post space. In Group 1, a 2-bottle universal adhesive G2 Bond Universal + EverX Flow were tested. In group 2, a single-component universal adhesive G-Premio Bond + EverX Flow were used. In groups 3 and 4 the same materials are tested, but after cleaning of the canal walls with 17% EDTA and final irrigation with 5.25% NaOCl Ultrasound Activated. In the last three Groups (5-7) traditional prefabricated GC Fiber Posts 1.6 mm silanized with G-Multi Primer for 1 minute are cemented with a dual-cured composite resin cement (GradiaCore), after ultrasonic irrigation in the groups 6 and 7. In each group, 1 mm-thick slices from each sample (n=10) were cut for light microscope and SEM inspection for study materials adaption to the dentin and for measuring push-out strength of post / cemement material to the dentin / prefabricated post. These results were statistically analyzed: as the data distribution was not normal, the Kruskal-Wallis Analysis of Variance by Ranks had to be applied. The level of significance was set at p<0.05. Results: Push-out forces varied between 6.66-8.37 MPa. No statistically significant differences were recorded among the groups. Microscopic examination showed that ultrasonic irri-gation increased adaptation of the materials to the dentin surface. There was a trend of higher bond strength among the tested groups when EverX Flow was used. Also, the type of failure was more often cohesive when ultrasonic irrigation and two-step adhesive system were used. Conclusions: Within the limitations of this in vitro study, it may be concluded that when EverX Flow was used for intracanal anchorage in the post-endodontic recon-struction, similar push-out retentive forces and strength to those of traditional fiber posts cemented with particulate filler resin composite cements were achieved. Although further studies are necessary, EverX Flow represents an effective alternative to traditional fiber post adhesion in particular when used in combination with the two-step adhesive system and ultrasonic activation.

According to current guidelines, growth hormone (GH) therapy is strongly recommended in children and adolescents with GH deficiency (GHD) in order to accelerate growth rate and attain normal adult height. The diagnosis of GHD requires demonstration of decreased GH secretion in stimulation tests, below the established threshold value. Currently, GHD in children is classified as secondary insulin-like growth factor-1 (IGF-1) deficiency. Most of children diagnosed with isolated GHD presents with normal GH secretion at the attainment of near-final height or even in mid-puberty. The most important clinical problems, related to the diagnosis of isolated GHD in children and to optimal duration of rhGH therapy include: arbitrary definition of subnormal GH peak in stimulation tests, disregarding factors influencing GH secretion, insufficient diagnostic accuracy and poor reproducibility of GH stimulation tests, discrepancies between spontaneous and stimulated GH secretion, clinical entity of neurosecretory dysfunction, discrepancies between IGF-1 concentrations and results of GH stimulation tests, significance of IGF-1 deficiency for the diagnosis of GHD, a need for validation IGF-1 reference ranges. Many of these issues have remained unresolved for 25 years or even longer. It seems that finding solutions to them should optimize diagnostics and therapy of children with short stature.

Among the components of a healthy life, sexuality is an essential part, contributing not only to psychophysical well-being, but also to the social well-being of women and, consequently to their quality of life. A poorly investigated standpoint is the acceptability of a contraceptive method, not only in terms of tolerability and metabolic neutrality, but also concerning the impact that it can have on sexual life. In this context, we will provide an overview of the different methods of contraception and their effects on female sexuality from the biological changes, to organic, social, and psychological factors, which can all shape sexuality.A MEDLINE/PUBMED review of the literature between 2010 and 2021 was conducted using the following key words/phrases: hormonal contraception, contraceptives, female sexual function, libido, sexual arousal and desire, and sexual pain. Recent studies have supported the effects of contraceptives on women’s sexuality, describing a variety of positive and negative events on several domains of the sexual function (desire, arous-al, orgasm, pain, enjoyment). However, satisfaction with sexual activity depends on factors that extend beyond sexual functioning alone. A more holistic approach is needed to better under-stand the multitude of factors linked to women’s sexuality and contraception. Contraceptive counselling must necessarily consider these important elements since they are closely related to good compliance.

With the automotive industry moving towards automated driving, sensing is increasingly important in enabling technology. The virtual sensors allow data fusion from various vehicle sensors and provide a prediction for measurement that is hard or too expensive to measure in another way or in the case of demand on continuous detection. In this paper, virtual sensing is discussed for the case of vehicle suspension control, where information about the relative velocity of the unsprung mass for each vehicle corner is required. The corresponding goal can be identified as a regression task with multi-input sequence input. The hypothesis is that the state-of-art method of Bidirectional Long-Short Term Memory (BiLSMT) can solve it. In this paper, a virtual sensor has been proposed and developed by training a neural network model. The simulations have been performed using an experimentally validated full vehicle model in IPG Carmaker. Simulations provided the reference data which was used for Neural Network (NN) training. The extensive dataset covering 26 scenarios has been used to obtain training, validation and testing data. The Bayesian Search was used to select the best neural network structure using root mean square error as a metric. The best network is made of 167 BiLSTM, 256 fully connected hidden units and 4 output units. Error histograms and spectral analysis of the predicted signal compared to the reference signal are presented. The results demonstrate the good applicability of neural network-based virtual sensors to estimate vehicle unsprung mass relative velocity.

It is crucial to forecast the water demand accurately for supplying water efficiently and stably in a water supply system. In particular, accurately forecasting short-term water demand helps in saving energy and reducing operating costs. With the introduction of the Smart Water Grid (SWG) in a water supply system, the amount of water consumption is obtained in real time through an advanced metering infrastructure (AMI) sensor, which can be used for forecasting the short-term water demand. The models widely used for water demand forecasting include the autoregressive integrated moving average, radial basis function-artificial neural network, quantitative multi-model predictor plus, and long short-term memory. However, there is a lack of research on assessing the performance of models and forecasting the short-term water demand by applying the data on the amount of water consumption by purpose and the pipe diameter of an end-use level of the SWG demonstration plant in each demand forecasting model. Therefore, in this study, the short-term water demand was forecasted for each model using the data collected from the AMI, and the performance of each model was assessed. The Smart Water Grid Research Group installed ultrasonic-wave-type AMI sensors in the block 112 located in YeongJong Island, Incheon, and the actual data used for operating the SWG demonstration plant were adopted. The performance of the model was assessed by using the residual, root mean square error (RMSE), normalized root mean square error (NRMSE), Nash–Sutcliffe efficiency (NSE), and Pearson correlation coefficient (PCC) as indices. The water demand forecast was slightly underestimated in models that employed the assessment results based on the RMSE and NRMSE. Furthermore, the forecasting accuracy was low for the NSE due to a large number of negative values; the correlation between the observed and forecasted values of the PCC was not high, and it was difficult to forecast the peak amount of water consumption. Therefore, as the short-term water demand forecasting models using only time and the amount of water consumption have limitations in reflecting the characteristics of consumers, a water supply system can be managed more precisely if other factors (weather, customer behavior, etc.) influencing the water demand are applied.