Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

Drilling centers are collaborative environments dedicated to facilitate decision-making in the well construction, where multidisciplinary teams work to support operations. The oil operators usually have drilling centers with different types of ergonomic features with considerable potential of integration, creating the opportunity to an Ergonomic Workplace Analysis. This paper aims to present the analysis of infrastructure requirements of one specific company in Brazil. The method was based on a survey with employees, which, coped with a statistical analysis, enabled understanding the impact of the layout requirements. The result is an approach to design collaborative environments, standardizing and defining models for the industry.

In this research, catalytic steam reforming acetic acid derived from the aqueous portion of bio-oil for hydrogen production was investigated by using different Ni/ATC (Attapulgite Clay) catalysts prepared by precipitation, impregnation and mechanical blending methods. The fresh and reduced catalysts were characterized by XRD, N₂ adsorption-desorption, TEM and H₂-TPR. The comprehensive results demonstrated that the interaction between active metallic Ni and ATC carrier was significantly improved in Ni/ATC catalyst prepared by precipitation method, and in which the mean Ni particle size was the smallest (~13 nm) resulted in the highest metal dispersion (7.5%). The catalytic performance of the three catalysts was evaluated through the process of steam reforming of acetic acid in a fixed-bed reactor under atmospheric pressure at two different temperatures, such as 550 ℃ and 650 ℃. Results showed that the Ni/ATC (PM-N/ATC) prepared by precipitation method, achieved the highest H₂ yield of ~82% and little lower acetic acid conversion efficiency of ~85% than that (~95%) of Ni/ATC (IM-NATC) prepared by impregnation method. In addition, the deactivation catalysts after reaction for 4 h were analyzed by XRD, TGA-DTG and TEM, which demonstrated that the catalyst deactivation was not caused by the amount of carbon deposition, but owed to the significant agglomeration and sintering of Ni particles in the carrier.

Making sustainable business models, especially in manufacturing, is a key approach for organizations to adopt in order to create more sustainable processes. New technologies are needed to implement new policies that can minimize the economic, environmental, and social consequences of industrial activity. Sustainable maintenance is decisive to guarantee the availability, reliability, and safety of assets, surpassing traditional maintenance policies and embracing the principles of the circular economy. The main objective of Maintenance 4.0 is to increase the lifespan and availability of the equipment by preventing unplanned downtime, reducing planned shutdowns, and improving safety. This research focuses on the integration of advanced Industry 4.0 tools with conventional monitoring and control techniques in process industries. The article suggests that while complex neural networks might provide good predictions, simpler solutions can generalize well and have lower prediction errors because they can capture the underlying relationships between variables in a specific asset better. The combination of maintenance function, Industry 4.0 tools, and advanced statistical solutions to obtain sustainable processes can facilitate organizational change and aid decision-making in equipment management. These solutions seek to reduce interruptions and expenses through the unnecessary utilization of labor and spare parts. All these considerations improve communication and data processing, resulting in improved maintenance of a hydrogen compressor in the petrochemical industry.

Since the discovery of the phenomenon of surface-enhanced Raman scattering (SERS), it has gradually become an important tool for the analysis of material compositions and structures. The applications of SERS have been expanded from the fields of environmental and materials science to biomedicine, due to the extremely high sensitivity and non-destructiveness of SERS-based analytical technology that even allows single molecule detection. This article provides a comprehensive overview of Surface-Enhanced Raman Scattering (SERS) phenomenon. The content is divided into several main sections: basic principles and the significance of Raman spectroscopy; historical advancements and technological progress in SERS, as well as various practical applications across different fields. We also discussed how electromagnetic fields contribute to the SERS effect, the role of chemical interactions in enhancing Raman signals, modeling and computational approaches to understand and predict SERS effects.

Lithium-metal batteries offer high energy density, but are challenged by dendrite growth, which can lead to short circuits and battery failure. Various models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. In this study, a simple simulation model to predict macroscale dendrite growth on lithium metal electrodes is presented. The model uses a 3D single-particle diffusion-limited aggregation (DLA) algorithm, biased by the electric field, to simulate dendrite growth. The model was validated using 3D MRI of dendrite growth in a lithium symmetric cell. It was used to simulate deposition between consecutive measured dendrite structures. The model also introduces an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. This parameter was calibrated using the overlap and displacement of deposition sites metrics, achieving optimal results at c = 0.7. The model predictions were in good agreement with experimental data confirmed by fractal dimension analysis. This model provides a useful tool for predicting and studying dendrite formation in lithium and other metal batteries.

Innovative urban farming plays a vital role in enhancing food security, nutrition, livelihood resilience, and environmental sustainability in Sub-Saharan African cities. Despite this, agricultural policy and extension services have largely ignored urban contexts. Recently, national and international initiatives have started addressing this oversight by promoting circular innovations in urban food systems. To boost productivity, it is crucial to understand the challenges, opportunities, and prospects associated with innovative urban farming. Therefore, exploratory qualitative research, specifically focus group discussions, was conducted in Alimosho Local Government Area of Lagos, Nigeria, to gather the perspectives of various stakeholders. The analysis, using mind maps and a consensus index, compared views from private urban farming practitioners and local public sector representatives. Urban farmers see significant potential in innovative technologies such as hydroponics, aquaponics, recirculating aquaculture systems (RAS), drip irrigation, sack farming, and waste upcycling to improve productivity. However, they express frustration over the lack of public sector support for embracing these circular innovations. Conversely, public sector representatives cite urban farmers' behavioral and educational shortcomings, particularly their lack of internal coordination and commitment to adopting innovations. These discrepancies underscore the complex interactions between urban farmers and local public agricultural policymakers and extension agents. The study highlights the need for fundamental changes in both public and private sectors to address the challenges in urban farming innovations. Establishing local-level dialogues, as demonstrated in this study, can provide valuable insights for developing urban farming and policy in Sub-Saharan Africa.

Natural language processing model (NLP) are used in chatbots to understand user input, interpret its meaning, and generate conversational responses to provide immediate and consistent assistance. This reduces problem-solving time and staff workload and increases user satisfaction. There are both rule-based chatbots, which use decision trees and are programmed to answer specific questions, and self-learning chatbots, which can handle more complex conversations through continuous learning about data and user interactions. However, only a few chatbots have been developed specifically for the Italian language. This work proposes an NLP model to develop a powerful and efficient Italian QA (Question Answering) chatbot that is easy to use for Italian Public Administration (PA). The proposed model is based on BERT (Bidirectional Encoder Representations from Transformer) architecture, where an Encoder/Decoder module and a Highway Network module have been added to perform more efficient filtering of input text. The Italian version of the Stanford Question Answering Dataset (SQuAD-IT) is used to test the proposed model. The proposed model is one of the first models developed for Italian language-specific chatbots.

Unmanned aerial vehicle (UAV) systems are widely used in many forest-related fields owing to their cost-intensive and precise surveying technology. This study classified erosion susceptibility (ES) in a timber harvesting area using machine learning (ML) and statistical approaches. In dataset generation for the training and testing process, the digital surface model (DSM) of difference (DoD) for July–June was utilized as a dependent variable, and six terrain maps of the DSM for June were used as independent variables. The ES threshold was set at 5 cm for the binary classification of ES pixels while processing using ML (e.g., random forest and extra gradient boost [XGB]) and statistical (e.g., logistic regression) algorithms for model development. The overall accuracy (OA), receiver operating characteristics, and area under the curve (AUC) were calculated for model accuracy and validation. Although the AUC of all models did not appear acceptable (AUC > 0.7), the XGB model showed the best performance regarding time duration, OA, and AUC by 2 h, 64%, and 0.63, respectively. Despite the low AUC and accuracy of the XGB model, the wheel tracks and edges of the operation road were determined to be susceptible areas in the ES map of the XGB.

The purpose of this article is to present a multi-criteria model designed to evaluate the potential of Sincelejo's cultural identity for tourism development. A descriptive quantitative methodology was used, which included a survey of 211 participants. A decision support method was applied, using the Weighted Additive Sum approach, following the multi-attribute method. Based on the information gathered, the elements of cultural identity shared by most of the criteria were identi-fied, highlighting the gastronomic, social, and cultural traditions and customs of the region, the results of which made it possible to design a composite indicator to evaluate the tourism potential of cultural identity in the area.

An accurate and timely precipitation forecast is essential for water resources management in hydropower, irrigation, and reservoir control. The conventional methods are limited by their inability to capture the high precipitation variability in time and space. In the present work, a wavelet-based deep learning approach is adopted to forecast precipitation using the lagged monthly rainfall, local climate variables, and global teleconnections such as IOD, PDO, NAO, and Nino 3.4 as predictors. The method was tested and validated over the Krishna River Basin in India. Overall, the forecasting accuracy was higher using the wavelet-based hybrid models than the single-scale models. The proposed multi-scale model was then applied to the different climatic regions of the country, and it was shown that the model could forecast the rainfall at reasonable accuracy for different climate zones of the country.

The paper presents a theoretical and empirical assessment of this social phenomenon. The achieved scientific solution-result (Main Finding) is presented a theoretical model of the develop-ment of public citizenship in a sustainable environment has been created for the case of Lithuania. First of all, the paper discusses the conceptual issues of the expression of modernisation of civil society, highlighting and justifying the interaction of social changes and sustainable environment in economic, social, political, environmental and cultural aspects, presenting the case of Lithuania (The Case of Lithuania). On the other hand, following the United Nations Sustainable Development Strategy document, the field of progress and resilience of Lithuanian society in modern society is discussed, identifying and analysing various criteria that have been empirically tested. The authors noted that the democratic cube model was used to create a theoretical model of the development of public citizenship in a harmonious environment, and the HDI (human development index model) was also integrated. The model created by the authors systematically explains the analysis of the relationship between the expression of modernisation changes identified in the research and the formation of civil society; secondly, it substantiates the process of interaction between modernisation changes and public citizenship, discussing four fields of expression. Practical applicability of the model: it will help researchers to conceptually analyse and empirically study public citizenship; will help public policymakers and implementers to manage effectively, ensuring quality changes in society and managing new challenges, and it will also contribute to the conceptual formation of the country's long-term development strategy.

Mobility models are used to evaluated the network protocols of the ad hoc network using the simulation. The random waypoint model is a model for mobility which is usually used for performance evaluation of ad-hoc mobile network. Mobile nodes have the dynamic mobility in the ad hoc network so the mobility model plays an important role to evaluate the protocol performance.In this article, we developed modify random waypoint mobility (MRWM) model based on random waypoint for the mobile ad hoc network. In this article, the comparative analysis of modifying random waypoint mobility and random waypoint model on the ad hoc On-Demand Distance Vector (AODV) routing protocol has been done for large wireless ad hoc network (100 nodes) with the random mobile environment for the 1800s simulation time. To enhance the confidence on the protocol widespread simulations were accomplished under heavy traffic (i.e. 80 nodes) condition. The proposed model protocol has been investigated with the performance metrics: throughput; packet delivery ratio; packet dropping ratio; the end to end delay and normalized routing overhead. The obtained results revealed that proposed modify random waypoint mobility model reduces the mobility as compared to the random waypoint mobility model and it is trace is more realist.

The rural population percentage decreased from 82.7% to 68.9% in 2011, even though there is an increase in the total rural population, which stands at 833.7 million, and the rural population were now more than three times compared to the population seven decades ago. Another observation is the decrease in cultivators percentage from 71.9% to 45.1 %, while agriculture labour increase from 28.1% to 54.9% during the same period. Despite the increase in irrigated land and net area sown, the average holdings' size under the farmers is continuously decreasing, and it requires a study to look into the reasons. The research probes the role of Minimum Support Price (MSP) in supporting farmers and measuring market price above MSP needed to help marginal and small farmers remain above the poverty level. It explains how different market rates above MSP have a different impact on different categories of agriculture landholding. The study works on developing a common model that relates the impact of MSP on different farmers categories. The model can be generalized to all crops and regions and useful in designing policies that focus on uplifting the income of agricultural farmers.

A micromechanical model is implemented to indicate the progressive fatigue problem of a laminated composite with a central circular hole under fatigue loading based on a finite element model. The mechanical properties of the composite lamina are represented based on the characteristics of the fiber and the matrix through a micromechanics model. An appropriate algorithm is then adopted to simulate fatigue damage development in the composite lamina. According to this algorithm, the stress field of the composite subjected to fatigue load is initially obtained using the finite element method. Finally, the predicted results of the stresses in the constituents i.e. fiber and matrix are determined according to the micromechanical bridging model. Finally applying proper damage driving relations leads to damage degree in each element. The proposed model is proven to be successful in the observation of the fatigue behavior with stiffness degradation in each element of the composite in each cycle. Results are reported and validated using those micromechanical models and experimental data available in the literature.

In this study is developed an aerobic and anaerobic stoichiometric model for Saccharomyces cerevisiae, compartmentalized in mitochondria and cytosol. This model considers the central metabolism of S. cerevisiae and it possesses the peculiarity of having catabolic and anabolic biochemical reactions for the synthesis of the higher alcohols contained in tequila; involving 94 metabolites and 117 reactions; of which 93 correspond to biochemical internal reactions and 24 to transport fluxes between the medium and the cell. The model is validated under aerobic and anaerobic conditions for the main fermentation metabolites and it coincides with experimental results and those in silico reported in the literature. This model is used to obtain three different physiological states of S. cerevisiae through of estimation of its distributions of fluxes calculated from experimental data reported in literature of fermentation in continuous culture during the tequila production under different dilution rate (0.04-0.12 h^-1). The model developed constitutes a tool for the estimation of flux distribution maps during fermentation processes for the production of tequila, which could permit estimate yields and visualize different fermentation scenarios.

Coastal areas in the South Coast of West Java Province have the potential to develop marine ecotourism, one of which is the Pangandaran area which must be transferred into economic value by not damaging natural resources. Marine ecotourism development is not only intended to raise foreign exchange for local governments, but are also expected to play a role in maintaining natural resources sustainably. This research aims to analyze the sustainable synergistic marine ecotourism development model. The method used in this research using quantitative descriptive method. The Quantitative descriptive method is used to describe the general condition of the research area, using primary and secondary data. The technique of taking respondents using accidental sampling as many as 50 respondents consisting of tourists, public figures, fishermen who have side jobs as a provider of marine ecotourism services. The analysis tool used is through a Rapfish model approach to measuring the synergistic model of sustainable development of marine ecotourism. Based on the results of a research on a sustainable synergistic marine ecotourism development model by measuring the ecological dimensions of environmental services in high conditions, the economic dimension of marine ecotourism is in moderate condition. Marine ecotourism technology in low conditions and social dimensions of marine ecotourism in low conditions. Model development of sustainable marine ecotourism synergistic with regard to the dimension of environmental, economic and social institutions should be able to form integrated from infrastructure to support marine ecotourism up to raise the level of income of fishermen who have a second job as a marine ecotourism providers. The infrastructure and regulatory dimensions are recommended to use the technology information to promote marine ecotourism optimally and regulations need to make marine ecotourism zoning rules and infrastructure improvements.

Energy harvesting is an emerging topic recently. This harvested energy is used to operate wireless sensors for different industrial monitoring and automation. To maximize the harvested energy in spinning systems, the harvester should vibrate at resonance for different spinning frequencies which can be achieved by active fitting. In this research, a novel methodology was developed by actively customizing the length of vibrating element beam to manipulate its natural frequency according to the spinning frequency. Different models were investigated numerically and experimentally to verify the proposed methodology. COMSOL Multiphysics software V5.1 was used to develop the numerical model, then and tip deformation and voltage difference results were benchmarked. The numerical model was verified experimentally by attaching a piezoelectric MIDE PPA 1021 which its vibrating length was varied by changing the supporting length from 0 mm to 12 mm to a spinning object having variable frequency from 0-200 Hz. It is found that the MIDE PPA 1021 beam vibrated at its resonance throughout the specified spinning frequencies. The proposed harvester can be applied in battery-free sensors being used in automotives, wind-mills blades, and rotating machinery.

Recent years' global shocks (e.g., the 2008 financial crisis and the COVID-19 pandemic) and environmental shocks - such as natural disasters - have heightened the vulnerability of developing countries to future shocks, and can compromise their development prospects. International institutions and researchers have advocated that the strengthening of productive capacities in these countries would help them enhance the resilience of their economies to shocks, and promote sustainable development. The present paper has examined the effect of productive capacities on economic growth and economic growth volatility in developing countries, in particular when they face a high level of structural economic vulnerability. The analysis covers 117 developing countries over the period 2000-2018. It shows that productive capacities do not only promote economic growth, but also reduce economic growth volatility. On the other hand, structural economic vulnerability reduces economic growth, in particular when it exceeds a certain level, and induces greater volatility of economic growth. Interestingly, the findings suggest that productive capacities promote economic growth and reduce economic growth volatility in countries that face a high degree of structural economic vulnerability. These findings support the recommendation by international institutions and researchers that if they were to enhance the resilience of their economies to shocks, and promote sustainable economic growth, developing countries (in particular the poorest ones) should strengthen their productive capacities.

The advancement of mechanization in forestry has bolstered productivity within the sector while simultaneously precipitating impacts necessitating deeper comprehension for the attainment of heightened levels of environmental sustainability in forestry management. The aim of this study was to apply an instrument for assessing the environmental damage and impacts of forest harvesting in commercial Eucalyptus plantations, based on a combination of methodologies. The environmental impact assessment methodology for forest harvesting was applied by combining interaction network approaches and impact assessment matrices. This methodology was then applied to a commercial Eucalyptus plantation in the municipality of São Pedro, state of São Paulo, Brazil. The results show that timber transportation activities had the highest positive and negative environmental indices. The most significant negative impacts refer to the depletion of water resources and erosion, while the positive impacts are regional development and job creation. The most significant changes, both positive and negative, were observed in a greater number of environmental impacts for the physical and anthropic environment, with fewer for the biotic environment. The combination of methodologies has potential as an expedient tool for planning forestry operations, providing practical results for decision-making, making production more sustainable and applicable to different crops and production systems.

This article serves as a preliminary introduction to the design of a new, open-source applied and computational category theory framework, named Categorica, built on top of the Wolfram Language. Categorica allows one to configure and manipulate abstract quivers, categories, groupoids, diagrams, functors and natural transformations, and to perform a vast array of automated abstract algebraic computations using (arbitrary combinations of) the above structures; to manipulate and abstractly reason about arbitrary universal properties, including products, coproducts, pullbacks, pushouts, limits and colimits; and to manipulate, visualize and compute with strict (symmetric) monoidal categories, including full support for automated string diagram rewriting and diagrammatic theorem-proving. In so doing, Categorica combines the capabilities of an abstract computer algebra framework (thus allowing one to compute directly with epimorphisms, monomorphisms, retractions, sections, spans, cospans, fibrations, etc.) with those of a powerful automated theorem-proving system (thus allowing one to convert universal properties and other abstract constructions into (higher-order) equational logic statements that can be reasoned about and proved using standard automated theorem-proving methods, as well as to prove category-theoretic statements directly using purely diagrammatic methods). Internally, Categorica relies upon state-of-the-art graph and hypergraph rewriting algorithms for its automated reasoning capabilities, and is able to convert seamlessly between diagrammatic/combinatorial reasoning on labeled graph representations and symbolic/abstract reasoning on underlying algebraic representations of all category-theoretic concepts. In this first of two articles introducing the design of the framework, we shall focus principally upon its handling of quivers, categories, diagrams, groupoids, functors and natural transformations, including demonstrations of both its algebraic manipulation and theorem-proving capabilities in each case.

Fiber-cement composites were prepared from cellulosic cotton residue (CCR) arise from sanding process (emerizing). The effect of different concentrations: 0.5% and 1%, and granulometry: thick (retained in a14 mesh sieve) and thin (retained in a 48 mesh sieve) of this residue were evaluated on tensile strength of cement slurries with seven (07) curing days. To characterize the CCR, TGA, FT-IR, SEM and XRD analysis were performed and the residue resistance in an alkaline environment was also evaluated. Splitting tensile strength test, known as Brazilian Test, was used to assess effects of the fibers on the mechanical behavior of cement matrix. Analyzing the results, the CCR proved to be resistant in an alkaline environment, meaning that it can withstand the alkaline environment of cement matrix. The results showed an improvement superior to 17% in tensile strength for 1% of CCR. Therefore, the CCR presents a great application potential in cement pastes used for oil well cementing that requires to increase its tensile strength, once a significant improvement was achieved with a low-residue employee.

The Baculoviridae, a family of insect-specific large DNA viruses, is widely used in both biotechnology and biological control. Its applied value stems from millions of years of evolution influenced by interactions with their hosts and the environment. To understand how ecological interactions, have shaped baculovirus diversification, we reconstructed a robust molecular phylogeny using 217 complete genomes and ~580 isolates for which at least one of four lepidopteran core genes was available. We then used a phylogenetic-concept-based approach (mPTP) to delimit 165 baculovirus species, including 38 species derived from new genetic data. Phylogenetic optimization of ecological characters revealed a general pattern of host conservatism punctuated by occasional shifts between closely related hosts and major shifts between lepidopteran superfamilies. Moreover, we found significant phylogenetic conservatism between baculoviruses and the type of plant growth (woody or herbaceous) associated with their insect hosts. In addition, we found that colonization of new ecological niches sometimes led to viral radiation. These macroevolutionary patterns show that besides selection during the infection process, baculovirus diversification was influenced by tritrophic interactions, explained by their persistence on plants and interactions in the midgut during horizontal transmission. This complete eco-evolutionary framework highlights the potential innovations that could still be harnessed from the diversity of baculoviruses.

Africa, home to the world's second-largest population of approximately 1.3 billion, grapples with significant challenges in meeting its medical needs, particularly in accessing quality healthcare services and products. The continent faces a continuous onslaught of emerging infectious diseases, exacerbating the strain on its already fragile public health infrastructure. The emergence of SARS-Cov2 has further exposed the deficiencies in Africa's public health response, ranging from limited diagnostic capabilities to difficulties in vaccine distribution. Compounding these challenges is vaccine hesitancy, which is prevalent in many regions due to varying levels of understanding and trust regarding vaccines. The disparities in vaccine literacy across the continent stem from a complex interplay of factors, including low literacy rates and a history of medical exploitation and misinformation campaigns. These factors have led to a pervasive distrust in medical institutions and healthcare providers among certain African communities. Despite concerted efforts by national governments, private entities, and international partners to address these challenges and improve vaccine literacy, significant hurdles persist. This manuscript examines the current state of education in vaccinology and related sciences in Africa, as well as the continent's efforts to address educational needs in vaccine development and manufacturing. It evaluates initiatives aimed at strengthening vaccine education and literacy, highlighting successful approaches and ongoing challenges. By assessing the progress made and identifying remaining obstacles, this review offers insights into how Africa can enhance its capacity in vaccinology and accelerate its contributions to global health security.

This study aimed to investigate the appearance and frequencies of the booroola polymorphism of the bone morphogenetic protein receptor 1b (BMPR1B) gene (FecB) and the embrapa polymorphism of the growth differentiation factor 9 (GDF9) gene (FecGE) in sheep in Thailand. A total of 454 cross-bred sheep blood samples were collected from four provinces in Thailand during August 2022 to July 2023. The polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method was used to identify the FecB and FecGE genotype. The history of ewe birth types was collected from the owners to analyze the association between Fecundity (Fec) genotypes and the history of birth types. The genotypic frequencies of FecB for homozygous genotype (B/B), heterozygous genotype (+/B) and wild-type (+/+) were 0.22%, 1.54%, and 98.24%, respectively. Meanwhile, the genotypic frequencies of FecGE for homozygous genotype (E/E), heterozygous genotype (+/E), and wildtype (+/+) were 0.00%, 2.42%, and 97.58%, respectively. Furthermore, three ewes exhibited both FecB and FecGE genotypes. Fisher’s exact test revealed that the possessor of FecB genotype was associated with multiple births (p<0.01). Both FecB and FecGE mutations were identified in crossbred sheep in Thailand. Sheep containing FecB allele is a better candidate to be selected to improve the prolificacy of crossbred sheep in Thailand.

Why are pieces of spaghetti generally broken into three to ten segments instead of two as one thinks? How can one obtain the desired number of fracture segments? For these problems, in this paper a strand of spaghetti is considered an elastic rod, and the finite-element software ABAQUS is used to simulate the detailed fracture dynamics of the elastic rod. By changing the size (length and diameter) of the rod, the relevant data on the fracture limit curvature and the number of fractured segments of the elastic rod are obtained. The ABAQUS simulation results confirm the scientific judgment of B. Audoly and S. Neukirch (Fragmentation of rods by cascading cracks: Why spaghetti does not break in half. Phys Rev Lett 95: 095505). Using dimensional analysis to fit the finite-element data, two relations of the elastic rod fracture dynamics are obtained: (1) the relationship between the fracture limit curvature and the diameter, and (2) the relationship between the number of fracture segments and the diameter-length ratio. Results reveal that when the length is constant, the larger the diameter (the smaller the diameter-length ratio D/L), the smaller the limit curvature; and the larger the diameter-length ratio D/L, the fewer the number of fractured segments. The relevant formulations can be used to obtain the desired number of broken segments of spaghetti by changing the diameter-to-length ratio.

The concept of a "circular economy" needs a more cogent theoretical anchor which will allow for transference of its goals and methods of attainment across cases. Tensions between ecological goals of this concept and the social implications of its implementation need to be addressed. This paper attempts to provide a theoretical framework for harnessing the strengths of a circular economy. Building on theories of social ecology which are predicated in Murray Bookchin's notion of "dialectical naturalism", the analysis presented here addresses some of the criticism of circular economic paradigms, such as their potential for stifling innovation or a neglect of human development challenges. A model for managing human "need" and "greed" within a circular economy framework is presented that also incorporates consumer choice and innovation. Planned obsolescence as a means of livelihood generation is also problematized with a view towards balancing durability of products on the one hand and ensuring throughput for manufacturing employment and innovation incentives on the other. Finally, the need for governance systems is considered to ensure that a planetary vision for a circular economy can be realized that efficiently harnesses local initiatives rather than an atomized and insular view of circularity.

Ultrasonic feedback energy is affected by the variety, planting and growth state of crops, it is difficult to fine application in precision agriculture systems. To this end, an ultrasonic sensor was mounted in a spray boom height detection system. Winter wheat was used as the test object to obtain feedback energy values for the spray boom height from the top of the wheat in the field during six critical growth stages: the standing stage, jointing stage, booting stage, heading stage, filling stage and maturity stage. The relationship between the actual value of the height from the spray boom to the top of wheat (Habw) and the detected value of the height from the spray boom to the top of wheat (Hdbw) was analyzed. A spray boom height detection model based on the ultrasonic sensor during the full growth cycle of wheat was determined. Field validation tests showed that the applicability of the spray boom height detection distance (Dd) of the spray boom height detection model proposed in the present study was 450 mm~950 mm. Within the applicable Dd range, the detection error of the detection model was ≤5 cm during the full growth cycle. This study provides an ultrasonic detection model construction method, which improves the reliability and accuracy of ultrasonic boom height detection for different wheat growth stages.

Microplastic pollution (MP) in marine environments around the globe is severe and enough precautions have not been taken so far for its prevention. The focus of this study is to quantify MPs from beach sediment and seawater samples and to identify their distribution and types along the western coast of Sri Lanka from the Kelani River estuary to the Mahaoya estuary. Nine sites along this 42 km stretch were selected and random sampling was employed to collect a minimum of 8 sediment samples from each site between October and December 2021. Water samples were also collected, parallel to the sediments, from the ocean surface. FTIR analysis revealed that most of the MPs found were polyethylene(PE), polypropylene(PP), polystyrene(PS), polyethylene terephthalate(PET), and phenol formaldehyde resin. The mean abundance of MPs varied from 2.0 ± 0.6 items/L to 161.0 ± 15.7 items/L in water samples and, from 3.0 ± 0.3 items/m2 to 656.0 ± 34.5 items/m2 in sediment samples. The MPs found were identified in different shapes as fragments (80.2%), pellets (14.9%), fibers (2.7%) and foams (2.5%). Analysis revealed that the beach sediments were contaminated with PS, phenol formaldehyde resin, PET, PP and PE, while surface seawater is dominated by phenol formaldehyde resin, PS, PP and PE.

Background: This study aims to develop a research framework for the Social Life Cycle Assessment (S-LCA) of public services by conducting a systematic review of existing literature from 2013 to 2022. The research question focuses on how the results of the literature review can inform the advancement of S-LCA for public services. Methods: To answer this question, a qualitative and quantitative analysis of data from the Web of Science and Scopus databases was carried out. The quantitative analysis determined the total number of S-LCA and SO-LCA papers published by main journals, and main authors. The qualitative analysis highlighted the different themes and research objectives addressed in the work relating to the S-LCA of products/services and organizations. Results: The results show that 222 papers dealing with S-LCA were published in 94 journals and revealed a scarcity of work on public services, with most papers focusing on products/services and organizations. While there is a lack of direct scientific data, the study identified 17 actors, 74 impact sub-categories, and 178 indicators that could be considered for the S-LCA of public services. Conclusions: Due to the diverse nature of public services, it is crucial to develop specific stakeholder categories, sub-categories, and performance indicators for each public service. This will support the scientific community and S-LCA practitioners in applying assessing the social impacts of public services.

Objectives To develop and validate a prediction model for 28-day in-hospital mortality among adult patients critically ill with COVID-19 in the UK. Design Observational cohort study. Setting 287 adult critical care units in England, Wales and Northern Ireland, of which 260 admitted at least one eligible patient. Participants 10,933 patients with confirmed COVID-19 of whom 10,401 were eligible (excluding 532 patients with a duration of critical care less than 24 hours and 1 patient with unknown 28-day outcome): 8,666 development (March-April 2020) and 1,735 temporal validation (May-August 2020). Main outcome measures 28-day in-hospital mortality from start of critical care. Results Two models were developed using 14 patient level predictors selected from 30 candidate predictors, with and without adjustment for calendar time. In the temporal validation data, the model discrimination was maintained (c index 0.78) but calibration was poor, particularly for the model not adjusted for calendar time (ratio of observed to predicted mortality 0.74 versus 0.88 for the model adjusted for calendar time). Conclusions We developed and validated a prediction model for 28-day in-hospital mortality for patients critically ill with COVID-19. Although absolute predictions were inaccurate due to changing outcomes, the models will support risk-adjustment in analyses and monitoring changes in risk-adjusted outcome over time.

Parkinson's disease (PD) is a common neurodegenerative disorder characterized by the loss of dopaminergic neurons and the buildup of misfolded alpha-synuclein proteins. While its exact cause remains unknown, several factors may influence its development. This review examines how factors like chronic constipation, head injuries, cholesterol and uric acid levels, pesticide exposure, smoking, NSAIDs, and caffeine intake may affect PD risk. Chronic constipation appears to be linked to a higher risk of PD, potentially serving as an early warning sign. Head injuries, especially mild-to-moderate ones, may also play a role in increasing PD risk. Surprisingly, higher levels of cholesterol seem to protect against PD, while lower levels of uric acid might increase the risk in the presence of some differences according to gender type. Exposure to certain pesticides is associated with a higher PD risk. NSAIDs, on the other hand, may or may not have a protective effect, with conflicting evidence. Interestingly, caffeine and tobacco smoking appear to lower the PD risk. Further studies are needed to fully understand the complex interplay between these factors and PD. It's also important to remember that these are just general trends, and individual risk factors will vary depending on a person's unique genetic makeup, medical history, and overall health.

As prominent oil producers, the Gulf Cooperation Council (GCC) countries have played a significant role in the global energy market. However, as the world's attention increasingly shifts towards environmental sustainability, understanding the implications of the GCC's economic activities on CO2 emissions becomes indispensable. This research paper investigates the relationship between specific economic indicators and their impact on CO2 emissions in the GCC from 2001 to 2021. The study employs quantile regression, a powerful statistical method that estimates the conditional quantiles of a response variable given a set of predictor variables. The findings reveal several important insights: Financial institution efficiency is significant and negative at a 1% level at the lower (10th) -83537.3 and the higher quantile (90th) -549002.3. The relationship between GDP per capita and CO2 emissions varies across quantiles, highlighting the complexity of the growth-environment nexus. Total patents exhibit a positive and significant relationship with emissions, underscoring the importance of directing innovation towards environmentally sustainable solutions. Renewable energy consumption displays a nuanced relationship with CO2 emissions, with a stronger negative impact observed at higher consumption levels. This underscores the potential of renewable energy to mitigate emissions when integrated at scale. The study's outcomes hold crucial policy implications for the GCC countries as they seek to align economic growth with environmental sustainability. The findings emphasize the importance of fostering financial institution efficiency, promoting green innovation, and expanding renewable energy sources to reduce emissions

Molecular analysis is an easier means to identify and isolate a specific gene which has imperative function for growth, body composition , fat deposition, metabolic and skeletal traits as well as the molecular genetics selection on individual genes is a very efficient method to genetically improve economically important traits in chickens. Insulin- like growth factor 1 ( IGF-1)is a member of a heterogeneous group of peptides with important growth.Transforming growth factorβ ( TGF-β) belongs to a large family of growth and differentiation factors that play a pivotal role in a great variety of biological activities including morphogenesis, development and differentiation. DNA was extracted from 48 chickens sampled from three strains Lohman (17) , Sinai (24) and Gimmizah (7) IGF-1 gene and TGFβ2 gene were amplified using PCR protocol. Electrophoresis was carried out on the products of PCR , bands viewed on transilluminator. The size of IGF-1 gene was 675 bp while the size of TGFβ2 gene was 188bp. Sharp bands were purified and sequenced and used the dendrogram to show the relationships between other vertebrate species

(1) Background: In this work, a series of benzimidazole derivatives containing colchicine-like and cathechol-like moieties with methyl group substitution in the benzimidazole ring were evaluated for their in vitro antiproliferative, pro-apoptotic and antioxidative activity against highly invasive breast cancer cell line MDA-MB-231 and related impairment of tubulin dynamics; (2) Methods: MTT assay was used to evaluate the antiproliferative activity. In vitro tubulin polymerization assay as well as docking analysis was applied to prove the alterations in tubulin polymerization. Immunofluorescent and DAPI staining reveal the pro-apoptotic potential of benzimidazole derivatives and the effect on tubulin’s dynamics in living cells. The antioxidant activity was evaluated by scavenging of H2O2 and hypochlorite ion and ortho-phenanthroline tests; (3) Results: The compounds containing colchicine-like moiety displayed significant growth inhibitory effect against cancer cell line as was highlighted the role of methyl functional group on the tubulin aggregation and blockage of mitosis. All compounds showed an ability to scavenge H2O2, while the compounds with cathecol-like moiety were more effective against hypochlorite ion and chelation of iron; (4) Conclusions: The benzimidazole derivative containing colchicine-like moiety and methyl group substitution in benzimidazole ring is a potential antiproliferative and microtubule destabilizing agent.

Background and Objectives: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent world-wide. It progresses from simple steatosis to non-alcoholic steatohepatitis (NASH). Fibrosis is often present during NAFLD progression; however, factors determining which subjects develop NASH or fibrosis are unclear. Insulin-like growth factor binding proteins (IGFBP) is a family of secreted proteins involved in senescence and scarring mainly synthetized in the liver. Here we aimed to study the association of IGFBP and its induced senescence with the progression of NAFLD and liver fibrosis. Materials and Methods: 16 weeks old male C57BL/6 mice weighing 23 ± 3 g were fed either Methionine-Choline Deficient or control diet for 2, 8 or 12 weeks. Blood and liver samples were collected; histological assessment of NAFLD and fibrosis was performed. Fat contents were measured. Cellular senescence was evaluated in the liver. IGFBP levels were assessed in liver and serum. Data was expressed as Mean ± SD and analyzed by one-way ANOVA followed Tukey’s test. Lineal regression models were applied for NAFLD and fibrosis progression. p

Since the 20th century, numerous studies have detected or isolated parasites from the Trypanosomatidae family in various tick species. However, the status of ticks as vectors of medically or veterinary important Trypanosoma and Leishmania remains unclear. We conducted a systematic review and meta-analysis to pro-vide new insights into the potential vector status of Leishmania and Trypanosoma, which have medical and veterinary significance. We searched three databases (PubMed, Google Scholar, and Web of Science) from 1912 to June 30, 2023, resulting in 94 and 86 papers included in the qualitative and quantitative analyses, respectively. All identified field studies were conducted in endemic areas and investigated the presence of Trypanosoma and Leishmania parasite, DNA, or antigen in ticks. We recorded a pooled prevalence of Trypa-nosomatidae detection in ticks at 15.48% [7.99-24.61%], with significant variations depending on the year, detection method, and geographical area. Most of positive tick species belonged to the genera Amblyomma, Hyalomma, Ixodes, and Rhipicephalus. Experimental laboratory work on transmission routes proved potential vector competence in both the Argasidae and Ixodidae tick families. Although our systematic review and meta-analysis provide unambiguous evidence of the natural infection of ticks by Trypanosomatidae para-sites with some evidence of non-traditional transmission routes, it does not provide conclusive evidence on the role of ticks as biological or mechanical vectors for veterinary and medically interest protozoan Trypa-nosomatidae species. All these unambiguously demonstrates the need of additional investigations to address this point.

To investigate the impact of electrode structure on Electrical Stimulation Therapy (EST) for chronic wound healing, this study designed three variants of flexible microelectrodes (FMs) with Ag-Cu coverings (ACCs), each exhibiting distinct geometrical configurations: hexagonal, cross-shaped, and serpentine. These were integrated with PPY@PDA/PANI (3/6) hydrogel dressings. Comprehensive animal studies, coupled with detailed electrical and mechanical modeling and simulations, were conducted to assess their performance. Results indicated that the serpentine-shaped FM outperformed its counterparts in terms of flexibility and safety, exhibiting minimal thermal effects and a reduced risk of burns. Notably, FMs with metal coverings under 3% demonstrated promising potential for optoelectronic self-powering capabilities. Additionally, simulation data highlighted the significant influence of hydrogel non-uniformity on the distribution of electrical properties across the skin surface, providing critical insights for optimizing EST protocols when employing hydrogel dressings.

Most research on colostrum quality and colostrum management focuses on maternal IgG transfer to the newborn calf. However, in addition to being a source of antibodies, colostrum has other components that influence passive immune transfer, nutrition, gut maturation, and thermoregulation. In this review, the constituents in the bovine colostrum, as well as their variation, are explored and it is discussed the biological role of colostrum on the newborn calf. Protein is the nutrient in higher concentration and fat is the nutrient with the most variation. There is a pronounced amplitude in the total solids content of colostrum, but the average concentration does not vary much between studies (24.7%). Bovine colostrum major components can vary with several factors, such as the region, season, breed, dry period length, and nutritional aspects of the dam. Bioactive components, cells, and microbiome variability are less understood, besides IgG. Regardless, they play crucial distinct roles in the physiological development of the newborn. However, a multi-interactive effect between these components, cells, and microorganisms is more likely to occur. Recently researched components in colostrum like miRNA, nucleo(t)sides, the milk fat globule membrane, oligosaccharides, minor proteins, and the microbiome are examples of promising interest areas of study.

Developing a dependable gas and condensate value chain is vital for the ongoing prosperity of the oil and gas industries. To achieve this, NNPC is enhancing its production facilities by employing process modeling and simulation with the assistance of Symmetry iCON®. As a part of this initiative, a simulation model was created using the Symmetry iCON® pipe plexus solver to simulate the dynamic environment of the gas and condensate pipeline network in the Nigerian Southern region. To validate the model, data from a single month, specifically December 2022, was utilized. Furthermore, the algorithm was utilized to generate projections for the month of January 2023, providing valuable insights into its effectiveness. The study also highlighted the significance of accurately estimating the characteristics and properties of a pseudo component of C6+ in relation to thermodynamics and material properties, which was a particularly insightful finding. he assessment of this property has transitioned from being relatively unimportant to becoming critically important, primarily due to the lack of condensate data monitoring at specific terminals. However, it's worth noting that the model currently has an error margin of 4-6% when predicting the data. As more information becomes available in the future, the model can be easily fine-tuned to better align with the actual conditions, allowing for more accurate predictions.

Analysing the human body by direct observation can lead to misinterpretation of underlying trends, whereas in vitro organ models facilitate their understanding. Likewise, in vitro organ models serve as valuable tools for assessing the toxicity of compounds. However, none of the existing models had the features we considered critical for toxicity assessment, so we developed an in vitro liver model for toxicity assessment. We used four different types of primary liver cells: Hepatic sinusoidal endothelial cells, hepatic stellate cells, Kupffer cells and hepatocytes. We cultured them in different combinations of composition and volume of cell medium, hepatocyte proportion of total cells, and addition of extracellular matrix. We added rifampicin, ibuprofen and 5-fluorouracil to this model and observed the microanatomy and physiology changes for a week with preclinical and clinical instruments. Among the different model configurations, we selected the feature combination of the in vitro model that had similar biomarker values to those measured by the clinical instruments used in clinical diagnostics. When we exposed the selected model configuration to rifampicin, ibuprofen and 5-fluorouracil, the viability of Kupffer cells and liver sinusoidal endothelial cells was significantly lower than in the untreated samples. In contrast, we observed negligible viability of hepatocytes compared to the untreated samples. We have built an in vitro liver model that resembles the liver microenvironment and we have analysed it with clinical instrumentation to facilitate the data translation. In this sense, we have established that Kupffer and LSEC cells are suitable candidates for the search for clinical diagnostic markers of liver function.

A new trend in cow's milk has emerged in the market called type A1 and A2 milk. These products have piqued the interest of both consumers and researchers. Recent studies suggest that A2 milk may have potential health benefits, which is why researchers are investigating this product further. It is interesting to note that the A1 and A2 milk types have area-specific characteristics compared to breed-specific characteristics. Extensive research has focused on milk derivatives obtained from cow’s milk, primarily through in vitro and animal studies. However, few clinical studies have been conducted in humans, and the results have been unsatisfactory. New molecular techniques for identifying A1 and A2 milk may help researchers to develop new studies that can clarify certain controversies surrounding A1 milk. It is essential to exercise extreme caution when interpreting the updated literature. It has the potential to spread panic worldwide and have negative economic implications. Therefore, this study aims to investigate the new aspects of A1 and A2 milk in different research areas and clarify some aspects regarding these two types of milk.

Based on DFT (density functional theory) using B3LYP/6-31G method, theoretical investigations applied to demonstrate the structural, electronic properties and stability of (N,Nˊ-(1,4-phenylene)bis(1,8-naphthalimide)) is more stable than the compound (N-(4-amino phenyl)1,8-naphthalimide) by (-1.2762 eV or -29.4299 Kcal.mol-1) depending on the values of HOMO, synthesis reaction of imide is spontaneous and endothermic at temperature 275˚C according to the values of ∆rS, ∆rG and ∆rH.

This study delves into the intricate dynamics of classical mechanics, exploring the interplay between force, mass, and energy. Through fundamental principles and mathematical formulations, it elucidates key relationships governing physical systems. Beginning with an overview of classical mechanics, the study establishes the foundational principles laid down by Newton, emphasizing concepts such as inertia, acceleration, and the relationship between force and motion. Central to the investigation is Newton's second law of motion, highlighting the direct proportionality between force and acceleration, and the inverse relationship between acceleration and mass when acted upon by a force. The study extends to the concept of effective mass, elucidating how the application of force influences the inertial mass of an object and contributes to its effective mass through the acquisition of kinetic energy. Furthermore, the study examines the total energy composition of systems, emphasizing the holistic nature of energy as a combination of potential and kinetic forms. Through the work-energy theorem, a direct link between force and kinetic energy is established, revealing how mechanical work done on an object results in changes in its kinetic energy and effective mass. Mathematical formulations and conceptual analyses provide deeper insights into the intricate relationships between force, mass, and energy, shedding light on the underlying mechanisms governing classical mechanical systems. Through validation against empirical observations and experimental data, the study ensures the accuracy and reliability of derived equations, contributing to a richer understanding of classical mechanics and paving the way for further exploration in the field of physics.

RNA transcripts play a crucial role as witnesses of gene expression health. Identifying disruptive short sequences in RNA transcription and regulation is essential for potentially treating diseases. Let’s delve into the mathematical intricacies of these sequences. We’ve previously devised a mathematical approach for defining a “healthy” sequence (10.14218/GE.2023.00079). This sequence is characterized by having at most four distinct nucleotides (denoted as nt≤4). It serves as the generator of a group denoted as fp. The desired properties of this sequence are as follows: fp should be close to a free group of rank nt−1, it must be aperiodic, fp should not have isolated singularities within its SL2(C) character variety (specifically within the corresponding Groebner basis). Now, let’s explore the concept of singularities. There are cubic surfaces associated with the character variety of a four-punctured sphere denoted as S24. When we encounter these singularities, we find ourselves dealing with some algebraic solutions of a dynamical second-order differential (and transcendental) equation known as the Painlevé VI equation (10.3390/dynamics4010001). In certain cases, S24 degenerates, in the sense that two punctures collapse, resulting in a “wild” dynamics governed by the Painlevé equations of an index lower than VI. In our paper, we provide examples of these fascinating mathematical structures within the context of miRNAs. Specifically, we find a clear relationship between decorated character varieties of Painlevé equations and the character variety calculated from the seed of oncomirs. These findings should find applications in cancer research.

The treasury of a DAO is purported to uphold the value of the DAO-issued token, and this study represents a pioneering effort to delve into these realms. We explore the relationship between the treasury and market capitalization in two DAO projects, namely Olympus DAO and KlimaDAO. Employing Granger Causality tests, we scrutinize the relationship between market capitalization and the treasuries of these DAOs. Furthermore, we use an EGARCH model to investigate the in-fluence of carbon offset tokens on KlimaDAO’s market capitalization, incorporating this variable into the model's conditional mean equation. The findings reveal a significant relationship between market capitalization and the treasuries of the DAOs, indicating a potential impact of market capitalization on treasuries. Additionally, the well-specified EGARCH model highlights the sig-nificance of carbon offset tokens, suggesting their potential to influence the overall market capi-talization of a DAO project and support its value.

Mitochondria are highly dynamic organelles that continuously change their shape. Their main function is ATP production; however, they are additionally involved in a variety of cellular phenomena, such as apoptosis, cell cycle, proliferation, differentiation, reprogramming, and aging. The change in mitochondrial morphology is closely related to the functionality of mitochondria. Normal mitochondrial dynamics are critical for cellular function, embryonic development, and tissue formation. Thus, defect in proteins involved in mitochondrial dynamics that control mitochondrial fusion and fission can affect cellular differentiation, proliferation, cellular reprogramming, and aging. Here we review the processes and proteins involved in mitochondrial dynamics and its various associated cellular phenomena.

Rice blast is the most harmful disease caused by Magnaporthe oryzae, leading to yield loss. The aim of the work is to improve resistance of rice to blast by introducing and pyramiding genes using conventional breeding technique and modern MAS analysis. Pyramiding is the combination of several resistance genes in one genotype, in this case it improves the resistance of rice to pathogens. MAS analysis revealed that 3 out of 4 studied domestic high-yielding rice cultivars (Bakanasski, Aisaule and Aru) contain 1 blast resistance gene (Pi-2). It was discovered 7 pyramidal lines with the introduction of a combination of 2 blast resistance genes (Pi-1 and Pi-ta) into 6 hybrids and 3 genes (Pi-1, Pi-33 and Pi-ta) into 1 line. Phytopathological test of local cultivars and pyramidal lines of rice showed that 3 out of 7 pyramidal lines have moderate resistance: 2 lines (F2 Bakanasski/7667 (var. vulgaris Koern.) and F2 Aisaule/7664 (var. italica Alef.)), containing 2 resistance genes (Pi-1 and Pi-ta) and 1 line – F2 Aisaule/7689 (var. zeravschanica Brsch.), carrying 3 genes (Pi-1, Pi-33 and Pi-ta), while the local cultivars Bakanasski, Aisaule and Aru are susceptible. As a result, the blast resistance of rice was improved by creating pyramidal lines. Pyramided lines can be used for germplasm exchange and in rice breeding programs to improve blast resistance in rice.

Asymmetric lithium salts, such as Lithium (difluoromethanesulfonyl)(trifluoromethanesulfonyl)imide (LiDFTFSI), have been demonstrated to surpass traditional symmetric lithium salts with improved Li+ conductivity and the capacity to generate stable solid electrolyte interphase (SEI) while maintaining compatibility with aluminum (Al0) current collector. However, the intrinsic reductive mechanism through which LiDFTFSI influences battery performance remains unclear and under debate. Herein, the detailed SEI reactions of LiDFTFSI-based electrolytes were investigated by combining density functional theory and molecular dynamics, aiming to clarify the formation process and atomic structure of SEI. Our results show that asymmetric DFTFSI– weakens the interaction between carbonate solvents and Li+, and substantially alter the solvation structure, exhibiting a well-balanced coordination capacity to bis(trifluoromethanesulfonyl)imide (TFSI–). Nanoseconds hybrid molecular dynamics simulation further reveals that the preferential decomposition of LiDFTFSI produces sufficient LiF and Li2O to facilitate a robust SEI. Moreover, the abundant F– generated from LiDFTFSI decomposition accumulates on the Al surface and subsequently combines with Al3+ from the current collector to form AlF3, potentially inhibiting corrosion of the current collector. Overall, these findings elucidate how LiDFTFSI regulates the solvation sheath and SEI structure, advancing the development of high-performance electrolytes compatible with current collector.

To increase the calculation speed of the computational fluid dynamics (CFD) based simulation for the gas-solid flow in fluidized beds, a Markov chain model (MCM) is developed to simulate the particle movement in a two-dimensional (2D) circulating fluidized bed (CFB) riser. As a statistic model, the MCM takes the CFD-discrete element method (DEM) results as samples to calculate the transition probability matrix of particles, which can describe the macroscopic regularities of the particle movement and guide the numerical simulation of the particle motion based on the Monte Carlo method. The particle distribution snapshots, residence time distribution (RTD) and mixing obtained from both the CFD-DEM and the MCM are compared. Results show that the computational speed of the MCM is faster than that of the CFD-DEM by about 2 orders of magnitude, and the difference of the mean particle residence time calculated by the two models is less than 2%. Besides, the MCM can well describe the time-averaged particle mixing compared to the CFD-DEM.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

Considering the unquestionable role of education in economic development, it has an impact at a macroeconomic level—contributing to enhancing productivity and innovation, reducing poverty and promoting social cohesion—and at a personal level—ensuring the improvement of knowledge, skills and individual capabilities and promoting social values like empowerment and equality. Notably, quality education is recognized as one of the Sustainable Development Goals (SDGs), which, jointly with other behaviors and attitudes, could impact the development of societies in other fields like health and well-being, cultural preservation, environmental sustainability and even peace and stability—all of them also listed as SDGs. However, the capacity, or not, to reach higher levels of compliance with quality in education (SDG 4) varies from country to country, according to the 2023 Sustainable Development Report results. Thus, the present study used a fuzzy set qualitative comparative analysis to assess the conditions for attaining higher levels of quality education based on the different indicators used to measure SDG 4. The analysis will allow comparisons of results among different countries and provide information about the initiatives that could be relevant to increasing quality education worldwide and for policymakers.

Today, enhancing food preservation and safety in the spirit of the bioeconomy suggests the use of biodegradable polymers such as poly-L-lactic acid (PLA) in food packaging and the replacement of chemical additives with natural antioxidant/antibacterial agents such as essential oils and their derivatives. Herein a novel carvacrol (CV) @ natural zeolite (NZ) nanohybrid was developed by employing a new vacuum adsorption desorption method which results in the adsorption of a high CV amount on NZ equal to 61.7 %wt. This CV@NZ nanohybrid was successfully dispersed in a PLA/triethyl citrate (TEC) matrix via a melt-extrusion process to obtain PLA/TEC/xCV@NZ nanocomposite films with 5, 10 and 15 CV@NZ %wt. content. PLA/TEC/xNZ films were also de-veloped for comparison. The overall study resulted in a biodegradable active film with the fol-lowing properties: 10%wt. CV@NZ content (PLA/TEC/10CV@NZ), self-healable properties, 22% higher tensile strength and 40% higher elongation at break, 45% higher water barrier and 40% higher oxygen barrier than pure PLA/TEC matrix. This film also provides high CV release content, high CV control release rate as well as significant antioxidant activity and antibacterial activity against the food pathogens Staphylococcus aureus and Salmonella enterica spp. enterica serovar Typhimurium. This film not only succeeds in extending the shelf-life of fresh pork minced, as shown by total variable count measurements in four days, but also prevents lipid oxidation of fresh minced pork and maintains the minced meat in higher nutritional conditions, as revealed by the heme iron content determination, and in a much better and acceptable sensory condition than the commercial packaging paper.

Computer simulation relies on well-defined experimental supported theories, models, and numerical methods. Computer simulation is an alternative to fill the gaps where the experiment is pricy or limited in engineering. Computer simulation is a high-fidelity computational technique that can work as an engineering innovation tool to try imaginative ideas beyond the available technologies. The accuracy of current computer simulation results is reliable enough to accelerate innovation for new materials and mechanical technologies without experimental validation. So, we may need to urge relying on computer simulation results without any experimental validation to accelerate engineering innovations. In addition, using high-fidelity computer simulation for engineering innovation acceleration without validation will be promoted if it is credited as a new classification. This subject and a related justification's classification in engineering have been discussed.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

mRNA-lipid nanoparticle (LNP) medicinal products can be considered a platform technology because the development process is similar for different diseases and conditions, with similar noncoding mRNA sequences and lipid nanoparticles and essentially unchanged manufacturing and analytical methods utilised often for different products. It is critical not to lose the momentum built using the platform approach during the development, regulatory approval and rollout of vaccines for SARS-CoV-2 and its variants. This Review proposes a set of modifications to existing regulatory requirements for mRNA products, based on a platform perspective for quality, manufacturing, preclinical and clinical data. For the first time, we address development and potential regulatory requirements when the mRNA sequences and LNP composition vary in different products as well. In addition, we propose considerations for self-amplifying mRNA, individualised oncology mRNA products and mRNA therapeutics. Providing a predictable development pathway for academic and commercial groups, so they can know in detail what product characterisation and data are required to develop a dossier for regulatory submission, has many potential benefits. These include reduced development and regulatory costs; faster consumer/patient access and more agile development of products in the face of pandemics; and for rare diseases where alternatives may not exist or to increase survival and the quality of life in cancer patients. Therefore, achieving consensus around platform approaches is both urgent and important. This approach with mRNA can be a template for similar platform frameworks for other therapeutics and vaccines to enable more efficient development and regulatory review.

Confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the influencing factors and practical strategies of urban carbon emissions and urban vibrancy, respectively, the unclear mutual nexus between them and the development strategy of collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and building carbon emission data, we have developed a reproducible method to estimate total carbon emission and emission intensity per capita. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each typology based on geographical location and the requirements of urban development policies of these four combination typologies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.

In current transportation hubs, it is a common phenomenon that passengers travel with necessary wheeled-luggage or suitcases. Due to the fact that most luggage occupies certain space within dense passenger crowds with giant mass inertia, its abnormal motions, such as drifting, frequently trig unavoidable local disturbances and turbulence in surrounding pedestrian flows, further increasing security risk. While, current researches primarily concentrate on examining the impact of luggage on crowd evacuation efficiency through scenario-based experiments and analyzing the spatial characteristics of crowd distribution in surveillance videos. There still lacks of the dynamic disturbance mechanisms and crowd stability associated with the combined motion of passengers and luggage. Therefore, this study considers the luggage-laden passenger as a deformable particle, the resulting disturbance on surrounding non-luggage passengers is analyzed and quantified into a nonlinear pressure term. Subsequently, the disturbance propagation model of passenger-luggage is developed by adapting the classical Aw-Rascle traffic flow model with incorporation of the pressure term. Simulation experiments of disturbances caused by luggage drifting and retrograding validate that the proposed propagation model aligns with real-world situations in transportation hubs. Quantitively, when luggage-laden passenger drifted left, the disturbing force of left side crowd can reach a peak of 238N in the passenger density of 3.0 p/m2, and the maximum difference between the left and right sides disturbing force can reach 153N. Furthermore, it is recommended that the proposed model can be applied to crowd flow analysis and risk assessment.

The tourism economy plays an essential role in supporting and driving tourism development. Therefore, studying its resilience is crucial for promoting sustainable and high-quality tourism development. Based on the core essentials of economic toughness, this paper constructs the tourism economic toughness evaluation index system, uses the entropy weight TOPSIS method to measure the tourism economic toughness of 10 counties and cities in the Yili River Valley in 2010-2019, analyzes its spatio-temporal evolution characteristics through the standard deviation ellipse, the traditional Markov chain, and the spatial Markov chain, and uses the grey correlation analysis method to analyze the driving factors of the tourism economic toughness. The results show that: (1) the economic resilience of tourism in the Yili River Valley shows a "W"-shaped time-series dynamic development trend with an average index of 0.28394, in which it experienced a decline from 0.28 to 0.27 during 2012-2013 and 2015-2016; (2) Spatial Pattern: The Yili River Valley tourism economic resilience is characterized by a clear uneven spatial distribution. The city of Yining, representing the northwestern region, exhibits a high level of resilience, while the southwestern region, endowed with rich tourism resources, demonstrates a medium level of resilience. Conversely, the central to southeastern counties and cities, such as ChabuchaerXibo Autonomous County and other counties and cities, are characterized by a lack of developed tourism facilities, which has resulted in a lower level of resilience and a weak development situation; (3) In terms of spatial and temporal evolution, the standard deviation ellipse is stabilized year by year pointing to "northwest-southeast", the clustering tendency is gradually enhanced, and five tourism economic resilience convergence clubs are identified, whose internal transition probability is significantly affected by spatial location, and which show obvious stability and neighborhood effects; (4) Regarding the driving factors, the impact of each driving factor on the resilience of the tourism economy reaches the statistical significance threshold of 0.77 or above. Among them, the number of employees in the tertiary industry, especially the input of high-quality talents in the tourism industry, plays a key role in improving the quality of tourism services and strengthening the industry's ability to withstand risks and development potential.

This study investigates the factors influencing the choice of online and offline shopping trips and their impacts on urban transportation, environment, and economy in Tehran, Iran. A questionnaire survey was conducted to collect data from 1,000 active e-commerce users who made successful orders in both online and offline services in the last 20 days of 2021 in areas 2 and 5 of Tehran. A deep neural network model was developed to estimate the type of shopping trip based on 10 indicators, such as age, gender, car ownership, delivery cost, product price, etc. The performance of the model was compared with three other algorithms: MLP, decision tree, and KNN. The results showed that the deep neural network model had the highest accuracy of 95.63%. The most important factors affecting the choice of shopping trips were delivery cost, delivery time, and product price. This study provides insights for transportation planners, e-commerce managers, and policymakers to design effective strategies for reducing transportation costs, pollutant emissions, urban traffic, and increasing user satisfaction and sustainable development.

With the increasingly stringent airworthiness standards, the noise generated during the rotorcraft flight is gradually attracting people’s attention. It widely operated helicopters at low altitudes because of their maneuverability. The way to reduce the noise caused by the complex airflow of the helicopter rotor system has progressively become a hot topic for researchers. Using a hybrid acoustic analysis method, this paper investigates the improvement of the noise and thrust of the helicopter’s tail rotor through the tail rotor structural parameters. For the basic model, the turbulence simulation is performed using an incompressible detached eddy simulation (DES) method, and the Lighthill acoustic analog equation is calculated using the finite element method (FEM). We verified the accuracy of the method through wind tunnel tests. We chose a series of structural parameters for sound simulation and fluid simulation calculations. The results indicate that the modified tail rotor noise reduced by 16.5 dBA and the total thrust increased by 19.9% from the prototype model. This work can enhance the duct tail rotor design to improve aerodynamic and aeroacoustic performance.

Accurate diagnosis for the 400 million people with rare diseases is critical for healthcare decisions, prognosis, understanding disease mechanisms, and identification of treatments. Despite advances in genome sequencing, barriers such as high interpretation costs, diagnostic expertise, throughput associated delays, and uncertain variant classifications persist, with demand exceeding capacity. Many variant classification methods focus narrowly on specific consequences, leading to the use of complex integrative pipelines that often overlook transcript variability and lack prediction transparency. To overcome these limitations, we introduce DITTO. This transparent, transcript-aware machine-learning method demonstrates superior overall performance in accuracy, recall, and precision when compared to existing tools. DITTO is publicly available at https://github.com/uab-cgds-worthey/DITTO

Multimodal knowledge graph completion necessitates the integration of information from multiple modalities (such as images and text) into the structural representation of entities to improve link prediction. However, most existing studies have overlooked the interaction between different modalities. To address this issue, this paper proposed a Transformer-based knowledge graph link prediction model (MM-Transformer) that fuses multimodal features. Different modal encoders are employed to extract structural, visual and textual features, and hybrid key-value calculations are performed on features from different modalities based on the Transformer architecture. The similarities of textual tags to structural tags and visual tags are calculated and aggregated respectively, and multimodal entity representations are modeled and optimized to reduce the heterogeneity of the representations. Experimental results demonstrate that, compared to the current multimodal state-of-the-art methods, the proposed method achieves significant performance improvements in knowledge graph link prediction tasks. This proves that the proposed method effectively addresses the problem of multimodal feature fusion in knowledge graph link prediction tasks.

Background: This study aimed to identify the association between occupational stress and depression-well-being by proposing a comprehensive and flexible job burden-capital model with its corresponding hypotheses. Methods: For this research, 1618 valid samples were gathered from the electronic manufacturing service industry in Hunan Province, China; self-rated questionnaires were administered to participants for data collection after obtaining their written consent. The proposed model was fitted and tested through structural equation model analysis. Results: Single-factor correlation analysis results indicated that coefficients between all items and dimensions had statistical significance. The final model demonstrated satisfactory global goodness of fit (CMIN/DF=5.37, AGFI=0.915, NNFI=0.945, IFI=0.952, RMSEA=0.052). Both the measurement and structural models showed acceptable path loadings. Job burden and capital were directly associated with depression and well-being or indirectly related to them through personality. Multi-group structural equation model analyses indicated general applicability of the proposed model to basic features of such a population. Gender, marriage and education led to differences in the relation between occupational stress and health outcomes. Conclusions: The job burden-capital model of occupational stress-depression and well-being was found to be more systematic and comprehensive than previous models.

This article develops a multi-perspective view on motivations and methods for tobamovirus purification through the ages, and presents a novel efficient, easy-to-use approach that can be well-adapted to different species of native and functionalized virions. We survey the various driving forces prompting researchers to enrich tobamoviruses: from the search for the causative agents of mosaic diseases in plants to their increasing recognition as versatile nanocarriers in biomedical and engineering applications. Best practice and also rarely applied options for the serial processing steps required for successful isolation of tobamoviruses are then reviewed. Adaptations for distinct particle species, pitfalls and 'forgotten' or underrepresented technologies are considered as well. The article is topped off with our own development of a method for virion preparation, rooted in historical protocols. It combines selective re-solubilization of polyethylene glycol (PEG) virion raw precipitates with density step gradient centrifugation in biocompatible iodixanol formulations, yielding ready-to-use particle suspensions. This newly established protocol and some considerations for perhaps worthwile further developments could serve as putative stepping stones towards preparation procedures appropriate for routine practical uses of the multivalent soft-matter nanorods.

Antioxidant, anti-inflammatory and chemo-preventive features have been reported for the carotenoid diatoxanthin. Diatoxanthin is only produced by a few groups of microalgae where it functions in photoprotection. Its large-scale production in microalgae is currently not feasible. In fact, rapid conversion into the inactive pigment diadinoxanthin is triggered when the cells are removed from the high-intensity light source, which will be the case during large-scale harvesting of microalgae biomass. Zeaxanthin epoxidase (ZEP) 2 and/or ZEP3 have been suggested to be responsible for the back-conversion of high-light accumulated diatoxanthin to diadinoxanthin in low light in diatoms. Using the CRISPR/Cas9 gene editing technology we knocked out the ZEP2 and ZEP3 genes in the already commercially used marine diatom Phaeodactylum tricornutum to investigate their role in the diadinoxanthin-diatoxanthin cycle, and to determine if one of the mutant strains could function as a diatoxanthin production line. Light shift experiments proved that ZEP3 encodes the enzyme converting diatoxanthin to diadinoxanthin in low light. Loss of ZEP3 caused the high-light accumulated diatoxanthin to be stable for several hours after the cultures had been returned to low light, suggesting that zep3 mutant strains could be suitable as commercial production lines of diatoxanthin.

Bovine respiratory disease (BRD) is the leading cause of morbidity and mortality in cattle raised in North America. At the feedlot, cattle are subject to metaphylactic treatment with macrolides to prevent BRD, a practice that may promote antimicrobial resistance and has resulted in an urgent need for novel strategies. Mannheimia haemolytica is one of the major bacterial agents of BRD. The inhibitory effects of two amphipathic, α-helical (PRW4, WRL3) and one β-sheet (WK2) antimicrobial peptides were evaluated against multidrug-resistant (MDR) M. haemolytica isolated from Alberta feedlots. WK2 was not cytotoxic against bovine turbinate (BT) cells by the MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide) assay. All three peptides inhibited M. haemolytica, with WK2, being the most efficacious against multiple isolates. At 8-16 µg/ml, WK2 was bactericidal against Mh 330 in broth and at 32 µg/ml in the presence of BT cells, reduced the population by 3 logs CFU/ml without causing cytotoxic effects. The membrane integrity of Mh 330 was examined using NPN (1-N-phenylnaphthylamine), and ONPG (o-Nitrophenyl β-D-galactopyranoside) with both the inner and outer membranes being compromised. Thus, WK2 may be a viable alternative to the use of macrolides as part of BRD prevention and treatment strategies.

Predicting ground reaction forces (GRFs) and ground reaction moments (GRMs) through a biomechanical model-based approach offers advantages for biomechanical analysis, particularly in situations where the application of a statistical model is limited by insufficient training data. However, the current prediction method is unsuitable for clinical applications due to its long computational time. The present study developed a new optical motion capture (OMC)-based method to predict GRFs, GRMs, and joint torques. The proposed approach performed the estimation process by distributing external forces, as determined by the equation of motion, between the left and right feet based on foot deformations, thereby predicting the GRFs and GRMs without relying on machine learning or optimization techniques. We investigated the prediction accuracy during walking by comparing a conventional analysis using a force plate with the OMC results. The comparison revealed excellent or strong correlations between the prediction and measurement for all GRFs, GRMs, and lower limb joint torques. The proposed method, which provides practical estimation with low computational cost, facilitates efficient biomechanical analysis and rapid feedback of analysis results, thereby increasing its applicability for kinetics estimation in clinical settings.

The objective of this study was to investigate the impact of adding fermented herbal extracts (FHE) derived from mulberry leaf (Morus alba), Javanese turmeric (Curcuma xanthorrhiza), and fingerroot (Boesenbergia rotunda) to the diet of tiger shrimp (Penaeus monodon) on their growth, moulting performance, feed efficiency, and nutrient retention. The main feed used in this trial was a commercially manufactured pellet, then five different doses of FHE supplementation were used: 0 mg/kg feed (P0, control), 50 mg/kg feed (P1), 100 mg/kg feed (P2), 150 mg/kg feed (P3), and 200 mg/kg feed (P4). Weight gain, average daily gain, and length gain of shrimps fed P2 was significantly higher than that of those fed the control diet. Similar result was observed in moulting performance. The application of P2 showed superior results in enhancing the FE of cultured shrimp. Thus, the protein and energy retention of P. monodon was significantly better in P2 treatment groups. P0 had the lowest crude protein, while shrimp given P2 and P3 diets had the highest crude protein content of any treatment group. Based on the findings, it is recommended that the juvenile tiger shrimp diet contain 100 mg/kg of FHE for the best effects.

The objective of this study was to evaluate the efficacy of fungal treatment (Pleurotus ostreatus) of urea-treated rice stubble on growth performance in slow-growing goats. Eighteen crossbred Thai native x Anglo-Nubian male goats (average body weight: 20.4 ± 2.0 kg) were randomly assigned to three experimental total mixed rations containing 35% rice stubble (RS) that was either untreated (URS), urea treated (UTRS) or treated with urea and fungi (UFTRS). URS and UTRS were cultivated and harvested from aseptically fungal spawn, incubated at 25-30 °C for 25 days. Indicators of growth performance were monitored and faeces were collected quantitatively to assess nutrient digestibility, during a 12-week feeding trial. All goats remained healthy throughout experiment. The goats fed UFTRS had a lower feed conversion ratio (kg feed/kg growth) compared to goats fed URS or UTRS. Compared to URS, dietary UFTRS increased nutrient digestibility of slow-growing goats, such as OM (+8.5%), CP (+5.5%), NDF (+39.2%), and ADF (+27.4%). Likewise, dietary UFTRS tended to increase rumen ammonia concentrations but rumen pH and volatile fatty acids were not affected by UFTRS. In conclusion, the present study indicates that the fungal treatment of RS is an effective tool to improve the growth performance of slow-growing goats.

Academics in healthcare grapple with the challenge of efficiently managing time while addressing diverse roles and responsibilities, leading to heightened levels of anxiety and stress within the academic community. The primary objective of this study is to offer a sustainable and systemic solution to the prevalent time-management challenges in healthcare academia, through the development of a predictive model. Employing systemic analysis software and adopting a systemic management approach, we crafted a model to address academic time-management concerns. The dynamics of time allocation for academicians are governed by three fundamental pillars: teaching, research, and administrative tasks. Significantly prioritizing administrative tasks depletes valuable academic time, making time management within academia excessively costly. This occurs as highly skilled individuals find themselves diverted towards administrative duties at the expense of their academic roles diminishing resilience levels. The proposed Timebooster Academic Systemic Model (TASM) advocates for the cultivation of soft skills in academia, en-compassing prioritization, seeking assistance, and addressing tendencies of perfectionism and procrastination. Additionally, TASM recommends the implementation of full automation, delegation of administrative responsibilities, and the mitigation of both overt and covert time-consuming elements. Furthermore, TASM reports on the pivotal role of the academic resilient manager-leaders, who actively contribute to their own satisfaction and academic growth by enhancing collaboration and teamwork. Time-management challenges emerge as a critical determinant of academic well-being and resilience. Systemic models, exemplified by TASM, offer a valuable framework for investigating prioritization concerns, identifying administrative impediments in academic processes, and managing low-value time-consuming elements. The TASM model, along with the systemic philosophy underpinning its design, presents an avenue for advancing the exploration of time-management phenomena within the context of healthcare academia.

Gelephu, located in the Himalayan region, has experienced one of the strongest development activities due to the suitable topography and geographic location, which has led to rapid urbanization. The main objective of the study is to perform land use land cover (LULC) mapping for Gelephu for 2016 and 2023 by a Random Forest (RF) classifier, using the Semi-automatic Classification Plugin (SCP) in QGIS and identify LULC changes. Furthermore, the study assessed the vegetation change dynamics within the study area by analysing the Normalized Difference Vegetation Index (NDVI) for 2016 and 2023. Additionally, the study characterized the resulting LULC change for Gelephu Thromde, a sub-administrative municipal entity, as a result of the notable intensity of the infrastructure development activities. The current study used a framework to collect Sentiniel-2 satellite data, which was then used for pre- and post-processing to create LULC and NDVI maps. The classification model achieved high accuracy, with an area under the curve (AUC) of up to 0.89. The corresponding LULC and NDVI statistics were analysed to determine the current status of the LULC and vegetation indices, respectively. The LULC change analysis reveals urban growth of 5.65% and 15.05% for Gelephu and Gelephu Thromde, respectively. The NDVI assessment shows significant deterioration in vegetation health with a 75.11% loss of healthy vegetation in Gelephu between 2016 and 2023. This research provides the first version of LULC and NDVI maps of Gelephu. The analysis results indicate possible future implications on sustainable development management in Gelephu Bhutan.

Transcription is carried out in most eukaryotes by three multimeric complexes (RNA polymerases I, II and III). However, plants contain two additional RNA polymerases (IV and V), which have evolved from RNA polymerase II. RNA polymerases II, IV and V contain both common and specific subunits that may specialize some of their functions. In this study, we conducted a search for the genes that putatively code for the specific subunits of RNA polymerases IV and V, as well as the corresponding of RNA polymerase II in olive. Based on the homology with the genes of Arabidopsis thaliana, we identified 13 genes that putatively code for the specific subunits of polymerases IV and V, and 16 genes that code for the corresponding specific subunits of polymerase II in olive. The transcriptomic analysis by RNA-Seq revealed that the expression of the RNA polymerases IV and V genes was induced during the initial stages of fruit development. Given that RNA polymerases IV and V are involved in the transcription of long non-coding RNAs, we investigated their expression and observed relevant changes in the expression of this type of RNAs. Particularly, the expression of the intergenic and intronic long non-coding RNAs tended to increase in the early steps of fruit development, suggesting their potential role in this process. The positive correlation between the expression of RNA polymerases IV and V subunits and the expression of non-coding RNAs supports the hypothesis that RNA polymerases IV and V may play a role in fruit development throughout the synthesis of this type of RNAs.

Background Glioblastoma (GBM) is a poor prognosis malignant grade 4 glioma. After surgical resection, standard therapy consists of concurrent radiotherapy (RT) and temozolomide (TMZ) followed by TMZ alone. The DC vaccination increased the amount of intratumoral-activated cytotoxic T lymphocytes and decreased the number of FoxP3-positive regulatory T cells. Based on these da-ta, we have developed a phase II protocol with the DC vaccine concomitant to standard RT-CT in patients undergoing surgery for GBM. Methods This is a single-arm, monocentric, phase II trial evaluating progression-free survival (PFS) and the safety of a DC vaccination integrated with standard therapy in resected GBM patients. The vaccine administration begins at the end of the RT-CT (Induction Phase) and is subsequently al-ternated with TMZ cycles (Maintenance Phase). The primary endpoints are PFS and safety. Among the secondary endpoints, are the in vitro and in vivo Immune response evaluations. Results By December 2022 , nine patients had been enrolled in the first step. Two patients progressed within three months after leukapheresis and none had experienced DC vaccine-related G3-4 tox-icity. Five patients had a positive DTH test against KLH. By June 2023. The median OS from leu-kapheresis was 23.1 months and 24.7 from surgery. Conclusions This combination therapy is well-tolerated, and we have achieved the two endpoints required for the first step. Therefore, the study will proceed to enroll the remaining 19 patients.

To curb the impacts arising from the agricultural sector the actions undertaken by policymakers, and ultimately by the farmers, are of paramount importance. However, finding the best strategy to reduce impacts, and especially assessing the effects of the interactions and mutual influence among farmers, is very difficult. To this aim, this paper shows an application of an agent-based model (ABM) coupled with life cycle assessment (LCA), which also includes multi objective optimization of farming activities (including both crop cultivation and livestock breeding) from an economic and environmental perspective. The environmental impacts are assessed using the impact assessment scores calculated with the Environmental Footprint (EF) 3.0 life cycle impact assessment (LCIA) method and the study is conducted “from cradle to farm gate”. The model is applied to all the farms in Luxembourg, whose network is constructed utilizing neighborhood interactions, through which a parameter known as farmer's green consciousness (GC) is updated at each time step. The optimization module is instantiated at the end of each time step, and decision variables (the number of livestock units and land allocation) are assigned based on profitability and specified environmental impact categories. If only profit optimization is considered (i.e. when farmers’ GC is de-activated), the results show a 9% reduction in the aggregated environmental impacts (obtained as EF “single score”) and a 5.5% increase in overall profitability. At the farm level, simulations display a clear trade-off between environmental sustainability and financial stability, with a 25% reduction in overall emissions possible if farming activities are carried using the EF single score impact in the objective function, though this results in an 8% reduction in profitability over ten years.

The aim of this research is to investigate and use a variety of immersive multisensory media techniques in order to create convincing digital models of fossilised tree trunks for use in XR. This is made possible via the use of geospatial data derived from sources such as aerial imaging using UAS, terrestrial material using cameras and also include both the visual and audio element for better immersion, accessible and explorable in 6 Degrees of Freedom (6DoF). Immersiveness is a key factor in order to result in output that is especially engaging to the user. Both conventional and alternative methods are explored and compared, emphasising in the advantages made possible via the help of Machine Learning Computational Photography. Material is collected using both UAS and terrestrial camera devices, including a 3D-360º camera with 6 sensors, using stitched panoramas as sources for photogrammetry processing. Difficulties such as capturing large free standing objects using terrestrial means were overcome using practical solutions involving mounts and remote streaming solutions. Conclusions indicated that superior fidelity can be achieved by the help of Machine Learning Computational Photography processes and higher resolutions and technical specs of equipment not necessarily translating to superior outputs.

Distributed/semi-distributed models are considered to be sensitive to the spatial resolution of the data input. In this paper, we take a small catchment in high urbanized Yangtze River Delta, Qinhuai catchment as study area, to analyze the impact of spatial resolution of precipitation and the potential evapotranspiration (PET) on the long-term runoff and flood runoff process. The data source includes the TRMM precipitation data, FEWS download PET data, and the interpolated metrological station data. GIS/RS technique was used to collect and pre-process the geographical, precipitation and PET series, which were then served as the input of CREST (Coupled Routing and Excess Storage) model to simulate the runoff process. The results clearly showed that, the CREST model is applicable to the Qinhuai catchment; the spatial resolution of precipitation had strong influence on the modelled runoff results and the metrological precipitation data cannot be substituted by the TRMM data in small catchment; the CREST model was not sensitive to the spatial resolution of the PET data, while the estimation fourmula of the PET data was correlated with the model quality. This paper focused on the small urbanized catchment, suggesting the influential explanatory variables for the model performance, and providing reliable reference for the study in similar area.

Gestational diabetes mellitus (GDM) pathophysiology comprises clinical and genetic factors. In fact, GDM has been associated with several single nucleotide polymorphisms (SNPs). This study aimed to build a prediction model of GDM combining clinical and genetic risk factors. A total of 1588 pregnant women from the San Carlos Cohort participated in the present study, 1069 (67.3%) Caucasian (CAU) and 519 (32.7%) Latin American (LAT), 255 (16.1%) with GDM. The incidence of GDM was similar in both groups (16.1% CAU and 16.0% LAT). Genotyping was performed by IPLEX Mass ARRAY PCR selecting 110 SNPs based on literature references. The SNPs showing the strongest likelihood of GDM development were the rs10830963, rs7651090 and rs1371614 in CAU and rs1387153 and the rs9368222 in LAT. Clinical variables including age, pre-pregnancy body mass index and fasting plasma glucose (FPG) at 12 gestational weeks predicted the risk of GDM (AUC 0.648, 95%CI 0.601-0.695 in CAU; AUC 0.688, 95%CI 0.628-9.748 in LAT) and adding the SNPs modestly improved prediction (AUC 0.722, 95%CI 0.680-0.764 in CAU; AUC 0.769, 95%CI 0.711-0.826 in LAT). In conclusion, adding genetic variants enhanced the prediction model of GDM risk in CAU and LAT pregnant women. An RCT is underway to demonstrate its usefulness

(1) Objective: This study aims to lay a foundation for non-contact intensive care monitoring of premature babies. (2) Methods: Arterial oxygen saturation and heart rate were measured using a monochrome camera and a time division multiplex controlled lightning with three different wavelengths (660 nm, 810 nm and 940 nm) on a piglet model. (3) Results: Using the camera system and our newly designed algorithm for further analysis, the detection of a heartbeat and the calculation of oxygen saturation was evaluated. In motionless individuals, heartbeat and respiration were separated clearly during light breathing and only minor intervention. In this case, the mean difference between non-contact and contact saturation measurements was 0.7% (RMSE = 3.8%, MAE = 2.93%). (4) Conclusions: The new sensor has proven effective under ideal animal experiment conditions. The results allow a systematic improvement for the further development of contactless vital sign monitoring systems. The results presented here are a major step towards the development of an incubator with non-contact sensor systems for use in the neonatal intensive care unit.

Dairy farming dates to the early Neolithic era in Europe, Middle East, Asia, and Africa. Over time, the industry has evolved from small ranches and farmhouses milk processing units to large, intensive dairy farms and big, high-tech processing units. Despite the industry’s contribution to the economy and food production, multiple studies have tainted cattle farming as a main contributor to climate change. This study explores literature in dairy farming and sustainable development goals (SDGs) to identify current scholarly developments since the formulation and adoption of SDGs in 2015 and themes for future research. This paper argues that dairy animals are not the source of sustainability shortfalls as depicted by multiple scholarly articles, but human processes associated with dairy farming. Data was analyzed using R package, Excel, NVIVO, and VoS Viewer. A review of literature shows that dairy farming and its contribution to sustainability has gained more intensive scientific interest since 2015. Moreover, livestock management, feed production and management, stakeholder’s management, logistics and supply chain management, and waste management are the sources of environmental adversities associated with dairy farming. Notably, these are human processes developed from commercialization of dairy farming and involve multiple stakeholders across the supply chain. While solutions are embedded within these processes, innovation emerges as a key driver of sustainability and source of opportunities to strengthen sustainability in dairy farming sector and achieve the sustainable development goals.

Background: to analyze the impact of COVID-19 pandemic on characteristics and management of uveal melanoma (UM) in National Referral Center in Poland. Materials and Methods: the retrospective analysis of 1336 patients who were newly diagnosed with UM at Department of Ophthalmology and Ophthalmic Oncology, Jagiellonian University Collegium Medicum Krakow, Poland between January 1, 2018 and December 31, 2021. The demographic and clinical data were compiled including localization, size and treatment methods of tumors. Results: In total, 728 patients with UM were included before COVID-19 pandemic in years 2018-2019 and 608 were included during COVID-19 pandemic in years 2020-2021. Fixed-base dynamics indicators for the incidence of uveal melanoma (base year 2018) in the national referral center in Poland were 80.22% and 86.81% in year 2020 and 2021, respectively. UMs were statistically significantly larger and more frequently localized anterior to the equator of the eye globe in year 2021 than in year 2018 (Chi^2 Pearson test p= 0.0001 and p= 0.0077, respectively). The rate of patients treated with enucleation increased from 15.94% in year 2018 to 26.90% in year 2021 (Chi^2 Pearson test p=0.0005). Conclusions: statistically significant differences were found in the management of uveal melanoma in National Referral Center in Poland during COVID-19 pandemic with tumors being larger, more frequently localized anterior to the equator of the eye globe and more often enucleated.

This study presents the comparison of heat transfer capacity and pressure drop characteristics between a basic fin-tube heat exchanger and a modified heat exchanger with the structural change of branch tubes and coiled turbulators. All experiments were carried out using an air-enthalpy type calorimeter based on the method described in ASHRAE standards, under heat exchanger experimental conditions. 14 different kinds of heat exchangers were used for the experiment. Cooling and heating capacities of the turbulator heat exchanger were excellent, compared to the basic one. As the insertion ratio of the coiled turbulator and the number of row increased, the heat transfer performance increased. However, the capacity per unit area was more effective in 4 rows than 6 rows, and the cooling performance of the 6 row turbulator heat exchanger (100% turbulator insert ratio) was down to about 6% than that of 4 row one. As the water flow rate and the turbulator insertion ratio increased, the pressure drop of the water side increased. This trend was more pronounced in 6 rows. In the cooling condition, the pressure drop on the air side was slightly increased due to the generation of condensed water, but was insignificant under the heating condition. The power consumption of the pump was more affected by the water flow rate than the coiled turbulator. The equivalent hydraulic diameter of a tube by the turbulator was reduced and then the heat transfer performance was improved. Thus, the tube diameter was smaller, the heat flux was better.

Assessment of extreme events and climate change on reservoir inflow is important for water and power stressed countries. Projected climate is subject to uncertainties related to climate change scenarios and Global Circulation Models (GCMs’). Extreme climatic events will increase with the rise in temperature as mentioned in the AR5 of the IPCC. This paper discusses the consequences of climate change that include extreme events on discharge. Historical climatic and gauging data were collected from different stations within a watershed. The observed flow data was used for calibration and validation of SWAT model. Downscaling was performed on future GCMs’ temperature and precipitation data, and plausible extreme events were generated. Corrected climatic data was applied to project the influence of climate change. Results showed a large uncertainty in discharge using different GCMs’ and different emissions scenarios. The annual tendency of the GCMs’ is bi-vocal: six GCMs’ projected a rise in annual flow, while one GCM projected a decrease in flow. The change in average seasonal flow is more as compared to annual variations. Changes in winter and spring discharge are mostly positive, even with the decrease in precipitation. The changes in flows are generally negative for summer and autumn due to early snowmelt from an increase in temperature. The change in average seasonal flows under RCPs’ 4.5 and 8.5 are projected to vary from -29.1 to 130.7% and -49.4 to 171%, respectively. In the medium range (RCP 4.5) impact scenario, the uncertainty range of average runoff is relatively low. While in the high range (RCP 8.5) impact scenario, this range is significantly larger. RCP 8.5 covered a wide range of uncertainties, while RCP 4.5 covered a short range of possibilities. These outcomes suggest that it is important to consider the influence of climate change on water resources to frame appropriate guidelines for planning and management.

Contemporary financial systems, despite their inherent complexity and resilience, face significant challenges related to transparency, efficiency, and security. Notable deficiencies in transparency and emerging security vulnerabilities underscore the pressing need for innovative solutions in current financial practices. Hence, this paper introduces a financial distribution system based on a consortium blockchain. Given organizations’ preference for keeping sensitive data private, the partially decentralized nature of consortium blockchains plays a pivotal role in the proposed system’s architecture. Moreover, within this system, the proposed smart contract serves a dual role. It functions not only as a transactional tool but also as a specialized solution designed to improve transactional procedures. Its multifaceted capabilities include ensuring data accuracy, facilitating swift execution, and ensuring adherence to regulatory requirements within the dynamic financial distribution landscape. Through rigorous testing and empirical analysis utilizing the Hyperledger Besu platform, it has been observed that the performance of the proposed system surpasses traditional blockchain frameworks. The results unequivocally demonstrate the proposed system’s proficiency in handling high-frequency financial transactions with minimal computational burden.

Reactive NO_x is one of the major air pollutants, which also plays a key role as greenhouse gas. Many research efforts have been devoted to not only detection of NO_x in air but also abatement of NO_x emission. The aim of this mini review is to provide a panoramic snapshot of the electrochemical analysis methods for the emission and detection of NO_x in atmosphere, with special emphasis on NO_x sensor. The electrochemical detecting mechanism and materials for fabricating electrochemical gas sensors are discussed and the prospects and challenges in this area are also evaluated. This work will serve as a useful source to inform the interested audience of the latest developments and applications in the field of NO_x emission and electrochemical detection.

Background/Objectives: Laryngeal squamous cell carcinoma (LSCC) is the most prevalent form, accounting for nearly 95% of laryngeal cancer. This study evaluates the effectiveness of chemotherapy and radiation therapy combined for treating LSCC, along with exploring miR-449a as a potential biomarker for predicting treatment response and prognosis in LSCC patients. Patients and Methods: The study had 81 patients with advanced laryngeal cancer and 50 healthy controls. Chemotherapy was given for two cycles, followed by an evaluation of the response. Disease progression cases had surgical resection and radiation therapy. Positive responders had radiation therapy after a third cycle. The study also assessed miR-449a expression levels in tumor tissue, normal tissue, and serum samples from control subjects. Results: 31% achieved a complete response, and 49% showed a partial response. 31% of complete response participants maintained laryngeal integrity and vocal function. After radiation therapy, 74% of 65 patients responded completely and preserved laryngeal functions. Lower MiR-449a levels found in LSCC tissues and serum samples were correlated with aggressive tumor behavior, poor prognostic markers, and decreased survival. Conversely, elevated levels were seen in treatment responders. Conclusions: Combined chemo and radiotherapy preserve vocal function and improve outcomes in LSCC. MiR-449a is a promising indicator for diagnosis and treatment efficacy in LSCC.

Climate change, increasing global food needs, and decreasing global resources brought a new era to human every-day live with a strong turn to circular economy and sustainability. Food packaging and preservation is an important global sector where enchanced research is carried out. New methods such as active packaging and chemical preservatives replacement are of major interest. Many researchers were published studies about active packaging films, replacement of chemical preservatives with natural originated extracts, and novel incorporation procedures. Sequentially to our and other scientists’ previous work, in this paper we present the results of the improvement which was achieved by incorporating the developed nanohybrid of natural-halloysite/carvacrol-extract, the pure natural carvacrol extract and the commonly used low-density-polyethylene. The well known corona-treatment procedure was employed to produced the optimum final film and the results shown that the low-density-polyethylene incorporated with natural-halloysite/carvacrol-extract nanohybrid and coated external with extra carvacrol extract exhibited aproximatelly 100% higher Young-Modulus values, slightly decreased ultimate strength by 20%, and almost stable elonation at break properties. The water-vapor and oxygen barrier increased by 40%. Finally, the antioxidant activity of the corrona-treated film increased by 28% compared to the similar but untreated product because of the lower release rate of the carvacrol.

A novel scaffold design has been created to enhance tissue engineering and regenerative medicine by optimizing the controlled, prolonged release of hepatocyte growth factor (HGF), a powerful chemoattractant for endogenous mesenchymal stem cells. We present a new stacked scaffold that is made up of three different fibrin gel layers, each of which has HGF integrated into the matrix. The design attempts to preserve HGF's regenerative properties for long periods of time, which is necessary for complex tissue regeneration. These multi-layered fibrin gels have been mechanically evaluated using rheometry, and their degradation behavior has been studied using D-Dimer ELISA. Understanding the kinetics of HGF release from this novel scaffold configuration is essential for understanding HGF's long-term sustained bioactivity. A range of cell-based tests were carried out to verify the functionality of HGF following extended incorporation. These tests included 2-photon microscopy using phalloidin staining to examine cellular morphology, SEM analysis for scaffold-cell interactions, and scratch and scatter assays to assess migration and motility. The analyses show that the novel stacking scaffold promotes vital cellular processes for tissue regeneration in addition to supporting HGF's bioactivity. This scaffold design was developed for in situ tissue engineering. Using the body as a bioreactor, the scaffold should recruit mesenchymal stem cells from their niche thus combining the regenerative abilities of HGF and MSCs to promote tissue remodeling and wound repair.

Mother's milk is the complete food of choice and the best nutritional source for its bio-immunological richness and its microbiota composed of lactic acid bacteria. The aim of this study was to assess the probiotic potential of the lactic acid bacteria present in breast milk. 34 samples (colostrum, transition milk, mature milk) were taken from breastfeeding women. They were enriched and inoculated onto MRS and MRS+cysteine agar. The sensitivity profile of the bacterial strains was assessed using the diffusion method. PCR was used to search for resistance genes specific to the target bacteria after extraction of the genomic DNA from the isolated strains. Their probiotic capacity was explored by assessing their ability to develop under different conditions. Forty-nine bacterial strains were isolated and Lactobacillus acidophilus was the most representative species in the three types of milk. These strains were able to develop both in hostile conditions and over a wide range of temperatures. They also showed antibacterial activity against pathogenic strains of Salmonella typhimurium, multi-resistant Staphylococcus aureus and Escherichia coli (ATCC25299). These results show that breast milk has potential probiotic activity that can be explored further to make the most of it, particularly in terms of antibiotic therapy.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

We consider creation of entanglement between the remote electron spins using a magnetic nanoparticle attached to a cantilever tip (CT). We assume that the frequency of the CT vibrations matches the Larmor frequency of the spin (the CT-spin resonance). In the conditions of the CT-spin resonance the CT-spin system is described by the exactly solvable Jaynes-Kummings model. In this work using the evolution operator of the Jaynes-Kummings model we show that a movable CT can create an entangled state between the remote spins. The most striking result is that the entanglement between the remote spins can be achieved without measurement of the vibrational state of the CT.

This paper presents a novel framework for Collaborative Learning Networks (CLNs) in classrooms, aiming to optimize resource utilization and enhance educational services. The study addresses the inefficiencies of traditional education models, particularly the underutilization of computational resources and limited access to specialized software. The proposed framework employs a mix of task-based and random allocation strategies to distribute computational tasks within the CLN, considering each device’s capabilities and task requirements. The findings show significant improvement in resource utilization, facilitated by device-to-device collaboration and task sharing, thus enriching students’ learning experiences. This work contributes to optimizing resource utilization in education, advancing the field of CLNs.

Viral co-infections, where a host is infected by multiple viruses simultaneously, are common in the human population. Human viral co-infections can lead to complex interactions between viruses and the host immune system, affecting the clinical outcome and posing challenges for treatment. Understanding the types, mechanisms, impacts, and identification methods of human viral co-infections is crucial for the prevention and control of viral diseases. In this review, we first introduce the significance of studying human viral co-infections and summarize the current research progress and gaps in this field. We then classify human viral co-infections into four types based on the pathogenic properties and species of the viruses involved. Next, we discuss the molecular mechanisms of viral co-infections, focusing on virus-virus interactions, host immune responses, and clinical manifestations. We also summarize the experimental and computational methods for identifying viral co-infections, emphasizing the latest advances in high-throughput sequencing and bioinformatics approaches. Finally, we highlight the challenges and future directions in human viral co-infection research, aiming to provide new insights and strategies for the prevention, control, diagnosis, and treatment of viral diseases. This review provides a comprehensive overview of the current knowledge and future perspectives on human viral co-infections, and underscores the need for interdisciplinary collaborations to address this complex and important topic.

The objective of this research is to establish a better understanding of the current landscape of digital learning research and sustainable development by using CiteSpace. First, we retrieved published publications from the Web of Science (2004–2022). Following that, we examined the primary research strengths and important subjects of digital learning from two perspectives: collaboration networks (including collaborative networks across countries, institutions, and authors) and co-citation networks. We examined the co-citation network from three perspectives: cluster analysis, the most active citers, top references. Furthermore, referenced journals, popular themes, and rising trends were examined. These findings indicate the primary study subjects in the field of digital learning, the most intriguing research literature, and each period's emerging research hotspots. Finally, we proposed further study ideas for future paths.

In this paper, the neural network is developed for solving the inverse photoacoustic problem aiming to estimate thermo-elastic properties and thickness of a semiconductor sample. The idea was that these sample properties be estimated from the phase characteristic because the phase measurements are more sensitive. The neural network has been trained on a large basis of photoacoustic phases calculated from a theoretical Si n-type model in the range of 20Hz to 20kHz, on which random Gaussian noise has been applied. It is shown that high accuracy and precision could be reached in solving of inverse photoacoustic problem using only phase measurement.

Contamination in coastal regions attributed to fluoride and nitrate cannot be disregarded, given the substantial environmental and public health issues they present worldwide. Maintaining water quality is crucial for environmental well-being. This comprehensive study was performed to assess the spatial as well as indexical water quality, identify contamination sources, and evaluate associated health risks pertaining to nitrate and flouride in AlHassa region, KSA. The physiochemical results revealed a pervasive pollution of the overall groundwater. The dominant water type was Na-Cl, indicating saltwater intrusion and reverse ion exchange impact. Spatiotemporal variations in physicochemical properties suggest diverse hydrochemical mechanisms, with geogenic factors primarily influencing groundwater chemistry. The groundwater pollution index varied between 0.8426 and 4.7172, classifying samples as moderately to very highly polluted. Similarly, the synthetic pollution index (in the range of 0.5021-4.0715) revealed that none of the samples had excellent water quality, with various degrees of pollution categories. Nitrate Health Quotient (HQ) values indicated chronic human health risks ranging from low to severe, with newborns being the most vulnerable. Household use of nitrate-rich groundwater for showering and cleaning did not pose significant health risks. Fluoride HQ decreased with age, and children faced the highest risk of fluorosis. The Hazard Index (HI) yielded moderate to high-risk values. Nitrate risks were 1.21 times higher than fluoride risks, as per average HI assessment. All samples fell into the vulnerable category based on the Total Hazard Index (THI), with 88.89% classified as very high risk. This research provides valuable insights into groundwater quality, guiding water authorities, inhabitants, and researchers in identifying safe water sources and vulnerable human populations. The results highlight the need for appropriate treatment techniques and long-term coastal groundwater management plans.

This longitudinal study examined the development of executive function and calibration accuracy in pre-adolescents. Two hundred sixty two students(127 females) from grades 4 (n = 91), 5 (n = 89) and 6 (n = 82) took measures of executive function and performance calibration in a sport task, three times within a period of 20 months. A latent growth curve modeling analysis showed a significant relation between the rates of change of executive function and calibration. Further, the results showed a dynamic interplay in the development of executive function and calibration. There were significant inter-individual differences in the estimated population means both in executive function and calibration and in the rate of change of executive function, but not in the rate of change for calibration. The age of the participants had a positive effect only on the estimated population mean of executive function.

Lower urinary tract dysfunction (LUTD) was associated with bladder inflammation and tissue hypoxia with oxidative stress. This study investigated the profiles of urine inflammatory and oxidative stress biomarkers in females with LUTD and to develop a urine biomarker-based decision tree model for the prediction. Urine samples were collected from 31 female patients with detrusor overactivity (DO), 45 with dysfunctional voiding (DV), and 114 with interstitial cystitis/ bladder pain syndrome (IC/BPS). The targeted analytes included 15 inflammatory cytokines and 3 oxidative stress biomarkers (8-hydroxy-2-deoxyguanosin, 8-isoprostane, and total antioxidant capacity [TAC]). Different female LUTD groups had distinct urine biomarker profiles, including IL-1β, IL-2, IL-8, IL-10, eotaxin, CXCL10, MIP-1β, RANTES, TNFα, VEGF, NGF, BDNF, 8-isoprostane, and TAC. The urine biomarker-based decision tree, using IL-8, IL-10, CXCL10, TNFα, NGF, and BDNF as nodes, demonstrated an overall accuracy rate of 85.3%. DO, DV, and IC/BPS accuracy rates were 74.2%, 73.3%, and 93.0%, respectively. Internal validation revealed similar overall accuracy rate. Random forest models supported the significance and importance of all selected nodes in this decision tree model. Urine inflammatory and oxidative stress biomarker profiles of different female LUTDs were different. This internally validated urine biomarker-based decision tree model predicted different female LUTDs with high accuracy.

This research experiments with a sustainability assessment based on the Sustainable Development Goals (SDGs) of Agenda 2030 through a process of their territorialisation and implementation. This process enables the development of a Spatial Decision Support System (SDSS) that can be integrated with strategic environmental assessments in urban planning. The assessment takes place on the transversality of the sustainability concept by trying to consider together the three dimensions (environmental, social and economic) into a single assessment through the Spatial Sustainability Assessment Model (SSAM) by integrating Geographic Information Systems (GIS) and multi-criteria analyses. Economic development, social equity, and ecological integrity represent three common visions for rethinking peri-urban edges.

In this study, we present data on the effects of condensed (CTs) and hydrolysable (HTs) tannins, polyphenols extracted from plants, at different concentrations on zebrafish development to identify the range of concentrations with toxic effects. Zebrafish embryos were exposed to CTs and HTs at two different concentration ranges (5.0, 10.0, 20.0 μgL-1 and 5.0, 10.0, 20.0 mgL-1) for 72 h. The toxicity parameters were observed up to 72 h of treatment. The uptake of CTs and HTs by zebrafish larvae was assessed by HPLC analysis. The qRT-PCR analysis was performed to evaluate gene expression of cd63, zhe1 and klf4, involved in the hatching process of zebrafish. CTs and HTs at 5.0, 10.0, and 20.0 μgL-1 were not toxic. On the contrary, at 5.0, 10.0, and 20.0 mgL-1 HTs induced a delayed in hatching starting from 48 h of treatment, while CTs showed a delayed in hatching mainly at 48h. The analysis of gene expression showed a down-regulation in the group exposed to HTs confirming the hatching data. We believe that this study is important to define the optimal doses of CTs and HTs, to be employed in different application fields such as chemical and animal feed industry, and medical science.