A complete understanding of groundwater dynamics and its interaction with surface water underthe impact of agricultural activities is vital for local agriculture, ecology, and residents of dry regions, which is not commonly recognized in arid areas. This research outlines the geochemical characteristics, recharge sources, and potential factors impacting groundwater quality in a new land reclamation located in the small basin of Abu Mina, which is part of the Western Nile Delta region.1 Thirty‑one groundwater samples and two surface water samples were collected in 2021 to represent the Pleistocene aquifer and were subjected to multivariate statistical, hydrochemical, and stable isotope analyses. Data analysis demonstrates that Na+ > Ca2+ > Mg2+ > K+ and SO4 2– > Cl– > HCO3 – > NO3 – are the predominant cations and anions, respectively. Groundwater salinity ranged from 465.60 to 6455.18 mg/l, with slightly alkaline. Most of the water samples fall into one of three types of facies: Ca–Cl, Na–Cl, and Mixed Ca–Mg–Cl, in decreasing order. The meteoric genesis index (r2) indicates that deep meteoric water percolation dominates the Pleistocene aquifer. The aquiline diagrams, correlation matrix, and different ionic ratios indicate that evaporation, reverse ion exchange reactions, and the dissolution of carbonate and silicate minerals are the main processes governing groundwater chemistry. Factor analysis (FA) indicated that three factors explain groundwater hydrochemistry, accounting for 71.98% of the total variance. According to the rotating components matrix (F1–F3), the chemistry of the Quaternary aquifer is principally affected by evaporation, ion exchange reactions, and anthropogenic influences. Additionally, salinity increases due to the return flow of irrigation activities and mixing between old and recent water. The stable isotopes (δ18O and δ2H) indicate that the Quaternary aquifer receives groundwater recharge through the return flow of excess irrigation and canal seepage. Under desert reclamation conditions, groundwater salinization processes should be given special consideration. All groundwater samples are appropriate for agricultural irrigation based on the Sodium Adsorption Ratio (SAR), Permeability Index (PI), Percent Sodium (%Na), and Residual Sodium Carbonate (RSC).

NLR genes are crucial components of the effector-triggered immunity (ETI) system, which recognizes pathogens and initiates immune responses. While NLR genes in many plants have been extensively studied, the diversity of NLR genes in citrus remains largely unknown. In this study, we systematically identified 1,585 NLR genes across the genomes of 10 citrus species. Significant variations in NLR gene copy numbers were observed among these citrus species, with gene duplication and recombination identified as major driving forces. Citrus NLR genes were classified into TIR-NBS-LRR (TNL), CC-NBS-LRR (CNL), RPW8-NBS-LRR (RNL), and NL categories. It is believed that TNL, RNL, and CNL genes originated from NL genes through the acquisition of TIR, RPW8 domains and CC motifs, respectively, followed by the random loss of the corresponding domains. Phylogenetic analysis revealed that citrus NLR genes originated with the species and underwent adaptive evolution, potentially playing crucial roles in the global colonization of citrus. This study is importance for comprehensively understanding citrus NLR genes and provides a foundational dataset for research aimed at breeding disease-resistant citrus varieties.

Plastic pollution is a pressing environmental challenge, necessitating the development of biodegradable alternatives like polyhydroxybutyrate (PHB). This study focuses on optimizing PHB production by Neobacillus niacinii GS1, a bacterium isolated from a municipal dumping site. By utilizing agricultural residues such as corn flour, wheat bran, and peptone as substrates, we aimed to establish an eco-friendly method for biopolymer production, contributing to sustainable waste management and bioplastic innovation. The bacterium was identified using morphological, biochemical, and molecular techniques. The optimization process involved adjusting variables such as inoculum age, inoculum size, incubation time, agitation rate, incubation temperature, pH of the medium, carbon sources and nitrogen sources. Response surface methodology (RSM) was employed to identify optimal conditions, with the highest PHB yield of 61.1% achieved under specific conditions: 37°C, pH 7, and an agitation rate of 150 rpm. These findings underscore the potential of Neobacillus niacinii GS1 in converting agro-industrial residues into valuable biopolymers, promoting sustainable bioplastic production, and advancing waste valorization efforts through the use of eco-friendly materials.

The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.The growing energy consumption in the cryptocurrency and artificial intelligence (AI) industries is becoming an increasingly critical issue. This paper discusses methods for addressing this problem using data compression algorithms and the optimization of computational processes. Specifically, we examine research results that demonstrate the potential for significantly reducing energy consumption through mathematical solutions, such as the Riemann Hypothesis. We also present the notarized test results of an image compression program developed by the author. The work was presented at a UN panel session and an ECO webinar, receiving recommendations for implementation in ECO member countries. Additionally, modern technologies for accelerating cryptographic methods and algorithms are considered.

Rift Valley Fever (RVF), is a viral zoonotic disease predominant in East Africa and transmitted by Aedes mosquitoes carrying the virus. Using the systematic literature review approach, the present study evaluated machine learning techniques and time series approaches to find literature on the impact of climatic changes on RVF outbreaks published between 1930 and 2024. The literature search involved databases including PubMed, PLOS ONE, JSTOR, Web of Science, Google Scholar, and SCOPUS (Kenmoe et al., 2023).The results show that most of the articles were published between 2018 and 2022, and most of the articles were from United States, France, and Kenya. We conducted a detailed review of the articles using the PRISMA 2020 flow chart, screening and qualifying 10,015 articles. Some articles revealed significant gaps in both internal and external validation. Therefore, future research should focus on developing multi-disciplinary models that incorporate climatic condition, geographical, biological, and social factors.

The forest is a complex and dynamic ecosystem that requires integrated planning and management, considering economic, social, environmental, and ecological dimensions. Different stakeholders often have conflicting goals, making optimal management decisions challenging. This study aims to balance increasing economic benefits for forest users with reducing negative environmental impacts, aligning the interests of managers, government institutions, and policymakers. To achieve this, multi-objective optimization methods and game theory were used. The objectives were to maximize the net present value of wood harvesting and the amount of carbon sequestration. The study was conducted in the Shafarood forest in northern Iran. Data collected included stumpage prices, variable harvesting costs, tree density per ha, volume per ha, growth rates, interest rates, carbon sequestration, and labor costs. Using this data, models were developed for the expected mean price of various species, stored carbon, logistics growth, and labor. After adjusting the objective functions and model constraints, the range of Pareto optimal solutions for the multi-objective model and the Nash equilibrium for the game theory model were determined using the epsilon-constrain method. The results indicate that both modeling approaches can identify optimal points for balancing economic and environmental goals. The Pareto optimal range and Nash equilibrium allow for informed decision-making in managing the Hyrcanian forests, accommodating different user goals. Therefore, both models are effective tools for forest management planning and provide a framework for achieving sustainable management practices.

It aims to utilize more solar energy, which is both environmentally friendly and easily accessible, to satisfy the energy needs of developing countries. Therefore, maximum power generation should be provided from photovoltaic panels. Several maximum power point tracking(MPPT) methods are utilized for the maximum power generation in photovoltaic panels systems under different weather conditions. In this paper, a novel hybrid and clever maximum power point monitoring fuzzy logic controller maximum power point tracking algorithm established on the perturb and observe (PO) algorithm is presented. The proposed fuzzy logic controller algorithm and the incremental conductivity maximum power point tracking algorithm were simulated in MATLAB/SIMULINK environment and evaluated by comparing the results. Four Sharp ND-F4Q295 solar panels, two in series and two in parallel were used for the simulation. The hybrid method proposed here was found to reduce the voltage and current ripple compared to the classical incremental conductivity method. The proposed intelligent method has been shown to stabilise the system more quickly in the event of sudden weather changes.

The context of this research is given by a socio-economic situation in which new and more demanding challenges appear. Using wood, a naturally occurring, renewable, and biodegradable material, in construction is becoming more and more necessary due to the awareness of the negative impact on the environment of the construction industry and the energy crisis limitations. The purpose of this study is to analyze what do we really know about the use of wood as a building material. The search for an answer to the research question implies the use of a scientometric literature analysis, followed by an in-depth and expert analysis. The research results indicate a major preoccupation for studying engineering wood structures, like the Cross laminated Timber, the structural behaviour of the structures, the implementation of new IT discoveries in wood construction technology and a more recent approach regarding the environmental impact of wood construction, mostly the possibility of using wood waste to prolong the life of wood as a building material. The results also show a need to create new models of understanding the structural behaviour of wood that consider the fibrous and cellular nature of the material. Wood as a construction material is an extremely complex material and for a better understanding of it, there is a need to have an interdisciplinary approach in which to combine the knowledge of construction with that of forestry, ecology, biology and others.

The problem can be stated as over 3 billion choices to improve 14 disease states with 9 optimization goals (some of the optimization goals are diametrically opposed) to improve dyspnea, shortness of breath, fatigability, exercise intolerance, edema, swelling, fluid retention, and arrhythmias. The goal is to increase Left Ventricular Outflow Integral, reduce mitral regurgitation, increase longitudinal conduction velocities and restore synchrony of the septum to the ventricle that needs it the most. A method to accomplish this task for the spectrum of cardiac disease is presented.

Falls are a major public health problem among older adults, therefore predicting the risk of having falls in the near future is of great importance for health and social systems worldwide. Nowadays, prospective fall risk assessment relies on clinical and functional mobility assessment tools, such as the Timed Up and Go (TUG) test. Recently, wearable inertial measurement unit (IMU) sensors measurements have been proposed for fall detection. We hypothesize that the IMU readings captured during TUG test realizations can provide prospective fall risk prediction. In this study, analysis by deep learning convolutional neural networks (CNN) and recurrent neural networks (RNN) were used for fall prediction based on features extracted from IMU data acquired during TUG test realizations. Data is obtained from a cohort of 106 older adults wear-ing a wireless inertial sensor with a sampling frequency of 100 Hz while performing the TUG test. Prospective fall incidence was obtained in a six-month follow-up period and used as the ground truth for the supervised training and validations of the deep learning and competing machine learning approaches. Further data collection could lead to potential fall risk biomarker. A repeated hold out cross-validation process using 75 subjects for training and 31 subjects for testing was carried out. Best results were achieved by a bidirectional long short-term memory (BLSTM), obtaining an accuracy of 0.83 and AUC of 0.73 with good sensitivity and specificity values.

In-situ production of PC components is the same process as in a factory, with the order of installing processed rebar, pouring concrete, curing, and storage. In-situ production not only reduces environmental impact by more than 14.58% compared to factory production, but also reduces costs by up to 39.4% compared to factory production when increasing the number of in-situ produced PC components. The stock yard area is more than 5 times larger than the production area, which accounts for a significantly higher proportion. Research on in-situ production of PC components is mainly focused on the production area. PC component yard layout planning for in-situ production can effectively reduce carbon dioxide emissions and improve construction efficiency. Therefore, the purpose of this study is to develop an environmental impact minimization model for in-situ production of PC components. As a result of applying the developed model, the improved dung beetle optimization algorithm optimization improved the adjacent correlation by 22.79% and reduced carbon dioxide emissions by 18.33% compared to the dung beetle optimization algorithm, which verifies its efficiency. The proposed environmental impact minimization model can support the construction, reconstruction, and functional upgrade of logistics centers, contributing to low carbon dioxide in the logistics industry.

Amidst the Covid-19 pandemic, open spaces provided solace for people during social isolation but also heighten risks, particularly in areas lacking proper management. This was especially critical in regions already facing challenges like low human development, extreme poverty, limited healthcare access, as seen in Pará Coastal Zone (NE Brazil). To address this, this study introduced the Pandemic Risk Index (PRI), evaluating Covid-19 risk using demographic, social, economic, and epidemiological indicators. Applied to municipalities in the area, PRI highlighted preexisting vulnerabilities shaping the region’s pandemic response and overall risk. PRI analysis revealed temporal and spatial variations in risk levels, identifying areas where the use of open spaces like beaches, presented a higher transmission risk. The results emphasized the significance of effective governance, improved healthcare, and containment measures, lowers risk levels. affect risk levels, and becomes one of the most important challenges, especially in remote areas with limited services and access.

The objective of this research is to show the family farms as an energy consumers and producers from renewable sources. It is a possibility for them to reduce operating costs. Time scope of the study covers the period 2014-2022. The data source is the Farm Accountancy Data Network. On the basis of the value of energy output and energy costs the rankings were prepared. After the ranks were awarded, the strong position of the Netherlands and Germany was indicated. It has been shown that the production and consumption of energy depend on the economic size of the farm, and do not depend on the type of production. As such, panel models were built for energy output and costs. The determinants that affect them were indicated. Energy output depends on total inputs – the higher they are, the higher the output. The energy costs are related to utilized agricultural area, total output and family farm income. The results obtained are discussed within the framework of recommendations of the European Commission.

Previously, we described that Adenine, Thymine, Cytosine, and Guanine nucleobases were superconductors in a quantum superposition of phases on each side of the central Hydrogen bond acting as a Josephson Junction. Genomic DNA has two strands wrapped helically around one another, but during transcription, they are separated by the RNA polymerase II to form a molecular condensate called the transcription bubble. Successive steps involve the bubble translocation along the gene body. This work discusses how quantum information transfer and entanglement can be achieved by modeling DNA as a combination of ? nonperturbative circuits quantum electrodynamics with nine Radio-Frequency- Superconducting Quantum Interference Devices (SQUIDs) inside. A bus can be coupled capacitively to a single-mode microwave resonator. The cavity mode and the bus can mediate long-range, fast interaction between neighboring and distant DNA SQUID qubits. RNA polymerase II produces decoherence during transcription. This enzyme is a multifunctional biomolecular machine working like an artificially engineered device. Phosphorylation reactions catalyzed by protein kinases constitute the driving force. The coupling between n phosphorylation pulses and any particular SQUID qubit can be obtained selectively via frequency matching.

IT strategic planning is a critical driver of performance for small and medium-sized enterprises (SMEs), improving operational efficiency, decision-making, and innovation. This systematic review synthesizes findings from eighty studies, highlighting the varied impacts of IT strategic planning on SME performance. Quantitative analysis was used in 45% of studies, while thematic analysis was applied in 25%, showing the growing preference for data-driven insights. Cloud-based solutions, adopted in 11.25% of cases, and hybrid IT setups (17.5%) were identified as key enablers of flexibility and cost-efficiency. SMEs aligning IT strategies with business goals reported up to 20% reductions in operational costs and 15% improvements in customer satisfaction. However, 60% of studies identified resource constraints as a major challenge to IT adoption. Despite these barriers, 60% of studies demonstrated that effective IT planning enhances long-term sustainability and competitive advantage. This review provides actionable insights and strategic frameworks to guide SMEs in optimizing their IT investments for sustainable growth.

To date, the percentage composition of the lung microbiome in bronchopulmonary cancer has not been summarized. Existing studies on identifying the lung microbiome in bronchopulmonary cancer through 16S rRNA sequencing have shown variable results regarding the abundance of bacterial taxa. Objectives: To identify the predominant bacteria in the lung microbiome of bronchopulmonary cancer from samples collected through bronchoalveolar lavage and to assess their potential role in the diagnosis of bronchopulmonary cancers. Data Sources: A systematic review of English articles using MEDLINE, Embase, and Web of Science. Search terms included lung microbiome, lung cancer, bronchoalveolar lavage. Study Selection: Randomized clinical trials that investigated the lung microbiome in bronchopulmonary cancer with samples collected via bronchoalveolar lavage. Data Extraction: Independent extraction of articles using predefined data fields, including study quality indicators. Data Synthesis: Nine studies met the inclusion criteria, focusing on those that utilized a percentage expression of the microbiome at the phylum or genus level. There was noted heterogeneity between studies, both in terms of phylum and genus, with a relatively constant percentage of the Firmicutes phylum and the genera Streptococcus and Veillonella being mentioned. Significant differences were also observed regarding the inclusion criteria for study participants, the method of sample collection, and data processing. Conclusions: To date, there is no consistent percentage pattern at the phylum or genus level in bronchopulmonary cancer, with the predominance of a phylum or genus varying across different patient cohorts, resulting in non-overlapping outcomes.

This study explores the comparative performance of FinBERT, GPT-4, and Logistic Regression for sentiment analysis and stock index prediction using the NGX All-Share Index dataset. By leveraging advanced language models like GPT-4 and FinBERT, alongside a traditional machine learning model, Logistic Regression, we aim to classify market sentiment, generate sentiment score, and predict market price movements. The models were assessed using metrics such as accuracy, precision, recall, F1 score, and ROC AUC. Results indicate that Logistic Regression outperformed both FinBERT and GPT-4, with an accuracy of 81.83% and a ROC AUC of 89.76%. GPT-4 predefined approach exhibited a lower accuracy of 54.19% but demonstrated strong potential in handling complex data. FinBERT, while offering more sophisticated analysis, was computationally demanding and yielded a moderate performance. Hyperparameter optimization using Optuna and cross-validation techniques ensured the robustness of the models. This study highlights the strengths and limitations of these approaches in stock market prediction and presents Logistic Regression as the most efficient model for this task, with FinBERT and GPT-4 showing promise for future exploration.

Abstract: This article advances the discourse on sustainable and energy-efficient software by examining the performance and energy efficiency of intelligent algorithms within the framework of green and sustainable computing. Building on previous research, it explores the theoretical implications of Bremermann's Limit on efforts to enhance computer performance through more extensive methods. The study presents an empirical investigation into heuristic methods for search and optimisation, demonstrating the energy efficiency of various algorithms in both simple and complex tasks. It also identifies key factors influencing the energy consumption of algorithms and their potential impact on computational processes. Furthermore, the article discusses cognitive concepts and their interplay with computational intelligence, highlighting the role of cognition in the evolution of intelligent algorithms. The conclusion offers insights into the future directions of research in this area, emphasising the need for continued exploration of energy-efficient computing methodologies.

Worldwide cervical cancer ranks the fourth most common cancer in women. In Tanzania, cervical cancer is the leading female malignancy, and a major cause of cancer related mortality and morbidity in women. More than 80% of women with cervical cancer, are diagnosed with late stage, in which curative treatment options are not available. To explore the knowledge, perceptions and behaviors among women with late-stage cervical cancer who attended Bugando Medical Centre (BMC) from November to December 2021. Explorative qualitative study was conducted from November to December 2021 at BMC, involving 11 participants who had late-stage cervical cancer. Purposive sampling was used to recruit information-rich participants. Data collection was done by using open ended In-depth interview guide. The guide contained information on social demographic characteristics, knowledge on cervical disease, perceptions towards cervical cancer symptoms, health care seeking behaviors. Data was transcribed and analysed manually by using thematic approach. The coded texts generated three themes and nine subthemes. The results showed that Participants had poor knowledge about cervical cancer disease, causes, risk factors, prevention measures and treatment options which had impact on the perceptions and health care seeking behaviors. Participants misinterpreted the symptoms with other urogenital conditions like fungus and urinary tract infection. Misconception about the symptoms and disease, long distance to health facilities and financial challenges hindered involvement in seeking biomedical intervention and witch doctors were given a priority. Findings from this study suggest a strong need of intensive health promotion efforts and education through mass media and outreaches to improve adequate and correct knowledge and awareness about cervical cancer disease, causes, symptoms, risk factors, prevention and treatment options. Health care providers and policy makers should include cervical cancer disease as a topic of everyday health education in the health facilities.

Background: Mitral regurgitation (MR) is a common valvular disorder linked to high morbidity and mortality. For patients unsuitable for surgery, transcatheter mitral edge-to-edge repair (TEER) with the MitraClip® G4 system offers an alternative. This study aims to evaluate procedural, echocardiographic, functional, and quality of life (QoL) outcomes in patients who underwent TEER with the MitraClip® G4 system, along with possible predictors of New York Heart Associa-tion (NYHA) class I at 30 days and 1 year. Methods: Patients with moderate-to-severe (3+) or se-vere (4+) degenerative MR (DMR) or functional MR (FMR), classified as NYHA class III or IV, who underwent TEER with the MitraClip® G4 system at our center between January 2021 and De-cember 2023 were included. Results: A total of 83 patients [71% FMR, 66% male, median (IQR) age 70 (11) years] underwent TEER, with 100% procedural success. MR ≤2+ was achieved in 100% and 98% of patients at 30 days and 1 year, respectively. NYHA class I or II was achieved in 100% and 96.8% of patients at 30 days and 1 year, respectively. The Kansas City Cardiomyopathy Ques-tionnaire (KCCQ) score improved from 51±20 at baseline to 69±15 at 30 days (p< 0.001) and 70.5±15 at 1 year (p< 0.001). Lower baseline N-terminal pro-brain natriuretic peptide (NT-proBNP) pre-dicted achieving NYHA class I at 30 days (HR: 0.63, 95% CI: 0.41–0.95, p = 0.030), while lower Eu-ropean System for Cardiac Operative Risk Evaluation II (EuroSCORE II) and NT-proBNP pre-dicted it at 1 year [(HR: 0.50, 95% CI: 0.28–0.89, p = 0.019), (HR: 0.67, 95% CI: 0.44–0.99, p = 0.049), respectively]. Conclusions: The MitraClip® G4 system provides significant improvements in MR severity, functional class, and QoL. Lower NT-proBNP and EuroSCORE II were strong predictors of achieving optimal functional status (NYHA class I).

Large language models (LLMs) are essential to the field of natural language processing, and as their applications expand, security risks have become increasingly prominent. This paper introduces a novel benchmark for evaluating LLMs security, termed CS-Eval, designed to effectively assess the models' ability to address vulnerabilities. CS-Eval targets seven key security risks: ethical dilemmas, marginal topics, error detection, detailed event handling, cognitive bias, logical reasoning, and privacy identification, and establishes a Multi-Security Hazard Dataset (MSHD). The evaluated models include GPT-4o, Llama-3-70B, Claude-3-Opus, ERNIE-4.0, Abab-6.5, Qwen1.5-110B, Gemini-1.5-Pro, Doubao-Pro, SenseChat-V5, and GLM-4. We analyzed each model's performance in relation to these security risks and provided recommendations for improvement. Experimental results demonstrate varying levels of effectiveness across models, with GPT-4o exhibiting the best overall performance. Moreover, the relationship between security enhancement and model capa-bility is nonlinear, indicating that improving safety requires a multifaceted approach, considering various factors in both development and application.

GRP78/BiP, a stress-induced protein and autoantigen in rheumatoid arthritis (RA), exhibits different expressions in various biological fluids and tissues, namely serum, synovial fluid (SF), and synovium, all of which are pertinent to the disease activity and progression of RA; however, there is a scarcity of data linking both intracellular and extracellular GRP78/Bip to the disease activity and progression of RA within the realm of rheumatology. This study was undertaken to investigate the differential expression of intracellular and extracellular GRP78/Bip in serum, SF, and synovium, and to determine their association with disease activity and progression in patients with RA. Patients with RA, osteoarthritis (OA), and traumatic meniscal injury (TMI) without radiographic OA were consecutively recruited for the study at our hospital. Among the patients with RA, six different subgroups were established based on their disease activity and progression. Disease activity was measured using the DAS28 criterion, while disease progression was evaluated using the Steinbrocker classification grade. This study conducted a comprehensive analysis of the differential expressions of GRP78/Bip in multiple sample types, including serum, SF, and synovium, with the specific emphasis on their associations with disease activity and progression in RA. The expression levels of GRP78/Bip, TNF-α, and IL-10 were significantly elevated in serum, SF, and synovium of patients with RA when compared to both the CON and iCON groups(P

AYOLO differentiates itself with a new fusion architecture proposal that leverages the strengths of unsupervised learning and integrates the Vision Transformer approach, taking the YOLO series models as a reference. This innovation enables the model to effectively use rich, unlabeled data, providing a new example in pre-training methodology for YOLO architectures. On the benchmark COCO val2017 dataset, AYOLO demonstrates its superiority by achieving an impressive 38.7 % AP while maintaining an outstanding rendering speed of 239 FPS (Frame Per Second) on the Tesla K80 GPU. This performance outperforms the previous state-of-the-art YOLO v6-3.0 by a significant margin of +2.2 % AP while maintaining comparable FPS. AYOLO is presented as a demonstration of the potential of integrating complex information fusion techniques with supervised pretraining in improving the precision and speed of object detection models.

Thermomechanical, biocompatibility, durability, workability and economic characteristics are the main criteria for development of dental alloys. In an attempt to provide a noble metal dental alloy without the known adverse metallic elements, a novel alloy was produced with the addition of Germanium (Ge), as a more cost-effective alternative to other precious alloying metals. The objective of this investigation was to analyse the thermomechanical and biocompatibility properties of a high noble Au-Pt-Ge porcelain-fused-to-metal (PFM) alloy. These properties were investigated with DSC analysis, hardness, density and coefficient of thermal expansion (CTE) measurements, as well as with metallographic and SEM/EDX microstructure investigations, with the production of a test PFM dental bridge, static immersion testing for metal ion release, and an in vitro primary biocompatibility testing on human gingival cells. The results of the thermomechanical testing showed alloy properties suitable for dental restorations and clinical use, with somewhat lower hardness, making the alloy not suitable for extensive multiunit fixed restorations. The microstructure investigations showed segregations of Ge in a homogeneous alloy matrix, which reduce the alloy’s mechanical properties. The produced PFM dental bridge shows excellent workability of the alloy in a dental laboratory setting, as well as a high standard of the final dental restoration. The ion release was negligible, well below any harmful quantities, while the cell viability examination showed significantly higher viability ratings on polished alloy samples as compared to as-cast samples. The results showed that a dental substructure in direct contact with oral tissue and fluids should be highly polished.

Background: Although insect-based foods (IBF) have been recently proposed as a way to face climate crisis and starvation, they encounter the aversion from Western countries which perceive fear, disgust, and high risk. The contribution of psychology research to food choices highlighted how decisions are taken not only through reasoned attitudes and goal-directed behavior, but also through more automatic associations (dual-systems models). Methods: In this paper we investigated people’s disposition towards IBF by combining (a) explicit attitudes (as assessed via self-report scales), (b) automatic associations (as measured via indirect measures), (c) intention to taste, and comparing different profiles based on (d) psychological factors, including decision-making style, food neophobia, and trust in science and scientist. A pilot sample of 175 Italian University students participated in the study. Results: The analyses on the general sample highlighted rather negative attitudes. The cluster analysis identified 4 decision-making profiles: “the gut-feeling”, “the suspicious”, “the vicarious”, and “the mind”. It revealed more favorable opinions in “the mind” profile, characterized by a rational decision-making style and high trust in science, and very aversive reactions from “the suspicious” profile, characterized by high food neophobia and low trust in science. Conclusions: The results underline the importance of psychological factors in interpreting people's reactions to IBF and changes in dietary habits based on the decision-making process. They suggest possible strategies to promote eco-friendly diets

Arabidopsis and other model plants can help improve crops by providing a reference framework for assembling omics data from plants. Arabidopsis is extensively studied and has a well-curated and annotated genome, making it an excellent reference organism for comparative analysis. When there is no reference genome of the organism being investigated, using the genome of Arabidopsis may be an option thanks to the extensive knowledge we have about it. Crop improvement can help to increase crop yields, improve crop quality, and enhance resistance to pests, diseases, and environmental stresses. It can also help to reduce the use of pesticides and fertilizers, which can have negative impacts on the environment and human health. Therefore, crop improvement is essential to ensure food security and sustainability in the face of global challenges such as climate change, land degradation, and water scarcity. The assembly process of genomic, transcriptomic, and metabolomic data from plants using carefully selected information on Arabidopsis can lead to a deeper understanding of organisms and their potential for genetic improvement for crops. However, the successful integration of data requires expertise and experience to design more effective and precise crop improvement strategies.

Abstract: Background: The acute hemodynamic response to isometric exercise in hypertensive patients ischemic heart disease (IHD) has been poorly investigated. The aim of this study was to assess acute changes on left ventricular myocardial work (MW) during isometric bilateral knee extension in patients with IHD. Methods. Twenty stable hypertensive patients with IHD and ten healthy, age-matched controls (HC) were enrolled. All subjects performed an isometric knee extension exercise, at 30% of their maximal voluntary contraction. The effort was maintained for three minutes or until exhaustion. At baseline, at peak exercise and after 10 minutes of recovery, echocardiography evaluation was perforned, blood pressure (BP) and heart rate (HR) were measurement. Results. The exercise was well tolerated by all subjects. At peak exercise: systolic BP in the IHD was significantly higher than HC (37.6± 7.2 vs 8.4±2.3 mmHg; p = 0.002). HC group had a greater increase in HR than IHD (19.7±6.2 vs 8.4±2.2 bpm; p = 0.009). E/E’ ratio increased in IHD and was unchanged in control. Global work index increased significantly in IHD compared to HC (+15% vs +3%; p 0.026). Global constructive work increased significantly in IHD compared to HC (+29.8% vs +7.4 respectively, p 0.031). Global waste work increased by 92.3% in IHD and was unchanged in HC. Global work efficiency decreased in IHD (-18%) while was unchanged in HC (between-groups p 0.019). Stroke volume decreased in IHD. and was unchanged in HC. Cardiac output was unchanged in IHD while it increased in HC. Conclusion: In patients with hypertension and underlying IHD, an acute isometric load caused a great increase in systolic BP and LV filling pressure. It follows a mostly ineffective increase of MW that fails to maintain stroke volume.

The expansion of the universe yields two consistent differing values of a Hubble constant depending on the methods of measurement. The aim of this work was to explain the Hubble tension by properties of space corresponding to a radially inhomogeneous metrics. A cosmological model described by this perturbed FLRW metric has zero pressure and exponentially expanding space. But the observed space appears to be compressed in the radial direction. We consider the dependence of the change rate of photon energy on the direction of its motion. It is found applying the principle of the photon's energy integral extremum and the relationship between it and the energy of a material particle obtained using Lagrange mechanics. The change rate of a photon's energy varies depending on whether it moves in a radial direction or has an angular component. The metric coefficients are determined by the difference in measurements of the Hubble constant using gravitational lensing and a distance ladder.

Background: anti-calcitonin gene-related peptide monoclonal antibodies (CGRP mAbs) are clinically effective in preventing migraine attacks, photophobia, and migraine aura associated with headaches. However, no study has yet investigated the effectiveness of CGRP mAbs in preventing migraine aura without headache. Case report: A female patient of 49 years old has a long history (since age 10) of photosensitivity and typical migraine aura without headache. The symptoms slightly responded to oral medication but did not completely resolve. Just one day after the administration of galcanezumab, her photo-hypersensitivity and migraine aura had completely resolved. Consequently, the administration of the oral migraine preventive medication was discontinued. Monthly galcanezumab at the dose of 120 mg has been continuously given and she did not experience any aura or headache so far. Conclusions: CGRP mAbs is considered an effective treatment method in preventing migraine aura without headache. Although CGRP mAbs do not penetrate the blood-brain barrier, trigeminal nerve stabilization with CGRP mAb can prevent cortical spreading depression.

Background: Rod fracture (RF) is the most common cause of revision in adult spinal deformity (ASD) surgery, and various treatment strategies for preventing RF are reported in literature. This retrospective study involving 139 ASD patients (aged ≥65 years and a minimum 2-year follow-up) who underwent long-segment fixation from T10 to sacrum with pedicle subtraction osteotomy (PSO), analyzed long-term results, including radiographical parameters and the incidence of recurrent RF (re-RF), to determine the most effective revision method for preventing RF. Methods: Patients were classified into three groups according to the revision method performed for RF; simple rod replacement (SR group, n=17), lateral lumbar interbody fusion around PSO site (LLIF group, n=8), and accessory rod integration (AR group, n=22). Baseline characteristics, radiographical and clinical parameters were analyzed. Results: RF occurred in 47 patients (34%) at an average of 28 months following primary deformity correction. Re-RF occurred in six patients (13%) at an average of 37 months. Re-RF occurred most commonly in SR group (p=.048). Every re-RF in SR group occurred at the PSO site, none occurred in LLIF group, and one in AR group occurred near L4–5. After both primary deformity correction and revision surgery, spinopelvic parameters had shown favorable results and clinical outcomes had improved in all three groups, without significant intergroup differences. Conclusions: Accessory rod integration or additional LLIF around the PSO site seems to provide greater strength and stability to the previously fused segments than simple rod replacement, which demonstrates the need for additional support in revision surgery for RF after PSO.

(1) Background: Efficient detection and rectification of defects or post-processing manufacturing conditions such as sharp edges and burrs on metal components are crucial for quality control in precision manufacturing industries. (2) Methods: This paper describes a lab-scale integrated system for real-time and automated metal edge image detection using a customized YOLOv5 ma-chine vision algorithm. The YOLOv5 algorithm and model were developed and embedded in the NVIDIA Jetson Nano microprocessor. The YOLOv5 model can detect three different image types for classification i.e., normal, sharp, and burrs edge on aluminum workpiece blocks. An integrated system connects the NVIDIA Jetson Nano with an embedded YOLOv5 model to a Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator to perform selective automated chamfer-ing and grinding when certain condition defects are detected. (3) Results: The model demonstrates durable performance, achieving a mean average precision of 0.886 across defect classes with minimal misclassifications. The Mitsubishi Electric Melfa RV-2F-1D1-S15 manipulator received input from the machine vision system and performed an automated chamfering and grinding process accordingly; (4) Conclusions: By integrating camera, embedded deep learning in the microprocessor and manipulator, automated chamfering and grinding process in metal component shapes can be efficiently rectified. This tailored solution promises to improve productivity and consistency in metal manufacturing and remanufacturing.

Heavy rains result in significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended periods of intense precipitation. Accurate rainfall prediction is crucial in nations such as Bangladesh, where agriculture is the predominant field of occupation. The efficiency of machine learning methods is enhanced by the nonlinearity of rainfall, surpassing the effectiveness of other approaches. This study proposes the novel combination of rainfall occurrence prediction, rainfall amount prediction, and daily average temperature prediction. This research implements machine learning techniques and an ensemble-based classifier to predict rainfall occurrence, as well as machine learning regressor models and an ensemble-based regressor to predict the rainfall amount and daily average temperature, using the Bangladesh Weather Dataset. The ensemble classifier demonstrated an accuracy of 83.41% and a recall of 78.17%, exhibiting the best performance in predicting when it will rain, but its precision was the lowest, at 51.16%. The ensemble regression model outperformed the base models, including linear regression, random forest, and support vector regression in rainfall amount prediction, with the lowest mean absolute error of 0.36 and root mean squared error of 0.90. Additionally, this model provided the most precise daily average temperature prediction results with the lowest mean absolute error of 0.42 and root mean squared error of 0.54, highlighting its superiority over the other regression models in forecasting temperature. Ensemble approaches consistently exhibit superior task performance metrics.

In recent years, consensus algorithms have gained significant importance in the context of blockchain networks. These algorithms play a crucial role in allowing network participants to reach agreements on the state of the Blockchain without needing a central authority. The present study focuses on carrying out a systematic mapping of these consensus algorithms to explore in detail their use, benefits, and challenges in the context of blockchain networks. Understanding consensus algorithms is essential to appreciating how blockchain networks achieve the reliability and integrity of their distributed ledgers. These algorithms allow network nodes to reach agreement on the validity of transactions and the creation of new blocks on the Blockchain. In this sense, consensus algorithms are the engine that drives trust in these decentralized networks. Numerous authors have contributed to the development and understanding of consensus algorithms in the context of blockchain networks. For example, Satoshi Nakamoto's (2008) original paper on Bitcoin introduced proof-of-work (PoW) as a method of achieving consensus on the network. This revolutionary concept paved the way for numerous cryptocurrencies and blockchain systems. Despite advances in this field, significant challenges remain: centralization, fair token distribution, scalability, and sustainability. The energy consumption of blockchain networks, particularly those using algorithms such as Proof of Work, Proof of Stake, Delegated Proof of Stake, Proof of Authority, and hybrid algorithms (Proof of Work/Proof of Stake), has raised concerns about their environmental impact, motivating the scientific and technological community to investigate more sustainable alternatives that promise to reduce energy consumption and contribute to climate change mitigation. Furthermore, interoperability between different blockchains and security in specific environments, such as IoT, are areas that still require significant research attention. This systematic mapping not only seeks to shed light on the current state of consensus algorithms in blockchain, but also their impact on sustainability, identifying those algorithms that, in addition to guaranteeing integrity and security, minimize the environmental footprint, promoting a more efficient use of energy resources, being a relevant approach in a context in which the adoption of sustainable technologies has become a global priority. Understanding and improving these algorithms are critical to unlocking the full potential of blockchain technology in a variety of applications and industry sectors.

Vitamin D deficiencies are very common in pregnant women, raising concerns about adverse health outcomes in children. This issue has hardly been studied in multiple pregnancies, the prevalence of which has been steadily increasing. Therefore, our study investigated the relationship between newborns’ anthropometric parameters and the concentration of 25(OH)D in maternal blood of women with twin pregnancies and umbilical cord blood. The study included 50 women who gave birth after the 36th week of twin gestation. 25(OH)D was determined in maternal blood collected during the antenatal period and in the umbilical cord blood of 100 newborns. Anthropometric parameters of the newborns (birth weight, length, head and chest circumference) were obtained from hospital records. Data on nutrition and lifestyle during pregnancy were collected from the patients during an interview conducted by a dietitian. No relationship between maternal and neonatal cord blood vitamin D concentrations and any of the anthropometric parameters of the newborns was found. However, only 6% of the mothers and 13% of the newborns had vitamin D deficiency (≤20 ng/mL). The type of pregnancy and maternal height were the main factors associated with neonatal size. Newborns from dichorionic pregnancies were on average 202 g heavier (p

Plastics are very versatile materials that have contributed to the development of society since the 19th century, however, their mismanagement has led to an accumulation of waste in almost every ecosystem affecting the fauna of the planet. In this way recently, studies have shown that some insects might be able to adapt, consuming a wide range of hydrocarbon base polymers. In this work it was studied the adaptive capacity of Zophobas morio larvae when feeding on different synthetic polymers derived from petroleum. Four 30-day larval feeding treatments were carried out with synthetic polymers as expanded polystyrene (PS), low density polyethylene (PBD), polyisoprene (PI) and butyl rubber (BT), in addition a positive control of organic diet was included. Intestinal bacteria were isolated from the treatments and identified by Sanger sequencing. To analyze the chemical composition and physical form of the frass produced, fourier transform infrared spectroscopy (FITR) and images of the feces surface were taken with scanning electron microscopy (SEM) respectively. Zophobas morio larvae were able to consume 54% of PS in 30 days, equivalent to 3.2 mg/d/larva. Nine bacterial strains associated with the decomposition of synthetic polymers were identified in the intestine of the larvae. As for the physicochemical analysis of the feces, FTIR spectra showed the scission of bands corresponding to functional groups of the synthetic polymers in the comparison of the plastic diet treatments versus the feces of antibiotic-treated and plastic-fed larvae, while the comparison of spectra of the plastic and control treatments also identified differences in the absorption peaks. SEM imaging demonstrated that superworm feces differed in dependence on the substrate consumed. The findings demonstrate that Zophobas morio larvae possess a gut biological complex that allows them to feed and survive by consuming various petroleum-derived polymers.

The study aimed to optimize protocols for the joint extraction of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) from 0.025 x 106 CFU of Candida albicans, aiming to overcome challenges in the extraction of these genetic materials. From this, treated Silicon Carbide (SiC) granules were added to fungal samples from methods 1, 2 and 3 obtained from aliquots of BHI or Sabouraud medium to cause cell lysis and enable the isolation of these macromolecules by phenol and chloroform. The concentration and integrity of nucleic acids were obtained, respectively, by spectrophotometry using A260/A280 ratios and 1% agarose gel electrophoresis. Therefore, method 3 is the one that most comprises samples considered pure of both DNA and RNA, simultaneously. Furthermore, the presence of intact RNAs corresponding to the base pair size, such as 5.8 S rRNA and tRNA, was verified during electrophoresis, taking into account the particularities of RNA, which makes it very unstable and easily degraded. Therefore, it results in a faster and simpler method in addition to obtaining promising results using minimal amounts of biological sample, thus offering a valuable alternative for small laboratories working with molecular biology.

Aspergillus flavus is a medically relevant fungus, particularly in tropical regions. Thermotolerance is a key virulence factor as an opportunistic pathogen, as well as its ability to form biofilms, which can impact therapeutic outcomes. To know how temperature affects the growth and biofilm formation of an A. flavus isolate, we assessed its growth on solid media and described the morphological changes during conidial germination. We also examined formation and composition of in vitro biofilm incubated at different temperatures and compared the susceptibility of planktonic and biofilm cells to antifungal agents. Our results showed that temperature promotes conidiation on solid media. The radial growth was higher at 28 °C, but total conidia was higher at 37 °C, however, conidial density was stronger at 42 °C. Moreover, incubation at 37 °C accelerates conidial germination and is the optimal temperature for biofilm formation. We described four distinct phases in A. flavus biofilm development: initiation (0-12h), consolidation (12-48h), maturation (48-72h), and dispersion (&gt;72h), where the presence of aspergillar heads was notable at 42 °C. Carbohydrates and proteins are the main components of the extracellular matrix. We observed lipid droplets within the biofilm hyphae, and mature biofilms are resistant to amphotericin B and itraconazole, with MICs exceeding 16 g/mL for both antifungals, regardless of the incubation temperature.

Lung and colon cancers are among the leading causes of cancer-related mortality worldwide. Early and accurate detection of these cancers is crucial for effective treatment and improved patient outcomes. False or incorrect detection is more harmful. Accurately detecting cancer in a patient's tissue is crucial to their effective treatment. While analyzing tissue samples is complicated and time-consuming, deep learning techniques have made it possible to complete this process more efficiently and accurately. As a result, researchers can study more patients in a shorter amount of time and at a lower cost. Much research has been conducted to investigate deep learning models that require great computational ability and resources. However, None of these have had a 100% accurate detection rate for these life-threatening malignancies. Misclassified or falsely detecting cancer can have more harmful consequences. This research proposes a new lightweight, parameter-efficient, and mobile-embedded deep learning model based on a 1D convolutional neural network with Squeeze-and-Excitation layers for efficient lung and colon cancer detection. This proposed model diagnoses and classifies lung squamous cell carcinomas and adenocarcinoma of the lung and colon from digital pathology images. Extensive experiment demonstrates that our proposed model achieves 100% accuracy for detecting lung, colon, and lung and colon cancers from the histopathological (LC25000) lung and colon datasets, which is considered the best accuracy for around 0.35 million trainable parameters and around 6.4 million flops. Compared with the existing results, our proposed architecture shows state-of-the-art performance in lung, colon, and lung and colon cancer detection.

In today's interconnected global business landscape, companies face numerous risks, particularly from their supply chains. Supplier disruptions can cause financial damages, inefficiencies, and reduced competitive edge, highlighting the need for robust supplier risk management. This paper scrutinizes recent studies on supplier risk management spanning 2020 to 2023. This review examines supplier risk management, focusing on supplier evaluation, payment terms, contract duration, and buyer-supplier collaboration as critical factors influencing risk mitigation and control in the procurement of goods and services. Through a systematic literature review, the study identifies the multifaceted nature of supplier risks, including production delays, quality issues, and financial challenges. It underscores the importance of proactive risk identification, comprehensive supplier evaluation, strategic payment terms, appropriate contract durations, and robust buyer-supplier collaboration. Despite the benefits, challenges remain, such as understanding regulatory impacts and ensuring methodological consistency. This review contributes to the discourse on supplier risk management, offering insights for future research and practical applications to enhance resilience and operational efficiency in global supply chains. Based on the findings, future studies should concentrate on formulating holistic supplier risks management approaches that nurture buyer-supplier collaboration and harness innovative technologies to enhance risk management practices.

There are various types of charged particles in the space environment, which can cause different types of radiation damage to materials and devices, leading to on-orbit failures and even accidents of spacecraft. Developing lightweight and efficient radiation shielding materials is an effective approach to improve the inherent protection of spacecraft. The protective performance of different materials against proton and electron spectra in the Earth’s radiation belts is evaluated by Geant4 simulation. Based on the simulation results, suitable hardening components are selected to design composite materials, and B4C/PEEK composites with different B4C content were successfully prepared. Experimental results demonstrate that the simulated and experimental results for the electron, proton and neutron shielding performance of B4C/PEEK composites are consistent. These composites exhibit excellent radiation shielding capabilities against electrons, protons and neutrons, and the radiation protection performance improves with increasing B4C content in the B4C/PEEK composite materials.

Human induced climate change led to the recognition of Community-Based Adaptation (CBA) as one of the remedies in building adaptive capacity and resilience of communities. It recognizes the agency of communities in shaping the optimal strategy. CBA interventions are vital in addressing community needs and contextual specificities, enable participatory and inclusive engagement and thereby facilitate more profound adaptation impacts. Agent-based modelling (ABM) has the potential to be an invaluable approach in developing tailored adaptation strategies considering capacities, resources, skills, priorities, needs and the cultural context of local communities. ABM allows to capture the complex relationships among various components within community decision making on CBA actions, the CBA actions themselves and the environment, however available tools provide for various degrees of freedom in agent definition, sensitivity analysis, scalability, experiment design and output analysis to name a few. This article assesses all available ABM modelling and simulation tools for its application in the context of CBA. NetLogo emerges as the most suitable tool for incorporating and handling the special features of CBA. It is closely followed by GAMA and Envision. The article provides insights and guidance to researchers and practitioners in selecting ABM tools aligned with the specific requirements of their CBA simulations and projects.

Health and social needs exist along a dynamic continuum. Recognizing that health status is inextricably impacted by social determinants of health, community paramedicine has opportunities and a responsibility to reduce inequities. The objective of this scoping review was to investigate peer-reviewed and grey literature to explore how community paramedicine supports community needs along a health and social continuum. We conducted a scoping review of English language literature using the JBI Scoping Review methodology. We searched CINAHL, EMBASE, MEDLINE, Google Scholar, and organisational websites. 30 peer-reviewed and 13 grey literature articles met inclusion criteria. The findings describe the ways community paramedicine models evolved from minimising system pressures on emergency health services to addressing health and social needs. A key recommendation across the literature was the need to meaningfully engage communities early in program development to understand how best to implement and co-design integrated service models addressing specific community needs, though there was a lack of evidence to guide this approach. There is a notable lack of evidence pertaining to optimising technologies in program design and implementation. Results highlight opportunities to determine best practices for conducting holistic community needs assessments that include equitable stakeholder engagement and enhancing education to prepare paramedics for expanded roles. Community paramedicine provides opportunities to better meet the needs of structurally marginalised communities. There is a social responsibility and opportunity to engage communities to co-design service delivery, advance paramedic education, and enhance interprofessional collaboration to better support community needs and generate upstream solutions for individuals and communities

The resurgence of mpox, particularly in regions where the virus was previously non-endemic, has reignited interest in the cross-protective potential of smallpox antibodies. Given the genetic similarity between the mpox virus and variola virus (the causative agent of smallpox), antibodies generated against smallpox may offer a degree of protection against mpox. This study investigates the presence of smallpox and mpox antibodies in human immunoglobulin preparations containing IgG against both mpox virus and vaccinia virus using ELISA techniques. The implications of these findings for post-exposure prophylaxis (PEP) are discussed in light of the recent mpox outbreaks in Africa, as reported by the World Health Organization (WHO). Current data suggest that smallpox vaccination, even decades after eradication, may confer substantial protection against mpox, thereby serving as a critical tool in managing emerging outbreaks. Further the presence of mpox antibodies in immunoglobulin preparations was also significant. These findings strongly suggest the potential use of intravenous immunoglobulins preparation as possible therapeutic strategy. Particularly it also underscores the need for further research into the development of targeted post-exposure prophylaxis strategies that leverage the cross-reactivity of orthopoxvirus antibodies.

Background Hepatic resection offers promising outcomes for patients with hepatocellular carcinoma (HCC) but can be constrained by factors like patient suitability. Continuous advancements in laparoscopic and robotic technologies have made minimally invasive hepatectomies (MIH) viable alternatives to open hepatectomies with benefits in recovery and complications. Materials and Methods We completed a retrospective review on 138 HCC patients at the Hume-Lee Transplant Center that underwent OH or MIH between 2010 and 2020. Univariate and multivariate analyses were completed on demographic, clinical, and tumor-specific data were collected to assess the impact of these variables on overall and disease-free survival at 1-, 3-, and 5-years. Preoperative metrics like length of hospital stay (LOS) and operation duration were also evaluated. Results Of the 109 OH and 29 MIH patients, MIH patients demonstrated shorter LOS and operative times. However, overall survival (OS) and disease-free survival (DFS) were similar between groups, with no significant variations in 1-, 3-, and 5-year survival rates. Ages > 60 years and lack of preoperative transcatheter arterial chemoembolization (TACE) were significant predictors of inferior OS and DFS in multivariate analyses. Conclusions MIH is an efficient substitute to OH with comparable survival even in older patients. The reduced LOS and operation time enhances its feasibility, and older patients previously denied for curative resection may qualify for MIH. Preoperative TACE also enhances survival outcomes, emphasizing its general role in managing resectable HCCs. Both robotic and laparoscopic hepatectomies offer acceptable short- and long-term clinical outcomes, highlighting MIH as the standard choice for HCC patients.

Almost fifty years after the introduction of antibiotics as an antimicrobial agent, we are facing an increasing number of multi-resistant microbials. The search for new antimicrobial compounds emerged as a promising alternative to treatments for diseases caused by microorganisms such as fungi and bacteria. Thus, endophytic fungi are gaining recognition for their varied metabolites that may possess antimicrobial properties. In our research, we isolated, from Calea pinnatifida, six endophytic fungi identified as Colletotrichum siamense, Colletotrichum karstii, Hypomontagnella barbarensis, Neopestalotiopsis clavispora, Nigrospora sacchari-officinarum and Annulohypoxylon moriforme. Analyses of microbial secondary metabolites by utilizing UHPLC-HR-ESI-MS/MS in conjunction with molecular networking, allowed the annotation of 9 compounds, 5 of them from GNPS spectral library match, and 4 from prediction-based on the molecular network, and allowed the isolation of two pairs of Acyl-tetronic acid derivatives distereoisomeric identified by NMR spectrum. In this sense, the present study underlines the value of endophytic fungi as a reservoir of potentially new antimicrobial agents.

Aims: To investigate the road safety concerns associated with pet dogs in New Zealand. Methods: An online survey was developed and offered to New Zealanders via SurveyMonkey© from 8 January 2019 to 31 March 2019) using New Zealand residents aged ≥18 years. Questions about the demographics of respondents and their number of pet dogs along with those about dog transportation and dog roaming were asked. Results: Of 2744 respondents who completed the online survey, 1494 (65%) owned a dog. Of these, 1511 completed the question about how their dog or dogs travel in vehicles. Of these, 2 % (n = 29) let their dog sit on the driver's knee, and 12 % (n = 179) allowed their dog to sit on the passenger's knee. Only 7 % (n = 105) allowed the dog to roam free throughout the vehicle, while 44% (n = 663) allowed their dogs to roam free in the backseat area only and 19% (n = 280) put their dog in the boot/hatchback area. A minority of respondents (2%, n = 22) indicated they let their dog travel unrestrained on the deck of a ute or truck. Approximately half, 51% (n = 767) of the respondents, indicated that they restrained or crated their dogs in or on the vehicle. Respondents also expressed concerns about roaming dogs being a road safety hazard with the themes of responsible ownership, physical and psychological harm, and the dangers of rescue altruism emerging. Conclusions: Increased awareness about proper pet restraints during transportation and preventing dogs from roaming, especially near and on roads, is crucial to ensure traffic safety for humans and dogs.

Covariant scalar field quantization, nicknamed $(\vp^r)_n$, where $r$ denotes the power of the interaction term and $n=s+1$ where $s$ is the spatial dimension and $1$ adds time. Models such that $r<2n/(n-2)$ can be treated by canonical quantization, while models such that $r>2n/(n-2)$ are trivial, or, if treated as a unit, emerge as `free theories'. Models such as $r=2n/(n-2)$, e.g., $r=n=4$, again using canonical quantization also become `free theories', which must be considered quantum failures. However, there exists a different approach called affine quantization that promotes a different set of classical variables to become the basic quantum operators and it offers different results. It is well-known that the canonical quantization of $\varphi^4_4$ fails. Here is how to fix it along with many other problems.

Train fault detection primarily relies on the comparative analysis of collected images with standard reference images, making the accurate identification of the collected image types crucial for effective fault detection. Most current systems achieve indirect classification of carriage images by using Automatic Equipment Identification (AEI) devices to recognize carriage identification number information while capturing the carriage images. However, damaged RF tags or blurred characters can hinder these devices, obstructing further train fault analyses. The unique features of carriage linear array images, such as high resolution, extreme aspect ratios, and local non-linear distortions, pose significant challenges to recognition algorithms. This paper proposes a method specifically designed to recognize these types of images. First, we implement an object detection algorithm to locate the positions of key components in carriage linear array images. By leveraging the spatial layout mapping between these key components and the carriage type classification, we simplify the complex image recognition problem into a sparse point set alignment task. To ensure effective registration despite local distortions in the linear array images, we propose a weighted radial basis function. To address the issues of unknown matching relationships and quantity mismatches during point set alignment, the proposed objective function is designed to maximize the similarity between Gaussian mixtures of point sets, thereby determining the RBF weights. Extensive experiments have demonstrated that our algorithm achieves 100% recognition accuracy under local nonlinear scale distortions of less than 15%. The algorithm remains effective even in the presence of some false positives or missed detections in key component object detection results. It can accurately identify the target category from 79 image categories within 24 milliseconds on an i7 CPU without GPU support. It is of great significance to reduce system cost and promote automatic exterior fault detection for train rolling stock.

This paper presents a comprehensive exploration of Data Quality terminology, revealing a significant lack of standardisation in the field. We propose a novel approach to aggregating disparate Data Quality terms used to describe the multiple facets of Data Quality, under common umbrella terms, with a focus on the ISO 25012 standard. Our aim is to design a Data Quality Data Model that serves as a universally applicable framework for Data Quality assessment. We introduce four additional Data Quality dimensions: Governance, Usefulness, Quantity, and Semantics, enhancing specificity, complementing the framework established by the ISO 25012 standard, and understanding of Data Quality aspects. The ISO 25012 standard, while tailored for software development, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of software development across a multitude of domains. In contrast, frameworks like ALCOA+ that are specific to certain domains lack the ability to be generalised. The model we propose can be seen as a "Rosetta Stone" for Data Quality terminology, facilitating a seamless communication of Data Quality between different domains when collaboration is required to tackle cross-domain projects or challenges.

COPD is a "heterogeneous lung condition characterized by chronic respiratory symptoms. It is a leading cause of death and illness and is associated with a substantial economic burden worldwide. Furthermore, it is marked by frequent exacerbations, which significantly contribute to morbidity and mortality, as well as the overall burden. These exacerbations are frequently triggered by bacterial infections.So far, the most commonly implicated Bacteria are Pseudomonas aeruginosa, Klebsiella pneumoniae, Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae.The advent of culture-independent techniques, such as next-generation sequencing techniques (NGS), allows us to characterise the lung microbiome in different disease states.

Purpose: This study investigates the influence of human brands on the formation of Generation Z's digital identity. Given the increasing importance of the digital space in this generation's life, understanding how their identity is shaped by human brands in this environment is crucial. Method: Using a netnographic approach, qualitative data was collected and analyzed from 500 posts and interactions of Gen Z users on Instagram and TikTok platforms. Thematic content analysis was conducted using NVivo 12 software, with inter-coder reliability confirmed (κ = 0.85). Findings: Results indicate that human brands influence Gen Z's digital identity through three main mechanisms: 1) lifestyle emulation (β = 0.42, p < 0.001), 2) value alignment (β = 0.38, p < 0.001), and 3) self-presentation modeling (β = 0.35, p < 0.01). Multiple regression analysis showed these three factors explain 67\% of the variance in digital identity formation (R² = 0.67, F(3,496) = 335.42, p < 0.001). Conclusion: This study demonstrates that human brands play a significant role in shaping Generation Z's digital identity. Understanding this influence can help marketers and policymakers design more effective strategies for engaging with this generation.

The advancement of agricultural technology is intrinsically linked to the evolution of the agricultural industry, with the latter being contingent upon technological progress for its sustained enhancement. In the context of China, a nation of significant agricultural stature, the globalization of agricultural science and technology (Agri-Sci-Tech) has emerged as a pivotal narrative within the country's agricultural growth trajectory. The strategic alignment of China's Agri-Sci-Tech industry with its global counterparts is driven by the ambition to assimilate sophisticated agricultural production techniques and managerial acumen from its Western counterparts, thereby catalyzing the international expansion of the nation's Agri-Sci-Tech endeavors. This scholarly discourse delves into the imperative of interdisciplinary convergence, underscoring the paradigm and intrinsic attributes associated with the cultivation of agricultural expertise. It scrutinizes the prevailing state of foreign language instruction within the academic curriculum, positing strategic interventions and recommendations for the development of a robust agricultural foreign language talent cultivation framework. The treatise accentuates the criticality of cultivating a multifaceted agricultural talent pool proficient in foreign languages, advocating for a "professional+foreign language" model that facilitates international dialog within China's Agri-Sci-Tech industry and bolsters the creation of globally recognized brands. Moreover, the cultivation of a "foreign language+professional" composite talent model is posited as a transformative solution to the existing impasse in foreign language education in China, offering a novel pathway for educational innovation and global competitiveness within the agricultural domain.

Contextualising the narrative on energy efficiency as the first fuel in the clean energy transition, the energy efficiency first principle (EE1) was introduced in EU energy and climate policy with Energy Union communication in 2015 and made nonbinding with the EU Governance Regulation in 2018. In 2023, the EU legislators made EE1 legally binding to apply in policy, planning and investment decisions exceeding 100 million euros each and 175 million euros in the transport sector across all sectors that affect the energy system. By applying the multiple streams framework, this article analyses the role of policy entrepreneurs in the policy process and politics of making EE1 binding through qualitative text analysis of policy documents, position papers and reports from EU negotiations. The analysis shows how nonprofit and nongovernmental organisations such as the Regulatory Assistance Project, the European Climate Foundation and the Energy Efficiency Financial Institutions Group, as well as the European Commission and the rotating Council Presidency, were critical policy entrepreneurs for coupling the problem, policy and politics streams. The coupling was performed once a policy window was opened by the Paris Agreement on climate change, the entering office of the Ursula von der Leyen European Commission in late 2019, immediately presenting the European Grean Deal as the EU’s climate action plan and green growth strategy, and the energy crisis in Europe following Russia’s second war on Ukraine, leading the Commission to present the REPowerEU Plan and the EU Save Energy Strategy. This paper provides better knowledge of the policy processes and politics related to EE1, contextualising and concretising the concept of energy efficiency as the first fuel into binding law.

As a pioneering transdisciplinary field, innovationology transcends the traditional boundaries of academic disciplines and development practices to address persistent global challenges. This article provides a comprehensive exploration of innovationology, delving deep into its philosophical foundations, ontological commitments, and ethical imperatives. By unpacking the core tenets of this innovative approach, the paper positions it as a robust and holistic science capable of catalyzing transformative innovation and sustainable development worldwide. Innovationology represents a paradigm shift that moves beyond conventional innovation and development theories, embracing a systems-oriented, community-centric, and decolonial praxis. Drawing on a synthesis of theoretical perspectives and empirical insights, the article examines innovationology's ontological commitment to marginalized communities as active agents of change, its epistemological grounding in pluralistic knowledge systems, and its ethical imperatives of social justice, environmental sustainability, and inclusive innovation. This in-depth analysis strengthens the conceptual and theoretical foundations of innovationology, providing critical guidance for its practical implementation and continued refinement as a cutting-edge transdisciplinary science for global transformation.

This review highlights recent innovations in food packaging, emphasizing the shift from conventional petroleum-based materials to bio-based alternatives and smart packaging systems. Bio-based materials, such as starch, cellulose, and polyhydroxyalkanoates (PHA), offer sustainable solutions due to their biodegradability and reduced environmental impact. These materials are positioned as eco-friendly alternatives to traditional plastics, but face challenges related to production costs and scalability. Additionally, advancements in smart packaging technologies, including sensor and indicator systems, provide real-time food quality monitoring, enhancing food safety and reducing waste. Active packaging technologies, incorporating natural antioxidants and moisture control, extend product shelf life and improve food preservation. Also, these biopolymers usually present lower CO2 footprint, energetic cost, and water consumption during their production, in comparison to traditionally used synthetic plastics. The review identifies critical challenges, such as regulatory barriers and technological limitations, but also outlines significant opportunities for future research and innovation in the food packaging sector, aiming for more efficient, safer, and environmentally sustainable packaging solutions.

Objectives: The aim of this study was to determine why and how adults with attention deficit hyperactivity disorder (ADHD) experience variable impairment and identify the processes and strategies adults with ADHD use to develop positive self-regulation skills. Methods: A mixed cohort of 11 participants (6 female and 5 male) from a university, an adult ADHD clinic and an ADHD support group in the UK were interviewed online between September 2021 and February 2022. Data were collected and analysed simultaneously, inspired by constructivist grounded theory methodology. Results: Participants described a “polar awareness of difference” from others in terms of engagement and ADHD characteristics, and a “polar awareness of consciousness” experienced as the states of chaotic attention and hyperfocus, both of which impact core perceptions of self. Using an infinity paradigm, the results demonstrate unskilled attempts to self-regulate within and between these states using self-absorption or self-transcendence strategies including their inherent challenges and energetic cost. Our results further indicate that at the centre of this dynamic paradigm creative awareness strategies exist, which exemplify polarity awareness and the regulation of that awareness supported by an authentic inner compass (AIC). Conclusions: This paper presents the empirical foundation for the ADHD Creative Awareness Theory (CAT) – a new theory for understanding the experience of ADHD consciousness and environmental engagement. Practice implications are explored, and recommendations include use of the CAT as a framework for understanding and development of inherent self-regulation skills for adults with ADHD.

No consensus exists on whether acute aerobic exercise alters the glomerular filtration rate in older adults. Objective: To assess the immediate effects of three aerobic exercise intensities on the estimated glomerular filtration rate (eGFR) in healthy, sedentary older adults. Methods: Eighteen healthy, sedentary older adults (12 men and eight women) voluntarily participated in the study. The participants underwent three standardized aerobic exercise tests (100%, 80%, and 60% of the maximal heart rate) on a bicycle ergometer. Blood samples were collected to determine cholesterol, triacylglycerol, glucose, serum creatinine (Cr), Cystatin C (CysC) concentrations, and eGFR. Results: eGFR and serum concentrations of Cr and CysC were not modified at any exercise intensity. There was a negative correlation between blood total cholesterol vs. eGFR (R= -0.512, R= -0.582, R= -0.531; p &lt;0.05) at rest, 60%, and 100% of the maximal heart rate, respectively. Besides, a negative correlation existed between age vs. eGFR at 60% of the maximal heart rate (R= -0.516; p &lt;0.05). Conclusion: Short-time aerobic exercise of low, moderate, and vigorous intensity did not significantly affect eGFR and is considered safe for kidney function in healthy, sedentary older adults.

Nitrogen is essential for plant growth, yield and crop quality, and its deficiency limits food production worldwide. In addition, excessive fertilization and inefficient use of N can increase production costs and cause environmental problems. A possible solution to this problem is the application of biofertilizers, which improve N assimilation and enhance biomass and yield. Therefore, the objective of this research was to evaluate the impact of the application of the combination of green and red algal (Ulva lactuca, Solieria spp.), Rhizobium sp., Trichoderma asperel-lum and the combination of the above three biofertilizers on N assimilation. A completely ran-domized design was carried out with 10 plants per treatment and five treatments: T1=control; T2=algal extracts; T3=Rhizobium sp.; T4=T. asperellum; T5=T2+T3+T4. Analyses showed that the Rhi-zobium sp. treatment resulted in higher nitrate reductase enzyme activity quantified in maize leaves, which possibly favored more efficient phosphosynthetic activity, reflecting higher bio-mass accumulation and increased yield. The use of Rhizobium sp. showed increases in biomass (13.4%), yield (11.82%), SPAD values (12%), total chlorophyll (18.4%), carotenoids (13.6%), num-ber of leaves (11.4%) and plant height (11.27%) compared to the control. This results in nitrogen assimilation promises to be a key mechanism for sustainable agricultural practices in the future.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in Edge Computing (EC), Deep Learning (DL), and Deep Transfer Learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructure. When combined with medically oriented IoT setups, DL-architectures are suitable for integrating into pandemic-compliant medical infrastructure. The development of intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 5G wireless communication has enabled ultra-wide broadband wireless communication, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such set-ups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents the design of a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge- and cloud-computing frameworks, and a suite of DL-tools. The framework uses a composite attention-driven framework incorporating various DL-pre-trained models (DPTM) for protocol adherence and contact tracing. When connected to public networks, it can detect specific cyber-attacks. The results confirm the effectiveness of the proposed methodologies.

When we grow old, we experience loss of muscle strength and power, a condition commonly referred to as sarcopenia (ICD-10-CM code (M62.84)). Sarcopenia leads to disability in 5-10% of the population. In this review we emphasise that it is not a sudden event, but a deterioration that occurs over time but is only recognised as a disease when it becomes clinically manifest in the 6th-7th decade of life. Evidence from animal studies, elite athletes and longitudinal population studies all confers that once sarcopenia is manifested, the underlying process has been ongoing for decades. We present various hypotheses about the underlying cause(s) of this process and their supporting evidence, i.e. does this disease have a neurogenic, a myogenic or a scaffold-related origin? We briefly review various proposals to alleviate sarcopenia, including stem cell rejuvenation, combating scaffold degeneration, reducing senescent cell burden, skeletal muscle secretomes and muscle innervation. We conclude that although there are potential target candidates and ongoing preclinical and clinical trials with drug treatments, the only evidence-based intervention today is exercise. We present different execrcise programmes and discuss to what extent the inter-individual susceptibility to developing sarcopenia is due to our genetic predisposition or lifestyle factors.

This paper introduces a systematic approach to the design of Direct Current-to-Digital Converter (DCDC) specifically engineered to overcome the limitations of traditional current measurement methodologies in System-on-Chip (SoC) designs. The proposed DCDC addresses critical challenges such as high power consumption, large area requirements, and the need for intermediate analog signals. By incorporating a current mirror in a cascode topology and managing the current across multiple binary-sized branches with the Successive Approximation Register (SAR) logic, the design achieves precise current measurement. A simple comparator, coupled with an isolation circuit, ensures accurate and reliable sensing. Fabricated using the TSMC 180 nm process, the DCDC achieves 8-bit precision without the need for nonlinearity calibration, showcasing remarkable energy efficiency with an energy per conversion of 1.52 pJ, power consumption of 117 µW, and a compact area of 0.016 mm². This innovative approach not only reduces power consumption and area, but also provides a scalable and efficient solution for next-generation semiconductor technologies. The ability to conduct online measurements during both standard operations and in-field conditions significantly enhances the performance and reliability of SoCs, making this DCDC a promising advancement in the field.

Extreme Ultraviolet (EUV) photolithography process, is a cornerstone of semiconductor manufacturing and operates under demanding precision standards realized via nanometer-level overlay (OVL) error modeling. This procedure allows the machine to anticipate and correct OVL errors before impacting the wafer, thereby facilitating near-optimal image exposure while simultaneously minimizing the overall OVL error. Such models are usually high dimensional and exhibit rigorous statistical phenomena such as collinearities that play a crucial role in the process of tuning their parameters. Ordinary Least Squares (OLS) is the most widely used method for parameters tuning of Overlay models but in most cases it fails to compensate for such phenomena. In this paper we propose the usage of Ridge Regression, a widely known Machine Learning (ML) algorithm especially suitable for datasets that exhibit high multicollinearity. The proposed method has been applied in perturbed data from a 300 mm wafer fab and the results show reduced residuals when ridge regression is applied instead of OLS.

This paper intoduces a non-standard research technique to clarify how complex phenomena, such as those that are abundantly present in human physiology, can be faithfully described using fractal dynamical models with and without stochastic forces. This method for conducting research involves tracing the his- torical evolution of understaning an empirical medical process faciltated by the fractal calculus perspective. Herein we trace the analysis of the time series for heart rate varability (HRV) developed for diagnosing the cardiovascular health of a patient. This is done herein by introducing four (one empirical which en- tails three theoretical fractal models) distinct but related fractal models each one introduced to solve a particular problem arising from a fundamental defect in the previous model but in generalizing a model at one stage to resolve the problem associated with the defect another is invariably introduced by the re- placement model. It is through the utilization of the fractional calculus that the necessity for rethinking how to systematically incorporate additonal layers of complexity are revealed ultimately resulting in a 'complete' description of its empirical dynamics in fractal terms.

Respiratory infections with RSV account for an important part of hospital admissions for acute respiratory infections. Nirsevimab has been developed to reduce the hospital burden RSV (respiratory syncytial virus) infections: Compared with the product previously used, it has a stronger binding capacity to RSV F protein and a high affinity for FcRn (neonatal receptor for the Fc fragment of IgG), which extends its lifespan. Nirsevimab has been shown to be highly effective in reducing hospitalization rates for RSV infections but a large or unknown number of treated subjects have been excluded in clinical and post-marketing studies. However, analysis of these studies cannot exclude that in rare cases, nirsevimab facilitates and worsens RSV infection (or other respiratory infections). This could be attributable to ADE (antibody-dependent-enhancement) which has been observed with RSV F-protein antibodies in inactivated vaccine trials. This risk has been incompletely assessed in preclinical and clinical trials (incomplete exploration of nirsevimab effector functions and pharmacokinetics). ADE by disruption of the immune system (not studied and due to FcRn binding) could explain there is no reduction in all-cause hospital admissions in treated age groups. Given the high price of nirsevimab, the cost-effectiveness of mass immunization campaigns may therefore be debated from an economic as well as a scientific point of view.

With the development of economy, maritime transportation has increased rapidly, which has had a significant impact on the marine environment and resources. In recent years, Synthetic Aperture Radar (SAR) has been widely used to observe maritime environmental incident. Through the large coverage area of SAR, marine environmental emergencies, especially oil pollution, can be better monitored and responded to. This study first proposed an improved Full-scale Aggregated MobileUNet (FA-MobileUNet) model to achieve more complete detection results of oil spill areas. The convolutional block attention module (CBAM) in FA-MobileUNet was modified based on morphological concepts. By introducing the morphological attention module (MAM), the improved FA-MobileUNet model can reduce the fragments and holes in the detection results, providing complete oil spill areas which were more suitable for describing the location and scope of oil pollution incidents. In addition, to overcome the inherent category imbalance of the dataset, label smoothing was applied in model training to reduce the model’s overconfidence in majority class samples while improving the model’s generalization ability. The detection performance of the improved FA-MobileUNet model reached 84.55% mIoU (mean Intersection over Union), which was 17.15% higher than that of the original U-Net model. The improved FA-MobileUNet model was then examined by some oil pollution incidents. According to the detection results, if the oil spill was suspected to be caused by a ship, the corresponding Automatic Identification System (AIS) data were collected based on the acquisition time and geo-location of the SAR image to search for the ship suspected of causing oil pollution. Finally, some oil pollution incidents in Taiwan marine area were collected to verify the effectiveness of the proposed model for marine oil pollution monitoring. Experimental results showed that the extent of the detected oil spill was consistent with the oil pollution area recorded in the incident reports.

This study focuses on evaluating and improving the effectiveness of village fund management in Merauke Regency, South Papua. Village fund management faces unique challenges due to the region's geographical and socio-economic conditions. Organizational behavior transformation is critical, particularly in addressing weaknesses in organizational structure, communication, work culture, and monitoring systems, to achieve sustainable development outcomes. A qualitative case study approach was employed, with data collected through in-depth interviews, participant observation, and document analysis between May and August 2024. Merauke Regency was selected for its diversity in management practices. Thematic analysis and triangulation techniques were used to ensure the reliability and validity of findings. The findings reveal that transforming the organizational structure from hierarchical to flexible team-based models, improving communication, and fostering a positive work culture significantly enhanced village fund management. These changes led to better coordination, responsiveness, efficiency, and transparency. Increased community participation and strengthened monitoring and evaluation also contributed to more effective development outcomes.This study provides practical recommendations for policymakers to design more sustainable and inclusive village fund management strategies. Data-driven and participatory approaches are crucial for maximizing development impacts and ensuring socio-economic benefits at the village level.

This paper introduces the self-supervised pretext task of rare visual token enhancement: given an image, the model is trained to push the rarest visual tokens far from all the others. This pretext task slightly-but-consistently improves baseline performances for both registration and few-shot change detection on OSCD, LEVIR-CD and S2looking.

Bearing fault diagnosis is crucial for ensuring the stable operation of mechanical equipment. With the continuous development of deep learning technology, Convolutional Neural Networks (CNNs) have demonstrated significant advantages in the field of fault diagnosis. This paper proposes a new method that combines various CNN architectures to improve the accuracy of bearing fault diagnosis. We designed five different convolutional network structures, including SerConv, ResConv, One-Shot Aggregation Convolution (OSAConv), Cross-Stage Aggregation Convolution (CSAConv), and MD-DAConv. Experimental results on the Case Western Reserve University (CWRU) bearing dataset show that the proposed method exhibits high accuracy and robustness in fault diagnosis. The results indicate that strategies such as multi-directional, multi-scale, and residual connections play a crucial role in enhancing the depth and breadth of feature extraction, while simple and effective feature fusion and information transmission mechanisms are key to ensuring the robustness and generalization ability of the model.

Social responsibility is imperative for its important function in the growth of societies and organizations. However, the research on corporate social responsibility (OSR) has predominantly focused on the actions of organizations. Not much has been discussed regarding the employee's behaviours in promoting the development of social responsibility within organizations. CI is known for its significant role in the organization's various outcomes, Although a conceptualization and some empirical support have been provided for its relationship with the behaviour of employees, However, the effect of CI on employee socially responsible behaviours has not yet been investigated. In this article, following OC and OI theories, we improve the research model which can describe mechanisms underpinning through which CI, OI and POS support actions for employees' social responsibility. Data was collected from members of service professionals in Saudi Arabia and examined using Structural Equation Modeling- Partial Least Squares. The findings indicated that CI actions and activities develop the skills that motivate employees to support social responsibility inside and outside the organization. Moreover, our research paper explained the mechanism under which CI activities reshape employees' socially responsible manners. The current study is among the first to look at how to modify the socially responsible behaviour of employees across organizations, which can be a starting point for transforming those employees into a sustainable society.

The P versus NP problem is a fundamental question in computer science. It asks whether problems whose solutions can be quickly verified can also be quickly solved. Here, "quickly" refers to computational time that grows proportionally to the size of the input (polynomial time). While the problem's roots trace back to a 1955 letter from John Nash, its formalization is attributed to Stephen Cook and Leonid Levin. Despite extensive research, a definitive answer remains elusive. Closely tied to this is the concept of NP-completeness. If a single NP-complete problem could be solved efficiently, it would imply that all problems in NP can be solved efficiently, proving that P equals NP. This work posits that MONOTONE ONE-IN-THREE 3SAT, a notoriously difficult NP-complete problem, can be solved efficiently, thereby establishing the equivalence of P and NP.

Background/Objectives: Chemerin, which is a multifunctional cytokine and adipokine, has been implicated in inflammatory and metabolic processes and might play a role in upper gastrointes-tinal (GI) malignancies, particularly gastric and esophageal cancer. The aim of this review is to explore the role of chemerin in the pathophysiology of upper GI cancers, as well as its potential as a biomarker for early detection and as a therapeutic target. Methods: A comprehensive review of recent studies about chemerin's biochemical properties and interaction with its receptors, as well as its effects on inflammatory responses, immune regulation and metabolic processes was con-ducted. Clinical implications of chemerin on gastric and esophageal cancer were analyzed, whereas the potential therapeutic strategies targeting chemerin were discussed. Results: Elevated chemerin levels are associated with poor prognosis in gastric cancer and promote invasiveness and metastasis in esophageal cancer. Chemerin receptor antagonists show promising results in inhibiting cancer cell migration, invasion and progression. Conclusions: Chemerin could repre-sent a valuable prognostic biomarker and therapeutic target for upper GI cancers. Future observa-tional studies should validate its clinical applications and investigate the efficacy of chemerin in-hibitors as potential therapeutic targets.

In the context of armed conflicts, regions often face infrastructure destruction and economic difficulties. The development of tourism can be a key factor in recovery and economic growth. Geospatial platforms for interactive charitable tourism offer a promising solution for attracting tourists and stimulating the development of affected regions. The aim of this study is to assess the effectiveness of geospatial platforms for interactive charitable tourism as a tool for attracting tourists and promoting the recovery and development of regions affected by armed conflicts. The study employed regression and correlation analysis methods. The analysis revealed significant positive correlations for all variables. The correlation coefficient for audience reach was 0.75, for the effectiveness of attracting donations - 0.8, for the economic impact on the region - 0.9, for user interaction with the platform - 0.85, for platform sustainability - 0.78, and for social impact - 0.92. The t-statistic values exceeded the critical thresholds, and the p-values were below 0.05, confirming the statistical significance of the results. The coefficient of determination was 0.91, indicating a high explanatory power of the model. For the four obtained clusters, approaches to the development and use of geospatial platforms were justified. For regions of cluster 1 with high audience reach (0.75) and moderate effectiveness of attracting donations (0.8), it is recommended to use technologies to improve connectivity and effectively promote their strengths. For regions of cluster 2 with high sustainability and high tourism potential (high social impact = 0.92), it is necessary to include advanced geospatial solutions to increase visitor engagement and optimize recovery strategies, building on existing strengths to attract more tourists and stimulate sustainable development. For regions of cluster 3, significant investments are required both in geospatial platforms and in tourism infrastructure to benefit from high tourism potential (economic impact on the region = 0.9). Strategic development and targeted marketing should be a priority to attract tourists and stimulate economic growth. For regions of cluster 4 with low sustainability and tourism potential (low user interaction with the platform = 0.85 and platform sustainability = 0.78) require comprehensive support, as they are in a crisis situation.

T-cell redirecting therapies, which include chimeric antigen receptor T-cells (CAR-T) and bispecific antibodies (BSAs), have revolutionized the treatment of relapsed\refractory large B-cell lymphoma (LBCL). Expanding clinical experience with these advanced therapies shows the potential for optimization of their use with combination or consolidation strategies, which necessitates prognostic stratification of patients. While traditional clinical prognostic factors identified in the era of chemotherapy are characterized by limited value, the tumor microenvironment (TME) is becoming a new prognostic cluster. We examine how the heterogeneity of LBCL, characterized by variations in tumor parameters and differences in TME immune cell composition, immune checkpoint expression, and cytokine milieu, correlates with both positive responses and resistance to treatment. While classical parameters such as histological subtype, cell of origin, and target antigen expression lack proven prognostic value for T-cell redirecting therapies, the density and functional state of tumor-infiltrating lymphocytes, tumor-associated macrophages, and immune checkpoint molecules are shown to be critical determinants of therapeutic success, particularly in CAR-T therapy. We identify several gaps in current knowledge and suggest that insights gained from CAR-T experience could be instrumental in refining BSA applications. This report also highlights limitations in current knowledge, as TME data derive from a limited number of registrational trials with varying methodologies, complicating cross-study comparisons and often focusing on immediate response metrics rather than long-term outcomes. By dissecting the complex interactions within the TME, this review aims to identify new prognostic factors and targets, ultimately fostering more effective and tailored treatment strategies for LBCL patients.

In the dynamic realm of agribusiness construction, firms are striving to bolster their operational efficiency and profitability amidst the global shift from traditional farming to commercial agriculture. This transition has intensified the demand for sophisticated infrastructure development, presenting new challenges for commercial managers. A critical hurdle is the accurate estimation of profitability for prospective contracts, a task often reliant on intuition rather than data-driven methods. To address this, we propose the development of a mathematical model utilising machine learning techniques to predict contract profitability and identify influential factors. This model aims to aid in bid decision-making, financial forecasting, and enhancing competitiveness in the marketplace. Furthermore, it would provide valuable insights into how altering specific contract attributes might affect predicted profitability. The implementation of such a system necessitates close collaboration between IT professionals and construction executives. This study delineates the development of this predictive model, outlining the data analysis process and the application of machine learning algorithms to tackle this complex commercial challenge. The ultimate goal is to bridge the gap between intuitive estimation and data-driven prediction, thereby enhancing the financial performance and strategic decision-making capabilities of firms in the agribusiness construction sector.

Global demand for food shows an increasing trend, which implies that volumes of food waste also increase. These wastes contain bioactive compounds that are not properly utilized or valorized. It was reported that ginger waste contains phenolic compounds with high antioxidant activity. Thus, this study aimed to evaluate the effect of temperature, time, and particle size on the total phenolic content (TPC) and antioxidant activity (AA) of ginger (Zingiber officinale R.) waste aqueous extract using the Pressurized Liquid Extraction (PLE) method with water as the sole solvent. Box-Behnken design was used. The sample was 1.60 kg of dried ginger peel powder. Data analysis was performed with Minitab ® 19.1 (64-bit). TPC ranged from 10.42 to 14.1 mg GAE/g, and AA (DPPH method) ranged from 72.9 to 111.9 μmol TE/g. The model explained 81.07% of AA’s total variability. Positive correlation was found between TPC and AA (Pearson's ρ = 0.58, p &lt; 0.05). Response optimization values were 126 °C and 38 min. Temperature was a significant factor (p &lt; 0.05) influencing AA, while time and particle size were not significant. Higher temperatures, longer extraction times and smaller particle sizes increase TPC and AA of the ginger waste aqueous extract.

The effective macroscopic model describing interactions of a viscous compressible heat-conducting fluid and a two-level fine bristly thermoelastic structure is derived from microstructure by means of the Allaire–Briane multi-scale convergence method. This new model naturally generalizes the isothermal formulation earlier constructed by the authors in 2020, see in Siberian Electronic Mathematical Reports, vol. 17 (https://doi.org/10.33048/semi.2020.17.100). In applications, the established model can be used, for example, in description, with account of heat transfer phenomemon, of airflow near surface of plant’s leaf, in simulation of epithelium surfaces of blood vessels, and in design of biotechnological devices operating in liquids.

There have been multiple technological advancements that promise to gradually enable devices to measure and record signals with high resolution and accuracy in the domain of Brain Computer Interfaces (BCI). Multi-modal BCIs have been able to gain significant traction given the potential to enhance signal processing by integrating different recording modalities. In this review, we explore the integration of multiple neuroimaging and neurophysiological modalities including Electroencephalography (EEG), Magnetoencephalography (MEG), Functional Magnetic Resonance Imaging (fMRI), Electrocorticography (ECoG), and Single-Unit Activity (SUA). This multimodal approach leverages the high temporal resolution of EEG and MEG with the spatial precision of fMRI, the invasive yet precise nature of ECoG, and the single-neuron specificity provided by SUA. The paper highlights the advantages of integrating multiple modalities, such as increased accuracy and reliability, and discusses the challenges and limitations of multimodal integration. Furthermore, we explain the data acquisition approaches for each of these modalities. We also demonstrate various software programs that help in extracting, cleaning, and refining the data. We conclude this paper with discussion on the available literature highlighting recent advances, challenges, and future directions for each of these modalities.

This paper presents the analysis of four error models that can appear in the field of adaptive systems, where adaptive laws for parameter estimation are represented as fractional order differential equations whose order can switch between an order within the range (0,1) and 1. These switched adaptive laws have been proposed in the past few years to improve the balance between control energy and system performance, which is often a challenge when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. Boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case.

Hepatocellular carcinoma (HCC) is a liver cancer that originates from hepatocytes. It is a primary health issue and one of the most common malignant tumors. The US FDA approved Sorafenib as the first-line anti-cancer drug for advanced HCC patients. A few patients with advanced HCC may benefit from Sorafenib treatment, but most of the patients eventually develop resistance to Sorafenib, which leads to its poor prognosis. Long-noncoding RNAs (lncRNA) have been found to play a critical role in tumorigenesis and the development of HCC, as well as various cancers. It also plays a vital role in tumor drug resistance, while the mechanisms of LncRNAs in HCC sorafenib resistance have not been characterized yet. In this review article, we summarized the molecular mechanisms, such as the involvement of transporters, receptors, cell death regulation, and other factors impacting sorafenib resistance in HCC.

The integration of digital technologies in managing technical and design information is trans-forming the fields of architecture and construction. This article presents a website developed to compile detailed information on the construction processes of buildings at Pontificia Universidad Javeriana (PUJ) campus. The site offers comprehensive construction records and interviews with designers, engineers and other professionals who collaborated to align design approaches with technical construction requirements. A chronological record of the construction processes was maintained, from inception to completion of the buildings. Digital tools include interactive modules that facilitate access and data updates, improving coordination and efficiency for man-aging future projects. Gamification elements and social media dissemination boost user engage-ment. The study also assesses the impact of the website on the university community, analyzing usability and user (N=235), among students, teachers, and staff members. Findings indicate that digital technologies optimize construction processes and improve the management of technical and design information, and reveal opportunities for future projects and research, benefiting both internal and external stakeholders.

Heusler compounds and alloys represent a rapidly expanding family of materials that exhibit novel properties of significant interest for advanced technological applications. Electronic band structure calculations play a pivotal role in advancing research in this area. In a recent publication [Özdoğan, K.; Galanakis, I. . Crystals 2024, 14, 693], we explored the properties of a new class of Heusler compounds based on Li, referred to as "p0-d Semi-Heusler Compounds." In this study, we take the research a step further by focusing on "d0-d Semi-Heusler Compounds," with the chemical formula KZ(Ga, Ge, As, or Se), where Z represents a transition metal. Our investigation centers on the structural, electronic, and magnetic properties of these compounds, particularly in relation to the three possible C1b structures. Most of these compounds are found to be magnetic and, notably, several among them exhibit half-metallicity making them appealing for applications in spintronics. Our findings provide a foundation for future experimental research on these materials.

Digital gait monitoring is increasingly used to assess locomotion and fall risk. The aim of this work is analyzing the changes in the foot-floor contact sequences of Parkinson’s Disease (PD) patients in the year following the implant for deep brain stimulation (DBS). During their best-ON condition, 30 PD patients underwent gait analysis at baseline (T0), at 3 months (T1), and 12 months (T2) after the DBS neurosurgery for bilateral high-frequency subthalamic nucleus stimulation. Thirty age-matched controls underwent gait analysis once. Each subject was equipped with bilateral foot-switches and a 5-minute walk was recorded, including both straight-line and turnings. The walking speed, turning time, stride time variability, percentage of atypical gait cycles, stance, swing, and double support duration were estimated. Overall, the gait performance of PD patients improved after DBS, as also confirmed by the decrease in the UPDRS-III score from 19.4±1.8 to 10.2±1.0 points (T0 vs. T2) (p < 0.001). In particular, the percentage change of atypical gait cycles of PD more affected side decreased at T1 (Straigh-line: -72%; Turnings: -43%) and at T2 (Straigh-line: -54%; Turnings: -20%). The percentage of atypical gait cycles proved an informative digital biomarker for quantifying PD gait changes after DBS, both in straight-line paths and turnings.

Search for the new non-invasive methods of investigating the functioning of human internal organs is an urgent task. One of these methods for assessing the functioning of the human res-piratory system is electromagnetic sensing, based on a significant difference in the dielectric permittivity of muscle tissue and air. During breathing, when the lungs are filled with air, the dielectric permittivity of the lungs decreases, which leads to a change in the level of the elec-tromagnetic signal passing through the body. Results of experiments on recording changes in the level of electromagnetic radiation passing through the human body, performed on an experimental device consisting of eight transmitting and receiving antennas located on opposite sides of the chest have been presented in the article. The possibility of visualizing the measured “pulmonograms” in the form of dynamic two-dimensional images showing the process of filling various parts of the lungs with air has been demonstrated.

Background/Objectives: Habituation and sensitization are opposite phenomena that play a role in the pathophysiology of episodic migraine and its progression to chronic migraine (CM). There have been just a few studies that have investigated these phenomena in patients with medication overuse headache (MOH) in comparison to those with chronic migraine (CM) and healthy controls (HCs), and the findings have been inconsistent. Methods: We measured and examined visual evoked potentials (VEPs) in 81 patients with MOH and 24 patients with CM, as well as 24 HCs. The VEPs were used to assess sensitization by analyzing the amplitude of the first block (100 sweeps), and to evaluate habituation by measuring the amplitude response decrement after 6 blocks. We further examined patients diagnosed with MOH based on their acute medication type and after a 3-week acute medication withdrawal program. Results: There were no significant differences between groups in terms of the first N1-P1 VEP amplitude block and its habituation. It was found that patients with MOH had a greater drop in the amplitude of the VEP P1-N2 complex after repeated stimulation than patients with CM or HC. The VEPs parameters showed no significant differences based on the specific overused drug and after a 3-week acute medication off. Conclusions: We propose that the results obtained in patients with MOH indicate an abnormal activation of inhibitory circuits in the parieto-occipital region in response to repeated modulatory stimuli.

Aim: This study aims to analyze the structure of the co-authorship network in Budd-Chiari syndrome (BCS) research from 2000 to 2023 using data from the Web of Science (WoS) Core Collection. The objective is to identify key researchers, evaluate the collaborative dynamics, and understand the evolution of scientific contributions in this field. Method: This study utilized network analysis techniques to evaluate 2,877 publications related to BCS research indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.Result: The results revealed a highly fragmented network structure in the early years (2000-2009), characterized by low network density (0.0017) and a large number of disconnected components (533). Key researchers such as Aurelie Plessier and Dominique Valla emerged as central figures in terms of degree and closeness centrality. In the 2010-2019 period, the network showed slight improvements, with density decreasing to 0.0016 but an increase in the number of components to 645, indicating persistent fragmentation. From 2020 to 2023, the network demonstrated increased cohesiveness with a density of 0.0032 and a reduction in components to 376. Influential researchers in the final period included Virginia Hernandez-Gea and Valerie Paradis, who played pivotal roles in connecting disparate research clusters.Conclusion: The co-authorship network analysis of BCS research over the past two decades highlights a gradual yet incomplete evolution towards greater collaboration and integration among researchers. While the network's cohesiveness has improved, significant fragmentation remains, suggesting that further efforts are needed to enhance global and interdisciplinary collaboration. Addressing these challenges is crucial for fostering a more connected and productive research community that can advance the understanding and management of BCS.

Using the data of China's A-share non-financial listed enterprises from 2015 to 2022 as a sample, this study empirically tests the impact of ESG rating disagreement on corporate debt financing cost. The results show that ESG rating disagreement significantly increases the debt financing costs of enterprises, and this result still holds after a series of robustness tests such as replacing the dependent and independent variables and PSM approach. Meanwhile, the heterogeneity analysis reveals that non-state-owned enterprises and enterprises in non-heavily polluting industries are more sensitive to ESG ratings disagreement. In addition, mechanism analysis shows that corporate financing constraints mediate the relationship between ESG rating disagreements and debt financing costs. The findings of this study provide important evidence for understanding the impact of ESG rating disagreement on the costs of debt financing, enrich the meaning of ESG rating disagreement, and reveal the significance of unifying ESG rating standards.

Background: Handheld cameras may increase access to teleretinal diabetic retinopathy screenings (TDRS), as they are more economical and nimbler than traditional desktop cameras. However, their use in safety-net clinical settings is less understood. Methods: The Los Angeles County Department of Public Health collaborated with the Los Angeles County Department of Health Services (DHS) to pilot the use of handheld cameras in DHS’s TDRS program. This study assessed screening metrics and interviewed TDRS coordinators and photographers about their experiences using these cameras. Results: Handheld cameras are harder to operate and performed less optimally on key screening metrics (e.g., image quality) compared to desktop cameras. However, this challenge appeared to be linked to the selected model, rather than to all handheld cameras. The adoption of handheld cameras in DHS was aided by their integration into an existing agency infrastructure that already supports the use of desktop cameras; these operational supports include an established workflow, a data platform, and experienced photographers. Conclusions: The use of handheld cameras for TDRS is a promising practice for assuring equitable access to screenings. Handheld cameras are portable, smaller in size, and lower in cost than desktop cameras. Future projects should invest in and assess the performance of higher-quality models of these cameras, especially their use in safety-net clinical settings.

People with aphantasia exhibit the inability to voluntarily generate or form mental imagery in their minds. Since the term “aphantasia” was proposed by Zeman et al. in 2015 to describe this, it has gained increasing attention from psychiatrists, neuroscientists, and clinicians. Previous studies have mainly focused on definition, prevalence, and measurement, its impacts on individuals’ cognitive and emotional processing, and theoretical frameworks synthesizing existing findings, which have contributed greatly to our understanding of aphantasia. However, there are still some debates regarding the conclusions derived from existing research and the theories that were constructed from various sources of evidence. Building upon existing endeavors, the current systematic review emphasizes that future research is much needed to refine definition and diagnosis of aphantasia, strengthen empirical investigations at behavioral and neural levels, and more importantly, develop or update theories. These multiple lines of efforts could lead to a deeper understanding of aphantasia and further guide researchers in future research directions.

Introduction: Transesophageal echocardiography (TEE) is a vital diagnostic tool in clinical prac-tice, particularly in transcatheter interventions where it aids in both pre-operative planning and intra-operative guidance. Traditional TEE probes often require general anesthesia due to patient discomfort. However, the development of miniaturized TEE probes presents a promising alter-native, enabling routine examinations and interventions with minimal sedation. This study evaluates the feasibility of performing a complete 2D/4D TEE protocol with the new 4D-mini-TEE probe in echocardiography departments and its application in transcatheter interventions. Methods: This prospective, observational study included 30 consecutive patients from two high-volume European hospitals (Interbalkan Medical Center, Thessaloniki, Greece, and Rennes University, France) undergoing TEE or transcatheter interventions. The new 4D-mini-TEE 9VT-D probe (GE Healthcare) was utilized. The quality of the images and the tolerance of the probe were assessed in the Cath lab during interventions and in the echocardiography department during routine TEE examinations. Results: Direct comparison of the 4D-mini-TEE probe with the standard 6VT-D probe, confirmed the excellent image quality of this new pediatric probe. Most of the patients required minimal sedation or local oropharyngeal anesthesia, with satisfactory tolerance reported. Most of the transcatheter procedures did not require general anesthesia and intubation, resulting in shorter procedural time. Both 2D and 4D imaging modalities offered adequate intra-operative guidance for transcatheter procedures. Conclusions: The 4D-mini-TEE probe delivers exceptional imaging capabilities for routine ex-aminations and transcatheter interventions without needing sedation. Its use reduces esophageal trauma and the need for general anesthesia, enhancing patient comfort and safety.

Purpose: Decomposition analysis of forecasting errors relating to time series generated by a 3PL logistics operator for ten distribution channels operated by the logistics operator. Design / methodology / approach: The studies were focused on the analysis of 10 distribution channels operated by the 3PL logistics operator who used a forecasting tool based on a modified ARIMA algorithm to prepare forecasts. In this paper, R environment was used. The studies focused on the visual analysis of forecasting error series, on the analysis of the basic parameters of the error time series distributions, on the analysis of STL decomposition and statistical tests relating to trend and seasonality. Findings: The forecasting error analysis indicates that there are different patterns and characteristics of errors for individual channels. The statistical test results for various channels display significant differences between forecast groups in some cases. This suggests that the forecasting tool can be more accurate for some than for other channels. Research limitations: Logistic operations are usually based on numerous variables which may influence forecast quality. Moreover, the absence of any information on the forecasting models and input data used may prevent complete understanding of error sources. Value of the paper: The studies described in this paper emphasized valuable conclusions which can be drawn from the analysis of time series forecasting errors in the context of logistic operations. The findings indicated the need for an adapted approach to forecasting for each and every channel, the importance of improving the forecasting tool and the potential to optimize the forecast accuracy by focusing on the trend and seasonality. For this reason, the analysis is an important input into the theory and practice relating to demand forecasting by logistics operators in distribution networks. The studies contribute to the works related to demand forecasting by logistics operators.

: Information on the detection of the presence and potential for degradation of synthetic polymers (SPs) under various environmental conditions is of increasing interest and concern to a wide range of specialists. At this stage, there is a need to understand the relationship between the main par-ticipants in the processes of (bio)degradation of SPs in various ecosystems (reservoirs with fresh and sea water, soils, etc.), namely the polymers themselves, the cells of microorganisms (MOs) partici-pating in their degradation, and humic substances (HSs), which constitute a macrocomponent of natural non-living organic matter of aquatic and soil ecosystems, formed and transformed in the processes of mineralization of bioorganic substances in environmental conditions. Analysis of the main mechanisms of their influence on each other and the effects produced that accelerate or inhibit polymer degradation can create the basis for scientifically based approaches to the most effective solution to the problem of degradation of SPs, including in the form of microplastics. This review is aimed at comparing various aspects of interactions of SPs, MOs and HSs in laboratory experiments (in vitro) and environmental investigations (in situ) aimed at the biodegradation of polymers, as well as pollutants (antibiotics and pesticides) that they absorb. Comparative calculations of deg-radation velocity of different SPs in different environments are presented. A special place in the analysis is given to the elemental chemical composition of HSs, which are most successfully in-volved in the biodegradation of SPs. In addition, the role of photooxidation and photoaging of polymers under the influence of the ultraviolet spectrum of solar radiation under environmental conditions on the (bio)degradation of SPs in the presence of HSs is discussed.

The growing emphasis on sustainability and environmental stewardship in the pharmaceutical industry necessitates innovative solutions to manage pharmaceutical waste, particularly medication bottles. This research paper explores the potential of integrating Radio Frequency Identification (RFID) and blockchain technology to enhance the traceability, accountability, and efficiency of recycling empty medication bottles. By utilizing RFID for real-time tracking and blockchain for secure, immutable data storage, this proposed system improves transparency across the supply chain and incentivizes consumer participation in recycling programs. The solution provides benefits to pharmaceutical manufacturers, regulators, and consumers, contributing to sustainability, regulatory compliance, and environmental protection.

This research centres on advancing the utilisation of a novel Deep Ensemble model called RicEns-Net for predicting crop yields through sophisticated multi-modal data engineering. Initially, data for the study were acquired from Ernst \& Young (EY), a prominent global technology and management consulting firm, as part of their annual data science challenge (EY Open Science Challenge 2023). The predictive models leverage multi-modal data by integrating radar and optical-type remote sensing data alongside meteorological data to create supplementary features essential for predicting crop yield. The primary data features are derived from spectral indices extracted from Sentinel 2 satellites, while more advanced features are generated through combinations and interactions of these fundamental features. Furthermore, data from Sentinel 1 and 3, along with meteorological measurements such as surface temperature and rainfall, account for the intricate natural factors influencing crop yield, creating a comprehensive set of data features based on existing research. To tackle the complexity introduced by numerous data features, extensive data engineering was conducted to identify the most predictive features among 100+ and mitigate the "curse of dimensionality," resulting in 15 features from 5 different modalities. The proposed RicEns-Net comprises multiple machine learning algorithms in a deep ensemble approach to leverage the best features of each of these techniques, achieving optimal performance. The proposed model achieves an MAE value of 341 kg/Ha for prediction, surpassing the performance of the EY Open Science Challenge 2023 winner models and state-of-the-art deep/machine learning approaches.

Background/Objectives: Obstructive Sleep Apnoea Syndrome (OSA) in children disrupts standard breathing patterns and sleep architecture, potentially leading to severe consequences. Early identification and intervention for OSA are crucial to prevent these issues. This study explored the relationships between waiting times for polysomnography (PSG), clinical history, age at the time of PSG, and PSG outcomes in children; Methods: Two hundred and fourteen children were prospectively enrolled. Data were extracted from medical records on the age at referral for PSG and at the time of its execution, the waiting time for PSG, diagnosis (primary snoring, mild, moderate, and severe OSA), history of snoring and apnoea, medications taken, surgical interventions, passive smoke exposure, and allergies. The records also included the Pediatric Sleep Questionnaire (PSQ); Results: The mean age at referral for PSG was 5.2 (2.8) years, the waiting time for PSG was 5.2 (5.0) months, and the age at the time of PSG was 5.6 (2.8) years. Children with a shorter history of OSA (≤12 months) underwent PSG at an earlier age (4.9 (2.7) years) compared to those with a more extended history (6.0 (2.7) years; p=0.006). A younger age at referral for PSG was associated with a lower PSQ score (r=0.167; p=0.015), a shorter history of apnoea (r=0.216; p=0.003) and snoring (r=0.187; p=0.023), but also with greater OSA severity, expressed as oxygen desaturation index (r=-0.152; p=0.026). In the correlation analysis, the waiting time for PSG, adjusted for age at referral, was longer in children with a prolonged history of apnoea (r=0.195; p=0.023) and snoring (r=0.259; p=0.002) but with a lower PSQ score (r=-0.172; p=0.045); Conclusions: Some children receive delayed treatment, increasing the risk of long-term complications. In evaluating children with suspected OSA, the duration of symptoms and the PSQ score at referral emerge as crucial factors that should be considered to optimise prioritisation for PSG access.

There is a growing awareness of the detrimental environmental impact of the construction industry, primarily due to the utilization of building materials that necessitate energy-intensive processes for their production. The objective of this paper is to explore whether mixing wood and cement could serve as a viable solution to mitigate this adverse environmental effect. The data was obtained by extensively investigating the Clarivate Web of Science database, adhering to the PRISMA technique guidelines, and subsequently processed using Bibliometrix and VOSviewer software. An in-depth critical analysis of the literature revealed a heightened interest in this subject matter in recent years, with the focal points evolving from initial experimental endeavors aimed at understanding the behavior of the innovative wood cement composites, primarily in terms of their mechanical properties, to assessing their environmental impact and the advantages associated with their use. The researchers suggest that optimal results can be achieved by treating the wood with tetraethyl orthosilicate, incorporating cellulose nanocrystal particles or wollastonite into the mixture, and utilizing wood from the Pinus species. Furthermore, it was discovered that a noticeable improvement in strength is only attainable if a low percentage of wood, up to 30%, when wood ash is employed. Ongoing research concerning wood and cement composites continues to progress, with regular advancements and breakthroughs, thus offering promising prospects for the development of superior and eco-friendly building materials.

The study focused on assessing passenger risks specifically at the platform-train interface (PTI). The primary aim of this investigation is to analyze how various factors, including station design, passenger flow, and weather conditions, impact the frequency and types of incidents occurring at this critical interaction point. To achieve this, the study utilized a comprehensive data analysis approach, examining records from the Valparaiso Metro for the period of 2022-2023. During this timeframe, a total of 361 incidents were documented, providing a substantial dataset for evaluating incident patterns and their correlations with different influencing factors. The analysis revealed that incidents are notably influenced by peak hour conditions and weekdays. Specifically, the study found that crowded conditions inside the train during peak hours, which typically occur in the morning and evening rush periods, contribute significantly to the occurrence of incidents. When examining incidents occurring on the platform, the study identified that these are closely related to the type of station and the presence of stairs for station access. Conversely, stations designed with more accessible features might experience fewer such incidents. Future studies will focus on incorporating additional factors and analysing new data to provide a more comprehensive understanding of incident dynamics.

The study put forward a data fusion approach for urban remote sensing that combines SAR (Synthetic Aperture Radar) and optical satellite data. By integrating datasets from different sensors and spatial-temporal scales, the technique aims to extract more accurate information. The fusion approach utilizes two methods: feature-based fusion, where relevant features are extracted and fused, and simple layer stacking (SLS), where the original datasets are directly stacked as multiple layers. This study extracted features using SAR textures (using Sentinel-1) and modified indices (using Landsat-8), and then classified these features using an XGBoost algorithm implemented in Python and Google Earth Engine. Researchers examined five cities, each representing a distinct climatic zone and urban dynamic: Cape Town, Guangzhou, Los Angeles, Mumbai, and Osaka. An accuracy assessment was conducted using random validation points, achieving an overall accuracy of 89.5% using the proposed MSFI method. A comparison was also performed with three well-known global products. The proposed approach, outperformed all three global products achived 89% accuracy while ESA (84%), ESRI (81%) and Dynamic World (82%). Additionally, Land surface temperature analysis was accomplished to investigate the relationship between extracted UIS and Land Surface Temperature (LST) across selected cities to show the practical use of proposed MSFI method. Los Angeles, a warm temperate city, showed the highest LST among all five cities. The datasets, along with the GEE and Python codes, are available at https://github.com/mnasarahmad/sls.

Background: Several GLP-1 receptor agonists (GLP-1 RAs) are used to treat type 2 diabetes (T2DM). Their cardio- and renal protective effects and substantial weight loss have been evident and progressively expanded their role in the American Diabetes Association (ADA) guidelines, which are endorsed by the European Association for the Study of Diabetes (EASD). The increased demand leads to a global shortage. Methods: We utilized a repeated cross-sectional design, drawing data from national prescribing databases, to analyze six GLP-1 RAs: Dulaglutide, Enatide, Liraglutide, Lisenatide, Semaglutide, and Tirzepatide. AutoRegressive Integrated Moving Average (ARIMA) models with exogenous variables were applied to assess trends over time and regions. Results: The prescription rates significantly differ between regions. Wales shows the highest prescribing for most GLP-1 RAs. The ARIMA models indicated a significant increase in their prescribing after ADA guidelines 2022 (e.g., Dulaglutide: Post-ADA effect of 15.22, 95% CI: [12.97, 17.47]). Following GLP-1 RAs shortages in July 2023, the prescribing rates, particularly for Semaglutide, increased (Shortage effect: 74.36, 95% CI: [71.92, 76.80]). Model diagnostics, including the Akaike Information Criterion (AIC) and Durbin-Watson statistics, confirmed the robustness of these trends. Conclusion: Informed decisions should be made by considering prescribing trends before and after important events such as the issuing of new guidelines or safety alerts.

The purpose of the study is to analyse and to identify specific structural characteristics of melanocytic nevi, to youth pacients. Using the optical microscope, could be possible a proper describtion related melanocytic nevi. In this study play a significant role, different factors, such as genetic, epigenetic, microbiomic and proteomic factors. Future directions refers to preventive and prophylactic methods. Disease diagnostic and management directions are also important key points.

Millet is a staple food in Northern Nigeria, widely used for the preparation of various millet-based dishes. However, traditional processing methods, such as de-husking, can significantly affect the grain's nutritional and antioxidant properties. This study evaluates the antioxidant composition and nutritional value of four millet varieties cultivated in Northern Nigeria, with a particular focus on the impact of de-husking on these parameters. Our analysis encompasses the proximate composition, phytochemical content, antioxidant vitamins, mineral elements, amino acid profile, and antioxidant activities of the millet samples. The findings reveal that all millet varieties contain significant levels of antioxidant compounds, including phenolics, flavonoids, and vitamins, although their concentrations varied due to genetic differences and environmental factors. Notably, in the mineral composition, higher (p