Emotion detection from the text is an important and challenging problem in text analytics. The opinion-mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.

Low-cost RGB-D cameras are increasingly used in several research fields including human-machine interaction, safety, robotics, biomedical engineering and even Reverse Engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras proved to be among the best suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e. ~160-10000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations on the RealSense D415. In particular, tests are carried out to assess the device performances in the near range (i.e. 100-1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e. the German VDI/VDE 2634 part 2 normative) with a number of literature-based strategies. Performance analysis is finally compared against latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in Reverse Engineering applications.

This study investigated live animal performance and carcass characteristics of Australian prime lambs fed oil based polyunsaturated fatty acid (PUFA) enriched pellets in a feedlot system. The tested hypothesis was that supplementation of lambs with a variety of dietary oil based PUFA enriched pellets would enhance growth and carcass characteristics compared with the control lambs on lucerne only. Seventy-two, 6 months old White Suffolk x Corriedale first-cross prime lambs with an average liveweight (LWT) of 35.7 ± 0.9 kg were allocated to six treatment groups in a completely randomised experimental design. The treatments were: (1) control: lucerne hay only; or lucerne hay plus wheat-based pellets infused with 50 ml/kg DM of oils from (2) rice bran (RBO); (3) canola (CO); (4) rumen protected (RPO); (5) flaxseed (FO) and (6) safflower (SO) dietary sources. All lambs had ad libitum access to lucerne hay and clean fresh water. Supplemented lambs were fed 1kg of pellet/head/day for 10 weeks. Feed intake, final LWT, average daily gain (ADG), body conformation and carcass characteristics of lambs in the supplemented groups were all greater than for the control group. SO lambs had the lowest ADG of 190.3 g/day. RBO and CO treatments had the lowest feed cost per unit gain of AU$ 3.0/kg. Supplemented lambs had similar over the hooks (OTH) incomes that were all higher than that of the control group. This empirical evidence-based data demonstrated that supplementation of lambs with RBO and CO had comparatively lower feed costs without compromising ADG, carcass characteristics and OTH income.

In the fifth generation communication system, millimeter wave (mmWave) networks will coexist with traditional micro wave (μWave) networks, which allows for higher data transmission rate and better user experience. In this paper, we give a comprehensive framework of mathematical models and analytical methods for multi-tier mmWave and μWave hybrid caching networks based on stochastic geometry. We propose an association strategy by assuming average biased-received power association and Rayleigh fading. Accordingly, by using a D-ball approximating blockage model of mmWave networks, expressions of the cell association probability and the coverage probability of the hybrid networks are derived. Also, we use the average successful delivery probability as the performance metric to analyze the existing caching placement strategies. Simulation results validate the accuracy of our analytical conclusions.

Objective: The primary objective of this study was to develop a logistic regression model to predict post-operative in-hospital mortality rates for patients undergoing emergency cardiac surgeries, with the aim of improving predictive accuracy over traditional risk assessment tools and enhancing patient outcomes and clinical decision-making. Methods: Data were collected from 4,855 patients who underwent emergency cardiac surgeries at a tertiary hospital between 2008 and 2017. The analysis incorporated demographic, anthropomet- ric, and clinical factors, including the ASA classification, emergency status, and various preoperative laboratory values. A logistic regression model was developed, and the Elixhauser Comorbidity Index was calculated using standard ICD-10 codes for its comprehensive assessment of comorbidities. Model performance was evaluated using metrics such as AUROC, AUPRC, accuracy, precision, recall, and F1 score. Results: The logistic regression model demonstrated strong predictive performance, with an AUROC of 0.939 and an AUPRC of 0.350. Key pre-operative factors identified included emergency operation status, ASA classification, and preoperative prothrombin time. The model significantly outperformed the traditional ASA classification system, which showed an AUROC of 0.524 and an AUPRC of 0.010. These findings suggest a substantial improvement in predicting post-operative mortality. Conclusion: The logistic regression model significantly improves the prediction of post-operative mortality in emergency cardiac surgeries compared to the ASA classification system. These findings highlight the potential of incorporating comprehensive pre-operative factors into predictive models to enhance clinical decision-making and patient outcomes. Implementing such models in routine clinical practice could lead to more accurate risk assessments, better resource allocation, and improved patient care.

The Balanced Scorecard (BSC) is a widely recognized strategic performance metric that has been extensively studied in large corporations but remains underexplored in the context of Small and Medium-sized Enterprises (SMEs). Understanding the effectiveness of BSC in aligning performance metrics with strategic goals and enhancing performance management in SMEs is critical for fostering growth and competitive advantage. This systematic review aims to evaluate the effectiveness of the Balanced Scorecard in measuring performance and strategy execution within SMEs, particularly focusing on financial and non-financial outcomes such as employee motivation, customer satisfaction, and business sustainability. A systematic review was conducted following the PRISMA framework, incorporating 136 studies sourced from Google Scholar, Web of Science, and SCOPUS. Eligibility criteria focused on publications from the last ten years that address the implementation of BSC in SMEs. Quantitative, qualitative, and mixed-method studies were included, with 49.63% of studies using quantitative methods, 26.67% qualitative, and 23.70% mixed-methods. Risk of bias was assessed using the Chrohane tool, revealing high risks in 90 studies due to incomplete outcome data and selective reporting. The review found that 74% of studies adopted a holistic approach by focusing on all four BSC perspectives (Financial, Customer, Internal Processes, and Learning and Growth). ROI emerged as the most frequently discussed key performance indicator, addressed in 36.84% of publications, while customer satisfaction and employee engagement were covered in 26.32% each. Geographic disparities were observed, with most of the research concentrated in Asia and Europe (40%) and only 8% in Africa. Despite positive outcomes such as improved business sustainability and competitive advantage (38.52% of studies), challenges persisted, including poor strategy execution and limited resources, necessitating careful customization of BSC to SME-specific contexts. The Balanced Scorecard demonstrates significant potential in enhancing performance management and strategic execution within SMEs, particularly when tailored to address unique constraints such as resource limitations. This review highlights the need for further research to standardize key performance indicators and explore sector-specific applications. By addressing these gaps, the BSC can be more effectively utilized to drive growth, agility, and long-term competitive advantage in SMEs.

Generally playing group management performance and financial management performance of sports clubs are assessed separately. We adopt a non-parametric methodology to assess overall performance first conceptualising overall management as a production process comprising of two serially-linked subprocesses; playing group management and financial management. Thereafter, we decompose overall performance to obtain estimates of performance at the subprocess level. Through this procedure it is possible to determine whether it is a sports club’s on-field performance or off-field performance or both may contribute towards its inefficiency, if any, in overall management. Further, a model is developed to determine targets for inefficient clubs to become overall efficient. The method is applied to 18 clubs in Australian rules football league. In 2021 season, the results reveal that on-field performance, on average, is better than off-field performance and variability in off-field performance is higher than that of on-field performance. Observed overall management inefficiency is mainly due to inefficiency in financial management. Results are robust to weighting scheme adopted in overall efficiency configuration.

Forensic speaker recognition plays a key role in criminal investigations, providing important conclusions for the justice system. The mandatory use of protection masks during the COVID-19 pandemic has posed a challenge for forensic speaker recognition, as they act as voice barriers or filters. Although the pandemic has been declared over, analyzing their impact in forensic speaker recognition contributes to a better understanding of the use of coverings as a voice disguise technique. This study aims to evaluate the impact of two types of face masks on an automatic forensic speech recognition system. For this purpose, the Multilingual Forensic Voice Database (FMVD), developed under the CERTAIN-FORS project, funded by the European Union, was used. Comparisons were made between dialog speech samples and reading samples collected without a mask, with a surgical mask, and with an FFP2 mask for both sexes in eight different languages. The performance metrics equal error rate (EER) and cost of likelihood ratio (Cllr) were calculated and analyzed. The results show that the presence of face masks has an impact on the performance metrics. The effect observed varies according to the language spoken, the gender of the speaker, and the type of mask.

The adoption of social media metrics by small and medium-sized enterprises (SMEs) has become a critical tool for engaging customers and enhancing strategic outcomes. However, many SMEs face challenges in effectively tracking and evaluating social media investments, making it difficult to assess the effectiveness of their strategies. This systematic review identifies and analyses key social media activities and performance metrics that contribute to SME strategic success. Through an extensive review of literature from Google Scholar, Web of Science, and Scopus, we selected 138 studies published between 2014 and 2024. The analysis reveals that engagement metrics (e.g., likes, shares), brand awareness (e.g., mentions), and conversion metrics (e.g., click-through rates) significantly influence organizational performance and long-term success. SMEs leveraging detailed engagement metrics reported a 25% increase in customer retention, while brand awareness metrics led to a 30% improvement in customer acquisition. The review highlights areas requiring further research, including a need for sector-specific metric analysis and a deeper understanding of emerging social media platforms. These results offer practical insights for SMEs to enhance their strategic use of social media and adapt to rapidly changing market dynamics.

This study investigated the effects of dietary garlic powder and fermented garlic powder supplementation at 1% and 2% levels on growth performance, digestive tract efficacy, blood biochemistry, immunity, and antioxidant status of Liza ramada (n=225 fish; 86.00±0.42 g) over a 60-day period. Fish fed diets supplemented with both forms of garlic at both levels exhibited significantly improved final body weight, weight gain, specific growth rate, and feed conversion ratio compared to the control group. Digestive enzyme activities (amylase, lipase, and protease) were significantly enhanced in all supplemented groups. Blood biochemical analysis revealed reduced glucose levels and increased total protein in garlic-supplemented groups, with no adverse effects on liver or kidney function markers. Immune parameters, including lysozyme activity, bactericidal activity against Streptococcus agalactiae, alternative complement pathway (ACP), and, respiratory burst (NBT) were significantly enhanced in garlic-supplemented groups, with fermented garlic showing more pronounced effects. Antioxidant enzyme activities (SOD, CAT, and GPx) were also significantly increased in all supplemented groups, particularly in those fed fermented garlic. No significant differences in survival rates were observed among treatments. The results suggest that both garlic powder and fermented garlic powder supplementation, especially at the 2% level, can effectively improve growth, feed utilization, immune function, and antioxidant status in L. ramada. Fermented garlic generally demonstrated superior effects, indicating its potential as a beneficial feed additive in aquaculture. Based on these findings, it is recommended to incorporate fermented garlic powder at a 2% level in L. ramada diets to optimize growth performance and health status. Further research is warranted to investigate the long-term effects and cost-effectiveness of this supplementation strategy in commercial aquaculture settings.

Purpose: This study's primary objective was to demonstrate a link between managerial traits and their significant impact on the financial performance of NIFTY 50-listed firms. These traits were age, gender, educational background, total time spent working. These criteria were seen as independent of the investigation. Design/ Method/ Approach: The data were collected from the official company websites, where the financial reports of the company have been published for the common people's access, and the traits of the management in terms of age, gender, and educational qualification have been covered from the Nifty 50 company’s websites.Findings: Regression analysis in terms of the multiple linear regression method has been applied to highlight the different managerial traits, i.e., gender as a trait of management personnel, that have a significant impact on the financial performance of the Nifty 50 companies, i.e., ROE and ROA.Originality/ Value: This study provided meaningful insights in terms of the different human traits that are prevailing in the management officials, which have a significant impact on the management decisions on the various parameters and have a significant impact on the financial performance of the companies in the Nifty 50.

The article considers the issues of ensuring operational performance and environmental sustainability of marine diesel engines by using biodiesel fuel. The research was conducted on 5S60ME-C8 MAN-B&W Diesel Group and 6DL-16 Daihatsu Diesel marine diesel engines, which were operated using RMG380 petroleum fuel and B10 and B30 biodiesel fuels. The efficiency of biofuel usage was assessed based on environmental (reduced nitrogen oxide concentration in exhaust gases) and economic (increased specific effective fuel consumption) criteria. It was found out that the use of B10 and B30 biofuels provides reduction of nitrogen oxides concentration in exhaust gases by 14.71–25.13 %, but at the same time increases specific effective fuel consumption by 1.55–6.01 %. Optimum fuel injection advance angles were determined that ensure the best thermal energy, economic and environmental performance of diesel engines. The optimum angle of biofuel supply advance is determined experimentally and should correspond to the limits recommended by the diesel engine operating instructions. It has been proven experimentally that the use of biofuel increases the environmental sustainability of marine diesel engines by 13.75–29.42 %. It increases the diesel engines environmental safety in case of emergency situations, as well as accidental and short-term emissions of exhaust gases with an increased content of nitrogen oxides into the atmosphere - phenomena that are possible in starting modes of diesel engine operation, as well as in modes of sudden load changes. It is the increase in the environmental friendliness of marine diesel engines in the case of using biofuel that is the most positive criterion and contributes to the intensity of biofuel use in power plants of sea vessels.

This study investigates the energy profile of a regional hospital in North America using an ASHRAE Level 2 Energy Audit. The audit aimed to assess the efficiency of existing systems, identify areas for improvement, and recommend cost-effective measures to reduce energy consumption and environmental impact. The analysis revealed a well-maintained heating system with efficient boilers. However, a critical concern emerged regarding operational risks due to the lack of redundancy in the cooling system. To address this vulnerability, the study recommends installing an air-cooled chiller for increased operational resilience. The economic feasibility of proposed improvements was evaluated, highlighting the balance between initial investment and long-term savings in energy costs and reduced greenhouse gas emissions. The findings contribute to the discourse on sustainable healthcare practices, emphasizing the importance of energy audits, strategic planning, and redundancy measures for optimized performance and environmental responsibility in hospitals.

In the dynamic realm of HPC (High-performance computing) and cloud-native applications, ensuring optimal network performance for Kubernetes Container Network Interfaces (CNIs) is critical. Traditional manual methods for evaluating network bandwidth and latency are prone to errors, time-consuming, and lack consistency. This paper introduces a novel approach that leverages Ansible to automate and coordinate network performance tests across diverse CNIs, performance profiles, and configurations. By automating these processes, we eliminate the potential for human errors, ensure the ability to replicate the process, and significantly reduce the time required for comprehensive testing. The use of Ansible playbooks facilitates the efficient and scalable deployment, configuration, and execution of performance tests, enabling comprehensive and standardized benchmarking across various environments. The set of Ansible playbooks, which we will make available online, has a significant real-world impact by providing DevOps teams with robust and reliable tools for consistently monitoring and enhancing network performance. This, in turn, enhances the stability and efficiency of cloud-native applications, accelerates the development cycle, and ensures that Kubernetes-based infrastructures can meet the demanding performance requirements of modern applications.

Popular geo-computer vision works make use of aerial imager with sizes ranging from 64 × 64 to 1024 × 1024 pixels without any overlap, although the learning process of deep learning models can be affected by the reduced semantic context or the lack of information near image boundaries. In this work, the impact of three image sizes (256 × 256, 512 × 512, and 1024 × 1024 pixels) and two image overlap levels (no overlap and 12.5% overlap) on the performance of road classification models was statistically evaluated. For this, two convolutional neural networks used in various tasks of geospatial object extraction were trained (using the same hyperparameters) on a large dataset (containing aerial image data covering 8650 km² of the Spanish territory that was labelled with binary road information) under twelve different scenarios, each scenario featuring a different combination of tile size and overlap. To assess their generalisation capacity, the performance of all resulting models was evaluated on a data from novel areas covering approximately 825 km². The performance metrics obtained were analysed using appropriate descriptive and inferential statistical techniques to evaluate the impact of the performance at distinct levels of the fixed factors (tile size, tile overlap, neural network architecture). Statistical tests were applied to study the main and interaction effects of the fixed factors on the performance. A significance level of 0.05 was applied to all the null hypothesis tests. The results were highly significant for the main effects (p-values lower than 0.001), while the two-way and three-way interaction effects among them had different levels of significance. The results indicate that the training of road classification models on images with a higher tile size (more semantic context) and tile overlap (additional border context and continuity) significantly impacts their performance. The best model was trained on a dataset featuring tiles with a size of 1024 × 1024 pixels and a 12.5% overlap and achieved a loss value of 0.0984, an F1 score of 0.8728, and an ROC-AUC score of 0.9766, together with an error rate of 3.5% on the test set.

Working capital management (WCM) is critical because it affects a company's profitability, liquidity, and investment decisions, all of which have an impact on financial performance. As a result, effective and efficient working capital management is an essential component for commercial and service businesses. Given the importance of the commercial and services industries to the Kenyan economy, the goal of this research was to look into the impact of working capital management on the financial performance of these firms, particularly those listed on the Nairobi Securities Exchange (NSE), from 2003 to 2022. Working capital management was measured using the average age of inventory, average collection period, average payment period, and cash conversion cycle, whereas financial performance was measured using return on asset, return on equity, and net operating profit margin. Using panel regression analysis, the results showed that the average inventory age, average collection period, average payment period, and cash conversion cycle were all negatively related to financial performance for NSE-listed commercial and services firms. Based on the findings, it is recommended that Kenyan commercial and services firms adopt prudent optimal working capital management practices in order to improve firm financial performance and maximize shareholder wealth.

Objective: To investigate whether the relationships between acute care occupational therapy (OT) and physical therapy (PT) utilization and community discharge are moderated by functional or physical performance at discharge among individuals hospitalized with traumatic brain injury (TBI). Setting: 14 acute care hospitals in the state of Colorado. Participants: We studied 5,599 adults hospitalized with TBI between June 2018 and April 2021. Design: In a secondary analysis of de-identified electronic health record (EHR) data, multivariable moderation logistic regression models were performed to calculate odds ratios (ORs) for the likelihood of community discharge among patients who utilized OT/PT services. Main Measures: Functional (activities of daily living [ADL]) and physical (mobility) performance at discharge, OT and PT utilization, and community discharge status. Results: Overall, 67% of patients discharged to the community. The mean age of the sample was 55 years (SD = 20 years). Most participants were male (64%) and non-Hispanic white (72%). Mean hospital length of stay was 6 days (SD = 6 days). Both OT and PT utilization (OT: OR= 1.21, 95% CI [1.11, 1.33]; PT: OR= 1.22, 95% CI [1.14, 1.30]) and discharge ADL and mobility scores (ADL: OR= 1.34, 95% CI [1.30, 1.39]; mobility: OR= 1.38, 95% CI [1.33, 1.42]) were significantly and positively associated with community discharge. The OT and PT utilization-by-discharge ADL and mobility interaction terms yielded slightly negative, but statistically significant moderation effects in both models (ORs= 0.99, 95% CIs [0.98, 1.00]); indicating the magnitude of the OT and PT utilization effect diminished as ADL and mobility scores increased. Several sociodemographic characteristics and clinical factors were also independently associated with community discharge in both models (p-values

Recently, research on ultra-high-performance concrete (UHPC) with the characteristics of ultra-high strength, high toughness, and high durability has been actively conducted from materials to actual structural applications. However, research on experimental methods that reflect the unique characteristics of UHPC, which are different from those of normal concrete, is insufficient. To solve the difficulties in applying the compressive creep test method for normal concrete to UHPC and to verify the validity of the recently proposed ASTM C 1856 standard test method, this study performed compressive creep tests on UHPC with variables of specimen size, sustained load intensity, fiber length, and strain measuring method. Two types of cylindrical specimens of ϕ100 × 200 mm and ϕ75 × 150 mm were used for a specimen size variable. The sustained load intensities of 10% to 40% based on the specified compressive strength of 150 MPa were applied. Two types of steel fiber lengths of 13 mm and 19.5 mm in the UHPC mix were used as a variable, and three strain measuring methods of the embedded strain gauge method, the attached strain gauge method, and the strain meter with stud measured the change in length of the UHPC specimens. The experimental results showed that the strain meter method using the standard rod had limitations due to the error in the verification process using the standard rod and the observation error of the measurer, and the sustained load intensity is preferable to 15% to 40% of the actual compressive strength. It was found that it is possible to perform a compressive creep test for UHPC using a specimen with a size of ϕ75 mm or more, and the fiber length did not affect the creep behavior of UHPC up to 19.5 mm. As a result of comparing the UHPC creep test results with the creep prediction model, the B3 model was found to most accurately predict the UHPC creep behavior. The creep coefficient pattern of the B3 model was very similar to the measured results, and well reflected the effects of the mixing ratio and compressive strength of UHPC.

In the rapidly evolving landscape of the AI era, the measurement of Computational Thinking ability among Computer Science (CS) University students is of paramount importance. This study introduces UniCTCheck, a novel method designed to assess the main components of Computational Thinking in CS students. Utilizing two key instruments, namely the web application CTScore and the psychometric scale CTProg, this research aims to precisely evaluate seven core components of Computational Thinking and six programming concepts skills essential for CS students. The study, conducted at Rey Juan Carlos University and Atlantic Technological University Galway, involved a diverse sample of students from different year levels and program specializations. Through a rigorous research design, including sampling strategies and data collection tools, this study seeks to address critical research questions related to measurement of variations in students' Computational Thinking and programming skills by gender, university level, and location. By shedding light on the significance of Computational Thinking and programming in the educational realm, this research contributes to the existing literature and underscores the essential role of computational skills in the modern era.

This study investigates the impact of intellectual capital on the sustainable performance of banks in Saudi Arabia, focusing on the period from 2012 to 2022. Utilizing data from ten major Saudi banks, the research examines how different components of intellectual capital—Human Capital Efficiency (HCE), Structural Capital Efficiency (SCE), and Capital Employed Efficiency (CEE)—influence key financial performance indicators such as Return on Equity (ROE) and Net Profit Margin (NPM). The study is grounded in the resource-based view of the firm, which suggests that a firm’s internal resources can provide a sustainable competitive advantage. The analysis employs panel data techniques, including fixed and random effects models, to assess the relationships between intellectual capital components and bank performance while controlling for variables like bank size, financial leverage, GDP growth, inflation, and the impact of the COVID-19 pandemic. The findings reveal that intellectual capital significantly enhances bank performance in Saudi Arabia, with human capital efficiency showing the most decisive influence on ROE and NPM. Structural capital and capital employed efficiency also contribute positively but vary in their impact on different performance measures. This research contributes to the theoretical and practical understanding of the role of intellectual capital in promoting sustainable banking practices. It underscores the importance of strategic management of intellectual capital in enhancing financial performance and achieving sustainability goals, particularly in the context of Saudi Arabia's Vision 2030. The study's insights are relevant for bank managers and policymakers who leverage intellectual capital for economic diversification and sustainable development.

A set of polymer composite bolted T-joints with novel configuration consisting of internal skeleton and external skin was fabricated using a prepreg-RTM co-curing molding process. Experiments were conducted to study their mechanical properties under bending load. A finite element model with a polymer resin area between the skin and skeleton was established and verified by the experimental results. Then, the damage propagation process and failure mechanism of the joint and the influence of three factors related to the layer characteristics of the skin and skeleton were investigated by the validated models. The results show that the bending stiffness and the yield limit load of the novel composite T-joint are 0.81 times and 1.65 times that of the 2A12 aluminum T-joint respectively, while at only 55.4% of its weight. The damage of the joint initiates within the resin area and leads to a degradation of the joint bending performances. The preferred stacking sequence of the skeleton is [0/+45/90/-45]ns when primarily subjected to bending loads. The decrease in bending performances is within 5% with the inclining angle of the skeleton less than 12 degrees. The more layers of 90° in the skin, the better bending performances of the joints, while the more layer of 0°, the poorer bending performances.

This study examines the dynamic relationship between Green Human Resource Management (Green HRM), Environmental Performance (EP), Green Employee Behavior (GEB), and Environmental Knowledge and Awareness (EKAW) within the Egyptian hospitality sector context. Using Pearson correlation coefficients and regression analyses on a sample of 400 staff members from hotels and tourist villages in Egypt, the study examines Green HRM practices’ influence on EP, mediated by GEB and moderated by EKAW. The findings reveal significant positive correlations between Green HRM practices and these mediators, indicating that comprehensive environmental strategies and incentive management are pivotal in promoting eco-friendly practices among employees. The study further confirms GEB and EKAW’s substantial mediating role in enhancing EP. The results also suggest that while EKAW and GEB independently contribute to EP, their interaction and the role of environmental awareness as a potential moderator warrant further examination. This research contributes to the literature on sustainable business practices by underscoring human resource strategies’ integral role in achieving environmental sustainability goals, highlighting the importance of incentivizing green practices, and cultivating an organizational culture that prioritizes EKAW. These insights are particularly valuable for organizations seeking to enhance their ecological footprint through effective Green HRM practices.

A sintered wick was formed in a heat pipe through the process of sintering a mixture of copper powder with particle sizes of 100㎛ and 200㎛, mixed with a pore-forming agent. On application to the heat pipes. The thermal resistance of heat pipes containing a sintered wick was calculated for different tilting angles (0°, 45°, and 90°) according to the type of powder, thickness of the sintered wick, and the filling ratio of the working fluid. The heat transfer efficiencies were evaluated through temperature tests. The capillary force, which is closely related to heat transfer efficiency, is determined by the effective pore radius resulting from the sintering of the copper powder. Furthermore, it was observed that the heat transfer performance of the heat pipe was at its best when the working fluid filling ratio was 30%. Additionally, the width of the wick was found to have a direct correlation with the heat transfer performance.

Moving object detection plays a crucial role in various applications, particularly in traffic surveillance and collision warning systems. However, the high cost of training and slow detection pose significant challenges to the practicality and real-time performance of existing methods. These challenges hinder the practicality and real-time performance of existing methods. The need for a reliable and efficient moving object detection system prompts the search for cost-effective strategies. Enhancing detection speed without sacrificing accuracy is crucial for real-time applications. Overcoming these challenges is essential to advance the field of moving object detection and improve its applicability in various domains. This paper presents a comprehensive review of the literature on moving object detection, proposes novel strategies to overcome the challenges of high training costs and slow detection. The study emphasizes the importance of reliable speed monitoring systems and advancements in control systems to enhance driver assistance on urban highways. Each frame in the dataset undergoes YOLOv6 processing to detect and classify objects, generating bounding box predictions and class probabilities. Optical flow, specifically the Lucas-Kanade method, is then utilized to compute motion vectors between consecutive frames. These motion vectors facilitate object tracking across frames, enabling refined bounding box positions and improving overall object tracking accuracy. The results demonstrate the effectiveness of the proposed technique in reducing training costs, improving detection speed, and maintaining a high level of accuracy when compared to existing methods. The paper concludes by summarizing the findings and highlighting the potential of the proposed strategy for precise and reliable moving object recognition in real-world traffic scenarios

Bifacial PV modules have become the pervasive choice for utility-scale solar photovoltaic (PV) installations. Understanding bifacial PV performance for commercial deployments is, therefore, critically important. Although there have been studies on the modeling of Bifacial PV performance, to the best of our knowledge, the comprehensive integration of racking and mounting geometry for Bifacial PV performance simulation is yet to be reported. In this work, we modeled the racking and mounting geometry for a field-deployed operational PV array. We then applied optical ray-trace simulation to study the impact of those structures on array performance. The study reveals that the support structures have a considerable impact on the rear surface irradiance and overall bifacial PV performance, amounting to as much as 30% shading in certain situations. The work analyzes the impact of installation parameters such as tilt, height, and row spacing on the bifacial PV array’s performance. Thus, a novel holistic approach to studying the sensitivity of various installation parameters’ impact on bifacial PV array performance, with the racking and mounting structure’s geometry integrated, has been demonstrated through the work. This knowledge and its practice enables site-specific design optimization for utility-scale bifacial solar PV arrays and synergistic PV applications such as agrivoltaics and carports, where maximizing power generation is not necessarily the sole objective.

Long-term storage may reduce the nutritional quality of brown rice, so the present study was aimed to evaluate the nutritive values and application in pig diets. In Exp.1, 18 Landrace × Yorkshire (L × Y) barrows with initial body weight (IBW) of 25.48 ± 3.21 kg were randomly assigned to three treatments, which included corn diet, one-year stored brown rice (BR1) diet and six-year stored brown rice (BR6) diet, to determine the digestible energy (DE) and metabolisable energy (ME) value of stored brown rice. In Exp. 2, 24 barrows (L × Y; IBW: 22.16 ± 2.42 kg) fixed with ileal T-cannula were randomly allotted to four dietary treatments, including corn diet, two stored brown rice diets and nitrogen-free diet, to evaluate the amino acid (AA) digestibility of stored brown rice. In Exp. 3 and 4, 108 crossbred weaned piglets (L × Y; IBW: 9.16 ± 0.89 kg) and 90 crossbred growing pigs (L × Y; IBW: 48.28 ± 3.51 kg) were allotted to 3 treatment diets, including control diet and two stored brown rice diets, respectively, to investigate the application of stored brown rice in weaned piglet and growing-finishing pig diets. Results showed that: 1) The DE and ME of corn and stored brown rice showed no significant differences (P > 0.05), while the apparent ileal digestibility (AID) of arginine, histidine, aspartic acid, and the standardized ileal digestibility (SID) of arginine and histidine were higher in stored brown rice compared to corn (P < 0.05). 2) Compared with the control diet, the stored brown rice diets showed no significant effects on growth performance, nutrient apparent total tract digestibility (ATTD) and serum biochemical indices (P > 0.05), but decreased digestive enzymes activities in duodenum, jejunum and ileum of piglets (P < 0.05). 3) Compared with the control diet, the stored brown rice diets showed no significant effects on growth performance, carcass traits, meat quality, fatty acids profiles in the longissimus dorsi muscle of growing-finishing pigs (P > 0.05). In conclusion, the brown rice stored for 6 years under good conditions has no obvious changes in available energy and nutrient values. Although it may reduce digestive enzyme activity in small intestine of the piglets, the stored brown rice showed no obvious adverse effect on growth performance and meat quality, and can be effectively used in pig diet.

Background and aims: Only one cohort study exists on the incidence of the risk of malnutrition (RM) in older adults, though numerous cross-sectional –and that cohort research– have reported several risk factors associated with the prevalence and incidence of this condition. However, alterations of body composition and impaired physical performance as exposition variables of RM have not been explored. This study assessed the incidence of RM and determined its association with excess fat mass, low total lean tissue, gait speed, and handgrip strength as exposition variables for RM in community-dwelling older adults. Methods: This is a secondary analysis in older adults (≥60 years old) derived from the study “Frailty, dynapenia, and sarcopenia in Mexican adults (FraDySMex)” a prospective cohort project conducted from 2014 to 2019 in Mexico City. At baseline, volunteers underwent body composition analysis and physical performance tests. Several covariates were identified through comprehensive geriatric assessment. At baseline and follow-up, RM was assessed using the long form of the mini nutritional assessment (MNA-LF) scale. Associations between the exposition variables and RM were assessed by multiple logistic regression. Results: The cohort included 241 subjects. Average age was 75.6±7.8 years; 83.4% were women. The mean follow-up period was 4.1 years, during which 28.6% of subjects developed RM. This condition was less likely to occur in subjects with excess fat mass index, even after adjusting for several covariates. RM was also more likely to occur in subjects with low total lean tissue index, according to both an unadjusted model and after adjusting for the corresponding variables. Regarding the association between RM and gait speed, RM was also more likely to occur in subjects with low gait speed according to both the unadjusted and adjusted models. Similar results were found for RM in relation to low handgrip strength; however, after adjusting for the associated covariates, models 1 and 2 no longer reached the level of significance. Conclusion: The risk of malnutrition diagnosed by MNA-LF was significantly less likely to occur among subjects with excess fat mass, but possibility was significantly higher in those with low total lean tissue and low gait speed after 4.1 years of follow-up in community-dwelling older adults. These results highlight that excess fat mass, low total lean tissue, and low gait speed are first than risk of malnutrition and not vice versa.

The objective of this study was to investigate the effects of body weight gain during the cashmere non-growing period on cashmere production performance and secondary hair follicle activity, to provide a theoretical basis for appropriate supplementary feeding of cashmere goats. Fifty Inner Mongolian cashmere goats aged 2-4 years old were randomly selected and weighed in May and September 2019, respectively. Based on body weight gain between the two weights, the experi-mental ewe goats were divided into two groups: 0-5.0 kg group (n = 30) and 5.0-10.0 kg group (n = 20). Skin samples and cashmere samples were collected. Results of Pearson correlation analysis showed that body weight gain during the cashmere non-growing period had a moderate and strong positive correlation with cashmere yield (P = 0.021) and cashmere staple length (P = 0.002) respectively, but did not correlate with cashmere diameter (P = 0.254). Compared with cashmere goats with a body weight gain of 0-5.0 kg, cashmere goats with a body weight gain of 5.0-10.0 kg had a 17.10% increment in cashmere yield (P = 0.037), 8.09% increase in cashmere staple length (P=0.045) and had no significant difference in cashmere diameter (P = 0.324). Results of Pearson correlation analysis showed that there was a strong positive correlation between body weight gain and the population of active secondary hair follicles in the skin of cashmere goats (P < 0.01). Compared with cashmere goats with a body weight gain of 0-5.0 kg, cashmere goats with a body weight gain of 5.0-10.0 kg had an increase in the population of active secondary hair follicles (P < 0.05). In conclusion, the body weight gain during the cashmere non-growing period had a sig-nificant effect on secondary hair follicle activity and cashmere production performance in cash-mere goats. Since body weight gain reflects nutritional level to a certain extent, this study suggests that nutritional manipulations such as supplementary feeding during cashmere non-growing periods can increase cashmere production performance. However, specific nutritional manipula-tions during the cashmere non-growing period need further research to increase cashmere pro-duction performance.

. Identifying students who might have difficulty in their course of studies ahead of time is crucial. There can be many reasons for performance issues, such as personality, family, social, and/or economic. We advocate that educational systems should use machine learning to predict students’ performance based on performance factors. This would allow educational professionals and institutions to put in place a preventive plan to help students towards achievements of their educational goals and success. In this chapter, we propose a student performance prediction method and evaluate its performance. We provide a taxonomy of performance factors that help to gauge students performance from different perspectives and give insights on the categories and features that have more significant impact on students’ performance. The results of this work can be used by education institutions to put in place a student-centric approach to tackle performance issues before they create long-term effects on student’s life. In addition, it will help education policymakers to introduce a tailored approach for the population in specific areas.

Sport centers are significant energy consumers. This article outlines the engineering design for a comprehensive energy performance upgrade of the indoor sport hall in Arkalochori, Greece, and presents the projected results. The indoor sport hall constitutes a major sport facility in the mainland of Crete, hosting a broad cluster of sport municipal activities and the official basketball games of the local team in the 2nd national category. Having being constructed in the mid ‘90s, the facility exhibits very low thermal performance, with considerably high U-factors for all constructive elements (from 4 to 5 W/m2∙K), still use of diesel oil for indoor space heating and domestic heat water production and ineffective, old lamps and luminaries covering the lighting needs of the facility. The energy performance upgrade of the indoor sport hall was holistically approached, through all possibly applicable passive and active measures: insulation of opaque surfaces and replacement of openings, installation of new stone wool panels, installation of heat pumps for indoor space conditioning, elimination of diesel oil for any final energy use, installation of a solar-combi system for domestic hot water production, upgrade of all indoor and outdoor lighting equipment, installation of solar tubes on the main sport hall roof for natural lighting and installation of a photovoltaic plant on the same roof for the compensation of the remaining electricity consumption. With the proposed measures, the indoor sport hall is upgraded to zero energy facility. The payback period of the investment was calculated at 26 years, on basis of the avoided energy resources procurement cost. This work has been funded by the Horizon 2020 project with the acronym “NESOI” and was awarded with the public award of the “Islands Gamechanger” competition of the NESOI project and the Clean Energy for EU Islands initiative.

Due to the high impact of the building sector on the environment, a growing interest focuses on insulating materials able to ensure good thermo-acoustic performance for the building envelope, in a sustainable and circular economy perspective. In this context, Moroccan natural gypsum was mixed with natural waste local materials. Thermal and acoustic properties of the samples were measured; they were compared to those of synthetic- and mineral-based gypsum plasters, manufactured with the same technique. A Small Hot Box apparatus was used for thermal characterization, whereas acoustic performance was investigated by means of a Kundt’s Tube. Natural and synthetic additives involved a reduction in density and an improvement in thermal performance. Conductivity values in the 0.181 – 0.238 W/mK range were obtained, depending on the type of natural additive, with respect to 0.275 – 0.323 W/mK of mineral-based gypsum plasters. The acoustic measurements showed that all the composites have similar performance in terms of acoustic absorption, whereas high Transmission Loss values were obtained for the natural additives (TL = 35 – 59 dB). Petiol of Palm and Stipa Tenacissima were found as the materials able to improve both thermal and acoustic properties.

Currently Single Board Computers (SBCs) are sufficiently powerful to run Real-Time Operating Systems (RTOSs) and applications with real-time attributes and requirements. SBCs serve as a foundation in Industrial Internet of Things (IIoT). The NXP Semiconductors produces a series of SBCs based on ARM-processors for a variety of industrial applications. The continuous increase in real-time data generated by IoT devices adds further research issues about the efficiency of such systems and applications. The purpose of this research was to investigate the timing performance of an NXP TWR-K70F120M device with μClinux OS on running concurrently tasks with real-time features and constraints. A custom-built multithreaded application with specific compute-intensive sorting and matrix operations was developed and applied to obtain measurements in specific timing metrics, including task’s execution time, threads waiting time, and response time, under different threads variations. The performance of this device was additionally benchmarked and validated against favorite platforms, a Raspberry Pi4 and BeagleBone AI SBCs. The experimental results showed that this device stands well both in terms of timing and efficiency metrics. Execution times were quite lower than the others, by approximately 56% in the case of two threads, and by 29% in the case of thirty-two threads configurations.

To ensure high-quality care, operationalize resilience and fill the knowledge gap regarding how to improve the prerequisites for resilient performance, it is necessary to understand how adaptive capacity unfolds in practice. Coronavirus 2019 stress-tested complex healthcare systems, and intensive care was driven to an unprecedented escalation. The main aim of this research was to explain the escalation process of intensive care from a microlevel perspective, including expressions of resilient performance, intervening conditions at the micro-meso-macrolevels and short- and long-term consequences. A secondary aim was to provide clinical recommendations regarding how to optimize the prerequisites for resilient performance in intensive care. A grounded theory methodology was used. First-person stories from different healthcare professionals (n70) in two Swedish regions were analyzed using the constant comparative method. A novel conceptual model of the escalation process of intensive care emerged, and 41 clinical recommendations concerning the optimal prerequisites for resilient performance were identified. Escalation was conceptualized as a transition from threatening chaos to temporary order through a complex process of adaptation. To prepare for the future, the components space, stuff, staff, system and science with associated continuity plans must be implemented, anchored and communicated to actors at all levels of the system.

Epidemiological studies have shown that consuming olive oil rich in phenolic bioactive compounds is associated with a lower risk of neurodegenerative diseases and better cognitive performance in aged populations. Since oxidative stress is a common hallmark of age-related cognitive decline, incorporating exogenous antioxidants could have beneficial effects on brain aging. In this review, we firstly summarize and critically discuss the current preclinical evidence and the potential neuroprotective mechanisms. Existing studies indicates that olive oil phenolic compounds can modulate and counteract oxidative stress and neuroinflammation, two relevant pathways linked to the onset and progression of neurodegenerative processes. Secondly, we summarize the current clinical evidence. In contrast to preclinical studies, there is no direct evidence in humans of the bioactivity of olive oil phenolic compounds. Instead, we have summarized current findings regarding nutritional interventions supplemented with olive oil on cognition. A growing body of research indicates that high consumption of olive oil phenolic compounds is associated with better preservation of cognitive performance, conferring an additional benefit, independent of the dietary pattern. In conclusion, the consumption olive oil rich in phenolic bioactive compounds has potential neuroprotective effects. Further research is needed to understand the underlying mechanisms and potential clinical applications.

In this paper, we develop a tractable mathematical model and emulation framework for communicating information through water using acoustic signals. Water is considered as one of the most complex mediums to model owing to its vastness and variety in characteristics depending on the scenario, what kind of water-body (lakes, rivers, tanks, sea etc.) and geographical location of the water-body is considered. Our proposed mathematical model involves the concept of damped harmonic oscillators to represent the medium (water) and Milne’s oscillator technique to map the interaction between the acoustic signal and water. Wave equations formulated for acoustic pressure and acoustic wave velocity are used to characterize the travelling acoustic signal. Signal strength, phase shift and time-delay generated from the mathematical model are fed to a Simulink-based emulator framework to generate channel samples and channel impulse responses. The emulator uses wide sense stationary uncorrelated scattering (WSSUS) assumption and finite sum-of-sinusoids (SOS) with uniformly distributed phase to generate the channel samples. Using the emulator platform it is possible to generate amplitude variation profile, Doppler shift and spread experienced by any travelling signal through different underwater communication scenarios. Such an emulator platform can be used to simulate different communication scenarios, underwater network topologies, data to train different learning models and predict performance of different modulation, multiplexing, error correction and multi-access techniques for underwater acoustic communications (UWAC) systems.

Steam boiler has a significant role in the industrial sector. The failure in boiler tubes significantly reduces the availability of the plant. Furthermore, deposits on the inner tube wall contribute to such failure by changing the thermal resistance of the wall, which causes a significant increase in the tube wall's temperature and, consequently, lower allowable stress. The presented thermal-fluid model is developed for the water tube. The model is implemented using ANSYS FLUENT to estimate the wall maximum water tube wall temperature considering the deposits layer and then determine the minimum thickness of the tube wall via ASME standards to assure safe operation. The model is verified via a mesh independence test. The minimum tube thickness is recommended and justified given the thermo-mechanical performance of the water tubes at several operating loads of 50, 75, and 100 %. These loads are implemented by different convective heat transfer coefficients of flue gases under different deposit thicknesses, thermal conductivity, and flue gas temperature. The present work contributes to the literature by recommending the minimum thickness of the water wall tubes and highlighting the thermo-mechanical effect of the deposits on water tube resilience under different operating conditions.

Lithium batteries and an increasing focus on CO₂ reduction have become an integral part of daily life and business for many people. Boron and boron compounds have been widely studied together in the history and development of lithium batteries. With a broad examination of battery components and systems but a boron-centric approach to raw materials, this review seeks to summarize past and recent studies on the following: which boron compounds are studied in lithium battery, in which parts of lithium batteries, what improvements are offered for battery performance, and what improvement mechanisms can be explained. The uniqueness of boron and its extensive application beyond batteries contextualizes the interesting similarity with studies on batteries. The paper predominantly focuses on lithium-ion batteries (LIBs) but also mentions other lithium batteries. At the end, the article aims to predict prospective trends for future studies that may lead to the successful and extensive use of boron compounds on a commercial scale.

There are evidences of shared biological mechanisms between obesity and hypertension during childhood in the adulthood, and loads of research literatures have proven that, it will cost the economies and health of nations profoundly if neglected. The Prevention and early diagnosis of cardiovascular risk factors such as overweight and hypertension is an essential strategy for control, effective treatment and prevention of its’ complications. The aim of the study is to assess the effect of school based Exercise and Life style Motivation Intervention (SEAL-MI) on adolescent's cardiovascular risk factors and academic performance. An experimental study was conducted among 1005 adolescents - 520 and 485 adolescents were randomly selected for control and study group, respectively. Demographic details and the data related to dietary habits, physical activity and sleep quality were collected by a structured interview questionnaire. The study group adolescents were given the SEAL-MI for six months which includes a school based rope exercise for 45 minutes per day for 5 days a week and a motivation intervention related to dietary habits, physical activity and sleep. Post-test 1 and 2 were done after 3 and 6 months of intervention. The prevalence of overweight among adolescents was 28.73% and prehypertension was 9.26%. Among overweight adolescents, the prevalence of prehypertension was found to be very high (32.25%). In post intervention, there was a significant reduction in weight, BP (p=0.000) and improvement in dietary habits, physical activity, sleep (p=0.000) and academic performance. A significant positive correlation was found between BMI and SBP (p=0.000) and BMI and academic performance (p=0.003). The linear regression analyses revealed that the gender (ß: 0.47, 95% CI: 0.39, 0.81), age (ß: 0.39, 95% CI: 0.17, 0.46), family income (ß: 0.2, 95% CI: 0.41, 0.5), residence (ß: 0.19, 95% CI: 0.01, 0.27) and type of family (ß: 0.25, 95% CI: 0.39, 0.02) had the strongest correlate with the BMI of the adolescents. Also, Mother’s education Mother’s education (ß: 0.35, 95% CI: 0.18, 0.59) had the strongest correlate with the SBP of the adolescents. In contrast, the DBP was negatively persuaded by age (ß: -0.36, 95% CI: 1.54, 0.29) and gender (ß: -0.26, 95% CI: 1.34, 0.12) of the adolescents. Regular practice of rope exercise and lifestyle modification such as diet, physical activity and sleep quality among adolescents prevent and control childhood CVD risk factors like overweight, hypertension. The SEAL-MI may lead to age appropriate development of adolescents and improves their academic performance and quality of life. Giving importance to the adolescents from urban habitat, from affluent nuclear family and catching them young will bring significant change in the disease burden.

Currently, most middleware application developers have two choices when designing or implementing API services: They can either stick with REST or explore the emerging GraphQL technology. Although REST is widely considered the standard method for API development, GraphQL is believed to overcome the main drawbacks of REST, especially data fetching issues. Yet, doubts remain as there are no experimental studies with convincing results to evaluate the performance of the two services. In this paper, we propose a research methodology to evaluate the performance of REST and GraphQL API services, which includes two main contributions. The first contribution is that the performance evaluation of the two services is conducted in the real operation of a massive and intensively accessible management information system. The second contribution is the fair and independent performance evaluation results obtained for both API services. The performance evaluation was studied using basic measures of QoS, including response time, throughput, CPU load, and memory usage. The results show that REST is still faster in response time and throughput, while GraphQL is very efficient in resource utilization, i.e., CPU load and memory utilization. Therefore, GraphQL is the right choice when data requirements change frequently and resource utilization is the most important consideration. REST is used when some data is frequently accessed called by multiple requests.

The Performance Assessment for California Teachers (PACT) is a high stakes summative assessment that was designed to measure pre-service teacher readiness. We examined the inter-rater reliability (IRR) of trained PACT evaluators who rated 19 candidates. As measured by Cohen’s weighted kappa, the overall IRR estimate was .17 (poor strength of agreement). IRR estimates ranged from -.29 (worse than expected by chance) to .54 (moderate strength of agreement); all were below the standard of .70 for consensus agreement. Follow up interviews of 10 evaluators revealed possible reasons we observed low IRR, such as departures from established PACT scoring protocol, and lack of, or inconsistent, use of a scoring aid document. Evaluators reported difficulties scoring the materials that candidates submitted, particularly the use of Academic Language. Cognitive Task Analysis (CTA) is suggested as a method to improve IRR in the PACT and other teacher performance assessments such as the edTPA.

This study was aimed to investigate the effect of xylan depolymerizing enzyme namely endo-xylanase (Xyn) combined with debranching enzymes namely arabinofuranosidase (Afd) and feruloyl esterase (FE) on digestion, growth performance and intestinal volatile fatty acid profile of piglets. The in vitro experiments were firstly conducted to examine the enzymological properties of Xyn, Afd and FE, the synergy among these enzymes, together with the effect of combination of these enzymes on digestion of piglet diet. The in vivo experiment was then implemented by allocating 270 35-d-old postweaning piglets into 3 treatment groups: control group, Xyn group and (Xyn+Afd+FE) group. Each group had 6 replicates (15 piglets/replicate). The results revealed a satisfying thermostability and pH stability of Xyn, Afd and FE. Combination of Xyn, Afd and FE had a superiority (P < 0.05) over Xyn alone and its combination with Afd or FE in promoting degradation of different bran fibers rich in arabinoxylan (Abx). Treatment with combination of Xyn, Afd and FE had advantages over Xyn alone to induce increasing trends (P < 0.10) of in vitro digestibility of dietary nutrients (dry matter, crude protein, crude ash and gross energy) and piglet growth performance (average daily gain, final body weight and feed efficiency), concurrent with a reduction (P < 0.05) of diarrhea rate and increases (P < 0.05) in cecal acetic acid, butyric acid and total volatile fatty acids concentrations as well as pH value of piglets. Collectively, combination of Xyn, Afd and FE was efficient in benefiting degradation of Abx in brans, as well as improving digestion, growth performance and intestinal volatile fatty acid profile of piglets.

This study aimed to assess the differences in the importance and performance of health awareness in Korean middle school students according to the types of online physical education classes they attended during the coronavirus disease-2019 pandemic. Overall, 583 participants were selected using a convenience sampling method; the data were obtained through an online survey using Google forms. Frequency analysis, reliability analysis, independent sample t-test, and importance-performance analysis were performed. First, the differences between importance and performance were found to be the most for sleep and physical activity management, and the least for disease and hygiene management. In addition, both the groups demonstrated higher importance and performance for hygiene and disease management. There were significant differences in the importance and performance of all the sub-factors. Second, hygiene, disease management, and mental health management were found in quadrant I in both the groups, while physical activity, sleep, and dietary habit management were in quadrant III. No factors were in quadrant II and IV.

Arabidopsis contains eight different h-type thioredoxins (Trx) being distributed in different cell organelles. Although Trx h2 is deemed to be localized in mitochondria, its subcellular localization and function remains a matter of debate. Here, Trx h2 localization and function were investigated using cell fractionation studies and reverse genetics. Differential centrifugation and immunodetection showed the Trx h2 protein to be distributed to the microsomal fraction rather than to mitochondrial preparations. To analyze whether Trx h2 has different roles than mitochondrial Trxs, Arabidopsis mutants lacking Trx h2 were compared with mutants deficient in mitochondrial Trx o1 and double mutants with joint deficiencies in both Trxs. Under constant medium light, trxh2 grew as the wild type, while trxo1 and trxo1h2 mutants showed impaired growth. This was accompanied by differences in the metabolite profiles. The trxo1 and trxo1h2 mutants clustered differently from the wild type during the night, revealing a decrease in ascorbate and glutathione redox states. In fluctuating light intensities, genotypic differences in growth rates were attenuated. Compared to the wild type, the fluctuating-light induced decrease in the NADPH/NADP ratio was diminished in the mutants, with the trxo1h2 double mutant showing the strongest effect. This was accompanied by an increase in the photosynthetic efficiency of the trxo1h2 mutant, specifically in the high light phases of fluctuating light. Conclusively, these results support the view that Trxs o1 and h2 are localized in different subcellular compartments, and have different effects on ascorbate and glutathione redox states and growth in medium light, but cooperatively affect NADP(H) redox state and photosynthetic efficiency in fluctuating light.

Lattice and code cryptography can replace existing schemes such as Elliptic Curve Cryptography because of their resistance to quantum computers. In support of public key infrastructures, the distribution, validation and storage of the cryptographic keys is then more complex to handle longer keys. This paper describes practical ways to generate keys from physical unclonable functions, for both lattice and code based cryptography. Handshakes between client devices containing the PUFs and a server are used to select sets of addressable positions in the PUFs, from which streams of bits called seeds are generated on demand. The public and private cryptographic key pairs are computed from these seeds together with additional streams of random numbers. The method allows the server to independently validate the public key generated by the PUF, and act as a certificate authority in the network. Technologies such as High performance computing, and graphic processing units can further enhance security by preventing attackers to make this independent validation when only equipped with less powerful computers.

The purpose of this study is to analyse the feasibility of using waste from granite gang saws (GCW-GS) to manufacture ultra-high performance, steel-fibre reinforced concrete (UHPFRC). These machines cut granite blocks by abrasion using a steel blade and slurry containing fine steel grit. The waste generated by gang saws (GCW-GS) contains up to 15% Fe2O3 and up to 5% CaO. This is the main difference from the waste produced by diamond saws (GCW-D). Consequently, the second objective of this study is to compare the results of the waste obtained with gang saws with that from diamond saws, in order to determine the influence of iron and calcium oxides. The waste from cutting granite with gang saws was used in different percentages to replace micronized quartz powder of natural origin in the manufacture of UHPRFC. All the test specimens were analysed to determine their compressive strength, elasticity modulus, flexural strength and indirect tensile strength. The final conclusion is that wastes from both gang saws and diamond saws can be used to manufacture UHPFRC with an improvement in the mechanical properties up to a 35% replacement. The results for GCW-GS are better, mainly due to the pozzolanic effect of the iron dioxide. For higher percentage replacements the mechanical properties are close to the control concrete with small decreases.

A significant attention has been made to the accurate detection of diabetes which is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the IoT e-healthcare environment. Internet of Things (IOT) has emerging role in healthcare services which delivers a system to analyze the medical data for diagnosis of diseases applied data mining methods. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a IOT based diagnosis system using machine learning methods, such as preprocessing of data, feature selection, and classification for the detection of diabetes disease in e- healthcare environment. Model validation and performance evaluation metrics have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning Decision Tree algorithms, such as Ada Boost and Random Forest are also used for feature selection and compared the classifier performance with wrapper based feature selection algorithms also. Machine learning classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the Decision Tree algorithm based on selected features improves the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high as compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and GL, DPF, and BMI are more significantly important features in the dataset for prediction of diabetes disease. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would be effectively detected diabetes disease and can easily be deployed in IOT wireless sensor technologies based e-healthcare environment.

This paper analyses the health and performance of 12-stage axial compressor of the TV3-117VM/VMA turboshaft operated in a desert environment. The results of the dimensional control of 4,800 worn blades are analysed to model the wear process. Operational experience and numerical simulations are used to assess the effectiveness of an Inlet Particle Separator. Numerical modal analysis is performed to generate the Campbell diagram of worn blades and identify resonant blade vibration which can lead to high cycle fatigue (HCF). It is shown that the gradual loss of the stall margin over time determines the serviceability limits of compressor blades. Recommendations setting out go / no-go criteria are made to maintenance and repair organisations.

Motor functions are deteriorated by aging. Some conditions may magnify this deterioration. To examine whether hemodialysis (HD) process would negatively impact gait and balance beyond diabetes condition among mid-age adults (48-64 years) and older adults (65+ years). One hundred and ninety-six subjects (age=66.2±9.1 years, body-mass-index=30.1±6.4 kg/m², female=56%) in 5 groups were recruited: mid-age adults with diabetes undergoing HD (Mid-age HD+, n=38) and without HD (Mid-age HD-, n=40); older adults with diabetes undergoing HD (Older HD+, n=36) and without HD (Older HD-, n=37); and non-diabetic older adults (Older DM-, n=45). Gait parameters (stride velocity, stride length, gait cycle time, and double support) and balance parameters (ankle, hip, and center of mass sways) were quantified using validated wearable platforms. Groups with diabetes had overall poorer gait and balance compared to the non-diabetic group (p<0.050). Among people with diabetes, the HD+ had significantly worsened gait and balance when comparing to the HD- (Cohen’s effect size d=0.63-2.32, p<0.050). Between-group difference was more pronounced among older adults with the largest effect size observed for stride length (d=2.32, p<0.001). Results suggested that deterioration in gait speed among the HD+ was correlated with age (r=-0.440, p<0.001), while this correlation was diminished among the HD-. Interestingly, results also suggested that poor gait in the Older HD- related to poor balance, while no correlation was observed between poor balance and poor gait among the Older HD+. Using objective assessments, results confirmed that the presence of diabetes can deteriorate gait and balance, and this deterioration can be magnified by HD process. Among non-HD people with diabetes, poor static balance described poor gait. However, among people with diabetes undergoing HD, age was a dominate factor describing poor gait irrespective of static balance. Results also suggested feasibility of using wearable platforms to quantify motor performance during routine dialysis clinic visits. These objective assessments may assist in identifying early deterioration in motor function, which in turn may promote timely intervention.

Cloud computing refers to applications delivered as services over the Internet. Cloud systems employ policies that are inherently dynamic in nature and that depend on temporal conditions defined in terms of external events, such as the measurement of bandwidth, use of hosts, intrusion detection or specific time events. In this paper, we investigate an optimized resource management scheme named v-Mapper. The basic premise of v-Mapper is to exploit application-awareness concepts using software-defined networking (SDN) features. This paper makes three key contributions to the field: (1) We propose a virtual machine (VM) placement scheme that can effectively mitigate the VM placement issues for data-intensive applications; (2) We propose a validation scheme that will ensure that a service is entertained only if there are sufficient resources available for its execution and (3) We present a scheduling policy that aims to eliminate network load constraints. An evaluation was carried out with various benchmarks and demonstrated that v-Mapper shows improved performance over other state-of-the-art approaches in terms of average task completion time, service delay time and bandwidth utilization. Given the growing importance of supporting large-scale data processing and analysis in datacentres, the v-Mapper system has the potential to make a positive impact in improving datacentre performance in the future.

This article reports the preparation of a novel functionalized magnetic nanoadsorbent through an addition reaction between Fe3O4 nanoparticles coated with allyl silica gel and 4-pyridinyl ethylene, detailed characterization of Fe3O4@SiO2-yl-VP was conducted. Among them, in the infrared spectrum, we can easily see that the absorption peak of C=C stretching vibration at 1660 cm-1 in the raw material disappears after the addition reaction, indicating the successful grafting of polymer on the surface of silica gel. The appearance of N element in the EDS spectrum also proves the successful completion of the addition reaction and the successful synthesis of Fe3O4@SiO2-yl-VP. At pH=5 and pH=7, it only takes half an hour for Fe3O4@SiO2-yl-VP to achieve maximum adsorption capacities of 85.06 and 73.78 mg/g for Hg (II) and Pb (II), respectively. The adsorption process conforms to the Langmuir model and pesudo first and pesudo second order kinetic models, and can be reused 11 times, demonstrating excellent reusability.

One of the goals of Oman vision 2040 is to attain a 30% of renewable energy mix, mainly from solar and wind energy projects for electricity generation by 2030, in alignment with the net zero emissions commitment by 2050. The adoption of residential rooftop solar PV installations supports achieving this target. This paper aims to find the suitable regions for rooftop solar PV installations by considering the factors, such as temperature levels, dust accumulation, humidity and population density. In the Middle East countries like Oman, dust from sandstorms significantly affects the performance of PV systems and is an important derating factor to consider when evaluating their performance as discussed in this paper. This paper starts by finding the suitable regions in Oman for solar PV projects based on geographical factors and then proceeds to find the best locations in the selected region. A rooftop solar PV system is designed, analyzed its performance and Levelized Cost of Electricity (LCOE) was calculated for smart bus stop load located in the selected region’s university of technology and applied sciences (UTAS) campus using analytical method. The results obtained through analytical method were compared with System Advisor Model (SAM) software. Key findings from the study: The northern region of Oman was identified as the most suitable region to install the solar PV systems. In the northern regions, Ibri is the best location for rooftop solar PV projects compared to other locations. However other locations are also competitively suitable for solar PV projects. The LCOE of all UTAS branch locations ranges from 8.5 ¢/kWhr to 9.7¢/kWhr. Due to local weather conditions, Salalah has a higher LCOE of solar PV system than other location. After finding the most suitable locations, attractive rooftop policies in solar PV of successful countries are discussed, based on that few strong strategies are suggested to develop rooftop solar PV projects in Oman.

Data governance has emerged as a critical factor in determining the effectiveness and long-term stability of IT systems in today’s data-driven environment. It encompasses frameworks that guide organizations in managing data collection, storage, processing, usage, and sharing, while ensuring alignment with business objectives, compliance with regulations, and adherence to ethical standards. However, the complexities associated with data governance, particularly in the context of new technological models, pose significant challenges. This systematic review aims to explore the role of data governance in enhancing data quality, system stability, and regulatory compliance. It seeks to identify common issues, effective practices, and strategic implications for implementing data governance frameworks across diverse industries, focusing particularly on small and medium enterprises (SMEs). We conducted a systematic review of 68 eligible studies published between 2014 and 2024, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. The included studies were assessed for risk of bias using the Cochrane Risk of Bias Assessment Tool. The review covered a range of study types, including qualitative (60%), quantitative (19%), and mixed-methods (21%) research, to provide a comprehensive understanding of data governance's impact on IT performance. The findings highlight that data governance significantly influences system success by improving data quality, operational efficiency, and regulatory compliance. Although qualitative studies predominantly emphasized in-depth analysis, a moderate representation of empirical validation through quantitative studies was observed. Common challenges identified include data migration issues, resistance to change, and budget constraints, particularly during system upgrades. Effective practices involve aligning data governance with business strategies, employing hybrid models, and regularly updating governance policies to adapt to technological advancements such as AI and big data. This review provides practical recommendations for IT managers and policymakers to enhance data governance frameworks. These include phased implementation approaches, the establishment of data governance committees, and the use of performance metrics to monitor data quality and system resilience. While significant progress has been made, further research is needed to address gaps related to emerging technologies and the unique challenges faced by SMEs in developing regions.

Efforts to achieve carbon neutrality, which aims to reduce the net carbon emissions to zero by decreasing carbon emissions from human activities and increasing carbon absorption, are actively underway. Additionally, the search for clean energy alternatives to fossil fuels has become a global research trend. This paper presents research on metal-air batteries, focusing on the development of energy supply technologies that do not generate carbon emissions during power generation and require less space for power generation compared to existing renewable energy sources. The proposed Mg-Air Battery (MAB) in this study uses magnesium as the metal anode and theoretically offers a maximum open-circuit voltage of 3.1V and a high energy density of 6.8kWh/kg. While previous research has primarily focused on designing small-capacity cells and maximizing the performance of metal anodes, this study differentiates itself by designing a large-capacity MAB cell and optimizing its electrical performance. For the large-capacity cell design, the weight, shape, and size of the anode were designed based on MAB performance factors, and research was conducted on manufacturing methods to optimize the performance of the air cathode. Furthermore, to enhance usability and extend the lifespan of the MAB cell, it was designed to allow electrolyte circulation, and the electrolyte circulation performance was verified through simulations of fluid flow within the cell. Based on the study of the power performance of the newly designed large-capacity MAB cell, the feasibility of con-structing a kW-class system using multiple Mg-Air battery cell stacks was confirmed.

To obtain high quality grain refiner, a new Al-5Ti-1B-1RE master alloy grain refiner was synthesised by melt-matching method. Thermodynamic calculations and kinetic analyses, which were seriously missing from the study of key issues during the preparation process, were investigated. The microstructure and properties, comparison of the refining effect of different grain refiners on industrial pure aluminium and its refining mechanism were analysed with the help of X-ray diffraction (XRD), optical microscopy (OM), scanning electron microscopy (SEM) and energy spectroscopy, transmission electron microscopy (TEM) and an electronic universal testing machine (CSS-44100). The experimental results show that the second-phase particles in the Al-5Ti-1B-1RE master alloy matrix synthesised by the melt-matching method are mainly TiB2、 A13Ti、Ti2A120RE, etc. The magnitude of the free energy ΔG of the synthesis reaction is calculated to be ΔGTiB2<ΔGAl3Ti<ΔGTi2A120RE. When the temperature is low (undercooling ΔT is large), the nucleation rate N mainly depends on the kinetic atomic diffusion activation energy Q, while the thermodynamic nucleation work becomes a secondary factor, and vice versa. The industrial pure aluminum refined by the new grain refiner has almost transformed from coarse columnar crystals to fine equiaxed crystals, with an average grain size of 70.2 μm, which was 36.18% and 20.66% smaller than that refined by domestic and imported Al-Ti-B wire master alloy grain refiner, and its mechanical properties of tensile strength σb was increased by 11.94%, 8.29%, and elongation at break δ was improved by 31.79%, 17.41%, respectively. The main mechanism for refining pure aluminum in Al-5Ti-1B-1RE master alloy is the formation of TiA13 on TiB2 particles, the release of RE atoms from Ti2A120RE phase surrounding TiAl3, and the partial transformation into TiA13 phase, which promotes the dual nucleation refinement mechanism and the pinning effect of rare earth elements on grain boundaries.

The production of a ton of steel in an integrated steel plant generates of approximately 1 ton of ferrous slag. A great part of ferrous slags is recycled in civil and road construction etc., but a huge amount is still dumped. Recycling ferrous slag as cement precursor was considered the upcycling route. Regulation (EU) 2024/1252 requires the Member States to provide information, until November 2026, on the critical raw materials (CRM) amount in their secondary resources and on quantification methods used for. This study addresses two synergic objectives: ensuring the reliability of XRFS results and screening for CRM in ferrous slags based on XRFS outcomes. The main novelty of the paper is the way of ensuring the reliability of the XRFS results based on weighted arithmetic mean and on the maximum likelihood approach. Secondly, the XRFS measurements carried on ferrous slags demonstrate that they contain significant amounts of CRM like Ba, Sr, Y, etc. XRFS cannot detect light CRM. Our preliminary LIBS measurements on ferrous slags disclosed the presence of Li and Be. The drawbacks of the XRFS technique impose further research to develop an integrated XRFS, LIBS and XRD procedure for comprehensive and trustworthy CRM screening in extractive waste piles.

The EFQM Model, established in 1991, serves as a framework for organizational excellence, helping achieve sustainable success through an integrated management system. The model's evo-lution is driven by the need to adapt to global trends, threats, and technological innovations such as: digital transformation, employee engagement and well-being, remote work, agile performance management, diversity, equity and inclusion (DEI), sustainability, AI, and big data. The new EFQM Model 2025, presented in Istanbul in June 2024, introduces significant changes, emphasiz-ing sustainability, innovation, and technology. These changes are analyzed based on the logic of three questions: Why, How, and What. Why: There is a necessity for better alignment of organizations with contemporary challenges through flexibility, risk resilience, and innovation capabilities in a dynamic business environ-ment, including alignment with the UN Sustainable Development Goals (SDGs). How: The model introduces an integrated approach to performance management and transfor-mation, incorporating advanced technologies such as AI and data analytics. What: Implementing the EFQM Model 2025 enhances organizational adaptability, innovation, and flexibility, improving financial, operational, and strategic outcomes. Better alignment with sustainability goals leads to long-term value and social responsibility. Improved stakeholder engagement methods enhance understanding and satisfaction. The use of advanced technologies supports innovation and operational efficiency, while better strategic and operational performance and risk management ensures greater stability and the ability to anticipate future challenges.

Age-related brain deterioration and Alzheimer's disease (AD) pose major challenges as misfolded proteins are deposited in the brain, leading to a decline in cognitive abilities and disruption of neural networks crucial for memory. This article addresses the impact of disrupted spike-timing-dependent plasticity (STDP) mechanisms in Alzheimer's disease and highlights the role of Transcranial magnetic stimulation (TMS) in the study of cortico-cortical spike-timing-dependent plasticity. Amidst this adversity, the role of STDP emerges as a key player in memory formation by synchronizing neural activity. Progressive neuronal dysfunction decreases neural plasticity and impairs manual dexterity and speed, echoing more general problems in elderly people. The findings highlight the potential of neurally-driven cortico-cortical paired associative stimulation (ccPAS) to revitalize neural plasticity and improve motor performance in elderly people, bridging the gap between age-related challenges and Alzheimer's research. This work opens a door to potential rehabilitation strategies for aging populations by unlocking the aging brain's ability to improve motor performance.

Background/Objectives: This study looks at how a kinematic chain exercise regimen that targets the lower, core, and upper body affects university shot put participants' shoulder muscle strength and throwing efficiency. This study fills an apparent research void on shot put training approaches by presenting a comprehensive kinematic chain workout program. It was anticipated that this method would improve performance the most, considering the complex biomechanical require-ments of the sport. Methodology: Athletes, seventy in age (19.87 ± 1.31 years), were assigned into two groups at random: experimental (n = 35) and control (n = 35). While the control group carried on with their usual training, the experimental group participated in an 8-week kinematic chain training program, pre- and post-training evaluations were carried out to evaluate shot put throwing ability, shoulder muscle strength, and participant satisfaction with the exercise regimen. Results: demonstrated that, when compared to the control group, the athletes in the kinematic chain program had a significantly improved throwing distance (p = 0.01) and shoulder muscle strength (p = 0.01). Furthermore, there was a significant difference (p = 0.005) in the athletes' satisfaction levels with the workout program among those in the experimental group. This implies that a kinematic chain-focused strategy improves performance and emphasizes the significance this type of training is for shot put athlete performance. Conclusion: As an improvement forward in sports science, the study's finding promotes the inclusion of kinematic chain workouts in shot put training programs and calls for more research on kinematic chain-based approaches in a variety of sports.

The rapid spread of African swine fever virus (ASFV), causing severe and often lethal disease in domestic pigs and Eurasian wild boar, continues to be a threat for pig populations and dependent industries. Despite scientific achievements that deepen our understanding of ASFV pathogenesis, alternative transmission routes for ASFV remain to be elucidated. We previously demonstrated the efficient transmission of ASFV from infected boars to naïve recipient gilts via artificial insemination, thereby highlighting the importance of surveillance in boar semen prior to shipment. Since accurate and reliable detection of even low amounts of ASFV in boar semen is key for disease prevention and control, we established a suitable diagnostic pipeline to efficiently detect ASFV genome in boar semen. Here, we assessed sensitivity, specificity and overall performance of various routine nucleic acid extraction kits as well as qPCR protocols in detecting ASFV genome in blood and semen of boars. Feasibility of respective kits and methods for future use in boar studs was also considered. Variability in sensitivity mostly concerned samples with low to very low amounts of ASFV genome. Ultimately, we defined a well-suited pipeline to precisely detect ASFV genome in boar semen, as early as 2 days post ASFV infection.

This paper aims to demonstrate how the detection of communities for epidemiological networks can be used to reduce the dimensionality of optimal control problems. A range of planted partition stochastic blockmodels are generated, where the underlying communities in the model vary in detectability. A high-performance computing workflow is then used to simulate and estimate reduced model dynamics for the networks, which in turn is used to find optimal control solutions.

Distracted driving is a major cause of road traffic crashes in Yaoundé. Partly due to scarcity of enforcement, lack of evidence and investigation of distraction safety performance indicator (SPI), hindering evidence-based intervention. This study aimed to address this evidence gap by evaluating the distraction SPI using proven methodology. Data on distracted driving (handheld mobile device; interaction; eating/smoking/drinking) were collected from roadside observation on 36 randomly selected road sections carefully spread to cover the city. SPI were computed and weighted with traffic volume to ensure representativeness of values. A total of 41,004 drivers were observed (38,248 in car; 1,116 in van; 977 in truck; 663 in bus). The prevalence of distracted driving in Yaoundé is 13.69% for the three distractions type combined. The prevalence is 7.84% for interaction, 4.89% for handled mobile device usage and 0.96% for eating/smoking/drinking. Leveraging these insights, a seven year (2024 - 2030) fighting strategy aiming at halving the prevalence was developed. The strategy contains intervention including on legislation/enforcement, which have been proven effective. This study, pioneer in Yaoundé, provide stakeholders with evidence of the issue and measures to implement and can also serve when developing road safety strategy.Future research should consider investigation at national level.

The study aimed to know the direct effect of strategic leadership on the quality of service represented by wireless communications services, in addition to knowing the indirect effect of strategic leadership on the quality of service when using performance as an intermediary variable. This research was conducted in government institutions in the Republic of Yemen. Represented by the offices of the Ministry of Communications and Information Technology in five Yemeni cities, it targeted senior management employees, boards of directors, and middle management concerned with strategic planning and strategic leadership. To measure and evaluate the factors of strategic leadership and service quality in Offices of the Yemeni Ministry of Communications Ministry of Communications and Information Technology of the Republic of Yemen. Moreover, performance. A questionnaire was formulated and distributed to the study sample, and data was collected through questionnaires for 150 individuals in some offices of the Yemeni Ministry of Communications. This study uses structural equation model analysis from the Partial Least Squares program through the Smart Plus 4 programs. The study reached a set of results, including that all models show a direct, positive and significant effect of strategic leadership on the quality of service represented by the quality of services in the offices of the Ministry of Communications. As well as the positive relationship between them, as well as the direct and positive impact of performance on the quality of service, as well as the positive and significant relationship between them. While the results indicated a positive and significant indirect effect of strategic leadership on service quality, there is also a positive and significant indirect relationship between strategic leadership on service quality when using performance as an intermediary variable.

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

With the constant growth of Software Defined Radio (SDR) technologies in wireless communications related fields the need for efficient ways to test and evaluate the Physical Layer (PHY) protocols developed for these technologies in real life traffic scenarios become more critical. This paper proposes a software testbed that enhances the creation of network environments that allow feeding GNU Radio applications with test traffic in a simple way and through an interoperable interface. This makes possible the use of any traffic generator, existent one or custom built to evaluate the GNU Radio application. In addition, the paper proposes an efficient way to collect PHY layer specific monitoring data that improves the performance of the critical components of the message delivery path by employing the Protocol Buffers library. The paper considers the entire testing and evaluation ecosystem and demonstrates how PHY layer specific monitoring information is collected, handled, stored, and processed as time series allowing complex visualization and real time monitoring.

Online streaming contents are creating greater service uncertainty, as consumers need to experience such contents before making a decision to continue to purchase them. Few studies have investigated the interaction between eWOM (online word-of-mouth) and online streaming content service characteristics in online streaming contents’ performance and explained the way this interaction can promote service characteristics’ role in service performance or remedy service uncertainty attributable to these characteristics. Thus, this paper examines the interaction between eWOM and service (webtoon) characteristics (i.e., author experience, genre (drama or fantasy), completion, transfer to pay service, and publication time (Wednesday)) on online streaming contents’ service performance measures such as publication period and gamification of contents. Author experience and genre interact with the number of reviews to affect gamification. The transfer to pay service interacts crucially with review ratings and the number of reviews to influence both publication period and gamification. Online streaming contents completion and publication time are interacting factors with review ratings that affected publication period. Service providers need to cope with service uncertainty in furthering online streaming contents service by considering service characteristics as well as customers’ responses through eWOM.

In medico-administrative database, certain prognostic factors cannot be taken into account. The main objective was to estimate the performance of two models based on two databases: Epi-thor clinical and medico-administrative databases. For each of the two databases, we randomly sampled a development dataset with 70% of the data and a validation dataset with 30%. Performance of models was assessed by Brier score, the area under the receiver operating characteristic (AUC ROC) curve and the calibration of the model. For Epithor and medico-administrative databases, the development dataset included 10,516 patients (with resp. 227 (2.16%) and 283(2.7%) deaths) and the validation dataset included 4,507 patients (with resp. 93 (2%) and 119 (2.64%) deaths). 15 predictors were selected in the models (including FEV, Body Mass Index, ASA score and TNM stage for Epithor). The Brier score values were similar in the models of the two databases. For validation data, the AUC ROC curve was 0.73 [0.68-0.78] for Epithor and 0.8 [0.76-0.84] for medico-administrative databases. The slope of the calibration plot was less than 1 for the two databases. This work shows the good performances of a model developed from a medico-administrative database, despite the absence of clinical variables used in practice by surgeons, such as FEV1, ASA score or TNM stage.

Long span cable-stayed bridge is the main bridge type with a span of more than 400 meters. It is generally designed as a double tower long-span structure with good spanning capacity and economic performance. Wind resistance safety performance is the main index to control the long-span cable-stayed bridge structure. During the operation of long-span cable-stayed bridge structure, because the service life of the cable is far less than the design life of the structure, the wind resistance performance of the structure will inevitably deteriorate significantly, which will seriously affect the structural service performance of symmetric cable-stayed bridge. Based on symmetric reliability theory, this paper takes flutter and static wind stability of long-span cable-stayed bridge structure as the main design control index, uses positive reliability theory to calculate the reliability index of symmetric cable-stayed bridge structure, uses inverse reliability theory to calculate the safety factor of symmetric cable-stayed bridge structure, and evaluates the wind resistant time-varying performance of long-span cable-stayed bridge structure by comprehensively considering the reliability index and safety factor. Taking a practical project of a long-span cable-stayed bridge as a specific case, the wind resistant time-varying reliability of the bridge during its operation for more than 30 years is analyzed, and the parameter sensitivity is analyzed. The results show that the wind resistance performance of the cable-stayed bridge structure is obviously affected by the cable, and the degradation of the cable performance will have an important impact on the wind resistance time-varying performance of the structure, especially the critical wind speed of the structure has obvious time-varying characteristics. The safety factor and reliability index can objectively evaluate the wind resistance time-varying performance of the long-span cable-stayed bridge structure.

Individuals diagnosed with systemic arterial hypertension (SAH) are considered risk groups for COVID-19 severity. This study assessed differences in cardiac autonomic function (CAF) and functional capacity (CF) in SAH individuals without COVID-19 infection compared to SAH individuals post-COVID-19. Participants comprised 40 SAH individuals aged 31 to 80 years old, grouped as SAH with COVID-19 (G1; n=21) and SAH without COVID-19 (G2; n=19). CAF was assessed via heart rate variability (HRV), measuring R-R intervals during a 10-minute supine period. Four HRV indices were analyzed through symbolic analysis: 0V%, 1V%, 2LV%, and 2UV%. FC assessment was performed by a 6-minute walk test (6MWT). G1 and G2 showed no difference between significant age, anthropometric, clinical and medication. G2 exhibited superior 6MWT performance, covering more distance (522±78 vs. 465±59 meters, p&lt;0.05). Specifically, G2 demonstrated a moderate, positive correlation between 6MWT and the 2LV% index (r=0.58; p&lt;0.05). Shorter walking distances were observed during 6MWT in SAH individuals post-COVID-19. However, the study did not find impaired cardiac autonomic function in SAH individuals post-COVID-19 compared to those without. This suggests that while COVID-19 impacted FC, CAF remained relatively stable in this population.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

This study examines the effect of a corporate citizenship-oriented high performance work system (CC-oriented HPWS) and the interactive influence of CC-oriented HPWS and person-organization fit (PO fit) on creativity at the individual level. Our analysis of field data collected from 303 employees in two branch companies with a two-phase collection method provides empirical support for our theoretical model. The results indicate that high performance work system (HPWS) reflecting corporate citizenship (CC) correlates positively with individual creativity. In addition, the moderating effect of PO fit is significant in the relationship between CC-oriented HPWS and creativity at the individual level. On this basis, this paper discusses the theoretical and practical implications of these findings for future research.

In order to investigate the relationship between government policy, the service and design of rural crowdsourcing spaces, the self-efficacy of returning youth, and the innovation performance of businesses, this study focuses on gathering data by distributing questionnaires to returning youth in rural crowdsourcing spaces and developing a structural model. The results demonstrate how important government policies are in encouraging youth entrepreneurship in their local communities. It has the potential to enhance both the development of rural crowdsourcing spaces and the self-efficacy of young entrepreneurs, thereby enhancing the innovative capabilities of local entrepreneurial businesses. Therefore, in order to encourage economic development in rural areas, the government should improve pertinent support measures.

The mobile payment has been emerging as an alternative to cash and credit cards and is rapidly evolving around the world. This study integrated the general model of innovation diffusion theory (IDT) with the technology acceptance model (TAM) to examine the impact of near-field-communication (NFC) mobile payment environment on the restaurant operating performance (ROP). This paper used convenience sampling to distribute questionnaires to restaurant owners and managers in order to investigate the impact of NFC mobile payment environment on ROP. A total of 279 valid questionnaires were collected. The empirical results show that sales growth, cost saving, flexibility, accessibility and trust &amp; safety had significantly positive impacts on ROP. After considering the restaurant size as a moderator for analysis, there are only two constructs- accessibility and trust &amp; safety had significant impacts on ROP. This empirical finding could provide restaurateurs with a reference to improve ROP by increasing the mobile payment environment.

The objective of this research is to analyze the effect of determinants of Business ethics (HR management ethics, ethics in corporate governance, and ethics in sales & marketing) on the organizational performance of the high-tech industry. The study further examines the influence of multiple serial mediations of human, structural, structural, and relational capital on the connection between Business ethics and organizational performance. Additionally, this research examines how technological change moderates the relationship between Business ethics and organizational performance. Data was collected from the information technology sector in various cities of South Asian countries through structured questionnaires. PLS-SEM modeling was used via Smart-PLS 4.0 software for analysis. The hypothesized & modified measurement and structural model were validated using the HTMT matrix, Fornell-Larcker criterion, R-square values, f-square values, outer loading, construct reliability and validity, path coefficient analysis, Blindfolding, and predictive relevance. The research results suggest that ethics in corporate governance, HR management ethics, and ethics in sales & marketing have a notable positive effect on business ethics, leading to a substantial and affirmative impact on the organizational performance of the high-tech industry. The findings further suggested that dimensions of intellectual capital (human capital, structural capital & relational capital) significantly moderate between exogenous and endogenous constructs. Moreover, technological change significantly & positively moderates the relationship between Business ethics and organizational performance of the high-tech industry. In conclusion, this research provides insights into how these variables interact and what potential mechanisms act upon them. Organizations can make informed decisions about improving their performance and ethical standards by understanding these relationships. This research offers substantial theoretical and managerial implications to research scholars and industry practitioners.

Promoting the use of solar energy resources has always faced the challenges of instability and supply-demand mismatch. The key to solving these issues is to efficiently store and utilize solar energy resources using high-performance heat storage devices. This study designed a high-performance shell-and-tube phase change thermal storage device and established a numerical model using ANSYS software to summarize the device's dynamic melting law. To verify the accuracy of the numerical simulation, a performance testing platform for the phase change thermal storage device was built to investigate the impact of factors such as inlet water temperature, inlet water flow rate, type of heat storage, and initial temperature of the device, and to reveal the change law of the device's performance. The results show that the inlet water temperature has the most significant impact on the device's heat storage and release performance. When the device's heat storage or release is used for heating, changing the inlet water flow rate has a weak and limited effect on the device's performance. However, when the device's heat release is used to provide domestic hot water, increasing the makeup water temperature and reducing the inlet water flow rate can significantly improve the device's effective heat release. Furthermore, based on the experimental validation of the model's correctness, this study further simulated and studied the impact of different factors on the device's heat storage process to optimize its structural design and provide technical references for the device's actual operation and installation. The results show that the placement of fins has a negligible effect on the performance of the heat storage device while reducing the fin spacing and increasing the fin thickness can significantly improve the melting efficiency of the phase change material (PCM). Additionally, the heat storage characteristics of the device are significantly better in the vertical installation mode than in the horizontal installation mode. This study provides theoretical guidance and technical references for the design and use of phase change thermal storage devices.

The best business relationships are built as relationships between people and organizations that work together to achieve common goals. In the current digital era, B2B collaboration relationships are essential for business success. The response time to customer requests can be a key factor in maintaining these relationships and obtaining a competitive advantage. The use of VBA automation for repetitive tasks can help speed up this process, but it is important that computer operators have medium knowledge about using VBA macros. This work deals with issues related to improving response times to requests received by email, using a macro program for repetitive tasks. The empirical study was conducted on an average of 50 daily emails for order confirmation, with an average writing time of 68 s per email, resulting in a potential reduction in working time for the operator of approximately 1 h/day, only for order confirmation. The results show a significant improvement in working time for computer operators, using VBA buttons for repetitive tasks.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diadnosis (CAD) schemes of medical images. This study aims to investigate and compare advantages and potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3,000 digital mammograms is assembled in which 1,496 images depict malignant lesions and 1,054 images depict benign lesions. Two CAD schemes are developed to classify breast lesions. The first scheme is developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme is built based a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes are trained and tested using a 10-fold cross-validation method. Several score fusion methods are also investigated to classify breast lesions. CAD classification performance is evaluated by the area under ROC curve (AUC). Results: ResNet50 model-based CAD scheme yields AUC = 0.85±0.02, which is significantly higher than radiomics feature-based CAD scheme with AUC = 0.77±0.02 (p < 0.01). Additionally, fusion of classification scores generated by two CAD schemes does not further improve classification performance. Conclusion: This study indicates that using deep transfer learning is more efficient to develop CAD schemes and enables to yield higher lesion classification performance than CAD schemes developed using radiomics-based technology.

Burrs form due to plastic deformation of materials during machining processes, such as milling and drilling. Deburring can be very difficult when the burrs are not easily accessible for removal. In this study, abrasive flow machining (AFM) was adopted for deburring the edges of milling specimens. Based on the experimental observations on AL6061 specimens, the deburring performance was characterized in terms of flow speed, the local curvature of the streamline near the burr edge, and shear stress. A new objective function that can predict the extent of deburring is proposed based on these characteristics and validated through experiments. Based on this new objective function, a prediction of deburring performance on the burr edge of the intersecting holes with offset was performed. The results between the predicted and experimental observation results were in reasonable agreement.

We have suggested earlier a new sustainable method for permafrost thermal stabilization that combines passive screening of solar radiation and precipitation with active solar-powered cooling of the near-surface soil layer thus preventing heat penetration in depth. Feasibility of this method has been shown by calculations, but needed experimental proof. In this article, we are presenting the results of soil temperature measurements obtained at the experimental implementation of this method outside of the permafrost area which actually meant higher thermal loads than in Polar Regions. We have shown that near-surface soil layer is kept frozen during the whole summer, even at air temperatures exceeding +30°C. Therefore, the method has been experimentally proven to be capable of sustaining soil frozen even in more extreme conditions than expected in permafrost areas. In addition to usual building and structure thermal stabilization, the method could be used to prevent the development of thermokarst, gas emission craters, and landslides; greenhouse gases, chemical, and biological pollution from the upper thawing layers at least in the area of human activities; protection against coastal erosion; and permafrost restoration after wildfires. Using commercially widely available components, the technology can be scaled up for virtually any size objects.

Heat enhancement is a topic of great interest nowadays due to its different application in industries. Porous material also known as metallic foam plays a major role in heat enhancement at the expenses of pressure drop. Flow in channels demonstrate the usefulness of this technology in heat extraction. In our current study, a porous strip attached to the channels walls is proposed as an alternative for heat enhancement. The thickness of the porous strip was varied for different Reynolds number. By maintaining laminar regime and using water as fluid, we determined an optimum thickness of porous material leading to the highest performance evaluation criterion. In our current study with the aspect ratio being the porous strip thickness over the channel width, an aspect ratio of 0.2 is found to be the alternative. A 40% increase in heat enhancement is detected in the presence of porous strip when compared to a clear channel case for a Reynolds number equal to 200 and improve further as the Reynolds number increase accordingly.

The effect of stress on task performance is complex, too much or too little negatively affects performance; and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor and workload related stress were more dominant in the medial and lateral PFC respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of negative affect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.

In the design of multi-loop Networked Control Systems (NCSs) wherein each control system is characterized by heterogeneous dynamics and associated with certain set of timing specifications and constraints, appropriate metrics need to be employed for the synthesis of control and networking policies to efficiently respond to the requirements of each control loop. Majority of the design approaches for sampling, scheduling and control policies include either time-based or event-based metrics to perform pertinent actions in response to the changes of the parameters of interest. We specifically focus in this article on Age-of-Information (AoI) as a recently-developed time-based metric and threshold-based triggering function as a generic event-based metric. As the NCS model, we consider multiple heterogeneous stochastic linear control systems that close their feedback loops over a shared-resource communication network. We investigate the co-design across the NCS, and discuss the pros and cons with AoI and ET approaches in terms of asymptotic control performance measured by linear-quadratic Gaussian (LQG) cost functions. In particular, sampling and scheduling policies combining AoI and stochastic event-triggered metrics are proposed. It is argued that pure AoI functions that generate decision variables solely upon minimizing the average age irrespective of control systems dynamics may not be able to improve the overall NCS performance even compared with pure randomized policies. Our theoretical analyses are successfully validated through several simulation scenarios.

Although different assessments and evaluations of Dual-Miller cycle performed, specified output power and thermal performance associated with engine determined. Besides, multi objective optimization of thermal efficiency, Ecological Coefficient of performance ( ) and Ecological function ( ) by the mean of NSGA-II technique and thermodynamic analysis performed. The Pareto optimal frontier obtaining the best optimum solution is chosen by fuzzy Bellman-Zadeh, LINMAP, and TOPSIS decision-making techniques. Based on the results, performances of dual-Miller cycles and their optimization are improved.

Objective: Lipids are secreted into milk as bilayer-coated structures: milk lipid globules (MLGs). Adipophilin (ADRP) and perilipin 3 (TIP47) are associated with MLGs in human breast milk; however, the role of these proteins in milk lipid secretion is not fully understood. The aim of the study was to investigate levels of ADRP, TIP47 and total lipid content in human breast milk, their mutual correlations and dynamics during lactation. Research Methods & Procedures: Milk samples from 22 healthy lactating women (Caucasian, Central European) were collected at five time points during lactation (1–3, 12–14, 29–30, 88–90 and 178–180 days postpartum). Mass spectrometry-based method was used for quantification of ADRP and TIP47 in the samples. The gravimetric method was used to determine milk total lipid content. Results: We observed distinctive trends in ADRP, TIP47 levels and lipid content in human breast milk during the first 6 months of lactation. We also found a significant association between lipid content and ADRP, lipid content and TIP47, and ADRP and TIP47 concentrations in breast milk at all sampling points. Moreover, we derived an equation for estimating the mean lipid content of milk based on ADRP concentration in human breast milk. Conclusions: A mass spectrometry-based method was developed for quantifying ADRP and TIP47 in human breast milk. Strong mutual correlations were found between ADRP, TIP47 and total lipid content in human breast milk.

The sustainable development of our world has gain particular attention of a wide range of decisional factors, civil society, business sector, and scientific community, seeing that the prosperity of people and society is possible with the aid of sustained and inclusive economic growth of all countries and regions. Educational environment has a decisive impact on changes in the way that societies are coping with national, regional, and global challenges and opportunities brought by sustainable development. Looking at the implications of HE on the progress of society, the paper addressed the lack of HE institutional capacity to integrate the principles and practices of sustainable development into all aspects of education and learning. The scope of research problem was bounded on the capability of HEI as organization and school to act as entrepreneurial university by combining the scope of its responsibility within the value chain through a practical and effective mechanism needed to align the strategy with sustainable development goals (SDGs). Embarking on the path of SDGs requires HEI to design, launch, implement, and customize specific processes architectures to govern the advance of sustainability approach. The authors applied the process scoping diagram to capture and conceptualize the educational model needed to guide the HEI through the process of change to embrace sustainability into organizational culture and daily operations. It has been used the SIPOC method (Supplier, Input, Process, Output, Customer) with Visio software tool to articulate processes relationships embedded in the educational model of HEI. The benefits relied on the organized view of the work processes needed to be performed to incorporate SDGs into the strategy of any entrepreneurial HEI. Finally, the authors shared their views on the scalability of the model which may be customized and harmonized in accordance with different HE circumstances and priorities. Implementing the proposed educational model requires long-term institutional commitment, transparency, continuous performance improvement, and communicating the strategy for SDGs and its achievements to wider stakeholders.

In this paper we introduce a computational model for the simulation of hypervelocity impact (HVI) phenomena which is based on the Discrete Element Method (DEM). Our paper constitutes the first application of DEM to the modeling and simulating of impact events for velocities beyond 5 kms⁻¹. We present here the results of a systematic numerical study on HVI of solids. For modeling the solids, we use discrete spherical particles that interact with each other via potentials. In our numerical investigations we are particularly interested in the dynamics of material fragmentation upon impact. We model a typical HVI experiment configuration where a sphere strikes a thin plate and investigate the properties of the resulting debris cloud. We provide a quantitative computational analysis of the resulting debris cloud caused by impact and a comprehensive parameter study by varying key parameters of our model. We compare our findings from the simulations with recent HVI experiments performed at our institute. Our findings are that the DEM method leads to very stable, energy–conserving simulations of HVI scenarios that map the experimental setup where a sphere strikes a thin plate at hypervelocity speed. Our chosen interaction model works particularly well in the velocity range where the local stresses caused by impact shock waves markedly exceed the ultimate material strength.

The critical role of manufacturing operations in enhancing the performance of Small and Medium Enterprises (SMEs) has become increasingly evident in recent years. This systematic review examines how manufacturing operations influence the performance of SMEs, analysing findings from 69 diverse research papers published between 2014 and 2024. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, the review focuses on key operational strategies, including lean manufacturing and advanced manufacturing technologies, assessing their effectiveness in improving business outcomes such as efficiency, productivity, and financial performance. The methodology included a thorough review of the research methods used in the studies, identifying potential biases, gaps, and highlighting the need for diverse approaches. While manufacturing operations have led to improved productivity and increased efficiency for many SMEs, challenges such as limited resources, lack of technical expertise, and resistance to change remain prevalent. Notably, a reporting bias was observed, with 48% of the literature composed of quantitative studies, 30% being mixed-method studies, and only 20% and 1% representing qualitative and exploratory studies, respectively. This imbalance suggests an overemphasis on quantitative methods, limiting the range of ideas and understanding available. Key strategies such as lean manufacturing and advanced technologies were shown to enhance productivity by 15% to 25% in SMEs. By adopting a broad approach, this review ensures that the findings are applicable across various sectors and geographies, offering valuable insights into best practices and challenges across diverse economic contexts. The broad scope also allows for the identification of under-researched areas, fostering a more comprehensive understanding of how manufacturing operations contribute to SME success. The review provides a decision-making framework for business leaders and policy recommendations to foster SME growth in a competitive, globalized market.

Fucoidans, known for their diverse biological properties such as anti-inflammatory, antiviral, antitumor, and immune-stimulatory effects, have recently gained attention for their potential benefits in exercise endurance, muscle mass, and anti-fatigue. However, the mechanisms by which fucoidans enhance exercise performance are still unclear. To investigate these effects, we administered 400 mg/kg/day of fucoidan extract derived from Undaria pinnatifida to sixty-four C57BL/6J mice over ten weeks. We evaluated changes in running activity, mitochondrial-related gene expression in skeletal muscle, and alterations in the intestinal microbiome. Our results showed that fucoidan supplementation significantly increased daily running distance and muscle mass by 25.5% and 10.4%, respectively, in mice on a standard chow diet, and with more modest effects observed in those on a high-fat diet (HFD). Additionally, fucoidan supplementation led to a significant increase in beneficial gut bacteria, including Bacteroides/Prevotella, Akkermansia muciniphila, and Lactobacillus, along with a notable reduction in the Firmicutes/Bacteroidetes ratio, indicating improved gut microbiome health. Mechanistically, fucoidan supplementation upregulated the mRNA expression of key genes related to mitochondrial biogenesis and oxidative capacity, such as COX4, MYH1, PGC-1α, PPAR-γ, and IGF1, in both standard chow and HFD-fed mice. Our findings suggest that fucoidan supplementation enhances exercise performance, improves muscle function, and positively modulates the gut microbiome in mice, regardless of diet. These effects may be attributed to fucoidans' potential prebiotic role, promoting the abundance of beneficial gut bacteria and contributing to enhanced exercise performance, increased muscle strength, and improved recovery.

Digital recruitment systems have revolutionized the hiring paradigm, imparting exceptional efficiencies and extending the reach for both employers and job seekers. This investigation scrutinizes the efficacy of classical machine learning methodologies alongside advanced large language models (LLMs) in aligning resumes with job categories. Traditional matching techniques, such as Logistic Regression, Decision Trees, Naïve Bayes, and Support Vector Machines, are constrained by the necessity of manual feature extraction, limited feature representation, and performance degradation, particularly as dataset size escalates, rendering them less suitable for large-scale applications. Conversely, LLMs like GPT-4, GPT-3, and LLAMA, adeptly process unstructured textual content, capturing nuanced language and context with greater precision. We evaluate these methodologies utilizing two datasets comprising resumes and job descriptions to ascertain accuracy, efficiency, and scalability. Our results reveal that while conventional models excel with structured data, LLMs significantly enhance the interpretation and matching of intricate textual information. This study highlights the transformative potential of LLMs in recruitment, offering insights into their application and future research avenues.

In order to cope with the limited supply of feed for global animal production, there is a pressing need to explore alternative feed resources. Orange pulp, a by-product of agriculture and industry, has shown potential to positively or neutrally impact pig productive performance when included in their diet. However, there is a lack of research on the effects of fermented navel orange pulp (FNOP) on pig growth and productive performance. This study aimed to investigate the effects of FNOP as a dry matter substitute on pig’s growth performance, carcass characteristics, meat quality, meat nutrition, and serum biochemical indicators. The experiment involved 128 finishing Tibetan pigs, divided into four feed treatment groups, with varying levels (0%, 5%, 10% and 15%) of FNOP replacing dry matter in the basal diet. The results indicated that substituting 5% to 15% FNOP had no adverse effects on pig growth performance. However, at a 15% substitution rate, there was a decrease in serum growth hormone and IGF-1 levels, along with an increase in feed-to-gain ratio. A 10% FNOP replacement notably increased the eye muscle area of pigs. Additionally, 5% and 10% FNOP substitutions reduced the drip loss of pork. The study also found that substituting 5% to 15% FNOP increased unsaturated fatty acids and umami nucleotide contents in pork, as well as raised serum total protein and uric acid (nucleotide metabolism related-product) levels. These findings suggest that moderate FNOP substitution might enhance meat quality and maintain growth and productive performance in Tibetan pigs by improving protein synthesis and nucleotide metabolism, while also reducing feed costs. The optimal substitution ratio identified was 10%.

Nitraria tangutorum Bobrov is a berry shrub with white flowers and red fruits, which grows in the deserts in the Tibetan Plateau, Mongolia, and western China. Its fruit N. tangutorum fruit (NTF) contains various bioactive compounds, with anti-fatigue, anti-inflammatory, and neuroprotective functions. However, the high saccharide content of NTF makes it unsuitable for diabetic patients. In this study, we fermented NTF to obtain N. tangutorum fermented juice (NTJ), and N. tangutorum fermented residue (NTR), which are suitable for diabetics to consume. We characterized the bioactive compounds in NTF, NTR, and NTJ, and found that fermentation increased the diversity of bioactive compounds, and greatly reduced sucrose, glucose, and fructose content while generating trehalose, which has the potential to manage blood glucose levels. Further, NTJ displays anti-diabetic potential due to various compounds anti-diabetic properties. This study provides a basis for further clinical research on NTJ's anti-diabetic function in humans.

One of the needs of the teaching and learning processes is the lack of information during the process, such as the emotions of students. Emotions influence learning, the ability to process information and accurately understand what we are dealing with. They profoundly affect students’ academic engagement and performance. One new way of getting such information is monitoring students' emotions. This research aims to expose 6 groups of secondary school students to video recordings for further analysis and categorization of their emotions. A model of emotional recognition (ER) during classroom learning was designed, with a custom-made code, from recorded videos to images to identify faces, action units (AUs) and consequently the emotions of each of the students shown on the screen. In addition, the ER model was optimized, from detecting few students to many students during a class. Emotions were then analysed according to the academic year, subject, and comparing the emotions at the beginning and at the end of the class. The results indicate the presence of a variety of emotions in the classroom and we found significant differences in the presence of some emotions depending on the time of class, subject and academic year, although there are no clear patterns. We discussed how emotions influence students' academic performance, and we suggest future research focus on teachers utilizing tools like this to improve student well-being and performance.

This study investigates the specific factors affecting the usage intention of distributed ledger technology (DLT), such as availability, diversity and economic value from the perspective of a unified theory of technology acceptance. Users of DLT in public and private sectors were surveyed. Availability and economic value affect performance expectancy, while availability, diversity, and profitability have an influence on effort expectancy. Performance expectancy and transparency have a positive effect on the intention to use DLT which in turn exerts a positive effect on usage behavior. This study provides implications for researchers in that it attempts to investigate the factors affecting the usage intention of DLT based on the extended unified theory of acceptance and encompassing diverse industries that adopt DLT, such as the public, financial, medial, and logistics sectors.

This study delves into the dynamics of relational energy (RE) within organizational context, examining some of its antecedents and decedents. Specifically, it investigates the influence of psychological capital (PsyCap) and humor on RE, and subsequently, the latter’s impact on job performance (JB) mediated by job engagement (JE). A research model based on structural equation modelling carried out with 481 employees in private service industries demonstrates several key relationships. It reveals that both PsyCap and affiliative humor positively affect RE, while aggressive humor exerts a negative influence. Furthermore, RE shows positive association with JE and JP, with JE serving as a mediator. To the authors’ knowledge, this is the first study to present an integrated model encompassing this exact combination of influencers and consequences of RE as well as to be investigated within Western Balkans cultural context. Therefore, it represents a novel approach. Additionally, the research addresses crucial questions regarding the existence and strategic significance of RE within organizational interactions. The findings offer valuable insights for organizations seeking to enhance employee engagement, performance, and wellbeing – even during health crises such as COVID-19 – by fostering RE. The study advances the understanding of RE in organizational settings and provides a foundation for future research in this domain.

This research endeavors to illuminate the path toward efficient and optimized propeller design by delving into the realm of aerodynamics. Propellers, as vital mechanical marvels, constitute the core of numerous propulsion systems, spanning from aircraft and marine vessels to wind turbines. The pursuit of enhanced propeller efficiency and performance holds paramount importance in industries where propulsion assumes a critical role, underscoring the indispensable need for a profound understanding of aerodynamics. Through meticulously crafted theoretical insights and their practical applications, this study embarks on a journey into the fascinating realm of propeller aerodynamics. While the paper primarily addresses the aerodynamic aspects of propeller design and optimization, it serves as a foundational guide for those seeking to comprehend the intricacies of propeller engineering.The research journey commences with an in-depth exploration of the fundamental principles governing propeller performance. It unveils the intricate dynamics of airflow surrounding propeller blades, shedding light on their proficiency in generating thrust, lift, and torque. The bedrock of the theoretical insights is a comprehensive analysis encompassing airfoil profiles, blade geometry, and the underlying principles of blade element theory. In navigating the ensuing pages, the study embarks on the task of demystifying the intricate methods and calculations that propel the design and optimization of propellers. Leveraging cutting-edge methodologies, the research systematically assesses the aerodynamic characteristics of propellers across various operational conditions. These invaluable insights empower the tailoring of propeller designs with precision, aligning them seamlessly with specific performance objectives. The aim is not only to impart a comprehensive understanding of propeller aerodynamics but also to provide actionable insights for engineers and researchers striving to enhance propeller efficiency. This research endeavor serves as a guiding light for both propeller enthusiasts and professionals alike. It not only enriches the broader discourse on propeller technology but also equips readers with the requisite knowledge and tools to embark on their own quests for optimized propeller design. As propulsion technologies continue their evolution, the steadfast principles elucidated herein will persist, guiding future innovations and advancements in the realm of propeller engineering. This paper serves as a profound resource, offering new researchers a comprehensive understanding of propellers and their intricate design processes.

In order to reveal the impact of boundary-spanning activities of cooperative innovation teams on team innovation performance, this paper takes the panel data of 71 cooperative innovation teams from January to November 2022 as the research sample. It introduces intermediary variables (teamwork crafting and individual work crafting) to analyze the impact mechanism of boundary-spanning activities of teams on innovation performance, 71 teams were divided into 41 experimental groups and 30 control groups, and a quasi-natural experiment was conducted on the innovation performance of team boundary-spanning activities using the Double Difference Model (DID).Research has shown that boundary-spanning activities of collaborative innovation teams can promote team innovation performance. Team job crafting has a mediating effect on team innovation performance in boundary-spanning activities of collaborative innovation teams. Team job crafting and individual job crafting mediate between the boundary-spanning activities of collaborative innovation teams and team innovation performance. Further analysis using the double difference model found that compared to teams without boundary-spanning activities, teams with boundary-spanning activities can directly improve team innovation performance. When team reflection is vital, and task interdependence is high, it will promote team innovation performance. This research enriches the research on the effects of boundary-spanning activities of collaborative innovation teams, explores solutions based on quasi-nature, and provides a reference for improving the team innovation performance of collaborative innovation teams.

In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted integrated satellite-high altitude platform-terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting HAP's stability and RIS's reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipments (UEs) to the satellite. To aim at maximising system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle it effectively by traditional solving methods. Based on this, this paper studies deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance and execution time, and higher computing speed, making real-time decision making truly feasible。

Published data or available literature on sustainable building plan-design, construction, performance, and renovation criteria have covered some stages or some parts of each stage. These data usually have been published partially in many different papers―there have not been any papers that published these data together. Hence, this paper aims to collectively review these data and publish them together. The collection and review of these data were carried out by our twenty-five team members who specialized in sustainable urban, architectural, and civil engineering and construction management. The gathered and reviewed outputs were combined and validated based on a general group consensus. This consensus decision-making proceeded through two major group meetings with several follow-up meetings. The first major meeting was to combine and improve the gathered reviewed sustainable building criteria for Cambodia. The second major meeting was to validate the improved reviewed sustainable building criteria for Cambodia. The several follow-up meetings were to discuss the relevance and importance of the validated data “criteria and their classifications and descriptions” in all stages and more focused on their importance and applicability to Cambodia. The collective reviewed data in this paper would be useful to researchers in the fields. They could also be useful collective knowledge and information for policymakers from governmental agencies and development partners, particularly for sustainable building and construction companies.

The COVID-19 pandemic is one of the worst catastrophes that we have faced globally in recent years. It has not only taken its toll on the economic sector but also on the education sector. The social distancing norms that are in place as a direct response to the pandemic have turned conventional classroom teaching into a problematic minefield; as such, students all over the world have been forced into unprecedented situations that have served only to worsen the situation. The current pandemic has given rise to one of the worst crises the 21^st Century has ever seen, resulting in a surge of unemployment. Many companies have taken the route of firing employees or making redundancies, as they cannot afford the monthly reimbursement for staff. While this issue primarily concerns full-time workers, it also carries significant consequences for students – a considerable number of students are required to earn their daily living costs, and, without a job, they cannot pay their educational fees, accommodation costs, or living expenses. This comprehensive study briefly discusses the multitude of problems faced by students in the UK regarding higher education, as a direct result of the coronavirus pandemic. It contains six individual sections: a detailed introduction; the methodological procedures employed; educational disruptions, covering issues from hindrances in field research to examinations and student evaluations; personal problems experienced by students, such as accommodation and loss of income; concerns arising from the global pandemic; and finally, a conclusion and summary of the study’s findings.

The paper is dedicated to the topical problem of examining permafrost state and the processes of its geocryological changes by means of geophysical methods. To monitor the cryolithozone, we propose and scientifically substantiate a new technique of pulsed electromagnetic cross-well sounding. Based on the vector finite-element method, we created a mathematical model of the cross-well sounding process with a pulsed source in a three-dimensional spatially heterogeneous medium. A high-performance parallel computing algorithm was developed and verified. Through realistic geoelectric models of permafrost with a talik under a highway, constructed following the results of electrotomography field data interpretation, we numerically simulated the pulsed sounding on the computing resources of the Siberian Supercomputer Center of SB RAS. The simulation results suggest the proposed system of pulsed electromagnetic cross-well monitoring to be characterized by a high sensitivity to the presence and dimensions of the talik. The devised approach can be oriented to addressing a wide range of issues related to monitoring permafrost rocks under civil and industrial facilities, buildings and constructions.

Sex dimorphism starts during early embryogenesis and is further manifested in response to hormones during puberty. As this leads to physical divergence that is measurably different between sexes, males enjoy physical performance advantages over females within competitive sport. While this advantage is the underlying basis of the segregation into male and female sporting categories, these sex-based categories do not account for transgender persons who experience incongruence between their biological sex and their experienced gender identity. Accordingly, the International Olympic Committee determined criteria by which a transgender woman may be eligible to compete in the female category, requiring total serum testosterone levels to be suppressed below 10 nmol/L for at least 12 months prior to and during competition. Whether this regulation removes the male performance advantage has not been collectively scrutinized. Here, we aim to review how differences in biological characteristics between biological males and females affect sporting performance and assess whether evidence exists to support the assumption that testosterone suppression in transgender women removes the male performance advantage. In this review, we report that the performance gap between males and females amounts to 10-50% depending on sport. The performance gap is more pronounced in sporting activities relying on muscle mass and strength, particularly in the upper body. Longitudinal studies examining the effects of testosterone suppression on muscle mass and strength in transgender women consistently show very modest changes, where the loss of lean body mass, muscle area and strength typically amounts to approximately 5% after 1 year of treatment. Thus, current evidence shows that the biological advantage enjoyed by transgender women is only minimally reduced when testosterone is suppressed. Sports organizations may therefore be compelled to reassess current policies regarding participation of transgender women in the female category of sport.