Recently, environmental degradation has become an international issue due to the extensive economic activities around the globe. This aspect demands the policymakers' and new researchers’ emphasis. Hence, the present article examines the impact of corporate social responsibilities (CSR) to community and environment on the environmental performance (ENP) of the manufacturing industry in China. The article also investigates the mediating role of green innovation and environmental strategy among CSR to community, CSR to environment and ENP of manufacturing industry in China. The article gets the primary data from the selected employees of the manufacturing industry using questionnaires. The article also tests the data's validity and reliability, and association among variables using smart-PLS. The outcomes indicated that CSR to environment has a positive association with ENP. The outcomes also revealed that green innovation and environmental strategy significantly mediate among CSR to community, CSR to environment and ENP. The article guides the policymakers in making policies related to improve ENP using CSR to environment and green innovation.

This study examines if enterprises’ digital transformation enhances their sustainable development capabilities and reduces carbon emissions, thus enhancing carbon neutrality performance and implementing the "dual-carbon" strategy. It specifically explores the impact of Chinese firms’ digital transformation on carbon neutrality performance in 30 provinces and municipalities in China from 2011 to 2021. Based on the Difference-in-Difference method and spatial econometric modeling, this study finds a U-shaped relationship between enterprise digital transformation and carbon performance, with spatial spillover effects. It also finds that converting environmental charges into taxes can significantly improve carbon performance. In terms of the mechanism of action, digital transformation affects carbon performance by influencing energy use efficiency and adjusting the level of industrial structure advancement and rationalization. In terms of heterogeneity, this U-shaped relationship exists only in the eastern and central regions, and the significance is more substantial in the east; in addition, the impact of enterprise digital transformation on carbon performance is more evident in regions with higher R&D intensity and higher economic development levels. These findings imply that the government should further encourage and guide enterprise digital transformation, monitor the effects of environmental protection policies and select and implement carbon emission reduction strategies according to local conditions to improve carbon performance.

The purpose of this paper is to assess the differential impacts of transformational and transactional leadership styles on employee performance during change initiatives within commercial banks in Sierra Leone. The research study utilised a cross-sectional survey design, and data was collected from 903 participants employed in the banking industry. The study used the structural equation model as the investigative instrument to examine the hypotheses. The findings reveal that transformational and transactional leadership styles demonstrate a strong bearing on employee performance, with the latter having a bigger influence. Organisational citizenship behaviour has a mediating effect between leadership styles and employee performance, while employee commitment does not act as a mediator between transformational leadership and employee performance, but it acts as a mediator between transactional leadership and employee performance. Commercial banks need to devise training programmes that will advance both leadership styles to arouse, energise, or inspire followers to display favourable behaviour and commitment to attain organisational. The paper makes an insightful contribution to the existing leadership literature by ascertaining the significance of leadership styles on performance and the role of commitment and organisational citizenship behaviour as mediators of the nexus between leadership styles and performance in the Sierra Leone banking industry.

Sialyllactose (SL) is a functional human milk oligosaccharide essential for immune support, brain development, intestinal maturation, and antiviral defense. However, despite its established health benefits, the effect of SL on exercise performance and muscle mass in mice remains unknown. Here, we aimed to investigate, for the first time, the effects of 6′-SL on muscle functions. Seven-week-old male C57BL/6J mice were administered 100 mg/kg 6′-SL for 12 weeks, after which exhaustive treadmill performance, muscle strength, and muscle phenotype were examined. The administration of 6′-SL significantly improved exhaustive treadmill performance metrics, including distance and exhaustion time. Grip strength was also increased by 6′-SL administration. Additionally, 6′-SL increased muscle mass in both the gastrocnemius (GAS) and soleus. 6′-SL administration led to an increase in the minimum Feret’s diameter and the protein expression of total myosin heavy chain in the GAS muscle. In conclusion, 6′-SL administration in vivo led to increased running distance and time by increasing muscle mass and strength. These findings collectively indicate that 6′-SL is a potential agent for improving muscle health and exercise performance.

Due to the extraordinary mechanical properties of ultra-high-performance concrete (UHPC), the shear stirrups in UHPC beams could potentially be removed. The aim of this study is to determine the effect of beam height and steel fiber volume content on the shear behavior of non-stirrup UHPC beams under a larger shear span-depth ratio (up to 2.8). Eight beams were designed and fabricated, including six non-stirrup UHPC beams and two comparing stirrup-reinforced normal concrete (NC) beams. The experimental results demonstrated that the volume content of steel fiber could be a crucial factor affecting the ductility, cracking strength, and shear strength of non-stirrup UHPC beams and altering their failure modes. Additionally, the height of the beam had a considerable effect on its shear resistance. This study revealed that the French standard formulae were more accurate for the UHPC beams with larger shear span-depth ratios and PCI-2021 formulae greatly overestimate the shear capacity of UHPC beams with larger shear span-depth ratios. Xu’s formulae were more accurate for the steel fiber-reinforced UHPC beams with larger shear span-depth ratios.

This study aimed to solve the problems of sealing and tearing failure of packer’s rubber cylinders during CO2 downhole injection. Using the simulation software, Comsol, a rubber cylinder model in a supercritical CO2 (SC-CO2) environment was established. The thermal analogy method was used to simulate the CO2 diffusion and rubber cylinder swelling process, thus we analyzed the deformation and stress of the rubber cylinder caused by temperature, pressure and CO2 as swelling agent. Results showed that, in the SC-CO2 environment, under the influence of CO2 diffusion and the consequent swelling, the rubber cylinder body is prone to large deformation, and the maximum shear stress is significantly increased, leading to shear failure of the rubber cylinder. The reduction of the initial seating pressure can alleviate the impact of deformation, whereas the reduction of the maximum contact pressure could cause the rubber cylinder to lose its seal. We also analyzed the change rules between various factors and the maximum contact pressure of the rubber cylinder, which provides a theoretical basis and technical support for improving the sealing performance of the packer’s rubber cylinder in the SC-CO2 environment.

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.

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.

This qualitative study explores the challenges and successes in supplier development from the perspectives of procurement managers across diverse industries. Through in-depth interviews, key themes emerged, highlighting the critical role of communication, trust, performance measurement, capacity building, risk management, innovation, regulatory compliance, and technology integration in supplier relationship management. Procurement managers emphasized the importance of clear, continuous communication and trust-building activities to align objectives and foster collaborative partnerships. Performance measurement was crucial for evaluating and improving supplier performance, supported by capacity-building initiatives to enhance supplier capabilities. Risk management strategies focused on identifying and mitigating risks, while compliance efforts aimed to ensure adherence to industry standards. Innovation and technological integration were pivotal for driving efficiency and competitiveness, enabling faster time-to-market and improved product quality. Cultural sensitivity and relationship strengthening emerged as essential for navigating diverse cultural contexts and enhancing partnership effectiveness. Overall, the findings underscored positive outcomes including enhanced quality, cost efficiencies, and increased resilience within supply chains. This research contributes valuable insights into effective supplier management practices, emphasizing the strategic importance of supplier development initiatives for sustainable growth and competitive advantage in today's dynamic business environment.

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.

In recent years, with the increasing prominence of environmental and social issues, investors have been paying more attention to the ESG performance of enterprises, highlighting the importance of ESG factors in the financial field. This study is based on the theories of banking business models, stakeholder theory, risk management theory, and ESG investment theory. It uses the financial data and ESG scores of Chinese listed banks to deeply analyze the ESG factors and explore their impact on the liquidity risk of commercial banks. The research found that (1) good ESG performance can reduce the liquidity risk commercial banks face by improving bank value and financial performance. (2) ESG factors can also enhance the liquidity management level of commercial banks through standardization and sustainable business principles, thereby reducing the occurrence and impact of liquidity risk. Therefore, it is necessary to reduce the liquidity risk of banks and promote the sustainable development of commercial banks from four aspects: ESG performance and management, bank value, financial performance, and policy regulation.

Educational institutions must identify students who are academically struggling to provide them with necessary support to improve their performance. In this context, recommendation systems powered by deep learning techniques are vital for detecting and categorizing such students. These systems help students plan their future by uncovering patterns in their historical academic data. This study introduces a new deep learning model designed to classify academically underperforming students in educational settings. The model incorporates a Gated Recurrent Neural Network (GRU) and includes specific neural network features like a dense layer, max-pooling layer, and the ADAM optimization algorithm. The model's training and evaluation were conducted using a dataset comprising 15,165 student assessment records from various academic institutions. The performance of the developed GRU model was benchmarked against other educational recommendation systems, including Recurrent Neural Network models, AdaBoost, and the Artificial Immune Recognition System v2. The proposed GRU model demonstrated remarkable accuracy, achieving an overall rate of 99.70%.

This article investigates the effectiveness of feature extraction and selection techniques for enhancing the performance of classifiers accuracy in Type II Diabetes Mellitus (DM) detection using microarray gene data. To address the inherent high dimensionality of the data by employing three Feature Extraction (FE) methods are used namely, Short-Time Fourier Transform (STFT), Ridge Regression (RR), and Pearson Correlation Coefficient (PCC). To further refine the data, meta-heuristic algorithms like Bald Eagle Search Optimization (BESO) and Red Deer Optimization (RDO) are utilized for feature selection. The performance of seven classification techniques such as Non-Linear Regression - NLR, Linear Regression - LR, Gaussian Mixture Model - GMM, Expectation Maximization - EM, Logistic Regression - LoR, Softmax Discriminant Classifier - SDC, and Support Vector Machine with Radial Basis Function kernel - SVM-RBF are evaluated with and without feature selection. The analysis reveals that the combination of PCC with SVM-RBF achieved a promising accuracy of 92.85% even without feature selection. Notably, employing BESO with PCC and SVM-RBF maintained this high accuracy. However, the highest overall accuracy of 97.14% was achieved when RDO was used for feature selection alongside PCC and SVM-RBF. These findings highlight the potential of feature extraction and selection techniques, particularly RDO with PCC, in improving the accuracy of DM detection using microarray gene data.

This research addresses the formidable challenge of achieving precise trajectory tracking for autonomous vehicle formations in uncertain environments. It introduces an adaptive control system tailored for autonomous vehicles, with a primary focus on ensuring prescribed performance in trajectory tracking within a leader-follower formation paradigm. The system incorporates a guidance law that allows the leader vehicle to dynamically adjust desired yaw angles and speeds for follower vehicles based on the established reference trajectory, enhancing tracking accuracy and responsiveness to environmental changes. To address practical external disturbances, the study utilizes Radial Basis Function Neural Networks (RBFNN) in conjunction with second-order filters for error approximation. This approach is further strengthened by a carefully formulated adaptive law. The innovative integration of a barrier Lyapunov function with the backstepping method significantly enhances the system’s adaptability and robustness, ensuring trajectory tracking performance meets predetermined standards. Simulation results illustrate the control system’s adept handling of various external disturbances, consistently maintaining trajectory tracking errors within predefined limits. This underscores the system’s potential to markedly enhance the operational reliability and efficiency of autonomous vehicle formations in unpredictable environmental conditions.

Establishing systemic communications between the project plan, stakeholders, and the organization through integrated management meets stakeholders' demands and managerial expectations in project-oriented organizations. Performance assessment can be defined as a control system in any company that reviews its daily operations and evaluates how well it has achieved its goals. This study aims to investigate the impact of an integrated project management system on the financial performance of the Organization for Renovation, Development, and Equipment of Schools. This applied research is descriptive-analytical. The statistical population comprises 2,700 employees of the organization, with a sample size of 340 individuals examined. The reliability of the designed questionnaire was confirmed by experts in the organization using Cronbach's alpha (α=0.91). Data were analyzed using SPSS version 20 and AMOS 18 software through descriptive statistics and confirmatory factor analysis. The results indicate that the seven dimensions of the integrated project management system (cost management, quality management and continuous control, change management, systems and information management, human resources management, interaction and communication management, procurement management) have a positive and significant impact on the financial performance of the Organization for Renovation, Development, and Equipment of Schools.

The Inefficient water use, varying and low productivity in Kenya public irrigation schemes is a major concern. It is therefore necessary to periodically monitor and evaluate the performance of public irrigation schemes. The performance of public irrigation in western Kenya was assessed by combining benchmarking methodology and principal component analysis. The aim was to quantify and rank the performance of pumped public irrigation schemes in Kenya. Eleven benchmarking indicators were computed for the period from 2012 to 2016 and compared to global benchmark values. The indicators used fall under agricultural productivity, water supply and financial performance categories. The computed agricultural productivity was 36%–51% in Ahero, 23%–42% in West Kano and 26%–50% Bunyala irrigation scheme. Water supply performance in Ahero, West Kano and Bunyala irrigation schemes varied from 24% to 58%, 3% to 49% and 19% to 43% respectively. Financial performance varied from 46% to 54% in Ahero, 25% to 32% in West Kano and 54%–56% in Bunyala irrigation scheme. An average overall performance efficiency of 46%, 39% and 31% was obtained in Ahero, Bunyala and West Kano irrigation schemes respectively. The performance of the irrigation schemes is very poor and measures on improving performance are needed.

This study aimed to evaluate the effects of plasma treated metal contaminated water, used for irrigation, on plant growth. Zinc (Zn) is one of the more commonly used metals. It can enter the environment from industrial processes as particles released into the atmosphere or as wastewater discharged into waterways or the ground. Exposure to large amounts of zinc, even for a short time, can seriously affect human health. In this experiment, three different DBD O2 plasma treated zinc contaminated water and a control (tap water) were used, with Arabidopsis thaliana as the model plant. The treatments were: i) Control, ii) Zn water, iii) Zn+O3(30 min), and iv) Zn+O3(60 min). Arabidopsis plant exhibited maximum growth in the Zn+O3(30 min) treatment. All growth parameters, except leaf area, followed this trend: Zn+O3(30 min) > Control > Zn water > Zn+O3(60 min). Gene expression analysis revealed that reduced metal ion stress and controlled oxidation due to active oxygen species contributed to favorable/improved growth of Arabidopsis in the Zn+O3(30 min) treatment. Therefore, 30 minutes of DBD O2 plasma treated zinc contaminated water [Zn+O3(30 min)] can mitigate the adverse effects of excess zinc ions and promote the growth of Arabidopsis plants.

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

Identifying punch types as part of punch kinematic analysis is a key component for both coaches and players, providing critical insights into the variety and effectiveness of punches, which are essential for refining overall performance. Existing research indicates that punch type classification typically relies on data from either wearables or video data. Both approaches have their merits and demerits but require a significant amount of labeled data for modeling the classification algorithm. In this work, we propose a novel approach that significantly reduces the labeling effort by one-sixth by leveraging video data to enhance the training information of sensor data. This enriched training phase enables more accurate punch classification with reduced labeled data. Subsequently, the real-time punch identification and classification use only the wearable data. On a real dataset, with 15% of the labeling efforts, our approach achieved an accuracy rate of 91.41% for rear hand punch recognition and 91.91% for lead hand punch recognition, along with 92.33% and 94.56% for punch classification, respectively. Through the development of our Smart Boxer system, we aim to revolutionize punch analytics in boxing, providing comprehensive insights while enhancing the overall training and competitive experience for athletes and enriching fan engagement with the sport.

This qualitative study explores collaborative supplier relationships within the context of e-commerce, aiming to uncover the dynamics, challenges, strategies, impacts, and future trends shaping these partnerships. Through thematic analysis of interviews with e-commerce platform managers, supplier representatives, and industry experts, the study identifies three primary types of relationships: transactional, relational, and strategic. Critical factors influencing these relationships include trust, effective communication, technological integration, and regulatory compliance. Challenges such as information asymmetry, cultural differences, logistical complexities, and competitive pressures are examined, alongside strategies for enhancing collaboration, including clear communication protocols, performance evaluations, and technological advancements. The findings highlight the significant impact of collaborative supplier relationships on business performance, including cost efficiencies, supply chain resilience, innovation capacity, customer satisfaction, and market competitiveness. Looking forward, trends in digital transformation, sustainability initiatives, global supply chain networks, resilience-building strategies, and industry collaboration are discussed as shaping the future landscape of e-commerce partnerships. Embracing these trends presents opportunities for organizations to innovate, adapt, and sustain growth in the competitive digital marketplace.

In this research, the primary goal was to investigate the relationship between strategic planning and organizational performance in Iraq's manufacturing context. The study's primary data sources were 360 manager respondents. The structured questionnaire was used to collect primary data from manufacturing firms located throughout Iraq. To analyze the result, the researcher used descriptive statistics, correlation, and multiple regression analysis. SPSS version 16 software was used to conduct data analysis. The result reveals that the process of strategic planning has a beneficial effect on financial performance. Environmental scanning has a statistically significant positive effect on a company's non-financial performance. Management participation and planning formality positively and statistically significantly affect a business's non-financial performance at the 10 percent level. The domain of strategy and technique has not impacted the company's non-financial performance.

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.

Increasing population size and income growth are causing and increasing demand for food and protein of animal origin. Insects are an interesting alternative to conventional animal source proteins as they can convert by-products from the agricultural industry to biomass for commercial feed for livestock and potentially as a food source for human consumption. Microorganisms have been found to affect insects and can be accumulated via horizontal and vertical transmission. This study aimed to identify if the removal of egg- and substrate-associated microorganisms impact larval performance through the following parameters: development of biomass, final biomass, and the survival rate of house fly (Musca domestica) larvae. Four treatments were tested on substrate consisting of 10.81% alfalfa (Medicago sativa), 21.62% wheat bran (Triticum aestivum), and 67.57% water: (A) disinfected eggs and non-autoclaved substrate, (B) non-disinfected eggs and autoclaved substrate, (C) disinfected eggs and autoclaved substrate, and (D) a control without any removal of microbiota. The results showed a significant decrease in final biomass for the treatments with only disinfected eggs, only autoclaved substrate and both when comparing to the control, and a significant decrease in survival rate for B and D. Moreover, the development of biomass showed a significant difference between days within all treatments. Together this suggest that microorganisms of housefly eggs and the growth substrate plays an important role for growth, which is critical in commercial insect production. Further studies must be performed to examine these parameters in more commercially relevant substrates.

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%.

This study assessed the relationship between body posture displacements and cardiopulmonary function test (CPET) and skill related physical fitness tests. One hundred male (60%) and female collegiate athletes (22.2 ± 4yrs) with a normal body mass index (BMI up to 24.9) were assessed via the PostureScreen Mobile app to quantify postural displacements as head, thorax and pelvis rotations and translations. CPET and physical performance tests including the agility T-test, the vertical jump test, the stork static balance test (SSBT) and the dynamic Y-balance test (YBT) were performed. Spearman correlation (r) and p-values are reported. Postural parameters were found to have moderate-to-high associations with CPET and the agility test, moderate correlations with the vertical jump test and the SSBT (head and pelvic postures only), and weak correlations with the YBT. As postural parameters were more asymmetric, both CPET and performance skills scores were worse. For examples: 1) a medium positive correlation was found between cranio-vertebral angle (CVA) and vertical jump test (r = 0.54; p-value < 0.001) and SSBT (r = 0.57; p-value < 0.001), while a strong negative correlation was found between CVA and agility test (r = -0.86; p-value < 0.001). 2) A strong positive correlation was found between CVA and oxygen uptake efficiency slope, load watts VO2 at VT, VO2 / kg, load watts at respiratory compensation point (RCP), (r = 0.65 and r = 0.71; p < 0.001). Conversely, a significant negative correlation was found between CVA and VE / VO2 at VT (r = -0.61; p < 0.001). Postural rotations and translations of the head, thorax and pelvis were statistically correlated with physical performance skills and CPET in young collegiate athletes. There were moderate-to-high associations with cardiopulmonary functions and agility tests, moderate correlations with the vertical jump test, and weak correlations with the YBT. Postural alignment may be important for optimal physical performance and optimal cardiopulmonary function. Further research is necessary to elucidate reasons for these correlations as found in our sample of young and healthy athletes.

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.

In this age of global warming, academics and policymakers are increasingly concerned about firm environmental sustainability success. Therefore, this study aims to investigate whether Environmental, Social and Governance (ESG) performance impacts sustainability performance through the mediating effect of firm innovation. To this end, Structural Equation Modeling (SEM) was deployed to analyze data collected from the employees of manufacturing industries in an emerging economy like Bangladesh. The results revealed that ESG performance significantly enhances the innovation and sustainability performance of manufacturing industries, indicating that the higher the ESG performance of a firm, the greater its innovation and sustainability performance. Furthermore, the results confirmed that firm innovation performance fully mediates the relationship between ESG initiatives and sustainability performance. The findings of this study provide policymakers and industry authorities with valuable insight into the role of ESG and innovation performance in improving sustainability performance. Specifically, the study sheds knowledge on how firm ESG initiatives and innovation performance impact sustainability performance in the manufacturing sector of an emerging economy like Bangladesh.

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.

In this study, the team employed full-scale crash tests to investigate the impact of post-densification technology on the safety performance of separated central median W-beam barriers under low compaction conditions. By precisely simulating the design and construction environment of the central median, the study comprehensively evaluated the barrier's containment, redirection, buffering, and anti-intrusion performances, as well as the deformation of both the barrier and vehicles under various collision scenarios. The results indicate that under low compaction soil conditions, the A-level separated central median barriers reinforced with post-densification technology effectively meet the specified protection requirements. By comparing the influence of soil strength variations on barrier performance under simulated rainfall conditions, it was confirmed that enhancing soil compaction significantly improves soil stability, effectively increases the overall strength and stability of the barriers, reduces deformation, and significantly enhances safety performance. This study not only validates the effectiveness of the reinforcement scheme but also provides new perspectives for the scientific research of safety barriers and offers crucial technical support for the optimal design of road safety protection facilities.

The energy efficiency of existing buildings is a crucial factor in addressing Europe’s energy con-sumption challenges, accounting for nearly 40% of the total energy usage. The ongoing energy crisis, driven by the reliance on fossil fuels and increasing prices, has underscored the urgency of adopting sustainable and energy efficient practices especially to the existing building stock. Cyprus is par-ticularly vulnerable due to its heavy dependence on imported fuels for electricity generation and faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. Extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings. This study examines an existing cultural hall. The case study of the Cultural Hall represents the building stock before 2010 which is lacking thermal in-sulation, double glazing and any energy efficient element. It then offers an ideal case study to ex-plore sustainable solutions which can be easily adopted from the construction industry and from the building owners. The primary objective of this research is twofold: first, to develop a comprehensive retrofit plan to transform the cultural hall into a net-zero energy building; and second, to explore how this transformation can give value back to the local community. After running two simulation scenarios and providing two energy performance certificates, one with at the existing state and one with the intended retrofit measures, it is clear that the building can be a net zero energy consump-tion building. Some interesting results derived, present also how this building can provide value back to the community i.e. through solar carports for EV charging.

To enhance the uplift capacity and facilitate the installation of multi-row perfobond connectors at shallow burial depths, this study puts forward a novel notched T-perfobond connector. The design incorporates an integrated flange at the bottom of the connector and a notch at the edge of the hole. Through pull-out model tests on four notched T-perfobond connectors, this research investigates their failure mechanisms and pull-out capacities. Utilizing the explicit dynamics method in Abaqus, a finite element model of the pull-out resistance test for the notched T-perfobond connector is established and verified against experimental data. Furthermore, a detailed parametric analysis involving 54 models is conducted, examining crucial parameters such as rib dimensions, hole geometry, flange size, notch width, bar diameter, and material properties. Based on the combined experimental and numerical results, the paper assesses the suitability of current formulas for calculating the pull-out capacity of perfobond connectors and proposes a refined calculation method specifically for notched T-perfobond connectors. All the findings in this paper can serve as a reference in the design and construction of composite structures.

Ice and snow on roads in winter endanger traffic safety. The existing methods of snow and ice removal have various drawbacks, which need further research. Aiming at solving limitations about high labor intensity, low efficiency, serious environmental pollution of traditional snow and ice removal methods, the anti-icing coating technology of high-voltage conductors is used for reference, and in view of the characteristics of asphalt pavement deicing, an ice-suppressing material that can effectively reduce the adhesion between snow, ice and pavement is successfully developed. As there is no perfect evaluation index and method for ice-suppressing materials at present, this paper further evaluates the performance of ice-suppressing materials from the aspects of deicing performance, durability, adhesion and the impact of ice-suppressing materials on pavement performance. The test results show that the ice-suppressing material can effectively reduce the adhesion between the ice layer and the specimen, making it easier to remove. The ice-suppression material has good slow-release performance, and the specimens still have good deicing performance after 7 cycles of tests.

This paper presents preliminary operational performance results of a pilot grid-connected photovoltaic (PV) system designed and installed on the rooftop of the Ministry of Petroleum, Energy and Mining headquarter in Nouakchott (latitude of 18.1°N and the longitude of 16.0°W), Mauritania. The aim is for the government to demonstrate the relevance of using solar energy and to encourage the uptake of solar PV technology for commercial and residential building applications in Mauritania.. In this study, the grid-connected PV system has a peak power of 48 kW and the performance monitoring was carried out during one year, with a system that allow to measure DC power, inverter and system conversion efficiency, energy generated by the PV arrays, solar radiation in the inclination plane of panels, ambient temperature and module temperature. During this period, the PV plant was found to supply 65,668 kWh to the grid. The final yield ranged from 3.91 to 5.09 kWh/kWp/day. The performance ratio was found to vary from 69.69% to 89.35% and the annual capacity factor was found to be 19%. Finally, performance parameters were compared with other PV plants installed in the same region of Northern Africa. The outcome of this work is deemed important in assisting accurate PV system design and decision-making.

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.

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.

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.

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

This qualitative study explores the intricacies of building sustainable supplier relationships in the e-commerce sector, focusing on best practices and strategic approaches. Through in-depth interviews with procurement managers, supply chain experts, and supplier representatives, the research identifies four critical themes: trust, technology integration, ethical practices, and performance measurement. Trust is found to be the foundation of effective supplier relationships, fostering open communication and collaboration, which are essential for addressing challenges and co-creating solutions. The integration of digital technologies such as supply chain management software, blockchain, and the Internet of Things significantly enhances transparency, decision-making, and operational efficiency, providing a competitive edge. Ethical practices, including adherence to fair labor standards and environmental sustainability, are increasingly crucial as consumer and regulatory expectations evolve, necessitating alignment between e-commerce companies and their suppliers. Performance measurement and continuous improvement are vital for maintaining quality and efficiency, with clear metrics and regular evaluations driving constructive feedback and optimization of supply chain processes. The study underscores the interdependence of these elements in fostering resilient and adaptable supplier relationships. The findings offer practical guidance for e-commerce businesses and suppliers, emphasizing the need for a holistic approach to supplier management that balances trust, technology, ethics, and performance. This approach not only enhances operational effectiveness but also ensures the long-term sustainability of supplier relationships in the dynamic e-commerce landscape. The research contributes to the theoretical understanding of supplier relationship management, providing a comprehensive framework for future studies and practical applications in the e-commerce industry.

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.

The paper deals with the design of a systematic procedure for implementing strategy changes into internal business processes for a project-oriented production type of organization. In the first part of the contribution, a summary of the theoretical starting points for the field of strategic management and change management is presented. In the second part, the contribution deals with the analysis of the current situation in the area of the impact of the change of strategy on the management of business processes. In the last part, the proposal of the procedure for implementing strategy changes into internal business processes and the verification results are presented. The proposed procedure includes steps where the selection and verification of key performance indicators at individual levels of management plus the quantification of the impact of the change in strategy on the processes takes place. The management can thus monitor and evaluate the chosen processes in accordance with the fulfilment of the chosen strategy of the company. The last chapter presents the verification of the proposal for the systematic introduction of changes into the processes.

In order to study the effects of soybean isoflavones on the growth performance and lipid metabolism of juvenile Chinese mitten crab, six experimental diets were formulated by gradient supplementation 0%, 0.004% and 0.008% soybean isoflavones at different dietary lipid levels (10% and 15%), which were named to NF-0 group (10% fat and 0% SIF), NF-0.004 group (10% fat and 0.004% SIF), NF-0.008 group (10% fat and 0.008% SIF), HF-0 group (15% fat and 0% SIF), HF-0.004 group (15% fat and 0.004% SIF) and HF-0.008 group (15% fat and 0.008% SIF). All crabs with an initial weight of 0.4 ± 0.03 g were fed for 8 weeks. The results showed that diets supplementation with 0.004% or 0.008% SIF significantly increased the weight gain and specific growth rate of crabs. Diets supplementation with 0.004% or 0.008% SIF significantly reduced the content of non-esterified free fatty acids and triglycerides in the hepatopancreas of crabs at 10% dietary lipid level. Dietary SIF significantly decreased the relative mRNA expressions of elongase of very long-chain fatty acids 6 (elovl6), triglyceride lipase (tgl), sterol regulatory element binding protein 1 (srebp-1), carnitine palmitoyltransferase-1a (cpt-1a), fatty acid transporter protein 4 (fatp4), carnitine palmitoyltransferase-2 (cpt-2), Δ9 fatty acyl desaturase (Δ9 fad), carnitine palmitoyltransferase-1b (cpt-1b), fatty acid binding protein 10 (fabp10) and microsomal triglyceride transfer protein (mttp) in the hepatopancreas of crabs. At 15% dietary lipid level, 0.008% SIF significantly increased the relative mRNA expressions of fatty acid binding protein 3 (fabp3), carnitine acetyltransferase (caat), fatp4, fabp10, tgl, cpt-1a, cpt-1b and cpt-2 and significantly down regulated the relative mRNA expressions of Δ9 fad and srebp-1. In conclusion, SIF can improve the growth and utilization of high-fat diet by inhibiting genes related to lipid synthesis and promoting lipid decomposition in juvenile Chinese mitten crab.

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.

In the current study, we assessed the effects of conventional and Ultra High Dose Rate (UHDR) irradiation on behavioral and cognitive performance one month following exposure and assessed whether these effects were associated with alterations in the number of immune cells in the hippocampus using flow cytometry. Conventional- and UHDR-irradiated mice showed impaired novel object recognition. During fear learning, conventional- and UHDR-irradiated mice moved less during the inter-stimulus-interval (ISI) and UHDR-irradiated mice also moved less during the baseline period (prior to the first tone). In irradiated mice, reduced activity levels were also seen in the home cage; conventional- and UHDR-irradiated mice moved less during the light period and UHDR-irradiated mice moved less during the dark period. Following behavioral and cognitive testing, infiltrating immune cells in the hippocampus were analyzed by flow cytometry. The percent Ly6G+ CD45+ cells in the hippocampus was lower in conventional- and UHDR-irradiated than sham-irradiated mice, suggesting that neutrophils might be particularly sensitive to radiation. The percent of Ly6G+ CD45+ cells in the hippocampus was positively correlated with the time spent exploring the novel object in the object recognition test, suggesting that the reduced percent Ly6G+ CD45+ cells in the hippocampus might mediate some of the detrimental radiation-induced cognitive effects.

Digital transformation provides new drivers for economic performance growth in enterprises, but can it further improve ESG performance and support sustainable development? Based on the perspective of resources and capabilities, this paper uses the relevant data of 1588 listed companies in strategic emerging industries from 2011 to 2021 to study the differential impact of digital transformation on enterprise ESG performance, and further tests the mediating role of absorptive capacity and the moderating role of regional digital level. The research results indicate that: firstly, digital transformation of enterprises can positively promote the improvement of ESG performance; Secondly, there are significant differences in the impact of digital transformation on the ESG performance of enterprises in terms of property rights and industry characteristics. The ESG performance of state-owned enterprises and high-end equipment manufacturing enterprises is more sensitive to digital transformation; Thirdly, absorptive capacity plays a mediating role between digital transformation and corporate ESG performance; Fourthly, the positive regulation of regional digital level promotes the ESG performance of enterprises through digital transformation. The research conclusion is based on a digital perspective, providing relevant insights for improving the ESG performance of strategic emerging industry enterprises and expanding their ESG development paths.

This paper proposed three-dimensional numerical simulation method by coupling of electrostatic and fluid fields to evaluating the performance of electrical sensor in the concentration measurement of gas/solid two-phase flow. Compared with the static numerical simulation, this real-time dynamic 3-D simulation method can research on a designed capacitance sensor combining the dynamic characteristics of the two-phase flows for concentration measurement. Several fluid-electrostatic models of transmission pipes with different sensor structures are built. Under different test positions and different particle concentrations, the flow characteristics and the corresponding electric signals can be obtained, and the correlation coefficient between the concentration values and the capacitance values are used for performance evaluation of the sensors. The effects of flow regimes on concentration measurement are also been investigated in this paper. To validate the results of simulation, an experimental platform with horizontal straight pipe for phase volume concentration measurement of solid/air two-phase flow is built, and the experimental results agree well with simulation conclusions. The simulation and test results show that the coupling models can give constructive reference opinions for the sensor design and collection of installation position in different transmission pipelines, which are very important for the practical process of pneumatic conveying system.

Surveys on the optimum seismic design of structures reveal that many investigations focus on how to minimize its initial cost while satisfying the performance constraints. Although reducing the initial cost while complying with the requirements of earthquake design codes of practice will significantly guarantee the life safety of occupants against earthquakes, it may still cause considerable economic losses to residents and fatalities. Accordingly, calculating the possible earthquake damages caused during the structure's lifetime may become essential from the optimal Life Cycle Cost (LCC) design point of view. LCC analysis is used to evaluate economic feasibility including construction, operation, occupancy, maintenance and end-of-life costs. For optimization, the population-based, meta-heuristic algorithm based on the Ideal Gas Molecular Movement (IGMM), has established its capability in solving highly nonlinear mono and multi-objective engineering problems. Therefore, this paper investigates the LCC -based mono and multi-objective optimum design of a 3-D four-story concrete building structure combined with the Endurance Time (ET) competence. The results show that, apart from substantially reducing the total number of analyses required, the proposed technique significantly affected dipping minor injury cost, rental, and income costs by 22%, 16%, and 16%, respectively. In total, a 10% reduction in all structural life-cycle costs was achieved, which could well be considered significant.

In this paper, it is defined that how the IPOs are affected by the underwriter reputation. What will happen to IPOs who are backed by reputed underwriter and who is not? And evaluate the IPOs short-run and long-run performance. Also try to explain the relationship between IPOs and underwriter. Also tried to compare some of the international return and Pakistani returns. Also determine the relation of the investor to the underwriter. There is a set of investors who have there own relationship with the underwriters how they will affect on the IPOs in the market.

The objective of this study was to investigate the relationship between high-performance work systems and a company’s social performance, taking into account the organizational climate as a situational variable. Specifically, this study aimed to analyze the impact of perceptions of a high-performance work system on the perception of a company’s social responsibility (CSR) among employees of Jordanian companies with an organizational size of 300 or more. This study used 175 valid questionnaires, and SPSS and AMOS 24 were used to test the hypotheses. The results showed that there was a positive relationship between the perception of a high-performance work system and the perception of a company’s CSR. Furthermore, this study found that a positive diversity climate could enhance the positive relationship between the two. These findings suggest that high-performance work systems can contribute to the development of sustainable human resource management systems and that a positive diversity climate is essential in shaping these systems. Based on these results, it is recommended that companies implement HPWSs and encourage workforce diversity to maximize the value of CSR activities and ensure their sustainability.

The nuclear reactor measurement system is a crucial component of the nuclear measurement system in a nuclear power plant. Its primary function is to measure the power distribution within the reactor core using detectors, calibrate, and provide data on core fuel consumption. Therefore, the study of the detectors' reliability is crucial for ensuring the safe operation of the nuclear power system. The article describes the operational mechanism of the gas ionization chamber detector utilized in the intermediate-range detector of the off-board nuclear measurement system. It also analyzes the movement of particles between the plates during the ionization process of the nuclear reaction taking place in the detector. The functional relationship between the current in the recombination region and the ionization region involved in the actual working condition, and the change with the increase of voltage between the plates, is deduced. A mathematical model is established for the number of pulses generated between the plates and the magnitude of the voltage between the plates. Subsequently, the change in the offset of the voltage corresponding to the inflection point of the ping curve and the change rule of the slope of the ping curve are investigated. Combined with the analysis of experimental data, the results show that the initial particle energy of Ar+, the primary sensitive particle on the detector surface, follows a normal distribution and is influenced by the energy loss associated with the initial velocity and the voltage between the plates. The trends of the inflection points and slopes of the ping curves can be utilized to evaluate the performance and operational status of the detector. Combined with the results of the experimental data analysis, the fit is good, and the inflection point currents in the normal state decrease by about 1.5% annually. The intelligent diagnostic system, based on the method studied in this paper, has been successfully implemented in the RPN digital retrofit and upgrade project at Qinshan No. 2 Plant.

Risk management strategies protect the company as a whole as well as employees from unforeseen financial impacts. As in the case of personal finance, preparing for certain scenarios helps to face any difficulties that the company may encounter and allows it to get out of this impasse.For any project to be successful, the way the project executing agency will deal with potential risks must be determined so that it can identify, mitigate or avoid problems when it needs to do so. Successful project managers realize that risk management is important, because the achievement of project objectives depends on planning, preparation, results and evaluation that contribute to the achievement of strategic goals, especially the sustainable development of societies.Risk management strategies work to protect the company as a whole as well as employees from unexpected financial effects, and as is the case in personal finance, preparing for certain scenarios helps to face any difficulties that the company may face and allows it to get out of this impasse.To ensure the success of your project, determine how you will handle potential risks so that you can identify, mitigate or avoid problems when you need to do so. Successful project managers understand that risk management is important, because the achievement of project objectives depends on planning, preparation, results, and evaluation that contribute to the achievement of strategic objectives.

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

Supplier Relationship Management (SRM) plays a pivotal role in enhancing delivery performance within the e-commerce sector, yet understanding its nuanced impacts remains crucial for optimizing operational efficiencies and customer satisfaction. This qualitative research explores the intricate dynamics of SRM in e-commerce, focusing on strategic alignment, technological integration, trust, collaboration, and risk management as key determinants of delivery reliability. Through in-depth interviews and thematic analysis, the study reveals that strategic alignment of supplier capabilities with firm objectives is fundamental for maintaining a responsive and synchronized supply chain. Technological integration, including AI-driven analytics and blockchain applications, emerges as critical for real-time monitoring and adaptive decision-making, thereby improving delivery accuracy and efficiency. Trust and collaboration are identified as essential pillars for building resilient supplier relationships, fostering transparent communication and joint problem-solving. Effective risk management practices, such as supplier diversification and contingency planning, mitigate disruptions and ensure consistent delivery performance. Despite these benefits, challenges such as technological adoption barriers, cultural differences, communication complexities, resource constraints, and supplier reliability issues persist, necessitating strategic interventions. Recommendations include investing in advanced SRM technologies, promoting cultural understanding, enhancing communication protocols, allocating adequate resources, and fostering collaborative initiatives with suppliers. These strategies aim to overcome challenges and optimize SRM practices, ultimately enhancing delivery performance and competitiveness in the e-commerce landscape.

This study aimed to analyze the effect of a flow training program based on mindfulness applied to young and adult elite handball athletes. A quantitative, quasi-experimental, descriptive data analysis approach was carried out. The sample included 105 athletes (51 female and 54 male). The athletes were divided into two groups: i) experimental (n = 53) and ii) control (n = 52). The results of the repeated ANOVA indicated that the experimental group achieved significant improvements compared to the control group in decision making (n2p=.086), mental well-being (n2p=.045), dispositional flow state (n2p=.103), non-judgment (n2p=.040), refocusing (n2p=.052) and mindful trait in daily life (n2p=.058). In the linear regression test, the factor non-judgment was identified as a predictor of decision making (F(103)=7.99, p=.006) and the predictor for the mental well-being were dispositional flow state (F(102)=29.2, p

The three-pronged sliding universal coupling is a new kind of three-pronged coupling. To study its kinematic and dynamic characteristics, directional cosine matrices are utilized as tools to analyze the coordinate systems by establishing simplified geometric models and corresponding motions. Through the analysis of the motions of the input and output shafts and employing the method of single-force element, a set of equilibrium equations for the forces acting on the output shaft and the input shaft are formulated for solution analysis. The research indicates that during the rotation process, there exists a small angular difference between the input shaft and the intermediate shaft, demonstrating the quasi-constant angular velocity characteristics of the new tripod sliding universal coupling. The curves of the forces and torques acting on each component of the coupling approximate sinusoidal curves. The optimal operating angle range for the new tripod universal coupling is at angle , during which the system exhibits good transmission and mechanical performance.

Asphalt binder is a temperature sensitive material, whose performance is greatly affected by the climate change of the environment. Improper selection of asphalt will cause a lot of damage and affect the durability of the road. The establishment of asphalt pavement performance zoning in Xinjiang, a vast area with great temperature difference, will provide reference for the selection of asphalt suitability, the refinement of pavement design and the sustainable development of road engineering. In this study, 11 years of temperature data in Xinjiang region were collected and analyzed, and 98% reliability of pavement design temperature was used to draw a performance grading map of asphalt pavement in Xinjiang region based on ArcGIS platform. Finally, Xinjiang region was divided into 9 performance zones. At the same time, the performance grade (PG) of five kinds of asphalt binders in Karamay were explored. The result shows that there is little difference in continuous PG span between different matrix asphalt binders, the lower penetration grade, the better high temperature performance, the worse low temperature performance. After adding SBS modifier, the continuous PG span can be higher than the matrix asphalt in 20 ℃. All these provide the basis for reasonable selection of asphalt binders in different areas of Xinjiang.

In order to explore the effects of glutamate on the growth performance, antioxidant capacity and protein metabolism of juvenile Chinese mitten crab. 0%, 1% and 2% glutamate were supplemented to low protein (30%) and normal protein (35%) diets respectively. There were 5 parallel tanks in each treatment, and the feeding duration was 8 weeks. The results showed that dietary glutamate did not significantly affect the weight gain of Chinese mitten crab. Diets supplemented with 2% glutamate significantly decreased the crude protein of crabs. The T-AOC of crabs fed the 30% protein diets was significantly lower than crabs fed the 35% protein diets. At 30% protein level, the superoxide dismutase (SOD) activity significantly increased with the increase of glutamate content. Dietary glutamate significantly down-regulated the relative expressions of mTOR, PI3K, S6K1 and 4EBP at 35% protein level. In conclusion, dietary glutamate cannot significantly increase the growth of Chinese mitten crab, but it can improve the antioxidant capacity in Chinese mitten crab under low protein condition.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

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.

Emergence of COVID-19 had profound impact on global stock markets, including Indian mar-ket. Key indices like NIFTY50 and Sensex dropped by approximately 40% within two months in 2020, which also affected major mutual funds, leading to significant depreciation of their Net Asset Value. Securities and Exchange Board of India (SEBI) allows mutual funds to use deriva-tives for hedging purposes, capped at 100% of the scheme's net assets covering debt, equity, and derivatives. However, circular amendment by SEBI in January-2019 permitted covered call strat-egies on constituent stocks of Sensex and NIFTY50 for mutual fund schemes, excluding ETFs and Index Funds. This study investigated whether mutual funds increased their derivative us-age as risk mitigation strategy during COVID-19 pandemic, analyzing derivative holdings from 2019-2023. Surprisingly, findings indicated a decline in derivative holdings in 2020 compared to pre-pandemic levels. Statistical tests and performance evaluation metrics such as T-test, F-test, Treynor Ratio, Sharpe Ratio, and Jensen's calculations were employed to assess mutual fund performance. Although derivative-holding mutual funds didn't outperform benchmarks in aver-age returns, it showcased significantly reduced volatility, offering investors favorable risk-return trade-offs. research sheds light on derivative utilization in Mutual Fund industry, potentially in-forming regulators, fund managers, and investors in their decision-making

Background: Manoeuvres esbarrada and volta sobre patas are fundamental movements in equestrian activities, yet their kinematics remain largely unexplored. Objectives: This observational study aimed to kinematically describe the movements of esbarrada and volta sobre patas in Criollo breed horses. Methods: Kinematic analysis was conducted on 31 Criollo horses executing esbarrada and volta sobre patas manoeuvres. Results: The esbarrada maneuver covered a distance of 4.28 ± 0.99m in 1.15 ± 0.11s at a velocity of 3.77 ± 0.55m/s. Angular kinematic variables revealed specific joint angles during the maneuver. In volta sobre patas, significant differences were found in spin duration and thoracic limb suspension between the first and second spins. Conclusions: Volta sobre patas demonstrated prolonged spinning duration and increased thoracic limb suspension in the second spin. Additionally, static conformation did not correlate with limb protraction angles during esbarrada.

This study investigated the impact of sustainability reporting on financial performance, with a focus on companies in the Turkish food, beverage and tobacco and textile, wearing apparel and leather sectors. The sustainability reports of 48 companies listed on the Istanbul Stock Exchange for 2022 were studied, and the quality of sustainability practices was determined by using a general index (Sustainability Reporting Disclosure Quality Index (SRDQI)) and three partial indices (Environmental Disclosure Quality Index, Social Disclosure Quality Index, and Corporate Governance Disclosure Quality Index (CGDQI)). To analyze the relationships between financial performance and sustainability practices two types of regression models were developed, based on which eight models were directly examined. The results indicate the complete absence of a statistically significant impact of SRDQI on all financial performance measures used. Among the partial indices, only CGDQI has a significant positive effect on the Assets Turnover Ratio. An analysis of the influence of control variables shows a multidirectional dependence of individual financial performance measures on the size of companies, their age, industry affiliation, as well as on the structure of capital used. Finally, this study provides directions for improving the institutional environment of sustainability reporting for Turkish companies.

We are in the era where various processes need to be online. However, data from digital learning platforms are still underutilised in higher education, yet, they contain student learning patterns, whose awareness would contribute to educational development. This limits development of adaptive teaching and learning mechanisms. In this paper, a model for data exploitation to dynamically study students progress is proposed. Variables to determine current students progress are defined and are used to group students into different clusters. K-means clustering is performed on real data consisting of students from a South African tertiary institution. Cluster migration is analysed and the corresponding learning patterns are revealed.

Performance appraisal is a fundamental indicator in public accountability to achieve the good governance principles. Hence, this study aims to analyze the performance measurement that can improve performance on government agencies in South Sulawesi, Indonesia. The study was conducted in qualitative approach. The research found that non-integrated models of performance measurement in South Sulawesi are inefficient and ineffective. Inefficiency happens because it uses time and cost resources separately, while being ineffective is because both models focuses on each goal instead on performance goals and the process is not supported by the actual management.

With the increasing use of data-driven modeling methods new approaches to complex problems in the field of wind energy can be addressed. Topics reviewed through literature include wake modeling, performance monitoring and controls applications, condition monitoring and fault detection, and other data-driven research. Literature shows the advantages of data-driven methods reduce computational expense or complexity, particularly in the cases of wake modeling and controls, as well as various data-driven methodologies’ aptitude for predictive modeling and classification, as in the cases of fault detection and diagnosis. Significant work exists for fault detection while less work is found for controls applications. A methodology for creating data-driven wind turbine models for arbitrary performance parameters is proposed. Results are presented utilizing the methodology to create wind turbine models relating active adaptive twist to steady-state rotor thrust as a performance parameter of interest. Resulting models are evaluated by comparing root-mean-square-error (RMSE) on both the training and validation data sets, with Gaussian Process Regression (GPR), deemed an accurate model for this application. The resulting model undergoes particle swarm optimization to determine the optimal aerostructure twist shape at a given wind speed with respect to the modeled performance parameter, aerodynamic thrust load. The optimization process shows an improvement of 3.15% in thrust loading for the 10 MW reference turbine, and 2.66% for the 15 MW reference turbine.

Non-compliant (NC) structures are often encountered in building construction in developing countries. This is a condition whereby erected buildings do not follow regulatory standards, especially in the provision of reinforcements. Therefore, under an earthquake load, the structures do not have sufficient resistance against such load. The structural elements of the building, which often have NC conditions, are the beam-column joints because they do not contain adequate shear reinforcements due to installation difficulty. This study aims to determine the structural performance of the reinforced concrete (RC) frame, which has a reinforcement deficiency in the beam-column joints. Numerical simulation was conducted on the Kakaletsis structure model by modifying the reinforcement details at the joints to represent the NC conditions. The non-linear modeling and analysis of the structural model were carried out using ATENA software. The analysis showed that the reduction in shear reinforcements in the NC model joint caused a decrease in load-carrying capacity by about 26.04%. A shift was also observed in the collapse pattern, i.e., from the beam to the end of the column, causing global failure and reducing the undamaged but increasing more severely damaged condition. This showed that the NC structure, due to the non-conformance of reinforcement, significantly reduced the structure's performance.

Vertical-axis wind turbines (VAWTs) are receiving more and more attention as they involve simple design, cope better with turbulence and are insensitive to wind direction which has a huge impact on the cost since the yaw mechanism is not needed. However, VAWTs still suffer from low conversion efficiency. As a results tremendous efforts are exerted to improve its efficiency which mainly focus on two methods regardless of whether the study is a CFD simulation or a field test or lab test experiment. An active approach which involves the modification of the rotor itself such as the blade design, angle, trailing and leading edges, inner blades, chord thickness, contra-rotating rotor, etc. While the second approach involves passive techniques where the flow is directed to optimally face the downwind rotor by mounting a guiding vane such as a diffuser or other shapes at the upwind position of the rotor. Among all techniques undertaken, the CRWT rotor technique seems to be the most effective with an output even comparable to that of Horizontal Axis Wind Turbines (HAWT), while the Savonius rotor received more attention compared to other designs of VAWTs. Apart from technological issues, it was also suggested that geographical issues such as proper site siting of the wind turbine rotor at a particular location where a uniform flow could be guaranteed is of paramount importance to ensure an effective conversion capacity of wind turbines.

The medical sector is of the utmost importance in every country for well-known reasons such as their economic development, national security, quality of life, and so on. Many researchers were dedicated to this topic, however, in Romania there are limited studies on this subject. One reason is that most of the businesses in this sector are individual enterprises or small family businesses. In our research paper we deploy several analysis methods to see the financial sustainability of companies operating in the medical industry. By analyzing a sample of 23931 companies and we have concluded that their performance has increase in the peak of the pandemic and that the financial stability is high. Our results are in line with other similar studies that were conducted in other regions.

Self-report measurement is frequently used in human sports to assess training adaptations [1, 2]. This is a technique novel to the equestrian sport. The aim of this study was to describe how elite event riders of Germany evaluate their own fitness as well as that of their horses for the entire duration of an eventing season. Additionally, correlations with possible influencing external factors such as type of training or duration of grazing were also investigated. Short online questionnaires were used to allow eight riders to describe the training of their horses. Five criteria (general performance, ability to concentrate, general state of mind, general muscular condition and emotional wellbeing) were used to assess both, their horse's, as well as their own wellbeing. A 1-10 rating scale was used to score each criterion resulting in an average of 38 points out of 50 (maximum wellbeing) for horse (Std. dev. 0.55) and rider (Std. dev. 0.65) respectively. A total of 120 questionnaires were analysed. A correlation could be demonstrated (Spearman's correlation coefficient = 0.5) between rider and horse score, whereby it only became a linear correlation above 30 points. In addition, the horse score correlates positively with its success at competitions. While the horses were rated similarly throughout the season, the riders’ perceived welfare was higher in the middle of the season compared with the beginning and end of the season. Regularly turned-out horses resulted in higher horse scores (Linear mixed model, p=0,04, n=120). It can be concluded that the self-report measurement of fatigue and wellbeing can be a useful tool for trainers and riders to better determine the horse’s fatigue and recovery. It should be noted that external factors influence both, rider and horse assessment.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

This research investigates the fatigue behaviour and fracture mechanics of high-performance concrete (HPC), including various compositions such as HPC with basalt aggregates (HPC-B), HPC with gravel (HPC-G), and high-strength coarse mortar (CM) static and cyclic tensile loading within the special priority program SPP 2020. The study aims to integrate fracture mechanics into structural analysis to enhance design guidelines for slender cross-sections and safety-related high-performance structural components. The experimental investigations reveal HPC-B's re-markable superiority, displaying higher compressive strength, modulus of elasticity, and tensile strength compared to HPC-G and CM. A modified disk-shaped compact tension (MDCT) based on ASTM standards, aided by digital image correlation (DIC) unveils fracture behaviour, empha-sizing fracture energy as a crucial parameter. HPC-B exhibits improved crack resistance and notch sensitivity reduction attributed to crushed basalt aggregates and enhanced interfacial transition zone (ITZ). The research scrutinizes factors like material characterization, aggregate morphology, stress levels, and displacement rate on crack formation. High-cycle fatigue tests show HPC-B's superior performance, and post-fatigue analysis reveals enhanced residual fracture toughness attributed to nano-level structural changes, stress redistribution and aggregate-matrix interac-tion. 3D image analysis via Computed Tomography (CT) scans captures mesostructural crack propagation and provide quantitative insights. This study advances the understanding of HPC fatigue behaviour, emphasizing the interplay of aggregate types, morphologies, and their dy-namic response to cyclic loading, offering valuable insights for optimizing design guidelines and fostering innovation in structural engineering.

Background: Monitoring the mobility of players during wheelchair sports is essential to support coaches in their understanding of the activity and in their training programming. However, the amount of information available from the monitoring tools, combined with a general approach to processing and a poor presentation of the data to the coaches, is not effective and remains unused. Thus, this study aimed to propose a simple and efficient algorithm for identifying locomotor tasks (static, forward/backward propulsion, pivot/tight/wide rotation) during wheelchair movements based on wheelchair kinematic data. Methods: A total of 36 wheelchair tennis and badminton players participated, completing at least one of three proposed tests: the star test, the figure-of-eight test, and the forward-backward test. Locomotor tasks were identified using a five steps procedure including data reduction, symbolic approximation and pattern logical search. Results: Using this method, 99% of locomotor tasks were properly identified for the star test, 95% for the figure-of-eight test and 100% for the forward-backward test. Conclusion: This method appears to be a valuable tool for a simple and clear identification and representation of locomotor tasks over extended periods of time. Future research should aim at applying this method to multiple wheelchair court sports matches or daily life declination.

Despite soccer popularity, comparatively little scientific information is available concerning the assessment of youth players’ physiological characteristics. The use of medical software integrating wearable sensors to provide accurate and objective data is necessary. In the present study, the objective assessment of physical performance of 128 adolescent soccer players at two timepoints (pre-season and end-season) has been performed. Participants completed a comprehensive test battery including stability, countermovement jumps (CMJ), plyometric jumps (PJ), agility, and quick feet tests. A single Inertial Measurement Unit (IMU) sensor provided quantitative data on various performance metrics. Principal Component Analysis (PCA) reduced the dataset to four principal components (PCs) explaining over 80% of the variance: power (PC1), balance (PC2), speed/agility (PC3), and stiffness (PC4). Paired samples t-test between the two timepoints showed no significant differences in the PCs between the two timepoints, indicating that the training program did not significantly alter the assessed physical attributes. However, subtle individual changes may still inform personalized training adjustments. The study highlights the importance of continuous monitoring and individualized training strategies to optimize athlete performance and reduce injury risk.

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.

The purpose of this study is to explore the effect of Environment, Social, and Governance (ESG) scores on Return on Assets (ROA), Risk-Adjusted Return on Capital (RAROC), and Tobin's Q on firm performance. This study used 37 non-financial companies listed on the IDX between 2018 and 2022 in Indonesia companies. Linear and nonlinear panel data regression methods were used on the ESG score and its pillars, resulting in eight different models. The study found a U-shaped impact of ESG scores and social and environmental pillars on ROA. There is a positive linear relationship between ESG scores and the environmental and governance pillars with RAROC, while the social pillar shows an inverted U-shaped effect. However, ESG and its environmental and social pillars have a negative linear relationship with Tobin's Q. The findings suggest the need to manage ESG initiatives strategically, aligning them with financial objectives and awareness of how ESG factors relate to profitability.

The study examines how the South African construction industry can nurture an entrepreneur and a large successful entrepreneurial construction company, even though the founder had no formal education and the company was founded during the Apartheid era. The question of whether entrepreneurs are born or are made is based on the age-old question of nurture and nature. The paper presents the narratives of a successful entrepreneur Mr. Sam Lubbe. The narratives presented are collected through a case study research approach. The data collected suggests that although Sam does not have any formal education, he succeeded based on nurturing given to him when he had the opportunity to work for a large South African construction company, his innate characteristics of self-confidence, task-result orientation, originality, future direction, and a unique business model which also helped him access international construction work opportunities.

An investigation was carried out to study the effects of television and internet on academic performance of senior secondary schools students in Rigachukun Inspectorate of Kaduna state. A well structured and designed questionnaire was adopted in eliciting information from the respondents. The respondents were sampled from schools within Kaduna state. The information obtained showed that the percentage of senior secondary school students who made use of internet for academic purpose was as much as those who could not operate a computer or even browse the internet. Good number of students applied internet mostly through phones and computer with internet access in solving their assignment. A larger percentage of students devoted their time to watching non-educative programs on television, even though it was discovered that some of them also watch educative programs. Positive impacts of television and internet are however obscuring and not glaring. Investigation carried out revealed that students in senior secondary schools need to be sensitized and oriented on how they can derive the best from internet and television. Schools should be encouraged in using television and internet as an instrument of learning and teaching.

Background.Nurse gives professional nursing service to patients according to competence owned. Based on these services in the end of every year, nurses are given incentives for medical services as financial incentive. Purpose. To analyze the impact of competence training and incentives medical services’ distribution upon the achievement of nurse’s performances in General Hospital of W. Z. Johannes Kupang. Matterial and method. Observational survey research with cross sectional design. Population. Nurses in General Hospital of W. Z. Johannes Kupang. Technique. Total sampling technique with number of samples 183 respondents. Inclusive criteria of samples are willing to be interviewed, nurse’s status, permanent offices without limitation from particular working unit or poly in hospital. Independent variable: (1) competence training; (2) Incentives for medical services. Dependent variable; Nurse’s performance achievement. Variables are measured by using Lickert scale. Information are recorded through questionnaire. Analyzes using linier regression. Result. There are impacts of competence training (α=.000;ß=.489)and distribution of incentives for medical services(α=.012; ß=.152)upon nurse’s performances achievement in General Hospital of Prof.W. Z. Johannes Kupang. Conclusion.The training of nursing service status that could increase competences among nurses with affair incentives for medical services’ distribution simultaneously end up in the increasing of nurse’s performances achievement in a hospital

Covid-19 is a disease caused by SARS-CoV-2, which has spread worldwide since the beginning of 2020. Several pharmaceutical and non-pharmaceutical strategies were proposed to contain the virus dissemination, including vaccination and lockdowns. One of the consequences of the pandemic was the denial or delay of access to convenient health care services, but also potentially the increase of adverse events within those services, like the number of hospital infections. Therefore, the main question here is: What happened to the performance and sustainability of hospitals? The main goal of this work was to test if the Portuguese public hospitals' performance has been affected by the SARS-CoV-2 pandemic. We used the Benefit-of-Doubt method integrated with the Malmquist Index to analyze the performance evolution over time. Then, we employed a multiple regression model to test whether some pandemic-related variables could explain the performance results. We considered a database of 40 Portuguese public hospitals evaluated from January 2017 to May 2022. The period 2017 to 2019 corresponds to the baseline (pre-pandemic), against which the remaining period will be compared (during the pandemic). We also considered fourteen variables characterizing hospital quality, divided into three main performance definitions (efficiency and productivity; access; safety and care appropriateness). As potential explanatory variables, we consider seven dimensions, including vaccination rate and the need for intensive care for Covid-19 infected people. Results suggest that Covid-19 pandemic features help explain the drop on access after 2020, but not the evolution of safety and appropriateness of care, which surpris-ingly increased the whole time.

Leadership performance by village leaders is essential to promote sustainable life among rural communities, especially fisheries community who living along coastal villages. Otherwise, previous studies found that performance issues among village’s leaders remain as serious problems, and need to find the best solution. This study was conducted to profile the job performance among village leaders based on demographic factors such as educational level, age and experiences as village leader. The data of this cross-sectional survey were collected by questionnaires on 300 respondents consist of members of village organization through stratified sampling’s technique, while the data was analysed by SPSS using items of mean, standard deviation, independent-sample t-test and anova. The finding shown that there were differences in job performance among village leaders on educational level, age and experiences. Interestingly, the finding told the best on job performance among village leaders are (i) the age between 41 to 50 years old; (iii) the experience between 11 to 20 years; and (iii) the higher educational level the higher job performance among them. This result can be using by government or any responsible parties to improve job performance among village leaders, especially for recruitment selection and for in-service training.

This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.

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.

Conventional heat treatment is not capable of converting a sufficient amount of retained austenite into martensite in high-carbon or high-carbon and high-alloyed iron alloys. Cryogenic treatment induces the following alterations in the microstructures: (i) a considerable reduction in the retained austenite amount, (ii) formation of refined martensite coupled with an increased number of lattice defects, such as dislocations and twins, (iii) changes in the precipitation kinetics of nano-sized transient carbides during tempering, and (iv) an increase in the number of small globular carbides. These microstructural alterations are reflected in mechanical property improvements and better dimensional stability. A common consequence of cryogenic treatment is a significant increase in the wear resistance of steels. The current review deals with all the mentioned microstructural changes as well as the variations in strength, toughness, wear performance, and corrosion resistance for a variety of iron alloys, such as carburising steels, hot work tool steels, bearing and eutectoid steels, and high-carbon and high alloyed ledeburitic cold work tool steels.

The study aimed to investigate the impact of incorporating deep learning strategies in mathematics instruction on students' problem-solving abilities, critical thinking skills, academic achievement, and mathematical skills development. The study population consisted of 280 students, with a sample size of 162 participants. Employing a purely quantitative approach, data collection utilized questionnaires and achievement tests as instruments. Data analysis involved simple linear regression, descriptive analysis, and Pearson correlation. The results of the study revealed that there was no significant enhancement observed in students' problem-solving abilities and critical thinking skills when deep learning strategies were incorporated into mathematics instruction. Additionally, students' attitudes towards deep learning activities in mathematics showed no effect on their academic achievement. Furthermore, students' engagement levels in deep learning activities did not impact their development of mathematical skills. Lastly, the frequency of deep learning activities in the mathematics classroom did not show a significant relationship with students' academic performance. These findings suggest that the implementation of deep learning strategies in mathematics instruction did not yield notable improvements in the specified areas based on the study outcomes.

Taiwan's bed and breakfast (B&B) development has been more than 25 years ago, from the first generation of retirement to the B&B industry, serving tourists from all over the world accom-modation, providing breakfast and accommodation mode to the present, after the change of employees from the second generation, more and more attention is paid to the cultivation and cultivation of business ability. With the advent of the era of AI technology networking, the ca-pabilities required for B&B operation are more focused on all aspects of service, and the good marketing ability of B&B can promote a green chimney-free tourism economy, which is also a favorite industry of various governments and the public. This study explores the basic vocational competency indicators required for running a B&B, with (1)EQ emotion management skills, (2)Financial management ability, (3)(SK) technical skills, and (4)Marketing and promotion management capabilities. Through the study method, the K-S Z test was determined by the con-sensus conclusion of expert reliability and validity p value

French academies almost all implement reactivity drills and small-sided games (SSG) the day preceding a match (MD-1). The present study aimed to determine the physical impact of different training durations on MD-1 on the subsequent matchday performance (MD). Eleven elite U19 academy soccer players con-ducted three typical training sessions lasting 45min (TS45), 60min (TS60), and 75min (TS75) on MD-1. Dur-ing TS60, warm-up, reactivity and SSG were 10, 15, and 24min, respectively, plus coaches' feedback or wa-ter breaks. Durations decreased and increased by 25% for TS45 and TS75, respectively. Tests were con-ducted on MD-4 (CONTROL) and MD before the match (TEST). Tests consisted of a counter movement jump (CMJ), 20m sprint, Illinois agility test (IAT), and Hooper questionnaire. CONTROL values were simi-lar over the three experimental conditions. TEST on MD revealed greater CMJ for TS45 (42.7 ± 5.1cm) compared to TS60 (40.5 ± 5.5cm, p=0.032) and TS75 (40.9 ± 5.7cm, p=0.037). 20m time was lower for TS45 (3.07 ± 0.10s, p<0.001) and TS60 (3.13 ± 0.10s, p=0.017) compared to TS75 (3.20 ± 0.10s). IAT time was lower on TS45 (14.82 ± 0.49s) compared to TS60 (15.43 ± 0.29s, p<0.001) and TS75 (15.24 ± 0.33s, p=0.006). Further-more, the Hooper index was lower at TEST for TS45 (7.64 ± 1.50) compared to TS60 (11.00 ± 3.49, p=0.014) and TS75 (9.73 ± 2.41, p=0.045) indicating a better readiness level. We concluded that as training session duration increases, performance decreases on MD. A 45min training session including reactivity and SSG exercises is therefore recommended on MD-1.

Hospital indebtedness is a complex and very diverse phenomenon. Thus, the goal of this study is the comparison of the financial performance of public hospitals in accordance with their ownership and size. The results of the research lead to the conclusion that the vast majority of public hospitals are indebted, and their ownership structure does not affect their financial condition. What is more, the statistical analysis depicted that large Marshall hospitals are less indebted than poviat-commune ones. In the group of medium-size hospitals, the situation was the opposite. Moreover, the study did not confirm the significant relationship between the size or ownership and the financial status of the hospital. The analysis conducted in the article is aimed at filling in the gap in studies comparing the indebtedness between different types of public hospitals.

Recycled asphalt mixture is a material remixed with old asphalt recycled material (RAP) and new aggregate, and its application is of great significance in environmental protection.Due to the wear and tear of the old asphalt mixture, the road performance of the recycled asphalt mixture will decrease. This paper uses IPP software to obtain the shape characteristics of the old and new aggregates, and found that the roundness of the old aggregate of 9.5mm-16mm is the most serious wear. Therefore, the 30% RAP, and the influence of the roundness of the recycled asphalt mixture on the road performance is studied.

Clustering is an important task in data mining that has become more challenging due to the ever-increasing size of available datasets. To cope with these big data scenarios, a high-performance clustering approach is required. Sparse grid clustering is a density-based clustering method that uses a sparse grid density estimation as its central building block. The underlying density estimation approach enables the detection of clusters with non-convex shapes and without a predetermined number of clusters. In this work, we introduce a new distributed and performance-portable variant of the sparse grid clustering algorithm that is suited for big data settings. Our compute kernels were implemented in OpenCL to enable portability across a wide range of architectures. For distributed environments, we added a manager-worker scheme that was implemented using MPI. In experiments on two supercomputers, Piz Daint and Hazel Hen, with up to 100 million data points in a 10-dimensional dataset, we show the performance and scalability of our approach. The dataset with 100 million data points was clustered in 1198s using 128 nodes of Piz Daint. This translates to an overall performance of 352TFLOPS. On the node-level, we provide results for two GPUs, Nvidia's Tesla P100 and the AMD FirePro W8100, and one processor-based platform that uses Intel Xeon E5-2680v3 processors. In these experiments, we achieved between 43% and 66% of the peak performance across all compute kernels and devices, demonstrating the performance portability of our approach.

With the increased emphasis on competition in academic settings, anxiety is becoming more common, which inevitably has some impact on students’ learning process and result. This study aimed to explore how competition-induced anxiety influences the subjective cognitive load (SCL), attention level and test score. Meanwhile, we investigated the mediating role of the behavioral inhibition system/behavioral activation system (BIS/BAS) in those processes. A total of 101 college students were recruited in Study 1 to learn five micro-lectures from massive open online courses (MOOC) under with and without competitive conditions. The results showed that participants’ state anxiety (SA) scores were higher after the experiment, participants in the competition condition had higher test scores, and the relationship between SA/ trait anxiety (TA) and SCL could be mediated by the BIS. To obtain more objective data on learning processes (attention levels), we conducted Study 2, which collected behavioral and EEG data from 42 college students during the online learning. The results showed that the competition group had higher SA, lower attention levels, and worse test scores, and the relationship between SA/TA and attention levels could be mediated through the BIS. The present study not only expands previous research by finding that BIS functioning plays an important role in the effects of anxiety on cognitive load and attention, but also offers implications for using competitive strategies to motivate students according to their aptitude.

Introduction: Academic success is primary goal of every student. It is described as the extent to which a student has successfully achieved his or her short and long-term educational goals. Several factors have been established to predict academic performance of students. Machine learning techniques have been employed in predicting students’ performance, but it has not been prevalent in developing countries like Nigeria and most studies did not consider class teachers’ end-of-the-year rating. Aim: The aim of this work is to develop a Radial Basis Function Neural Network (RBFNN) for prediction of secondary school students’ performance. Materials and Methods: We obtained data from school repository containing students’ raw score and classteachers’ rating from year one to year six. The data was labelled into pass or fail given the actual outcome of their examinations. Subjects were categorized into Mathematics, English and major, depending on the student’s specialization. Class-teachers’ ratings were also included in the dataset. The preprocessed dataset was used to train the RBFNN model. The impact of Principal Component Analysis (PCA) was also measured. Results: We set up four experiments in order to achieve our aim. The best result gave the sensitivity of 93.49%, specificity of 75%, accuracy of 86.59% and an AUC score of 94%. Other experiments gave a relatively low performance. Conclusion: This study helps students to get a projection of academic success even before sitting for the examination. This will also help parents and counsellors in knowing the direction of their counseling to each student. Teachers and parents should pay attention to class teacher ratings of the students as this is discovered to affect the prediction accuracy of their examination success.

The importance of this study may be defined by using the smart techniques to earned value indicators of residential buildings projects in Republic of Iraq, only one development intelligent forecasting model was presented to predict Schedule Performance Index (SPI), Cost Performance Index (CPI), and To Complete Cost Performance Indicator (TCPI) are defined as the dependent. The approach is principally influenced by the determining numerous factors which effect on the earned value management, that involves Iraqi historical data. In addition, six independent variables (F1: BAC, Budget at Completion., F2: AC, Actual Cost., F3, A%, Actual Percentage., F4: EV, Earned Value. F5: P%, Planning Percentage., and F6: PV, Planning Value) were arbitrarily designated and satisfactorily described for per construction project. It was found that ANN has the capability to envisage the dust storm with a great accuracy. The correlation coefficient (R) has been 90.00%, and typical accuracy percentage has been 89.00%.

Supplier Relationship Management (SRM) is a critical aspect of supply chain management, particularly in today's dynamic and interconnected business environment. This qualitative research study explores SRM practices within the context of supply chain dynamics, aiming to understand how organizations manage their supplier relationships to achieve strategic objectives. Through semi-structured interviews, document analysis, and participant observation, key themes emerge, including collaboration, trust, risk management, performance measurement, digital technology integration, sustainability, cultural alignment, supplier development, strategic alignment, emotional intelligence, and marketing perspectives. The findings underscore the importance of collaboration and trust in fostering strategic partnerships with suppliers, mitigating risks, and enhancing supply chain resilience. Effective risk management practices, performance measurement systems, and digital technology integration are essential for improving transparency, efficiency, and responsiveness in SRM. Sustainability considerations highlight the need for aligning SRM practices with corporate social responsibility goals and stakeholder expectations. Cultural alignment, supplier development programs, strategic alignment, emotional intelligence, and marketing perspectives further enrich the understanding of how organizations navigate their supplier relationships. This study contributes to the body of knowledge on SRM by providing empirical insights into the practices, challenges, and opportunities associated with managing supplier relationships in dynamic supply chains. The findings offer valuable guidance for practitioners and policymakers seeking to enhance their SRM practices and achieve competitive advantage in today's complex business landscape.

Clustering, or otherwise known as cluster analysis, is a learning problem that takes place without any human supervision. This technique has often been utilized, much efficiently, in data analysis, and serves for observing and identifying interesting, useful, or desired patterns in the said data. The clustering technique functions by performing a structured division of the data involved, in similar objects based on the characteristics that it identifies. This process results in the formation of groups, and each group that is formed, is called a cluster. A single said cluster consists of objects from the data, that have similarities among other objects found in the same cluster, and resemble differences when compared to objects identified from the data that now exist in other clusters. The process of clustering is very significant in various aspects of data analysis, as it determines and presents the intrinsic grouping of objects present in the data, based on their attributes, in a batch of unlabeled raw data. A textbook or otherwise said, good criteria, does not exist in this method of cluster analysis. That is because this process is so different and so customizable for every user, that needs it in his/her various and different needs. There is no outright best clustering algorithm, as it massively depends on the user’s scenario and needs. This paper is intended to compare and study two different clustering algorithms. The algorithms under investigation are k-mean and mean shift. These algorithms are compared according to the following factors: time complexity, training, prediction performance and accuracy of the clustering algorithms.

In this paper, the sol−gel method was used to synthesize powders of LaMnO3 (LMO), La0.85Ca0.15MnO3 (LCM), and La0.85Sr0.15MnO3 (LSM). We investigated the impact of substituting Ca and Sr at the A−site on the perovskite crystal structure and electrochemical capabilities of LMO. LCM retained its orthogonal structure in comparison to the parent LMO components, whereas LSM transitioned to a rhombic structure. At 0.5 A/g, the specific capacitance of LCM and LSM electrodes is 185.5 F/g and 248 F/g, respectively.The specific capacitance of LCM was more three times than that of the LMO electrode. Among the three samples (LMO, 22.25 m2 g−1; LSM, 31.56 m2 g−1), the LCM sample exhibited the highest specific surface area of 38.79 m2 g−1. The charge transfer resistances of the LMO, LCM, and LSM are 0.48 Ω, 0.36 Ω, and 0.38 Ω, correspondingly. The LCM electrode exhibits the greatest capacitance performance due to its more refined morphology, increased concentration of oxygen vacancy, and more complete utilization of the perovskite bulk structure. The above results demonstrate that Ca or Sr substitution of A−site compounds has great potential for supercapacitor applications.

Expensive cameras meant for research applications are usually characterized by the manufacturers and detailed specifications [1] are available for them. Suppliers of inexpensive cameras usually do not provide such detailed information about their cameras. This data set provides the acquisition speed and noise characteristics acquired from a monochrome 1.2 megapixel CMOS camera, the QHY5L-II M [2]. The source code provided along with this data set [3] can also be used to acquire similar data for other QHY cameras. This enables the use of such cost-effective cameras for other scientific applications in other fields, beyond the designed use in Astronomy.

The Individual Work Performance Questionnaire (IWPQ) can be defined as the individual be-haviour capable of generating value and competitive advantage for the organization. It is a mul-ti-dimensional construct, consisting of task performance, contextual performance and counter-productive work behavior. Albeit important, a comprehensive measure of individual job per-formance is still lacking in the Italian context. This paper describes the validation of an Italian adaptation of the IWPQ. A back translation procedure was used. To investigate the psychometric properties of the 18-item Italian IWPQ, 1053 participants (502 men, 47.7%; 551 women, 52.3%) were enrolled; the age of participants ranged between 19 and 69 (Mage=34.6, SD= 11.8), and for the oc-cupation, the sample is classified in blue collar 32.5%, pink collar 33% and white collar 34.5%. Exploratory and confirmatory factor analyses and multigroup confirmatory factor analyses were performed to test the structural factor of the IWPQ. The results supported the goodness of the IWPQ in the Italian context and the scales were found consistent with the original structure. One item was omitted due to a low factor loading, resulting in a final 17-item structure. Multigroup confirmatory factor analysis showed that the factor solution was invariant across both gender and occupational category and found evidence of metric invariance, uniqueness and scalar invariance, and structural invariance. Convergent and discriminant validity were also tested and showed.

Formation in soccer is among the most important tactical choices for a successful match.Herein, the simulations of 420000 match-plays have been performed varying the formation, the number of opponents that are actively pressing the team, the speed of the opponents in attempting a pass interception. Dribbling has been neglected. The match-play ends either with a successful series of passes from a central back to the line of the strikers or with the opponents that steal the ball. In this work, I demonstrate that 3-4-3 formation, which is among the most employed formations, relates to the highest probability of success.

In this paper, the effect of an exterior shading element (Iwan) on energy consumption in four different climatic regions, and for different geographical directions, has been investigated numerically and experimentally. By applying different materials and techniques and creating various elements and spaces, architects make hard climatic conditions more tolerable for residents. Iwan is one of the cooling elements which is used in different forms and dimensions in the Islamic architecture. In the present research, Iwan has been introduced as a climatic element in traditional and contemporary architectures and its role in reducing the energy consumption in buildings has been studied. In this respect, first, the thermal loads of a building without Iwan are computed by means of EnergyPlus software. Then, four different forms of Iwan are added to the above-mentioned structure along the four principal geographical directions, and the effect of Iwan on the reduction of thermal loads is analyzed for four different climates. Finally, the design parameters of Iwan, in terms of depth and form, that can help reduce the thermal loads in different climatic conditions are presented. The results show that the best position for using an Iwan is the south direction and the use of Iwan in temperate & humid, hot & humid, cold & mountainous and hot & dry climates could reduce the energy consumption in buildings by 32%, 26%, 14%, and 29%, respectively.

This study offers a concise summary of the research that has been done on alternative methods of HRM. A survey of 47 articles on the topic of creative human resource management uncovered three primary schools of thinking. At its core, innovation in HRM entails nothing more than that. One such topic worthy of investigation is the way human resource management approaches new situations. Third, research on inventiveness in the business world has given rise to the concept of "innovative HRM." Each of these three approaches, as well as the theoretical underpinnings of each, will be covered in this article. The note wraps up with a discussion of the significance of the findings for next research as well as clinical practice.