The purpose of this research is to present a bibliometric analysis of the literature on the ways in which the motivations of individual sports consumers impact the creation of sports infrastructure and the creation of sports-related policy. Design/methodology/approach: Based on the PRISMA approach and information gleaned from the Scopus database, 2605 publications were found to be pertinent to the subject. We conducted a literature analysis of trends and patterns using VOSviewer-based knowledge mapping. Findings: Recent years have seen a proliferation of scholarly publications on the topic of individual sports consumption motivation and its influence on policy formulation and infrastructure development. This suggests that interest in this field is expanding. The list of eminent journals, decision-makers, and organizations involved in this issue demonstrates its global influence. The interdisciplinary nature of the subject is reflected in the study’s emphasis on the most widely published authors and key research terminology. Originality/value: This study closes significant knowledge gaps regarding the complex interactions between societal, environmental, and individual factors that affect the motivation to consume sports and how these motivations influence decisions about sports infrastructure and policies. It does this by using bibliometric techniques and the most recent data. The project aims to create a more thorough picture of how public health policy, sports governance, and urban planning are impacted by the motivations behind sports consumption. Policy implications: Policymakers, planners, and sports organizations can use the results to generate more targeted and effective strategies for the development of sports infrastructure and policy formulation. The study highlights how important it is to make well-informed policy decisions and participate in customized involvement in order to improve public welfare and the overall sports consumer experience.

The rapid rise of live streaming commerce in China has transformed the retail environment, with electronic word-of-mouth (eWOM) emerging as a pivotal factor in shaping consumer behavior. As a digital evolution of traditional word-of-mouth, eWOM gains particular significance in live streaming contexts, where real-time interactions foster immediacy and engagement. This study investigates how eWOM influences consumer purchase intentions within Chinese live streaming platforms, employing the Information Adoption Model (IAM) as theoretical framework. Using a grounded theory approach, this research applies NVivo for data coding and analysis to explore the cognitive and emotional processes triggered by eWOM during live streaming. Findings indicate that argument quality, source credibility, and information quantity significantly enhance consumer trust and perceived usefulness of information, which, in turn, drives information adoption and purchase intention. Furthermore, the study reveals that social interaction between live streaming anchors and audiences amplifies the influence of consumers' internal states on information adoption. This study enhances the Information Adoption Model (IAM) by introducing social interaction as a moderator between consumers' internal states toward live streaming eWOM and their adoption of information, highlighting the value of social interaction in live streaming. It also incorporates information quantity, showing how eWOM quantity affects trust and perceived usefulness. Furthermore, the study contributes to exploring how factors like argument quality, source credibility, and information quantity shape consumer trust and perceived usefulness, offering insights into the cognitive and emotional processes of information adoption in live streaming.

Hazards are the primary cause of occupational accidents, as well as occupational safety and health issues. Therefore, identifying potential hazards is critical to reducing the consequences of accidents. Risk assessment is a widely employed hazard analysis method that mitigates and monitors potential hazards in our everyday lives and occupational environments. Risk assessment and hazard analysis are observing, collecting data, and generating a written report. During this process, safety engineers manually and periodically control, identify, and assess potential hazards and risks. Utilizing a mobile application as a tool might significantly decrease the time and paperwork involved in this process. This paper explains the sequential processes involved in developing a mobile application designed for hazard analysis for safety engineers. This study comprehensively discusses creating and integrating mobile application features for hazard analysis, adhering to the Unified Modeling Language (UML) approach. The mobile application was developed by implementing a 10-step approach. Safety engineers from the region were interviewed to extract the knowledge and opinions of experts regarding the application’s effectiveness, requirements, and features. These interview results are used during the requirement gathering phase of the mobile application design and development. Data collection was facilitated by utilizing voice notes, photos, and videos, enabling users to engage in a more convenient alternative to manual note-taking with this mobile application. The mobile application will automatically generate a report once the safety engineer completes the risk assessment.

This study investigates the performance assessment of methanol and water as working fluid in a solar-powered vapour absorption refrigeration system. This research clarifies the system’s performance across a spectrum of operating conditions. Furthermore, the HAP software was utilized to determine and scrutinize the cooling load, facilitating a comparative analysis between software-based results and theoretical calculations. To empirically substantiate the findings, this research investigates methanol-water as a superior refrigerant compared to traditional ammonia- water and LiBr-water systems. Through experimental analysis and its comparison with previous research, the methanol-water refrigeration system demonstrated higher cooling efficiency and better environmental compatibility. The system’s performance was evaluated under varying conditions, showing that methanol-water has a 1% higher coefficient of performance (COP) compared to ammonia-water systems, proving its superior effectiveness in solar-powered applications. This empirical model acts as a pivotal tool for understanding the dynamic relationship between methanol concentration (40%, 50%, 60%) and system performance. The results show that temperature of the evaporator (5–15 ℃), condenser (30 ℃–50 ℃), and absorber (25 ℃–50 ℃) are constant, the coefficient of performance (COP) increases with increase in generator temperature. Furthermore, increasing the evaporator temperature while keeping constant temperatures for the generator (70 ℃–100 ℃), condenser, and absorber improves the COP. The resulting data provides profound insights into optimizing refrigerant concentrations for improved efficiency.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study delves into the complex flow dynamics of magnetized bioconvective Ellis nanofluids, highlighting the critical roles of viscous dissipation and activation energy. By employing a MATLAB solver to tackle the boundary value problem, the research offers a thorough exploration of how these factors, along with oxytactic microorganism’s mobility, shape fluid behavior in magnetized systems. Our findings demonstrate that an increase in the magnetization factor  leads to a decrease in both velocity and temperature due to enhanced interparticle resistance from the Lorentz force. Additionally, streamline analysis reveals that higher mixed convection parameters  intensify flow concentration near surfaces, while increased slip parameters reduce shear stress and boundary layer thickness. Although isotherm analysis shows that higher Ellis fluid parameters enhance heat conduction, with greater porosity values promoting efficient thermal dissipation. These insights significantly advance our understanding of nanofluid dynamics, with promising implications for bioengineering and materials science, setting the stage for future research in this field.