The purpose of this study was to investigate the published literature on human resource management and school performance from January 2012 to December 2022. Numerous literature evaluations have been conducted on human resource management and organizational performance, but school or teacher performance has received less attention than organizational performance. The PICOC (population, intervention, comparison, outcome, and context) technique is integrated into each stage of the PSALSAR framework to assure the study’s objective and comparability. This in-depth research is conducted in three stages: identifying pertinent keywords, screening pertinent papers, and selecting pertinent publications for review utilizing the PRISMA (Preferred Reporting Items for Systematic Reviews and Mata Analysis) technique. This made a final database with 44 publications that met the study’s requirements for inclusion. This study reveals that HRM practices and school performance are correlated. The results of the research identify the eight most essential HRM practices for improving school performance, which included planning, organizing, recruitment and selection, training and development, performance management, employee relations and involvement, reward and compensation, health, safety, and work-life balance. Leadership style, motivation, satisfaction, productivity and task performance, competency, culture and climate, empowerment, and commitment were among the performance-influencing elements.

How to improve enterprise performance has been a research topic widely studied by scholars for a long time. As economic globalization deepens, the business competition becomes increasingly harsh. Technology-based small and medium-sized enterprises (SMEs) play an important role in the rapid development of the country’s economy, especially in China. This study aims to investigate the mediating effect of knowledge integration capability in the relationship between corporate social capital and enterprise performance. The sample group used in this study were 300 technology-based SMEs in China. The research tool was a questionnaire adapted from previous scholars, which passed assessment in terms of content validity and reliability. Data were analyzed using structural equation modelling. The results show that: 1) corporate social capital has a positive impact on enterprise performance, but the impact differs between well-performing and poor-performing enterprises; and 2) knowledge integration ability plays a mediating role in the relationship between corporate social capital and enterprise performance, and the mediating role is the same for both well-performing and poor-performing enterprises. But it played a partial mediating role in the good-performance comparison group and a complete mediating role in the poor-performance comparison group. This study is useful for enterprise management in cultivating and developing the abundant social capital of enterprises and expanding channels for knowledge integration ability to increase enterprise performance.

This research focused on the design and implementation of the flipped classroom approach for higher mathematics courses in medical colleges. Out of 120 students, 60 were assigned to the experimental group and 60 to the control group. In the continuous assessment, which included homework and quizzes, the average score of the experimental group was 85.5 ± 5.5, while that of the control group was 75.2 ± 8.1 (P < 0.05). For the final examination, the average score in the experimental group was 88.3 ± 6.2, compared to 78.1 ± 7.3 in the control group (P < 0.01). The participation rate of students in the experimental group was 80.5%, significantly higher than the 50.3% in the control group (P < 0.001). Regarding autonomous learning ability, the experimental group spent an average of 3.2 hours per week on self-study, compared to 1.5 hours in the control group (P < 0.005). Other potential evaluation indicators could involve the percentage of students achieving high scores (90% or above) in problem-solving tasks (25.8% in the experimental group vs. 10.3% in the control group, P < 0.05), and the improvement in retention of key concepts after one month (70.2% in the experimental group vs. 40.5% in the control group, P < 0.01). In conclusion, the flipped classroom approach holds substantial promise in elevating the learning efficacy of higher mathematics courses within medical colleges, offering valuable insights for educational innovation and improvement.

Objective: to determine the diagnostic performance of magnetic resonance hysterosalpingography (HSG-MRI), using laparoscopy as the reference method. Materials and methods: 22 patients were included. All underwent HSG-MRI with a 1.5 Tesla resonator and then laparoscopy with chromotubation. Two radiologists examined the MRIs, determining tubal patency by consensus. Descriptive and diagnostic performance analyses were performed. Results: HSG-MRI had a success rate of 91%. Study duration was 49 ± 15 minutes, volume injected 26 ± 16 cm³ and pain scale 30 ± 19 out of 100. Sensitivity and specificity of HSG-MRI were 100% for global and left Cotte test, and 25% and 93.3% for right Cotte test, respectively. There were 2 minor complications and no major complications. Discussion: our initial results demonstrated high sensitivity and specificity. Although other studies analyzed the ability of HSG-MRI to assess tubal patency with good results, the use of a flawed reference standard left room for reasonable doubt, preventing a recommendation based on solid evidence. However, when comparing our results with those published, we observed a high degree of concordance insofar as the positive effusion is correctly diagnosed with a specificity of 100% or with a percentage close to this figure.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.