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.

The objective of this study was to examine the impact of utilizing smart algorithms on enhancing the operational performance of sports facilities in the Kingdom of Saudi Arabia. These algorithms, based on principles and concepts of artificial intelligence, aim to achieve functions such as learning, decision-making, data analysis, pattern recognition, planning, and problem-solving. The study aimed to identify the extent to which smart algorithms are utilized in sports facilities, assess the level of operational performance, explore the correlation between the use of smart algorithms and operational performance, and predict the level of operational performance based on the use of smart algorithms. The study employed a descriptive approach, specifically utilizing a survey study method. Participants included chairmen and members of boards of directors, executive directors, sports directors, administrators, specialists, and members of various committees. The study sample was intentionally selected from different categories within the study population. Two questionnaires were used to collect data from 325 participants. The findings revealed a lack of utilization of smart algorithms in sports facilities in the Kingdom of Saudi Arabia, indicating a low level of operational performance. Additionally, a correlation was observed between the use of smart algorithms and operational performance, suggesting that the level of operational performance can be predicted based on the utilization of smart algorithms. The study concludes that the implementation of intelligent algorithms can enhance the operational performance of sports facilities in the Kingdom of Saudi Arabia. It provides valuable insights into the effects of utilizing smart algorithms on improving operational performance.

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%.

Human resource management practices are crucial, especially in the private healthcare sector. This could be because managing personnel in the healthcare sector is particularly challenging; therefore, meeting every employee's needs is crucial. Recently, the healthcare sector has experienced a scarcity and unbalanced distribution of employees due to job turnover. In addition, employee performance in the private healthcare sector has shown a slight drop due to the dissatisfaction of employees toward human resource practices such as unattractive compensation and rewards packages, bias in performance appraisal, lack of training and development, and many more. Therefore, this study is conducted to examine the impact of human resource practices on employees' job performance. Specifically, there are three main human resource practices observed as factors that contribute to an employee's job performance. The three human resource practices are compensation and benefits, performance appraisal, and training and development. There were four private hospitals operating in Selangor, Malaysia, chosen as a sample for this study. The private hospitals are KPJ Selangor Specialist Hospital, Columbia Asia Hospital Puchong, Assunta Hospital PJ, and Sunway Medical Centre. Out of these four private hospitals, there were about 291 employees working at the front desk: nurses, clinical workers, and administration staff were chosen as respondents in this study. The questionnaires were distributed to the respondents by hand. The data collected was analyzed using SPSS version 29. The findings indicate that employee job performance in Malaysian private hospitals is positively correlated with compensation and benefits. Employees feel motivated by compensation, which encourages them to increase their production and work more efficiently. Additionally, the findings also suggest that performance appraisal and training and development significantly contribute to employee job performance.

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.