Background: Increased research attention has recently been given to the topic of programming education, due to its significance in promoting students' critical thinking, complex problem-solving, creativity, and communication skills-commonly known as the '4Cs'. In particular, collaborative programming has been recognised as a promising instructional approach to promote students' cognitive competencies in programming education. However, simply offering students opportunities to collaborate will not necessarily improve their 4Cs. Thus, the potential of ChatGPT has been considered to address the drawbacks of conventional collaborative programming and to improve students' cognitive capabilities. Objective: To ascertain whether ChatGPT is a viable instructional tool in collaborative programming, this study proposes the ChatGPT-CIDI (Clarify, Ideate, Develop and Implement) learning model. Methods: A quasi-experimental method was implemented, wherein 61 students from two 12th-grade classes participated. One class (Group One) (n = 28, M-age = 17.25) was assigned to individual-based collaboration using the ChatGPT-CIDI model, while the second class (Group Two) (n = 33, M-age = 17.61) undertook team-based collaboration using the same model. Results and Conclusion: The empirical results demonstrated that this approach significantly improved students' critical thinking in Group One (individual-based), compared to Group Two (team-based). However, neither group experienced significant improvement in their complex problem-solving, creativity, or communication skills. This study concludes with implications for research and practice, while also indicating the research limitations to determine future research directions.
Ramadan fasting is a central religious practice observed by millions of Muslims worldwide, including individuals with diabetes. This expert consensus statement from the Arab Diabetes Forum aims to critically appraise the scientific validity and clinical utility of the International Diabetes Federation—Diabetes and Ramadan Alliance (IDF-DAR) risk stratification tool in the context of real-world Ramadan fasting. The IDF-DAR tool provides a structured framework for pre-Ramadan counseling, its scoring system being based on expert opinion rather than validated clinical outcomes. Observational evidence indicates that morbidity and mortality do not increase during Ramadan, even among high-risk individuals who choose to fast. Fasting has also been associated with favorable metabolic effects, including improved lipid profiles, modest weight reduction, enhanced insulin sensitivity, and potential neuroprotective and immunomodulatory benefits. Despite these findings, limited access to structured education and low attendance at pre-Ramadan clinics reduce the tool’s practical impact of current recommendations. This consensus emphasizes a patient-centered, culturally informed approach that respects autonomy and integrates individualized education and ongoing clinician-patient communication. A contextual re-evaluation of current risk stratification models is proposed to better align endocrine and metabolic guidance with real-world fasting practices and optimize diabetes care during Ramadan.
This study presents a comprehensive analysis of seven giraffid astragali from upper Miocene-Pliocene As-Sahabi deposits (Libya), combining morphological examination with machine learning approaches to resolve long-standing taxonomic uncertainties. Using Linear Discriminant Analysis (LDA)-the most accurate of four tested models (median accuracy = 0.61, kappa = 0.48)-we identified two distinct Samotherium species: S. major (4 specimens) and S. boissieri (3 specimens). Morphometric analyses revealed a significant size difference (S. major was larger by 10.1-12.3%, p < 0.05), supported by geographic segregation and stratigraphic distribution mirroring patterns observed in Samos, Greece. The S. boissieri specimens were exclusively found in the locality P32 (Qarat Makada Member, potentially unit T), while S. major occurred in both Unit U1 of Sahabi Formation and the overlying Qarat Weddah Formation. These findings confirm the extension of the Greco-Iranian bioprovince into North Africa during the late Miocene (MN11-13) and highlight As-Sahabi's importance as a biogeographic crossroads. Faunal associations suggest that S. boissieri inhabited more open landscapes compared with S. major's mixed-habitat preference, indicating ecological partitioning within the Samotherium species.
Supercritical generation technologies have emerged as a promising pathway toward clean and flexible energy conversion. Advancing these technologies demands highly accurate mathematical models capable of supporting innovative control strategies and enhancing energy efficiency. The modeling of industrial systems has evolved from simple first-principle formulations to sophisticated frameworks that preserve their physical foundations while being enhanced through optimization algorithms. As these optimization problems increase in size, sensitivity analysis becomes essential for identifying the most influential parameters, thereby reducing computational complexity without sacrificing accuracy. These critical parameters are then refined using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in both predictive accuracy and computational efficiency. The proposed model holds significant potential for power generation applications, particularly in advanced control system design and real-time performance monitoring. This study developed a supercritical power plant (SCPP) model that achieved improved accuracy while maintaining simplicity compared to existing models. A model of a 600 MW SCPP is first constructed and systematically analyzed to capture its key characteristics. Subsequently, a sensitivity analysis is conducted to identify the parameters with the greatest influence on model performance. These critical parameters are then tuned using an adaptive Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization method. The results demonstrate notable gains in accuracy. The proposed model offers promising potential for power generation applications, particularly in the areas of advanced control system design and real-time performance monitoring.
This study evaluates natural radioactivity levels in environmental samples from Al-Nafoura oil field and surrounding areas in Al-Jikharra, Libya. Fourteen samples (soil, plant, and water) were analyzed to assess radiological hazards associated with oil and gas activities. Radionuclide concentrations of 226Ra, 238U, 235U, 232Th, and 40K were measured using a high-purity germanium (HPGe) detector. Results show elevated radionuclide levels in several samples, with some exceeding recommended limits. Notably, 226Ra accumulation was pronounced in evaporation ponds, indicating localized radiological sources. Radiation hazard indices, including Raeq, I gamma, Hex, Dout, AEDout, and outdoor ELCR, were calculated; all remained within safe limits except for sample SO3-3, which exhibited Raeq of 2091 Bq/kg, surpassing the permissible threshold. AGDE values exceeded the world average in plant samples, signaling potential health risks. Water-source indicators stayed within acceptable ranges but neared other limits, underscoring the need for ongoing monitoring. The findings highlight radiological pollution risks for groundwater-dependent irrigation and agriculture, urging pollution reduction, water safety assurance, and protective measures for communities and ecosystems.
