A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by $25.8\%$ and data throughput by $26.86\%$. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

The traditional manual attendance system wastes\ud time over students’ responses, but it has worked well for small\ud numbers of students. This research presents a real-time group\ud face-detection system. This system will be used later for student\ud class attendance through automatic student identification. The\ud system architecture and its algorithm will be described in details.\ud The algorithm for the system was based on analyzing facial\ud properties and features in order to perform face detection for\ud checking students’ attendance in real time. The classroom’s\ud camera captures the students’ photo. Then, face detection will\ud be implemented automatically to generate a list of detected\ud student faces. Many experiments were adopted based on real\ud time video captured using digital cameras. The experimental\ud results showed that our approach of face detection offers realtime\ud processing speed with good acceptable detection ratio\ud equal to 94.73%

Network pharmacology is a powerful approach for studying complex drug–target interactions and biological pathways. However, existing tools often require extensive manual intervention and lack integrated analysis capabilities. Here, we present NeXus v1.2, an automated platform for network pharmacology and multi-method enrichment analysis including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) that addresses these limitations. NeXus v1.2 enables the seamless integration of multi-layer biological relationships, handling complex interactions between genes, compounds, and plants while maintaining analytical rigor. The platform implements three enrichment methodologies: Over-Representation Analysis (ORA), GSEA, and GSVA, circumventing limitations associated with arbitrary threshold-based approaches. NeXus v1.2 was validated using multiple datasets spanning 111 to 10,847 genes, demonstrating robust scalability and performance across dataset sizes. The platform was initially tested using a representative dataset comprising 111 genes, 32 compounds, and 3 plants, showing consistent performance in processing various relationship patterns, including shared compounds between plants and multitargeted genes. The processing time for this dataset was 4.8 s with peak memory usage of 480 MB. Large-scale validation with datasets up to 10,847 genes confirmed scalability, with linear time complexity and completion times under 3 min. NeXus v1.2 automatically generates comprehensive visualizations, including network maps, enrichment analyses, and relationship patterns, while maintaining the biological context of interactions. The tool successfully processed and analyzed enrichment patterns across multiple functional domains, generating publication-quality visualization outputs at 300 DPI resolution. The platform demonstrated enhanced automation in handling incomplete relationship data and maintaining analytical integrity across different biological layers. Compared to manual workflows requiring 15–25 min, NeXus v1.2 reduced the analysis time to under 5 s (>95% reduction) while ensuring the comprehensive coverage of biological relationships. NeXus v1.2 provides improved automation and integration for network pharmacology analysis, offering an efficient and user-friendly platform for complex biological network analysis. Its modular architecture enables the future integration of AI technologies and expansion into various therapeutic applications.

As the global demand for clean and sustainable energy grows, photovoltaics (PVs) have become an important technology in this industry. Thin-film and flexible PV modules offer noticeable advantages for irregular surface mounts and mobile applications. This study investigates the use of four machine learning models to detect different flexible PV module geometries based on power output data. Three identical flexible PV modules were mounted in flat, concave, and convex configurations and connected to batteries via solar chargers. The experimental results showed that all geometries fully charged their batteries within 6–7 h on a sunny day with the flat, concave-, and convex-shaped modules achieving a peak power of 95 W. On a cloudy day, the concave and convex modules recorded peak outputs of 72 W and 65 W, respectively. Simulation results showed that the XGBoost model delivered the best classification performance, showing 93% precision with the flat-mounted module and 98% recall across all geometries. In comparison, the KAN model recorded the lowest precision (78%) with the curved geometries. A calibration analysis on the ML models showed that Random Forest and XGBoost were well calibrated for the flat-mounted module. However, they also showed overconfidence and underconfidence issues with the curved module geometries.