Babu Banarasi Das University

The mechanical properties of Metal Powder Reinforced Polymer Matrix (MPRPM) composite materials are significantly influenced by various reinforcement parameters, such as the particle size of the reinforcement material and the loading weight% of the reinforcement metal powder in the matrix. In this study, composite samples were prepared by reinforcing metal powder into a polyethylene terephthalate (PET) matrix, followed by mechanical testing to evaluate tensile strength, flexural strength, and percentage elongation. A machine learning-based predictive models utilizing the Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithm were developed to estimate the effects of the particle size of the reinforcement and metal powder loading by weight% on these mechanical properties. The model demonstrated outstanding accuracy with $$\left( {R^{2} } \right)$$ values of 0.3851, 0.3526, and 0.1639 for tensile strength, flexural strength, and percentage elongation, respectively, for RF model and of 0.8785, 0.6786, and 0.6360 respectively for XGBoost model during testing. Experimental results show good values for MSE for RF model and XGBoost model both, for tensile strength (0.5025 and 0.1144 respectively), flexural strength (0.0918 and 0.0462 respectively) and elongation (0.3572 and 0.1268 respectively). Root Mean Squared Error (RMSE) values were also minimal to the value of 0.7089, 0.3030 and 0.5977 for RF and 0.842, 0.2149 and 0.7731 for XGBoost for tensile strength, flexural strength and percentage elongation respectively. highlighting the reliability of the model. This study establishes XGBoost as a robust and interpretable method for optimizing mechanical properties in MPRPM composites over RF after systematic implementation and study of Random Forest (RF) and Extreme Gradient Boost (XGBoost) algorithms to the obtained dataset. The results provide a foundation for enhancing material performance and optimizing reinforcement parameters, with future potential to extend predictions to other mechanical properties like compressive strength and shear strength.

Organizations today face growing environmental pressures, leading many to implement green human resource management practices that foster sustainability and encourage employees to engage in environmentally responsible behaviors. This study investigates the distinct impacts of green recruitment and selection—through environmentally friendly employer branding and preference for eco-conscious hires—and green training and education—via awareness programs and green knowledge sharing—on voluntary employee green behavior, defined as self-initiated eco-friendly actions beyond job requirements. Unlike prior research treating green human resource management holistically, this analysis isolates these practices' individual effects. Pre-tested measurement scales from established sources, assessed on five-point Likert scales with strong reliability, were used in a cross-sectional survey of employees from medium and large organizations. Multiple regression results confirm both practices significantly predict voluntary green behavior, with green training showing stronger influence, collectively explaining a substantial variance in outcomes. These findings advance theory by detailing practice-specific mechanisms and guide managers to prioritize green branding and training for enhanced sustainability, while recommending longitudinal studies for deeper causal insights.

India's livestock sector faces significant challenges in accurate breed identification, with traditional manual methods achieving only 65-70% accuracy while consuming 40-60% of Field Level Workers' (FLWs) time. This paper presents BreedVision, an AI-powered web system for automated cattle and buffalo breed recognition using YOLOv12 object detection trained on 3,683 annotated images covering 15 major Indian breeds. The model achieves 69.8% mean Average Precision at IoU 0.5, with breed-specific accuracies reaching 87% for Bargur, 85% for Dangi, and 83% for Ongole and Alambadi. The system integrates ReactJS frontend with WebRTC camera capture, Python Flask backend with OpenCV preprocessing, and Roboflow cloud inference, providing real-time classification with confidence scoring and Excel export for government BPA system integration. Field testing demonstrates 40-60% reduction in FLW workload while improving data accuracy to 70% overall.

Background:The management of mandibular fractures traditionally involves the use of titanium miniplates for internal fixation. However, resorbable plating systems offer potential advantages, such as eliminating the need for second-stage removal surgery and reducing long-term complications. To compare the mechanical stability and fracture resistance of resorbable versus titanium miniplates in simulated mandibular fracture models under standardized in vitro conditions.Methods:Forty synthetic polyurethane mandibles with standardized angle fractures were randomly assigned to two groups (n = 20 each): Group A (titanium miniplates, 2.0 mm system) and Group B (resorbable polylactic acid miniplates, 2.0 mm system). Fixation was performed using a standard two-hole miniplate on the superior border with monocortical screws. Biomechanical testing was performed using a universal testing machine to measure maximum load to failure and displacement at failure. Statistical analysis was conducted using independent t-tests (P < 0.05).Results:Group A showed significantly higher load to failure (295.7 ± 18.2 N) compared to Group B (187.6 ± 20.5 N) (P < 0.001). Displacement at failure was lower in Group A (1.62 ± 0.14 mm) than in Group B (2.38 ± 0.22 mm), also statistically significant (P < 0.001). All titanium constructs maintained integrity during testing, whereas 15% of the resorbable constructs exhibited screw loosening.Conclusion:Titanium miniplates provide superior mechanical stability and resistance to functional loading in simulated mandibular fractures compared to resorbable systems. While resorbable plates offer clinical advantages in pediatric or selected cases, caution is warranted in load-bearing areas.