This study evaluated the effects of solar UV-B (Ultraviolet-B) radiation on coffee seedlings at different altitudinal ranges in a tropical environment. A field experiment, conducted using a randomized complete block design, tested four coffee varieties—Fayette, Odisha, Koti, and Hangafa—under natural UV-B radiation and UV-B-blocking sheets at altitudes of 1450, 1850, and 2150 m, with three replications. The results revealed that altitude significantly influenced most growth parameters, except for specific leaf area and primary branch girth. The interaction between altitude and coffee variety affected plant height, primary branch length, and internode length (at p < 0.001, 0.01 and 0.05, respectively), with plant height and primary branch length decreasing as altitude increased. Altitude also impacted leaf area and physiological parameters such as photosynthetic rate (Pn) and transpiration rate. In contrast, the interaction of UV-B and altitude influenced stomatal conductance and intercellular CO2 levels. These findings demonstrate that coffee varieties respond differently to solar UV-B radiation at different altitudes. This highlights the need for location-specific coffee breeding/genetic selection/ in tropical environments to address adaptation to climate change, particularly in relation to increased UV radiation.
Sustainable leadership, through information credibility, promotes resourcefulness and adaptive solutions to nurture a culture of frugal innovation across different economies. As well, subsequently wasn’t extensively studied by initial investigations, mainly from the viewpoint of higher education institutions in Ethiopia. Guiding on complex theories of adaptive systems and effectuation, current investigation inspects the unintended consequence of information credibility on the nexus of sustainable leadership through frugal form of innovation. This research applied a quantitative strategy alongside a self-administered questionnaire. The investigation utilized data collected from 385 representatives from all 45 Universities in Ethiopia, selected purposive sampling technique. Structural equation modelling (SEM) with the assistance of AMOS system was incorporated to trial the anticipated postulates. The study outcomes empirically verified that sustainable leadership positively and considerably contributes towards frugal innovation through the mediation of information credibility. Composite measure of leadership sustainability, credibility of information along with frugal form of innovation in a single model extended the existing knowledge in entrepreneurship education and makes unique this study. This outcome manifested the nurturing of innovative conceptions along with understandings associated to the concept of sustainable leadership, which fundamentally substructure a noticeable influence of African universities to support information credibility to promote frugal innovation.
This work presents a modified physics informed neural network (PINN) framework that integrates gradient augmentation and an adaptive weighted loss to solve heat conduction problems with time-dependent thermal conductivity and source term. The method uses automatic differentiation to compute solution gradients, which are incorporated as auxiliary outputs to improve training smoothness and accuracy. An adaptive weighted loss mechanism dynamically balances the contributions of the governing equation residual, initial conditions, and boundary conditions during training, thereby enhancing convergence, stability, and robustness. The proposed adaptive loss weighted gradient-enhanced PINN framework is validated on a benchmark problem, both with and without a known analytical solution and its performance is compared against a standard PINN using fixed loss weights. Results demonstrate that the proposed approach achieve superior accuracy, even when trained with fewer collocation points. To further assess robustness and stability, a statistical error analysis based on variance and standard deviation was conducted. The findings indicate that the proposed method provides a reliable and scalable computational framework for variable-coefficient heat conduction problems in nonlinear field theories.
This study investigates the application of adaptive physics-informed neural networks (PINNs) to solve two-dimensional time-domain Maxwell’s equations in their differential form. The system is reformulated as hyperbolic-type equations governing the electric and magnetic field components. A fully connected neural network is constructed to approximate the solution while enforcing boundary and initial conditions. To improve convergence, a hybrid optimization strategy combining the Adam and L-BFGS algorithms is employed. The effects of key architectural choices, including the number of hidden layers, neurons, and activation functions, were systematically examined. A benchmark problem with known analytical solutions is used to evaluate accuracy, and errors are compared with a reference finite-difference time-domain solver. The results confirm that the adaptive PINNs provide an accurate and computationally efficient alternative for solving 2D Maxwell’s equations, with strong potential for extension to frequency-domain and three-dimensional problems.
Skin cancer is among the most frequent and fatal illnesses in the world, but early and correct diagnosis is one of the main challenges because of the complicated visual patterns of skin lesions and the absence of interpretable diagnostic devices. Conventional techniques are largely based on experienced dermatologists, but manual inspection is time-consuming, subjective, and liable to misinterpretation, with high false positive outputs. To address these issues, we present a unique DCNN architecture that employs the Swish activation function to effectively identify complex patterns in the skin lesion dataset. This model has remarkable performance in diagnosing skin cancer, as evidenced by a 98.31% accuracy rate, a 98.12% precision rate, a 98.01% recall rate, and an F1-score of 98.09%. Utilising several localised and global explainable artificial intelligence (XAI) approaches, we evaluate the model's predictions to ensure transparency and reliability in medical contexts. To reconcile the disparity between AI research and its use in healthcare, our findings underscore the necessity of integrating deep learning with explainability. These XAI solutions tackle critical concerns such as inclusiveness, transparency, and error control, providing medical practitioners with a comprehensible and reliable framework for assessing the model's reasoning process. The suggested technique establishes a reliable mechanism to assist physicians in the early and precise identification of skin cancer, while enhancing diagnostic accuracy. Future studies will focus on enhancing the model's computational efficiency and incorporating more datasets to ensure its durability and fairness across various demographic groupings.
