The Nile River Basin has long been governed by asymmetrical water arrangements that privileged downstream states and constrained upstream development, shaping persistent hydro-political tensions among riparian countries. Against this background, the present study aims to analyze Ethiopia’s strategic use of a fait accompli approach in the construction, filling, and early operation of the Grand Ethiopian Renaissance Dam (GERD) and to assess its effects on power relations and governance dynamics within the basin. The study adopts a qualitative research design based on systematic document analysis of official statements, policy documents, and scholarly sources related to the GERD process since 2011. The findings reveal that Ethiopia’s unilateral advancement of the dam functioned as an effective fait accompli, creating irreversible material conditions that significantly altered the strategic balance among Nile Basin states, particularly weakening Egypt’s historically dominant hydro-hegemonic position and reshaping Sudan’s strategic alignment. While this approach strengthened Ethiopia’s sovereignty, energy security, and upstream bargaining power, it also contributed to increased mistrust, securitization of water resources, and diminished prospects for consensus-based cooperation. The study concludes that although the GERD has redefined regional power relations and challenged entrenched governance hierarchies, its long-term sustainability will depend on a transition from unilateral control toward inclusive and cooperative water governance frameworks. Given the irreversible nature of the project, the GERD must be managed simultaneously as a national development asset and a shared regional resource to mitigate conflict risks and support durable basin-wide stability.

In the domain of apicultural colony management, the existence of queen in a beehive is indispensable for sustenance of the hive's overall health and productivity. Nonetheless, conventional methodologies for evaluating the status of the queen are labor-intensive and may intrude upon the colony. This research endeavors to explore the application of acoustic pattern classification to differentiate between queenright (the residence of a queen) and queenless (the non-residence of a queen) conditions within bee colonies. Through the monitoring and examining the sound signals pour out by bees, this work seek to discern distinct acoustic characteristics that are associated with each condition. Sophisticated algorithms in machine learning are utilized to effectively sort these sound patterns with remarkable precision, especially KNN and RF models with 94.7% and 93.6% mean accuracy respectively using traditional MFCC’s which is widely used feature extraction method. Further this work extended by employing Gabor-MFCC method, a new approach which is a hybrid method demonstrated improved accuracy and noise resilience by 2% to 3% across the models. Then GFCC and MFCC spectral fusion methods produce further improvements in accuracy. The findings indicate that specific sound frequencies and patterns serve as reliable indicators of the queen's status, thereby offering a non-invasive and automated solution for apiarists. This methodology presents substantial prospects for the enhancement of hive management practices by facilitating real-time, continuous surveillance of queen status, ultimately contributing to the sustainability and productivity of apicultural operations.

The task to manage the municipal solid waste (MSW) in the urban localities specifically in the cities which are growing faster is very challenging as the city administration need to cope up with the pace at which today’s cities are growing which is almost impossible without incorporating the latest technologies like Artificial Intelligence (AI). The static waste collection system which is being used conventionally currently facing issues like inadequate allocation of the resources required which in turn raises the overall costs of the operations and ultimately harm the environment, too. To overcome this, the present study introduces an innovative approach of combining Decision Support System (DSS) with machine learning driven waste generation forecasts. This study attempts to optimize the vehicle routing to improve the MSW collection efficacy in the city of Surat, India. The framework proposed here combines Long Short-Term Memory (LSTM) neural networks for MSW prediction with Capacitated Vehicle Routing Problem (CVRP) algorithms for the optimization of routes. We have developed our LSTM model using the dataset of past 16 years (2009-2024) obtained from Surat Municipal Corporation which achieved considerable forecasting accuracy with RMSE of 0.1095 and R² value of 0.93. The integrated DSS demonstrated considerable benefits in operations, cutting the overall collecting distance by 50.31% and the use of vehicles by 24% compared to traditional static routing methods. The technology uses predictive analytics to dynamically allocate the required resources helping the waste management authorities make decisions in real time. This study enhances sustainable urban development by offering a data-driven methodology that corresponds with the objectives of the Smart Cities Mission and Sustainable Development Goals (SDGs) 11, 12, and 13. The suggested architecture provides scalable methods for implementing a circular economy and making municipal waste management systems more environmentally friendly.

The study investigates the magneto hydrodynamic (MHD) behaviour of convective heat flow in a rotating rectangular hollow enclosure subjected to both non-isothermal and isothermal boundary conditions. The interaction of thermal buoyancy, rotational forces, and externally applied magnetic fields produces a complex flow structure, significantly influencing heat transfer characteristics within the cavity. A two–dimensional incompressible electrically conducting fluid is considered, and the governing equations are formulated using the Bossiness approximation, incorporating Coriolis acceleration and Lorentz force effects. The rectangular hollow is examined under mixed boundary conditions, wherein selected walls are maintained at uniform isothermal temperatures, while others follow a non-isothermal spatial distribution. Numerical simulations are employed to evaluate the influence of key controlling parameters, including Hartmann number, Taylor number, Prandtl number, and Rayleigh number. The results reveal the critical role of magnetic damping in suppressing convective currents, while rotation enhances secondary circulatory structures depending on angular velocity. The interplay between isothermal and non-isothermal hedges yields distinct thermal stratifications and modifies heat transfer rates along the cavity walls. Overall, the analysis provides insights into optimizing thermal regulation in rotating MHD systems with relevance to advanced cooling technologies, geophysical flows, and energy-conversion devices.. In this study we assume the left vertical wall of the cavity is at temperature Th (Th +Byλ) and right vertical wall temperature Tc (Th> Tc) the top and bottom horizontal surfaces of the cavity are adiabatic. Darcy law is to be obeyed to the flow inside the porous medium the properties of the fluid and porous medium are homogeneous, isotropic and constant expect variation of fluid density with temperature. The fluid and porous medium are in thermal equilibrium and flux of heat radiation in y - direction is negligible in comparison to that in x-direction. Galerkin Finite Element Method of three nodded triangular elements has been used to convert the partial differential equations into the matrix form equations. Results are presented in terms of stream functions and isotherms for various values of Aspect ratios, Radiation parameters, Rayleigh numbers, Nusselt numbers and Power law exponents( ).

The objective of this study was to determine the influence of the Pedagogical Model of Automaton Multimodal Responses on the strengthening of teaching practice in a higher education institution. A quantitative approach was adopted, with a pre-experimental design, through the application of a pre-test and a post-test in a single group of 40 teachers. The technique used was the survey and the instrument, a validated questionnaire. The results revealed that the Pedagogical Model of Automaton Multimodal Responses significantly influence the improvement of teaching practice, evidenced by the Student's T-test with a bilateral significance value of 0.000, which allowed the rejection of the null hypothesis. Likewise, a positive impact was observed in all the dimensions evaluated: personal, interpersonal, institutional, didactic and axiological. Consequently, it is concluded that implementing collegial work contributes to improving teacher performance, strengthening collaboration and professional learning, which in turn favorably affects the quality of the educational process

In this paper we give explicit formula for the solution of the generalized wave equation associated to the Besseloperator, as an application of our results we solve explicitly the generalized wave equations with the Morse potential and on the hyperbolic spaces.

If there is a function f : E(G) → {1, 2, 3, . . . , K} suchthat for any two adjacent vertices u and v of the samedegree, the sets S(u) and S(v) are equal, where S(u) ={f(uv), uv ∈ E(G)}, then a graph G = (V, E) is said tohave an adjacent vertex reducible edge coloring. The term“chromatic number of adjacent vertex reducible edge coloring” refers to the highest value of K. The purpose of this study is to ascertain the chromatic number for adjacent vertex reducible edge coloring of specific graphs, including mirror graphs, alternate triangular cycles, prism wheel graphs, double wheel graphs, antiprism graphs, crown (or sun) graphs, degree splitting graphs, and H-graph.

Tomato leaf diseases pose a severe danger to agricultural productivity and global food security. The automated tomato leaf disease detection system presented in this study is based on a hybrid deep learning model that combines the feature extraction capabilities of the VGG16 and ResNet50 architectures. The system was tested on a publicly available dataset of tomato leaf images against 4 baseline models: CNN, ResNet50, VGG16, and MobileNetV2. With a 97.99% accuracy, a 98.04% precision score, 97.99% recall score, and a 97.98% F1-Score, the suggested hybrid model performs better than individual models. This was verified after extensive trials with the datasets. The model's resilience and efficacy in reliably diagnosing a variety of tomato leaf diseases were further validated by visual studies such as confusion matrices, bar graphs, radar charts, and heatmaps. Since the model can capture both fine-grained textures and deep hierarchical characteristics, the hybrid model performs better, providing more thorough pattern identification. Future research should concentrate on improving the system for edge devices, extending to multi-crop disease detection, and integrating explainable AI approaches. The suggested approach presents a viable first step toward automated, scalable, and accurate plant disease monitoring, which will enhance crop management, lower the need for pesticides, and promote sustainable farming methods.

Energy efficiency has become a central concern in modern engineering systems due to increasing energy demand, environmental constraints, and the need for sustainable technological development. Mathematical modeling and optimization techniques play a critical role in analyzing energy consumption patterns, identifying inefficiencies, and designing systems that achieve optimal performance with minimal resource utilization. This research examines the application of mathematical models and optimization strategies for enhancing energy efficiency across diverse engineering systems, including thermal, electrical, mechanical, and industrial processes. The study emphasizes the formulation of system-level mathematical representations that capture operational constraints, energy flows, and performance objectives with high precision. Various optimization techniques, including linear and nonlinear programming, dynamic optimization, and heuristic-based approaches, are explored to evaluate their effectiveness in reducing energy losses while maintaining system reliability and functional stability. The research highlights how optimization-driven decision-making enables engineers to balance competing objectives such as cost, efficiency, and performance under real-world operating conditions. Special attention is given to constraint handling, sensitivity analysis, and parameter tuning, which are essential for achieving robust and scalable energy-efficient solutions. The findings demonstrate that integrating mathematical modeling with advanced optimization frameworks leads to significant improvements in energy utilization, operational efficiency, and system sustainability. Optimized models enable predictive insights, allowing engineers to anticipate performance degradation and implement proactive control strategies. Furthermore, the study underscores the importance of adaptive optimization techniques that can respond to variable operating conditions and evolving system demands. Overall, this research establishes that mathematically driven optimization is a powerful tool for enhancing energy efficiency in engineering systems, offering practical pathways for reducing energy consumption, operational costs, and environmental impact while supporting sustainable engineering practices.

Indoor autonomous vehicles face persistent localization challenges due to sensor failures, path restrictions, illumination changes, and dynamic obstacles.We propose a fail-safe, context-aware, multi-modal sensor fusion framework to ensure reliable navigation. The approach combines ensemble localization, confidence-weighted fusion via Kalman consensus, and a fail-safe decision algorithm integrated with adaptive path planning. Sensor weights are dynamically adjusted to maintain accuracy under partial or complete modality failures. We used the ”Haram” (pilgrimage ritual (Tawaf)) as an inspirational application for the simulation and evaluation of the proposed framework. For this purpose,we created an AV cart and replicated it by maintaining the challenges of an interior setting, a comparable path length, and occlusions. The work covers the equivalent environmental applicability to other complex environments such asairports, hospitals, and shopping malls. The framework is implemented in ROSwith a 3D-customized cart navigating circular and elliptical paths under simulated sensor networks. Performance evaluation using Average Displacement Error DE), Final Displacement Error (FDE), collisions, and trip completion time shows improved robustness and higher success rates than static fusion, even under repeated sensor failure scenarios.