With the increasing demand for sustainable and smart energy solutions, innovative charging technologies are becoming essential in modern electronic device usage. The main focus of this study was the design and fabrication of an Internet of Things (IoT)–enabled solar-based charging station. It was developed to provide a sustainable, efficient, and intelligent charging solution for electronic devices. The system integrates renewable solar energy with IoT connectivity to allow real-time monitoring and mobile notifications. Key components include an automated solar tracking mechanism, AC and DC power outputs, an adjustable pole, and a system status display, all of which enhance functionality and user convenience. This study employed both developmental and descriptive research methods. The developmental phase involved planning, designing, fabrication, assembly, system integration, testing, and evaluation of the charging station. The descriptive aspect focused on assessing system performance in terms of charging rate and battery consumption under standard operating conditions. Product quality was evaluated based on durability, functionality, and system features, while acceptability was measured in terms of sustainability and ease of use. A researcher-made instrument was administered to selected technical experts, industry practitioners, and academic instructors through purposive sampling to evaluate the system’s performance and acceptability. Findings revealed that the IoT-enabled solar-based charging station was successfully developed as a fully functional and reliable device. Performance testing demonstrated efficient charging capability and optimized battery usage within expected standards. Quality evaluation showed high ratings in durability and system features, while acceptability results reflected positive responses, with sustainability and ease of use receiving the highest scores.

A cooling tower is an inescapable piece of industrial and power-generation equipment the world over and constantly deposits vast amounts of warm and humidified air up the tower stacks at a rate of between 3 and 12 m/s. This upward-flowing airstream, which is a focused and predictable source of kinetic energy, has long been ignored as a potential source of wind energy recovery. This is a comprehensive review that summarizes the existing body of research on integration of wind turbine systems, which are mainly axial-flow turbines, cross flow turbines and hybrids, into or onto cooling tower buildings with the aim of producing electrical power. We discuss the thermodynamic properties of cooling tower exhaust plumes, the aerodynamic design limits of humid and thermally stratified airstreams, the mechanical and structural issues of turbine placement under wet environments and the empirical and theoretical energy output of pilot and commercial installations, based on over 80 peer-reviewed literatures, engineering reports, and patent analyses published between 1998 and 2025. Important results have shown that properly designed integrated wind turbines can recapture between 2 and 14 percent of the overall fan energy used by a mechanical draft cooling tower, and that theoretical recoverable power densities of 150-800 W/m² of rotor swept area are possible with respect to exhaust velocity, turbine diameter and placement geometry. Economic estimates indicate that simple payback times are 4 to 12 years with good conditions, with the lifecycle CO₂ avoidance of 15-85 tonnes per turbine-year. The review points to several research gaps such as the long-term impact of corrosive and high-humidity conditions on turbine materials, the role of variable thermal loading on turbine operation, and the necessity of having standardized testing procedures. Future research directions, design optimization strategies and policy frameworks are suggested to speed up deployment.

The global construction industry confronts the intersecting imperatives of digital transformation and environmental sustainability, with concrete supply chains representing a critical nexus where material carbon intensity, equipment fleet management, and logistics coordination converge. Despite substantial advances in low-carbon concrete technology, construction equipment optimization, and digital systems such as artificial intelligence, the Internet of Things, and building information modeling, these domains have developed in relative isolation. The empirical validation employed a one-way factorial experimental design with three treatment levels, comprising Baseline Sequential Optimization, Static Integrated Optimization, and Dynamic IoT-Informed Integrated Optimization, each executed across thirty independent stochastic replications. Statistical analysis using hyper volume indicators, Mann-Whitney U tests with Bonferroni correction, and Kruskal-Wallis tests with Dunn’s post-hoc comparisons revealed that the Dynamic Integrated condition achieved a mean hyper volume of 0.859, representing a statistically significant improvement of 33.6 percent over the Baseline condition and 10.0 percent over the Static Integrated condition. Disaggregated performance metrics demonstrated a mean cost reduction of 16.7 percent, a mean carbon emissions reduction of 15.7 percent, and a mean schedule deviation reduction of 53.8 percent for the Dynamic condition relative to Baseline. The Dynamic condition reduced rejected concrete loads from a mean of 8.4 to 1.6 per simulation run and virtually eliminated the logistics performance differential between theologically challenging and forgiving low-carbon mixes, demonstrating that real-time sensor feedback serves as a technological equalizer, enabling the safe deployment of carbon-ambitious formulations. Sensitivity analyses confirmed that the value proposition of the dynamic framework is maximized under conditions of high traffic congestion, reduced equipment reliability, and elevated material variability. TOPSIS multi-criteria decision analysis demonstrated that Dynamic Integrated solutions achieved the highest closeness coefficients across cost-prioritizing, carbon-prioritizing, and balanced stakeholder weighting scenarios. Expert panel validation identified contractual and procurement structure modification as the principal adoption barrier, with a mean rating of 4.67 on a five-point scale, underscoring that the remaining challenges are institutional rather than technological. The research contributes an empirically validated architectural synthesis that bridges low-carbon material science, equipment fleet management, and digital construction technologies, providing a pre-commercial decision-support prototype and an evidence-based implementation roadmap for achieving simultaneous cost, carbon, and schedule performance improvements in complex urban construction projects.

In this study, the main objective is to develop an energy management system to satisfy power production of a photovoltaic system with storage in building in tropical area. These projects are now the main challenge to participate in energy transition in key sectors like building. Furthermore, they enhance the resilience of the multi sources systems through a modern management hard skil based on artificial intelligence applied to renewable energies. We begin by the optimization of the PV system to enhance the production and at the end, a control and management of energy of the whole system is done. Adaptative technics based on Artificial Neural Networkare used for the optimization and system control due their good performances on non-linear applications. The energy management is an important task in hybrid system; it must have the ability to do the repartition of the energy in the different sources of the system. To meet demand, optimized energy must be distributed fairly in the whole system. The system is developed and simulated for a variation of the sunshine using MATLAB/Simulink. The simulation results indicate that the proposed controllers enhance the power of the system, a low harmonic distortion is noted and the management of the energyis assured in the whole system.

This system models the stock price of a bank using Geometric Brownian Motion(GBM) with a deterministic seasonal initial condition. The Stochastic Differential Equation (SDE) is formulated to capture annual dividend, earnings, and macroeconomic cycles in the Nigerian banking sector. Three sample paths are simulated examines the future share price dynamics of First Bank under a seasonal Stochastic Differential Equation (SDE) framework using three figures. The analysis considers stochastic Brownian paths, deterministic seasonal cycles, and sensitivity to amplitude parameter A.  Results show that while seasonality drives recurring patterns, random shocks and amplitude changes determine short-term dispersion and volatility. Under Lipschitz and linear growth conditions, existence and uniqueness of a strong solution is proved via Itô’s Lemma, yielding. To this end, the model provides a framework for timing entry and exit around quarterly results.