Public institutions often face challenges in managing energy efficiently across distributed facilities with limited technical personnel. Manual control of lighting and environmental conditions contributes to unnecessary energy consumption, especially outside regular operating hours. Emerging IoT platforms offer accessible alternatives for automation without the need for complex infrastructure. Integrating mobile applications with microcontroller-based systems can support real-time monitoring and control, reducing waste and simplifying operational routines. This project explores the development of an IoT-based energy management system enabling remote device control through applications that are compatible with Android and iOS. The architecture is based on ESP32 microcontrollers integrated with temperature and humidity sensors (DHT11), LEDs, and relays. Three platforms were tested: MQTT Dashboard, ESP RainMaker, and a custom mobile app developed using MIT App Inventor. MQTT was used for data transmission and command handling via public brokers. The mobile app allowed for profile selection, room identification, sensor data visualization, and lighting control. MQTT Dashboard provided a stable interface for real-time communication, while ESP RainMaker added automation features such as scheduled routines. All approaches demonstrated consistent functionality, with MIT App Inventor standing out for its ease of use and educational value. The system supports scalability, enabling expansion to other rooms and buildings. These results confirm the feasibility of implementing remote energy control through open technologies, offering a practical solution for improving efficiency and simplifying maintenance routines in public educational institutions.

The growing demand for intelligent energy use requires systems capable of predicting consumption behavior in real time and adapting to different operational environments. Traditional forecasting methods often lack flexibility when integrated into modern energy monitoring platforms. Advances in neural network architectures offer alternatives for capturing nonlinear and dynamic consumption patterns. Energy forecasting also plays a central role in optimizing distributed systems and reducing operational uncertainty in energy management. This study introduces an intelligent software system designed to perform real-time energy consumption forecasting, integrated with Energy Management Systems (EMSs). The proposed solution communicates with sensing devices via the MQTT protocol, allowing continuous data acquisition and flexible system integration. Two forecasting models were implemented: a hybrid ARIMAX-NN model that combines statistical methods with neural networks and a CNN-LSTM Autoencoder (CNN-LSTM-AE) model that captures temporal dependencies and nonlinear behaviors. Public datasets from residential and commercial buildings were used for model validation. The software adapts to different input configurations without requiring structural changes, supporting a wide range of metering devices and data formats. Forecast results are updated in real time and can be seamlessly integrated into operational environments. The system's modular design enables future expansions such as graphical interfaces and alert generation mechanisms. This approach provides a scalable foundation for supporting energy efficiency initiatives in residential, industrial, and commercial applications.

Foldable quadcopter represent a new frontier in aerial robotics technology. The ability of foldable quadcopter to reconfigure its geometry inflight and adapt its self to various flight scenarios offer enhanced agility, maneuverability, aerodynamic efficiency, and mission versatility compared to traditional quadcopter. However, the folding function introduces significant parameter variations such as center of gravity, inertia, and nonlinear dynamics in addition to inherent underactuation, coupling dynamics, and external disturbances. Thus, folding mechanism presents significant challenges to conventional control approaches. To solve the drawbacks of conventional control approach, this article proposed the development of prescribed performance adaptive sliding mode control (SMC) for foldable quadcopter UAV. It models the morphing quadcopter as rigid body system with five morphing formations (X, Y, H, O, and T). The prescribed performance SMC approach systematically addresses the time varying parameter and aerodynamic impact resulting from the morphing formation. Using Lyapunov theory, the adaptive SMC that ensures the error evolution is within prescribed performance bounds, the closed loop stability, and precise trajectory tracking under parametric and nonparametric uncertainties is designed.The effectiveness of the proposed control algorithm is evaluated and benchmarked via simulation in structured and unstructured environment against conventional sliding mode control (SMC), PID, and LQR control methods. The simulation results indicate the performance of the adaptive SMC, improved robustness, and adaptability compared to benchmarked control methods. The simulation results demonstrated that, adaptive control approach is a viable and effective solution for managing the complex dynamics and uncertainties of foldable quadcopter UAV.

Small Island Developing states face various challenges in the field of agriculture. One of the major challenges in Fiji is insufficient irrigation; therefore, it is crucial to develop a “smart irrigation system based on IoT” that is available for use through mobile and web applications. This article presents an innovative solution to sustainable farming that incorporates advanced IoT technologies. The system utilizes water flow, temperature, humidity, and soil moisture sensors, together with solenoid valves, and water pumps controlled using an ESP32 Node-MCU microcontroller. Continuous monitoring of real-time data on soil and environmental parameters is used to control the irrigation.

This significantly minimizes water wastage without compromising crop health. The system mainly focuses on specific crops such as ginger, turmeric, eggplant, chili, okra, etc., which are majorly grown in Fiji. The main aim is to make this IoT system efficient yet simple and user-friendly, to be managed by farmers without much technical knowledge. It allows farmers to select a crop type, and according to previously collected and stored data, the system automatically selects parameters to provide a conducive environment for the selected crop and manages irrigation efficiently without any human intervention. It provides a new and innovative approach to maintaining resilience, sustainability, and economic development of rural communities. Real-time testing across several agricultural instances shows the system’s effectiveness. This holistic approach demonstrates dedication to Fiji's agricultural sustainability as well as economic growth. It illustrates the integration of advanced technology to elevate Fiji's agriculture to a level of superior effectiveness and sustainability.

This study addressed crack formation and the rehabilitation of concrete elements, such as slabs and columns, in buildings located in areas with temperatures above 25 °C, where accelerated water evaporation significantly reduces structural strength. To mitigate this phenomenon, an intelligent automated monitoring and curing system was developed, based on thermal and humidity sensors and integrated controllers.

The research was applied, experimental, and explanatory, based on the hypothetical-deductive method. Physical models (test tubes, columns, and solid slabs) were used in a 1:2:3 ratio, integrating DS18B20 and HD-38 sensors. A visualization system was implemented in Proteus, with an LCD screen for data collection and analysis. Statistical analysis, with 95% confidence, revealed a moderate and significant correlation (r = 0.587; p = 0.001) between the environmental thermohygrometer and the DS18B20 sensor, indicating effective heat transfer. In contrast, the HD-38 sensor showed a low correlation (r = 0.143; p = 0.468), indicating a limited influence of ambient humidity on internal humidity.

Cracks were verified 120 minutes after the concrete was poured, a critical moment when the system acts by sprinkling water. The system repaired the initial cracks and consumed 1680 liters in 28 days, reducing water consumption by 20%. A critical evaporation peak was identified between 11:00 and 16:00 (UTC-5), not previously documented in tropical areas.

Isolated microgrids face operational challenges due to restricted generation capacity and high sensitivity to consumption fluctuations. Reliable short-term forecasting is essential to support decision-making in these constrained environments. Accurate short-term load forecasting plays a key role in the planning and operation of electrical power systems, especially in isolated microgrids with limited generation capacity. This study proposes a hybrid forecasting model that combines the Recursive Least Squares algorithm with the K-Nearest Neighbors method, enhanced through the application of Genetic Algorithm optimization techniques. The model integrates weather conditions, calendar variables, and historical consumption data to identify behavioral patterns and improve forecast performance. The implementation was carried out in MATLAB version 2024rd, using the Global Optimization Toolbox structures for Genetic Algorithm and Direct Search methods to fine-tune the model parameters. Real operational data from 2023 and 2024, collected from isolated electrical systems serving tourist areas, were used to validate the proposed model. The results show that the hybrid approach outperforms classical Recursive Least Squares and Artificial Neural Network models, particularly during periods of high demand variability. This improved forecasting capacity supports energy providers in making informed decisions for scheduling maintenance and operational actions, while ensuring efficient power generation at reduced costs. The methodology is adaptable to other isolated or small-scale power systems and contributes to improving the reliability and cost-effectiveness of energy planning in similar regional contexts.

Wing-in-Ground Effect (WIG) vehicles operating at low altitudes benefit from increased aerodynamic efficiency but require precise control systems to maintain stable flight near surfaces. The Lippisch configuration introduces additional complexity in height stabilization due to its inherent sensitivity to disturbances. WIG vehicles with a Lippisch configuration exhibit distinctive dynamic characteristics due to their operation close to water surfaces, posing challenges for achieving robust and stable height control. Traditionally, Proportional–Integral–Derivative (PID) controllers have been employed in autopilots built on the ArduPilot platform. However, these controllers are typically tuned for specific nominal conditions, revealing limitations when facing dynamic uncertainties and environmental disturbances. This paper presents a comparative analysis between a conventional PID controller and a hybrid approach combining PID with Model Reference Adaptive Control (MRAC), specifically for altitude control of a Lippisch-type WIG vehicle subjected to wind gust disturbances. The MRAC implementation is based on a stable reference model, enabling real-time adaptive adjustment of PID gains in response to disturbances and variations induced by ground effect. Simulation results, obtained in a MATLAB/Simulink environment integrated with ArduPilot (Mission Planner SITL), demonstrate that the hybrid PID+MRAC controller achieves improvements in tracking error reduction and settling time under wind gust conditions compared to the conventional PID controller. The integration of adaptive elements with traditional PID control contributes to more consistent performance in variable operating scenarios.

The Parasol microsatellite control system model aims to enhance the stability and performance of small satellite operations in low Earth orbit (LEO). This paper presents a simulation framework based on the Linear Quadratic Regulator (LQR) methodology, which is a widely recognized optimal control strategy. The LQR approach is particularly suited for systems requiring precise control with minimal energy expenditure, making it ideal for microsatellites that operate under strict power constraints. The simulation using the MATLAB/Simulink environment incorporates dynamic models of the microsatellite’s attitude and orbital mechanics, allowing for an assessment of the LQR controller’s effectiveness in maintaining desired orientation and trajectory. In this study, we first of all detailed the mathematical model of the Parasol’s position, including state-space representation, cost function definition, and feedback gain computation. Then we implemented the Linear Quadratic Regulatorcontroller in a closed-loop model in MATLAB/Simulink. After we provided attitude control data in the context of the Parasol microsatellite scenario and his geographic coordinates. Finally, the simulation results demonstrate the effectiveness of the implemented Linear Quadratic Regulator controller in stabilizing Parasol’s position. It shows the trajectory of the Parasol microsatellite in DCI coordinates and the simulation of the geographic coordinate with latitude equal to 61.9756 and longitude 108.119 correspondant to northeastern Siberia, Russia, within Yakutia. And the latitude equal to 1.41449 and longitude -57.7824 is located in Brazil near the Amazon rainforest region.

This study presents a multi-objective design optimization of a Brushless Permanent Magnet Outer Rotor (BPMOR) motor intended for electric scooter applications, focusing on the impact of rotor-mounted magnet geometry. Specifically, the permanent magnet (PM) thickness, magnet arc, and magnet reduction—key design variables—were investigated for their influence on three critical performance metrics: torque ripple, back-electromotive force total harmonic distortion (BEMF THD), and torque per rotor volume (TPRV). A limited set of finite element analysis (FEA) simulations was used to generate a sensitivity dataset, which served to train Gaussian Process Regression (GPR) surrogate models. These models enabled rapid motor performance prediction during optimization—without rerunning computationally intensive FEA—using Particle Swarm Optimization (PSO). The resulting Pareto front revealed the trade-offs between conflicting objectives and identified an optimal design region that satisfies practical engineering constraints: torque ripple ≤ 10% to reduce noise, vibration, and harshness (NVH), BEMF THD ≤ 5% to ensure smoother inverter operation and better control accuracy, and maximized TPRV to achieve high torque density while minimizing magnet material cost. The final design, validated through high-fidelity FEA, demonstrates a marked reduction in torque ripple and BEMF THD, along with a notable increase in TPRV. The proposed approach provides a computationally efficient design methodology for exploring rotor topology configurations, contributing to the design of compact, cost-effective, and high-performance electric traction motors.

Lithium-ion batteries play a crucial role in new electronic applications due to their high energy density, size, safety and efficiency, and they are considered a critical aspect of modern life. However, the increasing prevalence of fast-charging technology has raised user concerns about its impact on the immediate, daily performance of smartphone batteries. This paper investigates the hypothesis that fast charging diminishes a battery's duration throughout a single usage cycle compared to standard charging methods. We propose a comparative study analyzing the daily battery performance of modern smartphones under various charging regimens, including fast, normal, OEM (and third-party chargers). The methodology involves developing an experimental setup of three distinct electronics devices (a tablet, a laptop and a smartphone) to continuously measure parameters like voltage, current, temperature and state of charge, under different charging conditions to assess their direct effect on single-cycle discharge duration. Findings demonstrate that fast charging generates significantly more heat—a known factor in long-term degradation—and even with sophisticated thermal management they tend to last shorter through the day with increased cell aging. This research highlights the distinction between long-term battery health and immediate daily performance, aiming to clarify consumer misconceptions and underscore the importance of certified charging hardware.