The growing demand for sustainable materials for flexible electronics and energy storage applications has driven the development of biodegradable composite films with enhanced electrochemical functionality. This study systematically investigates the effect of MXene loading on the structure, morphology, and electrochemical performance of biodegradable PVA/ZnO/MXene/CNC composite films fabricated by aqueous solution casting. The main contribution of this work is the explicit establishment of a relationship between loading, structure, and electrochemical performance for this multicomponent biodegradable film system under controlled processing conditions. Films containing 20%, 25%, and 30% MXene were prepared with constant ZnO and CNC contents and characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), and cyclic voltammetry in 1 M KOH. The crystallinity increased from 20.06% to 27.58% and 44.74% with increasing MXene loading, while FESEM revealed progressively more homogeneous morphology and improved filler dispersion. These structural changes were accompanied by a marked enhancement in electrochemical response, with current density increasing from 425.18 to 876.71 and 1480.25 A/m², and specific capacitance rising from 0.921966 to 1.682536 and 2.860035 F/g for 20%, 25%, and 30% MXene, respectively. The 30% MXene film exhibited the best overall performance, indicating that higher MXene loading within the investigated range promotes more continuous conductive pathways and greater electroactive surface accessibility. These findings provide useful insight for designing biodegradable composite films for sustainable flexible energy-storage applications.

The utilization of renewable energy through rooftop photovoltaic (PV) systems serves as a strategic solution for mitigating climate change; however, their performance in tropical climates often exhibits a deviation between theoretical predictions and field reality. This study aims to evaluate the technical performance and economic viability of an on-grid 1.82 kWp rooftop PV system in Indonesia. The research employs a comparative quantitative approach by validating PVsyst simulation results against actual measurement data recorded from April to July 2024. The findings indicate a simulation overestimation, where actual energy production was 30.3% to 40.5% lower than PVsyst projections. A significant discrepancy was also observed in the Performance Ratio (PR), with the actual PR reaching only 55-59%, substantially lower than the simulated 81-82%. Despite these technical inconsistencies, the economic analysis confirms the project's financial feasibility. Under a 5.25% interest rate scenario, the study yielded a Net Present Value (NPV) of IDR 15.88 million, a Benefit-Cost Ratio (BCR) of 1.50, a Payback Period of 9.8 years, and a Levelized Cost of Electricity (LCOE) of IDR 974.88/kWh, more competitive than the national utility (PLN) tariffs. In conclusion, although tropical environmental factors such as high temperatures and dust accumulation reduce technical efficiency, rooftop PV investment in Indonesia maintains strong profitability and remains viable for implementation.

This paper investigates the impact of transformer grounding configurations on Transient Recovery Voltage (TRV) during fault current interruption in high-voltage power systems. The study evaluates three grounding schemes: Solid–Solid, Solid–Floating, and Solid–Resistance, applied on a step-down transformer located at the Tello substation. Each configuration was modeled and simulated using ETAP 19 software to observe TRV behavior under three-phase fault conditions. The results demonstrate significant variations in TRV profiles depending on the grounding type. The Solid–Solid configuration exhibits the highest TRV peak (22,500 V) and the steepest Rate of Rise of Recovery Voltage (RRRV), reaching 833.33 kV/ms, indicating severe stress on circuit breaker insulation. In contrast, the Solid–Floating configuration yields a moderate TRV peak (19,800 V) with less consistent waveform stability due to the absence of a defined secondary reference. Meanwhile, the Solid–Resistance scheme, using a 20 Ω resistor, produces the most damped TRV waveform with the lowest peak (17,100 V) and RRRV (approximately 589.66 kV/ms), offering improved insulation coordination. The comparative analysis confirms that controlled grounding through resistance effectively mitigates TRV magnitude and oscillation, making it a viable approach to enhance circuit breaker performance and reliability. These findings can be used to guide grounding system design in high-voltage substations to reduce the risk of re-ignition or insulation failure.

Carbon monoxide (CO) is a toxic, odorless gas commonly present in industrial processes and poses serious risks to occupational safety and health. This study proposes an optimized machine-learning-based approach to predict CO concentration using metal-oxide semiconductor (MOS) sensor arrays. The model was trained and evaluated on a public dataset comprising 650 time-series measurements from 14 thermally modulated MOS sensors, tested across CO concentrations ranging from 0 to 8.9 ppm under dynamic relative humidity (15%–75%). To optimize computational efficiency and mitigate multicollinearity, a multi-method feature selection strategy that combines Random Forest importance, Recursive Feature Elimination (RFE), and Mutual Information (MI) was implemented, successfully isolating sensors R10, R11, and R13 as the most robust predictors. A Random Forest Regression model, optimized via grid search and validated through five-fold cross-validation, was subsequently developed. The proposed framework demonstrated high predictive accuracy, achieving an R² of 0.884, Root Mean Square Error (RMSE) of 2.189 ppm, Mean Absolute Error (MAE) of 1.215 ppm, and Symmetric Mean Absolute Percentage Error (SMAPE) of 34.27%. These results highlight the potential of combining low-cost, feature-optimized MOS sensor arrays with ensemble machine learning for accurate, real-time gas monitoring. The framework provides a computationally efficient decision-support tool for the early detection of hazardous CO levels, contributing to safer process environments.

Infant body temperature stability is paramount, especially for preterm newborns unable to maintain their own thermal equilibrium. Here, we explore a Proportional-Integral-Derivative (PID) control algorithm implemented directly on a Nextion Human–Machine Interface (HMI) to regulate infant warmer temperature. Unlike typical systems where the microcontroller holds the major PID calculation and the HMI acts as a display only, this method integrates the PID logic into the HMI itself, with possible reductions of microcontroller load, minimization of communication delays, and hardware architecture simplification. Three trials at a constant setpoint of 37 °C with varying combinations of PID gains were used with a fixed experimental setup. Temperature response indicators like rise time, settling time, percent overshoot, and steady-state error were measured and compared. Results indicate that with gains of Kp = 1.50, Ki = 0.05, and Kd = 1.50, the system reached a steady state of 36.97 °C with just 2.16 % of an overshoot and a settling time of about 7 minutes and satisfied neonatal warmer requirements. The results confirm that PID control executed directly on the Nextion HMI can achieve temperature regulation performance comparable to conventional microcontroller-based implementations while improving system simplicity and code efficiency. It presents a good alternative choice of low-power and portable infant warmer and also of other embedded hot and cold control systems.

This study successfully designed and implemented a LabVIEW-based nickel laterite ore leaching process control system on a laboratory scale. This system integrates key hardware components such as Arduino Mega 2560, temperature sensor, volume sensor, jet bubble reactor, and LabVIEW-based user interface that allows automatic and real-time monitoring and control of process parameters. The calibration results showed high accuracy, with temperature measurement error values ​​of 0.04% and 0.015% compared to the calibrator. Volume measurements under five test conditions produced error values ​​ranging from 0.023% to 0.066%, with the best accuracy shown by readings via the LabVIEW HMI. Leaching process testing was carried out using variations in citric acid concentrations. The resulting filtrate volume showed a decrease from 173 mL at a concentration of 0.5 mol to 8 mL at a concentration of 2 mol, indicating that the higher the solution concentration, the greater the viscosity of the solution, thereby inhibiting mass transfer. The application of jet bubble technology has been shown to increase the efficiency of mixing and contact between the leaching solution and the ore, which accelerates the leaching process. Overall, the system shows high stability, accuracy, and reliability for laboratory scale applications. This system is considered suitable for use as a learning medium, an initial simulation tool for the APAL (Atmospheric Pressure Acid Leaching) process, and a means of supporting research in the development of efficient, energy-saving, and environmentally friendly nickel extraction technology.

This paper investigates the optimization of the Global Maximum Power Point (GMPP) and the simulation of Total Harmonic Distortion of Current (THDI) from an inverter connected to a nonlinear load. THDI variations are analyzed with respect to ambient temperature (T) and solar irradiance (G). The study also highlights how harmonic components negatively affect steady-state voltage stability in photovoltaic (PV) systems. The Queen Honey Bee Migration (QHBM) algorithm is applied to optimize GMPP while minimizing THDI. An off-grid PV-inverter system is modeled in MATLAB/Simulink. The model extracts THDI as a function of temperature and irradiance. Simulations cover irradiance from 794.8 to 994.2 W/m² and temperature from 20.0°C to 32.3°C, based on daily measurements from 08:25 to 16:50. The QHBM algorithm tracks GMPP effectively under fluctuating irradiance. Results show a 17.3% improvement in power extraction efficiency and a 32.8% reduction in THDI compared to conventional methods. The highest THDI occurs during low irradiance, particularly in the early morning and late afternoon. The algorithm converges in 0.18 seconds, outperforming other techniques. THDI increases during rapid irradiance and temperature changes. The proposed method ensures stable performance and lower THDI. Combining QHBM with active harmonic filters under low irradiance conditions is recommended to improve power quality and enhance system protection.

This study aims to analyze the insulation resistance value of a 555 WP monocrystalline solar module under the influence of solar irradiation through outdoor testing and insulation assessment. The primary focus is to understand the impact of solar exposure on insulation durability, a crucial factor in the long-term performance and safety of solar modules. The testing method follows the SNI/IEC 61215 standard, involving initial and final measurements using a calibrated insulation tester at the Energy Conversion Laboratory, BRIN. The results indicate a 19.54% degradation in insulation resistance after 15 days of solar exposure. Despite this decline, the module still meets the IEC 61215 criteria for insulation resistance, maintaining a resistance value above 40 MΩ for a module with a surface area of 2.583 m². A comparison of initial and final data reveals a decrease in resistance from 3.470 GΩ in the initial test to 2.792 GΩ in the final test. This reduction underscores the importance of paying closer attention to maintenance and routine testing to ensure the module's long-term reliability. This study provides new empirical evidence on the dynamics of short-term insulation degradation under tropical solar conditions, a topic that has been rarely quantified in field-based PV reliability research. In addition, this study makes significant contributions to the development of industry standards that aim to enhance the reliability of solar modules and manage renewable energy systems.

This paper presents a novel optimization algorithm, the NExIWAC (Natural Exponential Inertia Weight and Acceleration Coefficient) variant of Particle Swarm Optimization (PSO), for tuning PID controllers in DC motor speed control systems. The proposed NExIWAC algorithm improves control performance by dynamically adjusting the inertia weight and acceleration coefficients during optimization. To evaluate its effectiveness, the NExIWAC-tuned PID controller was compared against five established metaheuristic algorithms: Atomic Search Optimization (ASO), Sand Cat Swarm Optimization (SCSO), Grey Wolf Optimization (GWO), Invasive Weed Optimization (IWO), and Stochastic Fractal Search (SFS). The system's step response was analyzed under a reference speed demand of 1 p.u., with performance metrics including steady-state error, rise time, settling time, overshoot, and Integral of Time-weighted Absolute Error (ITAE). The NExIWAC algorithm demonstrated superior performance, achieving the fastest rise and settling times, zero steady-state error, and the lowest ITAE value among the tested algorithms. A robustness analysis was conducted by varying motor parameters, such as armature resistance and motor constant, by ±50%. The NExIWAC-PID controller exhibited stable and reliable performance under all conditions. Stability analysis through Bode plots and pole-zero mapping further confirmed the system's robust behavior, with a high phase margin and poles located in the left half of the complex plane. The results indicate that the NExIWAC algorithm is a powerful and reliable optimization tool for tuning PID controllers in DC motor applications, offering significant advantages in terms of precision, stability, and adaptability.

The effective operation of the IEEE 30 Bus power system requires economic dispatch optimization to minimize production costs, align energy supply with demand, and ensure system stability. This economic dispatch problem is complex due to its non-linear characteristics, interdependence between generators, and the need to combine cost minimization with power loss reduction. Conventional optimization techniques often struggle to find global solutions, easily get stuck in local optima, and require significant computational time. This study introduces the Orca Predation Algorithm (OPA) as a new approach to address these challenges. Inspired by the hunting behavior of orcas, OPA balances exploration and exploitation through two distinct phases: pursuit and attack. Evaluated on the IEEE 30-Bus system using power loss computation with coefficient B, the algorithm ensures that generator output power allocation meets demand at the lowest cost. OPA's performance is comprehensively compared with Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), and Bat Algorithm. The results consistently show that OPA achieves the lowest total cost of $772,754 while maintaining superior system stability and effectively minimizing power losses among the evaluated algorithms. These findings highlight the significant potential of OPA to enhance energy management and advance power system optimization.