Block pavements involve the placement of rectangular blocks in an area with a certain pattern. However, blocks will need to be cut to fit the area which will yield additional costs and waste. This study presents a model to minimize cutting loss in block pavements for any shape, improving previous studies that only considered rectangular bounding areas and disregarded block placement patterns. The model mathematically incorporates constraints such as amount, geometric boundary, overlap, and pattern; and uses the harmony search algorithm to optimize block layouts for stack bond, stretcher, and herringbone patterns. Results demonstrated improved layouts compared to existing pavements. Sensitivity analysis showed that while the pattern does not affect the results, cutting loss decreases with smaller block sizes, larger pavement areas, or zero convexity boundaries. It also showed that optimizing the block layout was able to follow construction guidelines of allowing additional blocks to 8% of the estimated quantity.
Lithium-ion batteries have emerged as critical enablers of electrified transport, renewable energy integration, and distributed power systems. Their deployment in real-world environments marked by variable loads, heterogeneous usage patterns, thermal fluctuations, and long-term degradation poses significant modeling and control challenges. Also increased complexity and dynamism of its electrochemistry pose significant challenges for conventional machine learning models used in battery management systems. These challenges include data non-stationarity, sensor anomalies, and aging-related performance drifts, which degrade prediction accuracy and compromise safety. This paper outlines a roadmap for integrating self-healing machine learning into next-generation battery management systems to enhance safety, longevity, and intelligence. It proposes an interdisciplinary framework combining online learning, meta learning, uncertainty quantification, and adaptive control for robust, continuous model correction. This Review analyses and classifies recent self-healing machine learning methodologies based on architectural and functional principles.
Financial fraud continues to be a significant concern for financial institutions, with ever-evolving methods of attack. This paper investigates the role of automated decision-making systems in enhancing financial fraud control by integrating Business Rules Management Systems (BRMS) with Artificial Intelligence (AI). By combining these two technologies, organizations can better detect and prevent fraudulent activities in real-time, ensuring compliance with regulations and improving risk management frameworks. The research explores the benefits of leveraging AI algorithms, such as machine learning models, in tandem with BRMS to automate decision-making processes, reduce false positives, and enhance detection accuracy. Through a case study of a leading financial institution, we demonstrate the efficiency of this integrated approach in reducing financial crime and improving operational efficiency. The findings underscore the importance of AI and BRMS in creating dynamic, adaptive fraud detection systems capable of addressing the complex challenges of modern financial fraud.
This work presents the modeling, control, and simulation of a three-phase grid-connected hybrid renewable energy system integrating solar photovoltaic (PV), wind energy conversion (WECS), and battery energy storage (BESS). The system is developed in MATLAB/Simulink with a unified DC link regulated at 700 V to coordinate the power flow among all subsystems. The wind energy unit employs a 5.79 kW wind turbine coupled to a PMSG, followed by rectification and a P&O MPPT-controlled boost converter. The solar PV subsystem consists of a 7.5 kW array with an Incremental Conductance MPPT and dedicated boost stage. A bidirectional DC–DC converter manages battery charge/discharge based on DC-bus voltage and resource availability. Grid interaction is achieved through a three-phase inverter operating under dq-axis current control with PLL-based synchronization. Simulation scenarios involving variable irradiance (100–1000 W/m²), fluctuating wind speeds, and different load conditions validate the system’s capability to maintain DC-link stability, ensure smooth battery transitions, and enable controlled grid import/export operations. Results confirm efficient maximum power extraction, robust inverter control, and reliable hybrid energy coordination suitable for modern grid-connected renewable applications.
This study presents the design, modeling, and performance evaluation of a standalone solar photovoltaic (PV)-powered induction motor water pumping system employing the Incremental Conductance (INC) algorithm for maximum power point tracking (MPPT). The system integrates a series–parallel configured PV array, a boost converter regulated by the INC MPPT method, and a three-level Neutral Point Clamped (NPC) inverter controlled through PID-based V/f regulation. A 5 kW squirrel-cage induction motor driving a centrifugal pump is used as the mechanical load. The MATLAB/Simulink model is developed to analyze system behavior under varying irradiance (800–1000 W/m²) and dynamic step changes. Simulation results show that the INC MPPT ensures accurate and oscillation-free tracking of the maximum power point, maintaining consistent DC-link voltage for inverter operation. Under standard conditions (1000 W/m²), the system delivers approximately 5.5 kW PV output with motor speed reaching 1500 rpm, whereas reduced irradiance (800 W/m²) lowers speed to around 1200 rpm while preserving stable torque–speed characteristics. The NPC inverter further improves output waveform quality by minimizing harmonic distortion. Overall, the proposed configuration demonstrates reliable dynamic response, efficient energy utilization, and robust performance suitable for rural and agricultural water pumping applications.
