This paper presents the design, implementation, and experimental validation of a real-time embedded photovoltaic (PV) emulator based on the two-diode model, using a dSPACE DS1103 platform for hardware validation. The proposed system aims to accurately reproduce the electrical behavior of PV modules under varying environmental conditions, including irradiance and temperature variations. The emulator architecture combines a lookup-table-based modelling approach with a programmable DC power source, enabling deterministic real-time execution and efficient implementation. A multi-level control structure is employed, integrating inner-loop regulation, model-based reference generation, and feedback control to ensure accurate tracking of the PV current-voltage (I-V) characteristics. Experimental results demonstrate that the emulator achieves high accuracy, with an approximation error of approximately 1.2% under standard operating conditions. The system exhibits stable dynamic behavior characterized by a time constant of approximately 0.5 s, with performance maintained across different sampling intervals and load conditions. Additional simulations confirm that the two-diode model preserves high accuracy over a temperature range of 15-60 degrees C, with deviations below 2%. The results highlight that the two-diode model provides an optimal trade-off between modelling accuracy and computational complexity for real-time embedded applications. The proposed emulator offers a flexible and reliable platform for laboratory validation of photovoltaic behavior and provides the foundation for future testing of maximum power point tracking (MPPT) algorithms, power electronic converters, and embedded control strategies under controlled conditions.

Photovoltaic (PV) systems are strongly affected by irradiance and temperature variability, with partial shading causing nonlinear P-V characteristics and multiple local maxima that hinder conventional MPPT techniques. From a computational cybernetics viewpoint, a PV system with maximum power point tracking (MPPT) can be modelled as an adaptive feedback-driven process interacting with a dynamic environment. This paper introduces an adaptive cybernetic framework in which a bio-inspired optimization algorithm is embedded in the MPPT loop to enhance global MPPT. The PV system is interpreted as a nonlinear self-regulating process capable of robust adaptation to environmental disturbances. Simulations under rapidly changing irradiance show that the proposed method improves global tracking performance compared to conventional MPPT strategies, confirming the relevance of computational cybernetics for intelligent PV energy systems.

The global transition to renewable energy demands not only the large-scale deployment of photovoltaic (PV) systems but also their seamless integration into smart grid infrastructures. Conventional PV optimization strategies face significant challenges, including environmental variability, partial shading, and decision-making latency. This paper introduces a novel Edge-AI-driven optimization framework for PV systems, enabling real-time, decentralized intelligence for maximum power point tracking (MPPT), predictive load management, and fault detection. Mathematical models of PV cells are developed to highlight inherent nonlinearities, followed by a comparative review of classical MPPT and metaheuristic algorithms. An Edge-AI architecture is then proposed, integrating reinforcement learning, lightweight deep learning models, and IoT protocols for efficient grid interaction. Simulation results demonstrate the superior performance of the Edge-AI approach compared to conventional methods in terms of tracking efficiency, decision latency, and fault detection accuracy. Finally, the paper discusses future perspectives on federated learning, distributed optimization, and cyber-resilience, positioning this framework as a key enabler for next-generation renewable network infrastructures.

The accurate modelling of solar cells is crucial for optimizing their performance and improving efficiency in photovoltaic systems. In this study, we revisit a genetic algorithm (GA) approach previously introduced in a 2010 research paper to determine the optimized parameters of a two-diode solar cell model. Using the latest version of MATLAB, we implement the genetic algorithm based on the methodology outlined in the original work. The same input parameters and constraints were used to assess the algorithm’s performance and compare the optimized results with those reported in the original study. The obtained results reveal key similarities and deviations from the initial findings, highlighting potential improvements due to advancements in computational efficiency and algorithmic refinements in modern MATLAB environments. This research provides valuable insights into the robustness of genetic algorithms in solar cell parameter identification and underscores the impact of software updates on numerical optimization techniques.

Partial shading conditions (PSC) pose significant challenges to the efficient operation of photovoltaic (PV) systems, as they create multiple local maxima in the powervoltage (P-V) curve, often misleading conventional Maximum Power Point Tracking (MPPT) algorithms. To address this, metaheuristic techniques have gained attention for their ability to identify the global maximum power point (GMPP) under such complex conditions. This paper provides a focused state-of-the-art review of MPPT methods, with particular emphasis on the Firefly Algorithm (FA) and its application in PV systems operating under PSC. The FA, inspired by the flashing behavior of fireflies, is examined in terms of its core mechanisms—light intensity, attractiveness, and randomness—and how these enable effective GMPP tracking. A critical review of recent studies is presented, analyzing FA-based MPPT approaches in terms of tracking accuracy, convergence speed, energy efficiency, and computational complexity. Hybrid techniques combining FA with other algorithms are also discussed, highlighting enhanced performance in dynamic shading scenarios. Additionally, a bibliometric analysis using VOSviewer was conducted to visualize research trends, coauthorship networks, and thematic clusters within this field. The study concludes by identifying key research gaps and future directions for optimizing FA-based MPPT strategies in partially shaded PV systems.

The implementation of Artificial Intelligence (AI) into Quality Management (QM) processes within the Automotive Industry 4.0 marks a pivotal shift towards enhanced efficiency, precision, and adaptability. As automotive manufacturers transition into a more interconnected and data-driven landscape, AI systems: machine learning, analysis of prediction and deep learning, are increasingly applied to monitor and optimize quality control systems. This paper explores the role of AI in automating quality inspection, detecting defects, and predicting failures, thus ensuring consistent product quality while minimizing human error. Furthermore, it examines the synergies between AI and Industry 4.0 systems and processes, as IoT, big data analytics, cyber-physical systems. The study highlights key benefits such as cost reduction, real-time decision-making, and continuous process improvement, while also addressing challenges such as data integration, system complexity, and workforce adaptation. Ultimately, this paper demonstrates how AI-driven QM strategies are transforming the automotive industry's ability to meet the demands of mass customization, sustainability, and global competitiveness.

Electric-powered micro unmanned aerial vehicles (UAVs) have been applied in a wide range of defense and civilian applications given their flexibility, portability, and versatility. However, they are highly susceptible to wind, which can compromise control stability. This study formulated a system that makes elevator control, pitch, and roll adjustments for enhanced longitudinal flight stability in UAVs. First, the basic aerodynamic coefficients of the UAV are calculated using a digital airborne tactical communications system. Subsequently, the longitudinal motion state-space equations of the UAV are used to derive the transfer function for the pitch angle θ and horizontal stabilizer δE. Simulink was used to compare the effects of traditional proportional-integral-derivative (PID) and fuzzy PID controllers on the longitudinal flight stability of the UAV, identifying the optimal PID values. Finally, actual flight tests confirmed that fuzzy PID significantly improves the longitudinal flight stability of the UAV.

This paper presents a Simulink model of a single-phase, 240 Vrms, 3500 W transformerless grid-connected photovoltaic (PV) system. The model integrates a Trina Solar TSM-250PA05.08 PV array, a DC link for stabilizing the output, and an H-Bridge inverter controlled by Maximum Power Point Tracking (MPPT) to optimize power extraction. The inverter synchronizes the AC output with the grid while RL filters minimize harmonic distortion. A 75 kVA transformer steps up the voltage for grid compatibility, and local loads simulate power consumption. Leakage current monitoring ensures safety, a key requirement in transformerless systems. The model allows for real-time monitoring of system performance, providing insights into the dynamic interaction between PV generation and grid connection. This work demonstrates the system's efficiency and safety, offering a practical solution for small-scale solar energy integration.

The objective of this paper is to propose a mathematical model representing the hydraulic behavior of a generic three-tank aquaponic system, capable to support future research on optimized sizing and suited control algorithms. The components of the system are a fish tank, a hydroponic reservoir, and a multi-task buffer water tank. The mathematical model is structural, representing the water-tanks and the water-flows between them, by differential equations, implemented in Matlab-Simulink.

The aim of this study is to develop a three-variable Karnaugh-Map (K-MAP) in hardware. K-MAPs are extensively used in digital logic design for simplifying Boolean expressions. Here, we automate the process of Boolean expression simplification by designing a K-MAP hardware circuit, that can show the simplified expression in real-time. The hardware is comprised of input, combinatory, comparator, and output units. The input unit accepts minterms from the DIP switches. These minterms are processed in combinatory unit against all the possible combinations of boxes in power-of-two. The output from the boxes with higher power-of-two is kept in the comparator unit and the result is displayed on LEDs in the output unit. The proposed hardware design can be used in a classroom setting to teach the concepts of Boolean function simplification and verify the practice problems.

This paper presents a comprehensive study integrating theoretical analysis and simulation modelling of residential photovoltaic (PV) systems. The first part focuses on the environmental, economic, and technological implications of PV adoption in residential settings, with particular emphasis on longterm energy savings, return on investment, and the role of net metering policies. Environmental benefits, including significant reductions in greenhouse gas emissions and decreased dependence on fossil fuels, are discussed in light of the European Green Deal's objectives for a carbon-neutral economy by 2050. Technological developments are explored through a comparative evaluation of monocrystalline, polycrystalline, bifacial, and passivated emitter and rear cell (PERC) PV modules. The integration of energy storage systems, particularly lithium-ion batteries, is examined for their potential to enhance self-consumption, grid resilience, and energy independence. The second part develops a detailed MATLAB/Simulink model for a single-phase, 240 Vrms, 3500 W transformerless grid-connected residential PV system. The model incorporates a Trina Solar TSM-250PA05.08 PV array, a DC link for output stabilization, and an H-Bridge inverter controlled via Maximum Power Point Tracking (MPPT) to optimize energy extraction. The inverter ensures synchronization with the grid, while RL filters reduce harmonic distortion. A 75 kVA transformer is employed to step up the voltage for grid compatibility, and local loads are included to simulate household consumption. Leakage current monitoring is implemented to address safety concerns, a critical factor in transformerless configurations. The simulation enables real-time analysis of system performance, providing insights into the dynamic interaction between PV generation and grid integration. The combined analysis underscores the technical and policy-aligned viability of residential PV systems as a cornerstone of sustainable energy transition.

The diagnosis of autism spectrum disorder during its early stages leads to better intervention programs, which result in developmental improvement. Traditional behavioral assessment methods demonstrate poor diagnosis performance with 82.3% accuracy and 80.1% precision, and 81.7% recall as well as 80.9% F1 score. The proposed research presents MultiModal-ASDNet as an advanced deep learning platform that combines convolutional neural networks (CNNs) for MRI examination with bidirectional long short-term memory (LSTM) networks for behavioral assessment as well as ensemble methods for genetic marker assessment. The attention-based fusion mechanism from the model actively combines genetic data with neuroimaging results and behavioral examination outcomes to deliver better diagnosis outcomes. Experimental measurements show the system exceeds present methods with 95.2% accuracy and 93.8% precision and 94.5% recall, and 94.1% F1 score success rate indications. The model displays strong reliability through comprehensive cross-validation and statistical validity tests, along with real-case ASD diagnosis deployments, which prove its general applicability. The study contributes to autism research through an objectively measurable monitoring tool that operates efficiently in minimal resource areas for designing patient-specific intervention plans.

Residential photovoltaic (PV) systems represent a growing trend in sustainable energy generation. Their efficiency is significantly influenced by installation parameters such as orientation, tilt angle, and shading. This paper conducts a comparative study of two PV systems under similar residential conditions: one with an optimal south-facing orientation, and a second that is partially shaded and primarily West-facing. The energy production of both systems is analyzed over a representative period. This analysis reveals the production losses attributed to shading and suboptimal orientation. Results demonstrate significant disparities in energy yield and efficiency, which underscores the necessity of proper system design for achieving long-term financial viability and energy independence. The findings of this study provide a better understanding of the practical challenges encountered in residential PV deployment, informing decisions related to system sizing and placement.

Since 2021 a new traffic control rule was proposed to the drivers running on highways: the two-second rule, stating that a driver should ideally stay at least two seconds behind the preceding vehicle. The two-second rule is easily understandable by drivers and is perfectly adapting distance gaps between vehicles to their actual speeds. On the other side this rule is hard to automate. The 2-second rule is deeply connected with the Constant Time to Collision criterion (CTTC), introduced in 2006, which is measuring the distance gap between following cars by the time required for the following cars to collide, assuming unchanged speeds for both vehicles. CTTC was experimentally tested in 2013, on the occasion of a doctoral thesis defended by F. Mensing and supported by the Renault group. This work is proposing to automate the 2 second rule by using CTTC curves as planners for the cruise controllers of any specific automobile model.

The integration of Artificial Intelligence (AI) into Quality Management (QM) systems is transforming how organizations approach automated decision-making. This paper explores the convergence of AI technologies such as machine learning, natural language processing, and predictive analytics with established QM frameworks, including ISO 9001 and Six Sigma, to enhance decision support systems (DSS). We propose a conceptual model that embeds AI into key QM processes, enabling real-time data analysis, anomaly detection, and adaptive process control. Case studies from manufacturing demonstrate how AI-driven DSS can improve accuracy, efficiency, and compliance while reducing human error. The study also examines the ethical and operational implications of AI in quality-centric environments, highlighting challenges in transparency, accountability, and system integration. Our findings suggest that synergizing AI with QM not only accelerates continuous improvement but also redefines quality assurance in the era of Industry 4.0.

There are numerous uses for picture fuzzy sets (PFS) in the context of engineering and scientific issues. Dombi operators can flexibly work on parameter assessment. To address this, we propose the weighted versions of the ordered, hybrid, and picture fuzzy Dombi averaging operators, which we refer to as PFDWA, PFDOWA, and PFDHWA, respectively. Additionally, we created the weighted form of the PFDWG, PFDOWG, and PFDHWG operators, which stand for picture fuzzy Dombi weighted geometric, order geometric, and hybrid geometric operators, respectively, and show their features. To build the model for a multiple attribute decision-making (MADM) method, we also use PFDWA and PFDWG operators. Finally, an example has been provided to illustrate the application of the suggested MADM technique.

The soft set-theoretic model acts as an essential methodology for handling the uncertainty in which parameterized issues appeared during the data analysis compared with fuzzy as well as advanced fuzzy sets (picture fuzzy set). In this proposed work, an effort is made to achieve the effect of picture fuzzy soft sets, as well as multicriteria group decision making (MCGDM) on picture fuzzy arguments where evaluation is executed on the evaluation of a group of experts. In this regard, the new averaging operators are introduced via weighted forms of a parameterized PFS setting, namely, a picture fuzzy soft average operator, a picture fuzzy soft geometric operator, and some properties of these proposed operators are stated. Finally, a model for the MCGDM technique has been introduced for these proposed operators and compared with the existing operators.

Dombi operations are introduced on two m-polar picture fuzzy (mPoPF) numbers in this chapter. The mPoPF Dombi weighted averaging (mPoPFDWA), mPoPF Dombi ordered weighted averaging (mPoPFDOWA), mPoPF Dombi hybrid weighted averaging (mPoPFDHWA), mPoPF Dombi weighted geometric (mPoPFDWG), and mPoPF Dombi hybrid weighted geometric (mPoPFDHWGA) operators have been proposed. Additionally, some qualities are established, including idempotency, boundedness, monotonicity, and commutativity. Next, using the mPoPFDWA and mPoPFDWG operators, we created the multiattribute decision-making (MADM) technique for the mPoPF environment. In order to choose the ideal location for the construction of a petrol station, we have provided an application of the MADM approach. Based on the operating parameter for the outcomes of the decision-making process, a sensitivity analysis of the current strategy is established.