The efficacy of Maximum Power Point Trackers (MPPT) in enhancing the efficiency of photovoltaic (PV) modules has been well documented. They facilitate the maximization of power output from the modules by ensuring impedance matching between the PV modules and the connected load. A range of MPPT techniques has been developed, varying in terms of complexity, tracking speed, cost, accuracy, sensor requirements, and hardware demands. The present paper is concerned with the design and modelling of an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT controller. The proposed system is designed to maximize the efficiency of PV modules under varying environmental conditions and offers a dynamic and adaptive control strategy to accommodate changes in temperature, irradiance, and load. The implementation of this strategy entails the definition of fuzzy rules, which are delineated in accordance with the operating conditions of the PV modules. These rules provide a logical structure that enables the system to make accurate and rapid decisions by using the input parameters of temperature, irradiance, and load to reach the maximum power point. The employment of fuzzy logic is instrumental in accommodating the intricate and non-linear characteristics inherent in the system, thereby facilitating the dynamic attainment of the optimal operating point in accordance with the prevailing environmental conditions. The efficacy of the proposed ANFIS-based MPPT controller is evaluated through extensive simulations conducted in the MATLAB/SIMULINK environment. The simulation results demonstrate the effectiveness of the system in operating under various load, temperature, and irradiance conditions. In addition, comparisons with the conventional Incremental Conductance (INC) MPPT technique indicate that the ANFIS-based controller provides a more stable and faster dynamic response, reducing oscillations around the maximum power point (MPP).

An acoustic emission and machine learning based pistachio classification system has been developed. This system performs feature extraction using Mel frequency cepstral coefficients (MFCC) and classification using support vector machine (SVM). This study revealed that when closed-shelled pistachios hit a steel plate, they have different frequency components compared to open-shelled pistachios. The audio signals of the samples selected for the classification process were recorded using a high sensitivity carbon microphone and MATLAB Analog Input Recorder. These recorded sounds were processed by applying a hamming window to remove ambient noise and make them more clearly analyzable. MFCC is one of the leading methods used to extract features representing audio signals. In this study, MFCCs are used to distinguish between open and closed shelled pistachios. By analyzing the frequency components in audio signals, this feature extraction method helps to identify the distinctive features of the signals. These features are given as input to a support vector machine algorithm called FITCSVM for classification. FITCSVM is an algorithm that can perform one-class and two-class (binary) classification on low or medium-sized prediction datasets. In this study, open and closed shelled pistachios were classified with high accuracy. The results show that the acoustic emission and machine learning based classification system has the potential to be used in the pistachio industry. In particular, distinguishing between open and closed shelled pistachios is of great importance for increasing product quality and improving processing processes. As a result, this research shows that MFCC and SVM algorithms can be used effectively in pistachio classification. The sound signals obtained by acoustic emission method were analyzed with MFCC to extract the features required for classification and FITCSVM was used to classify with high accuracy. Such innovative approaches can contribute to the development of more efficient and effective methods for processing agricultural products.

The increasing penetration of renewable energy sources and converter‐based distributed generation has significantly intensified power quality disturbance (PQD) challenges in modern distribution systems. Accurate and reliable multi‐class classification of disturbances is therefore essential to ensure grid stability and protect sensitive equipment. In this study, an explainable deep learning framework is proposed for multi‐class PQD classification using electrical structured descriptors (ESD), which provide physically interpretable representations of electrical signals. Three representative architectures—MLP, GRU and BiLSTM—are systematically evaluated under a unified preprocessing and stratified cross‐validation scheme. Model performance is assessed using imbalance‐aware metrics, particularly macro‐averaged measures. Experimental results demonstrate near‐saturated classification performance across all models. The GRU model achieves the best overall accuracy (0.9979) and macro‐F1 score (0.9980), while all models reach an identical macro‐AUC of 0.9998, indicating excellent class separability. These findings suggest that increasing architectural complexity does not necessarily yield significant performance gains when structured and physically meaningful features are employed. To enhance transparency, explainability analyses based on SHAP and LIME are integrated into the framework. The results reveal that RMS voltage, peak voltage and total harmonic distortion (THD) are the most influential features, aligning with established power system knowledge. The proposed framework provides a balanced solution in terms of accuracy, computational efficiency and interpretability, making it suitable for real‐time PQ monitoring in renewable‐integrated smart grid environments.

Occupational health and safety constitute an important part of today's working life and labor law. When the measures taken in line with the relevant laws and regulations in workplaces are implemented with a strict approach, it is possible to reduce work accidents and occupational diseases. As a result of these practices, important results are obtained in terms of the sustainability of workers, employers and social security systems. In this study, it is aimed to determine the effects of occupational health and safety training. Therefore, the training given to ÇEDAŞ AŞ employees was evaluated with the questionnaires applied before and after the training. As a result of this evaluation, it will be proven that occupational safety training has provided a significant success.

This study proposes an advanced EMS for a SHEV equipped with lithium-ion (Li-ion) batteries and supercapacitors (SC). The developed rule-based EMS optimizes power distribution between the internal combustion engine (ICE), electric motor (EM), and energy storage system (ESS), ensuring efficient energy utilization while enhancing battery lifespan and vehicle performance. The proposed EMS dynamically adjusts power flow based on real-time driving conditions and charge state, preventing excessive charge/discharge currents and improving regenerative braking efficiency. A comprehensive simulation was conducted using the ADVISOR (Advanced Vehicle Simulator) platform to evaluate vehicle performance under Urban Dynamometer Driving Schedule (UDDS) and New European Driving Cycle (NEDC) conditions. Results demonstrate that the EMS significantly enhances energy efficiency, reduces fuel consumption, and extends battery longevity while ensuring optimal power delivery. Comparative analysis reveals superior performance under NEDC conditions due to smoother acceleration and braking patterns. These findings highlight the effectiveness of integrating supercapacitors with Li-ion batteries in SHEV architectures, providing a viable solution for sustainable and efficient hybrid vehicle design.

The long-term durability of concrete structures is significantly affected by moisture. Excessive moisture may cause drying shrinkage, crack formation, and accelerated corrosion of embedded reinforcement; therefore, reliable and non-destructive moisture assessment is essential for structural durability evaluation. In this study, a controlled acoustic measurement method and a machine learning-based classification framework are presented for the non-destructive identification of moisture levels in concrete specimens. A magnet-assisted free-fall steel ball mechanism was used to generate standardized impacts instead of conventional manual hammer excitation. To reduce environmental vibration noise and capture internal material responses, acoustic signals were recorded using a piezoelectric contact microphone. Experiments were conducted on concrete specimens prepared at nine moisture levels under both large-sample (BIG) and small-sample (SMALL) conditions. Power Spectral Density (PSD) and Mel-Frequency Cepstral Coefficients (MFCC) were extracted from the recorded impact signals and used as input features. Individual machine learning classifiers were compared with a hybrid stacking ensemble model to evaluate discriminative performance and probabilistic reliability. The results showed that MFCC features provided higher classification performance than PSD features under both dataset conditions. For the BIG specimens, the MFCC-based model achieved an accuracy of 0.9872, whereas the PSD-based model achieved 0.9811. For the SMALL specimens, MFCC reached an accuracy of 0.9822, while PSD achieved 0.9750. The AUC-ROC values of the proposed model ranged from 0.9980 to 0.9996 in the multi-class classification of nine moisture levels. These findings demonstrate that controlled impact acoustics combined with MFCC-based representation and stacking-based ensemble learning provides a rapid, low-cost, and reliable NDT approach for concrete moisture classification.