Design and Control of A Standalone Photovoltaic Power System For Telecommunications In Isolated Regions of Algeria

In this paper, we describe the design, control, and management of a photovoltaic (PV) power supply system for a remote telecommunications facility situated in Bab Ezzouar, Algiers, Algeria, that maintains Base Transceiver Stations (BTS) in remote areas where the intermittent grid is either unavailable or non-existent. The proposal is developed and modeled in MATLAB/Simulink for a freestanding PV-battery hybrid system that utilizes a DC-DC boost converter regulated by a Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) algorithm. The proposed system endeavour’s to maximize solar energy harvesting while maintaining a constant energy supply for remotely situated telecommunication BTS without grid connection in a variety of environmental conditions. The simulation results indicate effective MPPT and battery charge-discharge management performance, ensuring BTS autonomy and serviceability 24/7, while demonstrating both technical feasibility and operational efficiency associated with the use of PV-based systems to meet the energy needs of isolated telecommunication infrastructure in North African regions, which have considerable solar potential

Evaluation of Vector Font Rendering and Voice Recognition in Integrated Hearing Support Systems

This paper focuses on the implementation of core functionalities for a Hearing Support System (HSS) and the validation of its engineering feasibility. The system is designed to address the limitations of conventional hearing aids, specifically their restricted personalized calibration and environmental adaptation. The proposed HSS is a smartphone application-based system characterized by key functions: personalized settings derived from individual audiogram profiles, environment-specific presets, and real-time speech translation with textual display. Regarding the system's auxiliary output, the implementation of a Hangul (Korean) display is presented. A comparative analysis between a low-cost ESP32-based implementation (utilizing bitmap fonts) and a Raspberry Pi-based counterpart (employing vector fonts) empirically validates the necessity of vector fonts for enabling font scaling functions, which are crucial for users with low vision. For speech recognition, the study adopts an approach that transforms one-dimensional time-series audio waveforms into two-dimensional 'sound images,' specifically spectrograms, which serve as input for a Convolutional Neural Network (CNN). Conclusively, this research successfully prototyped the core functionalities of the HSS at a Proof of Concept (PoC) level, utilizing tools, thereby confirming its integration feasibility. Nevertheless, several key areas are identified as future tasks for practical deployment: the refinement of preset functionalities, the elimination of dependencies on external APIs, and fundamental enhancements to speech recognition performance through the adoption of deeper CNN architectures

Optimizing K-Means Clustering Parameters for Mapping Smart Contract Transaction Characteristics: A Comparative Analysis of Evaluation Metrics in the IOTA Ecosystem

Smart contracts are already a major development in digital transaction automation thanks to blockchain technology, but their operational efficiency is still greatly impacted by resource consumption, transaction success rates, and gas cost dynamics. This study aims to optimize the K-Means Clustering algorithm's parameters in order to map the characteristics of smart contract transactions in the IOTA ecosystem and provide thorough insights into the efficiency of gas allocation. Using a massive dataset of 566,303 empirical transactions from the IOTA Tangle, three key metrics the Silhouette Coefficient, Davies-Bouldin Index, and Calinski-Harabasz Index were compared to verify the quality of the clustering. With a Silhouette Coefficient value of 0.9851, Davies-Bouldin Index of 0.4622, and Calinski-Harabasz Index of 741,423.92, quantitative evaluation results demonstrate that the 3- cluster structure performs better than two clusters. These results validate the 3-cluster model's ability to more accurately divide transactions into categories that are efficient, complex, and gas-inefficient. The results of this mapping can serve as the foundation for creating an automated recommendation system for optimizing transaction costs in decentralized networks. This study shows that the Gas Limit and Gas Consumed indicators are crucial predictors of transaction efficiency

Cardiovascular Disease Risk Classification Using Machine Learning with Weighted Feature Fusion and Explainable AI on Bangladeshi Clinical and Lifestyle Data

Cardiovascular disease (CVD) is one of the top causes of death across the world, and there is a need to develop early risk prediction models that can be accurate and interpreted. This study introduces a weighted feature fusion (WFF) model of machine learning to integrate clinical, lifestyle, and engineered features into an integrated machine learning model to improve the classification of CVD risk and the interpretability of the model. Several classifiers, such as the Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost (XGB), Bagging, Decision Tree were trained and tested based on fusion-based methods. The experimental findings indicated that the highest classification accuracy of the model at 91% obtained by the Random Forest model. Moreover, the model was better in terms of discrimination as ROCAUC scores were over 0.980447in all categories of CVD risk. Explainable AI algorithms, such as SHAP and LIME were used to provide transparency, when combined with feature fusion, leads to a significant improvement in accuracy, reliability, and interpretability of CVD risk prediction models that can lead to the development of data-driven healthcare decision support systems of trust

Design and Optimization of EMC Filtering Strategies for DC-DC Converters in Electric Vehicles Applications

The rapid electrification of vehicles intensifies electromagnetic interference (EMI) challenges in DC–DC converters, particularly isolated topologies used for high-voltage to low-voltage energy transfer. High-frequency switching generates common-mode (CM) and differential-mode (DM) conducted noise that threatens compliance with stringent CISPR 25 Class 5 standards. This paper proposes the design, modeling, and evaluation of a compact electromagnetic compatibility (EMC) filter capable of simultaneously suppressing CM and DM emissions in an isolated DC–DC converter for electric vehicle applications. The proposed passive filter combines a CM choke with Y-capacitors, a DM π-filter using X-capacitors and series inductors, and an RC damping branch to avoid resonances. The converter and filter were modeled in LTspice, and conducted emission spectra were evaluated using a Line Impedance Stabilization Network (LISN) with Fast Fourier Transform (FFT) analysis. Simulation results demonstrate that conducted emissions are reduced by about 40 dBµV, ensuring full compliance with CISPR 25 Class 5 limits. The proposed solution offers a cost-effective and practical approach to improve EMC margins and reliability in automotive DC–DC converters. The results presented in this study are based on circuit-level simulations, and experimental validation will be addressed in future work

A Critical Review of Fault Detection and Diagnosis in Crystalline Silicon Photovoltaic Systems: From Cell-Level Degradation to Array-Level Failures

The long-term reliability of photovoltaic (PV) systems depends on the timely detection and diagnosis of faults. Crystalline silicon (c-Si) technology dominates the global PV market, and is susceptible to a wide range of degradation modes. This review provides a structured analysis of these faults, categorizing them into material-level intrinsic defects, environmentally-induced extrinsic faults, and system-level interconnection faults. The review details the underlying mechanisms of key degradation modes, including Light- and Elevated Temperature-Induced Degradation (LeTID), Potential Induced Degradation (PID), and micro-crack propagation. A critical evaluation of corresponding Fault Detection and Diagnostic (FDD) methodologies follows. It encompasses laboratory-grade imaging techniques, field-deployable electrical analysis, and emerging data-driven approaches leveraging machine learning and unmanned aerial vehicles (UAVs). This synthesis reveals a fundamental trade-off between diagnostic resolution and operational scalability. To navigate this trade-off, the study analyzes the evolution towards integrated, tiered monitoring strategies and hybrid data-fusion techniques. Furthermore, the review identifies persistent research gaps, such as the need for explainable artificial intelligence (XAI), standardized datasets, robust transfer learning models, and cyber-secure FDD architectures. By bridging the fundamental science of cell degradation with the system-level engineering, this article serves as a roadmap for advancing predictive maintenance and ensuring the sustainability of large-scale PV infrastructure

A Context-Aware Itinerary Recommendation Model Based on CBR with Auto-Revise and Multi-Clustered Data Modeling

This study proposes an itinerary recommendation model based on Case-Based Reasoning (CBR), enhanced with an auto-revise mechanism and multi-cluster modeling using the DBSCAN algorithm. The model is developed from four primary data sources: historical travel cases, visit statistics, social media reviews, and contextual data. The auto-revise mechanism is activated when case similarity falls below 0.95, allowing solution adjustments based on six feature subsets: spatial, categorical, attraction, destination type, popularity, and visitor segmentation. Evaluation was conducted through 5-fold cross-validation and new-case testing, yielding F1-scores of 92.60% and 90.29%, respectively, while ranking performance remained consistently high across both evaluation scenarios. The model also demonstrated improvements in recommendation quality metrics, including novelty, diversity, and serendipity, alongside a reduction in average response latency from 25.53 ms to 20.09 ms. These results indicate that the proposed integrative CBR auto-revise approach, supported by contextual data and multi-cluster structuring, provides an adaptive and efficient itinerary recommendation framework suitable for real-time decision-support scenarios

A Stacked Classifier Model for Enhanced Student Performance Prediction in E-Learning Environments

The rapid growth of e-learning platforms has resulted in an enormous amount of student interaction data, creating opportunities to anticipate learning outcomes and implement timely interventions. In this research, a Stacked Classifier Model (SCM) is introduced to predict student performance using e-learning reaction data obtained from a Kaggle repository. The SCM employs a hierarchical ensemble approach by combining several base classifiers—K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), Random Forest (RF), Support Vector Machine (SVM), and Radial Basis Function (RBF) networks—to capitalize on their respective strengths while compensating for individual limitations. The dataset underwent careful preprocessing, including imputation, encoding, feature normalization, and temporal aggregation, to ensure the classifiers received high-quality input. Evaluation results indicate that the SCM outperforms each base model individually, demonstrating its capability to capture complex behavioral patterns in e-learning contexts. Overall, this study highlights the effectiveness of ensemble learning techniques in educational data mining, offering a solid foundation for adaptive learning, personalized interventions, and enhanced academic performance

A Novel Linguistic Summarization of Time Series Data Based on Enlarged Hedge Algebra Formalism and Genetic Algorithm

The linguistic summarization of time series data (TSD) has been examined extensively because the extracted knowledge represented as summary sentences in natural language is interpretable for all people. The existing extracting methods use manually designed fuzzy partitions of value domains, so the word semantic depends on the subjective opinions of designers. Besides, the number of linguistic words with the fuzzy set-based computational semantics used to describe the TSD, the quantifier, and the summarizer is usually limited to 7±2. That cardinality is not rich enough to describe the special characteristics in a certain period in the TSD. In this paper, enlarge hedge algebra is applied to create a mathematical formalism for automatically designing interpretable and scalable multi-level semantic structures for the corresponding value domains of linguistic variables and these structures can be arbitrarily extended as needed. The objectives of the applied genetic algorithm were also adjusted to improve the optimization goals. The experimental results on the patient admission data have shown that our proposed methods obtain the outstanding results in terms of accuracy, conciseness, and coverage

Benchmarking Linear vs. Non-Linear Predictive Models for Energy Demand Forecasting in Electrical Motor-Driven Compressors

In the LNG regasification plants, among the most energy-demanding components are the electrical motor driven compressors. Artificial Neural Networks (ANNs) and Extreme Gradient Boosting (XGBoost) feature superior flexibility in modelling nonlinear and dynamic compressor behaviour, unlike the conventional methods such as Multiple Linear Regression (MLR), which is limited by its assumption of linearity. This study focuses on data-driven techniques for compressor power consumption prediction, in which MLR, ANN and XGBOOST are compared. Here, real operational data collected from two Boil-off Gas (BOG) and two Regasification Terminal Export Compressors (RGTEC) were considered to develop and evaluate the models. The findings suggest that non-linear machine learning models provide superior predictive performance in comparison to those of the traditional linear approach. The highest prediction accuracy for most compressors is achieved using the ANN model, with a n R2 values of 94.6%, 99.64% and 99.64% for BOG-A, BOG-C and RGTEC-A, respectively. Meanwhile, the best performance was found for XGBOOST for RGTEC-B, with a R2 value of 94.17% and a significantly lower RMSE value. Contrary to that, MLR produced lower accuracy in several cases, particularly when subjected to complex operating conditions, in which linear assumptions were inadequate to capture system dynamics. In addition, this research features one of the first direct comparisons between linear and nonlinear models applied to real-time compressor data, unlike past research that depends on simulations or single-method analysis. It emphasizes the practical advantages of ANN and XGBoost for data-driven energy forecasting and operational optimizations in the gas industry