Dear Readers, It gives me great pleasure to announce the third regular issue of 2026. I would like to thank all the authors for their sound research and the editorial board for the extremely valuable reviews and suggestions for improvement. These contributions together with the support of the community and the generous support of the KOALA initiative enable us to run our journal and maintain its quality. I would still like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends. In this regular issue, I am very pleased to present 6 accepted papers by 27 authors from 6 countries: Brazil, India, Kingdom of Saudi Arabia, Morocco, Spain and Sri Lanka. In a collaborative effort between researchers from Morocco and Spain, Chaimae Moumouh, José A. García-Berná, Begoña Moros, Juan M. Carrillo de Gea, Mohamed Y. Chkouri, and José L. Fernández-Alemán address in their paper the critical challenges of privacy and security in Electronic Health Records (EHRs) by presenting a comprehensive bibliometric analysis of academic research in this field based on 3,077 publications indexed in Scopus over a 24-year period. The findings identify major publication trends, leading institutions, dominant languages, and the year with the highest research output, highlighting the growing academic relevance of EHR privacy and security and providing a structured overview of the field’s development. Jorge Arthur Schneider Aranda, Ricardo dos Santos Costa, Vitor Werner de Vargas, Paulo Ricardo da Silva Pereira, Jorge Luis Victória Barbosa, Marcelo Pinto Vianna, and Eleandro Luis Marques da Silva from Brazil research in their work the challenge of efficiently classifying electrical metrics in power distribution networks by proposing OntoFreya, an ontology-based model that applies semantic reasoning to interpret voltage, current, and contextual data. The results demonstrate that OntoFreya enables precise and scalable automatic classification, reducing specialist analysis effort while supporting context-aware inference across large datasets. Ayodhya Liyanage and Anuradha Mahasinghe from Sri Lanka investigate in their paper the lack of well‑posed state transition diagrams for basic quantum gates in the standard Quantum Turing Machine model by constructing rigorous diagrams for a universal quantum gate set. The results demonstrate that these diagrams satisfy the postulates of quantum mechanics, thereby providing a universal, fault‑tolerant basis for simulating quantum computations within the QTM framework. Moulay Youssef Ichahane, Noureddine Assad, and  Hassan Ouahmane from Morocco present in their work a multimodal diagnostic framework that combines case-based reasoning with deep learning to address the complexity and heterogeneity of rheumatoid arthritis diagnosis, integrating electronic health record data with deep learning–based analysis of chest X-ray images. Experimental evaluations show that the proposed approach significantly improves diagnostic accuracy and robustness compared to conventional CNN- and KNN-based methods, highlighting the framework’s relevance for advanced computer-aided medical diagnosis. Abdelhady Naguib and Abdulaziz Shehab from Saudi Arabia tackle in their article the problem of robust and energy-efficient localization in obstacle-rich wireless sensor networks by introducing a deterministic mobile anchor trajectory model, that integrates square spiral coverage with lightweight obstacle avoidance. A range of simulations demonstrate that the proposed approach achieves superior localization accuracy, higher node coverage, and reduced trajectory length compared to state-of-the-art path planning schemes. In a collaborative research between Saudi Arabia and India, Abdulhadi Altherwi, Md. Mottahir Alam, Mastoor M. Abushaega, Abdulmajeed Azyabi, Ahmed Hamzi, Shabbir Hassan and Asif Irshad Khan research the challenge of accurate and computationally efficient forecasting in Hybrid Renewable Energy Systems by proposing a hybrid Grey Wolf Optimization–Deep Belief Network (GWO-DBN) framework that integrates metaheuristic feature selection with deep learning. Validation on two real-world datasets demonstrates reduced prediction error and computational time, achieving high forecasting accuracy and improved energy efficiency for smart grid applications. Enjoy Reading! Warm regards, Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria

The diagnosis of rheumatoid arthritis (RA) diagnosis demands precise detection methods due to its complex symptomatology. This study presents a novel hybrid diagnostic framework that is the first to integrates Case-Based Reasoning (CBR) with deep learning and introduce three key innovations: (i) a dual-pathway architecture that combine electronic health records with imaging analysis, (ii) an Enhanced Clustering-Based K-nearest neighbors (ECB KNN) model for optimal feature selection, and (iii) a dynamic K-means clustering approach for handling class imbalance. We evaluated our framework using two comprehensive datasets: MIMIC-IV-Hosp, containing clinical data and MIMIC-CXR containing 377,110 chest X-ray images. The model employs a VGG16-based CNN for radiological feature extraction, with a particular focus on pulmonary manifestations, which is combined with our ECB KNN algorithm for patient-specific clinical data analysis. Using five-fold cross-validation, our framework is shown to achieve superior performance metrics (precision: 0.90-0.95, recall: 0.89-0.93, F1-score: 0.91) compared to baseline methods (traditional CNN: precision 0.82, recall 0.79; standard CBR: precision 0.85, recall 0.83). This significant improvement in diagnostic accuracy demonstrates the potential of our framework in terms of enhancing early RA detection and clinical decision support. The architecture of the model architecture is designed to allow for extensibility to other rheumatic conditions, thereby offering a comprehensive solution for multi-disease diagnosis in rheumatology.

The impact of technology on improving health and well-being of individuals is remarkable. EHealth boosts the transition from paper-based health records to Electronic Health Records (EHRs). The use of EHRs can lead to improve quality of care, costs and time. In eHealth systems the health data is stored in digital form, and can be exchanged or accessed securely by authorised users. It is worth noting that medical data is considered very confidential information. However, the privacy and security of medical data remains a critical issue. Any leak or breach in security can lead to serious privacy damages for patients. Despite the safeguards, training courses and the consciousness on keeping data safe, the human error continues to be a problem. The main purpose of this paper is to present a bibliometric overview on the academic research related to privacy and security in EHRs. For this purpose, the papers of this study were searched in the Scopus. A period of 24 years was considered for selecting the papers. The information gathered in the database identified a total of 3,077 publications. Some key findings revealed that in the year 2015 the highest number of publications was produced. The Harvard Medical School was the most prolific institution with 2.44% papers from the total number of publications. A total of 97.21% of the documents were written in English. Finally, the results provided in this manuscript allowed us to make a picture on the current relevance in academic literature on privacy and security in EHRs.

The integration of hybrid renewable energy systems (HRES) has introduced both opportunities and challenges in managing multisource power systems such as wind and solar. Accurate forecasting of HRES performance is critical to efficient planning and grid stability. This paper proposes a data efficient hybrid framework that combines Grey Wolf Optimization (GWO) for feature selection with Deep Belief Networks (DBN) for predictive modeling. GWO effectively selects relevant features from high dimensional environmental and system parameters, reducing computational burden and enhancing learning performance. The DBN is then trained on the optimized input set to forecast system performance. Two public datasets capturing wind and solar power production across distinct geographic conditions were used for validation. The proposed model significantly outperforms conventional methods, achieving a mean square error of 0.0207, RMSE of 0.144, and an energy efficiency of 98.32%. These results demonstrate the framework’s potential for deployment in smart grid forecasting environments.

The importance of localization algorithms is due to their uses in various wireless sensor network applications. A single anchor movement can be used to aid in localization to reduce the cost of using multiple anchors or equipping sensor nodes with GPS units, but the main challenge here is choosing the best path of movement while avoiding potential obstacles. This paper proposes a path planning algorithm called Square Spiral with Obstacle Avoidance (SQSPOA) which allows a mobile anchor node to track an optimal path while broadcasting its current coordinates to the unknown sensor nodes. During its movement, the mobile anchor node faces many obstacles that may hinder its mobility; but as a result of the superiority of the proposed algorithm the mobile anchor can avoid these obstacles while still broadcasting its coordinates to sensor nodes. The performance of the proposed algorithm was evaluated at the presence of variable-sized obstacles and was compared with recent path planning algorithms. Simulation results proved the superiority of the proposed algorithm with respect to localization error, percent of localized sensor node and trajectory length.

Power utilities demand large volumes of data used in power distribution networks. Among them are parameters representing possible technical failures, such as network’s short circuit current and voltage sag. Specialists find these parameters and detect technical failures. However, this process can become time-consuming. Thus, this article proposes an ontology called OntoFreya, which classifies voltage, current, or any electric metric, following the definitions of the regulatory agencies and reducing the time spent on this task. A series of 4402 axioms, 132 classes, and 40 data properties comprises OntoFreya. The ontology automatically inferred classifications for four hundred readings from energy samples, validating OntoFreya across three scenarios. The first and second scenarios classified current in amperes, and the third classified voltage in per-unit system (pu). The scenarios showed that OntoFreya automates the classification of electric metrics, reducing specialists’ time in detecting technical failures in a distribution network.

A quantum Turing machine (QTM) is an abstract model of computing that serves as the quantum counterpart of a classical Turing machine. Closely related to the probabilistic Turing machine, a QTM utilises quantum effects such as superposition, entanglement and unitary evolution. Despite its historical role as a framework for devising quantum algorithms and the significance of its classical counterpart in theoretical computing, very little attention has been paid in literature to the state transition of QTM’s for basic quantum gates. In this paper, we construct the state transition diagrams for a set of elementary quantum gates that consists of the Hadamard, CNOT, and T gates, providing a universal basis for fault tolerant quantum computation. We verify the necessary conditions to ensure that the designed state transition diagrams comply with the postulates of quantum mechanics, verifying their well formedness.

Job recommendation systems play a critical role in matching individuals with relevant career opportunities based on their skills and experiences. However, many existing systems struggle to balance precision and contextual relevance, leading to mismatches in job recommendations. In this paper we introduce JobMatcher, a multilayered recommendation system that integrates a well established technique, cosine similarity and KNN clustering with ChatGPT based evaluation. Initial recommendations are generated through content-based filtering and refined via clustering similar job descriptions aligned with user profiles by seniority and trajectory. To enhance contextual accuracy, GPT 3.5 turbo was prompted to act as an expert evaluator, scoring top recommendations based on skill relevance and career fit using structured and unbiased prompts. In a user study with seven domain experts and ten user profiles, system-selected jobs scored significantly higher (mean = 3.43 compared to 2.99 for KNN clustering, p = 0.0035), with moderate inter-rater agreement (Kendall’s W = 0.417). JobMatcher bridges algorithmic filtering with human like evaluation, offering a scalable, intelligent solution for improved job matching.

Dear Readers, It gives me great pleasure to announce the second regular issue of 2026. I would like to thank all the authors for their sound research and the editorial board and guest reviewers for the extremely valuable reviews and suggestions for improvement. These contributions together with the support of the community enable us to run our journal and maintain its quality.  I would still like to expand our editorial board: If you are a tenured associate professor or above with a good publication record, please apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and emerging trends.  In this regular issue, I am very pleased to present 6 accepted papers by 20 authors from 6 countries: Brazil, Germany, India, North Macedonia, Saudi Arabia, Türkiye.   Gustavo Lazarotto Schroeder, Wesllei Felipe Heckler, Rosemary Francisco, and Jorge Luis Victória Barbosa from Brazil address in their manuscript the growing problem of problematic smartphone use (PSU) by proposing OntoKratos, an ontology-based approach that models contextual, demographic, and mental health information to identify PSU and recommend personalized interventions through semantic reasoning. The research contributes a formal and reusable ontology with SWRL-based inference mechanisms, demonstrating through simulated data that OntoKratos effectively classifies PSU states, infers risk factors, and generates evidence-based intervention suggestions.  In a collaborative research between colleagues from North Macedonia and Germany, Aleksandar Velinov, Aleksandra Mileva, Simon Volpert, Sebastian Zillien, and Steffen Wendzel look into the steganographic analysis of different network protocols which becomes a necessary part of their security evaluation, to prevent their abuse as carriers of hidden messages. In this manuscript, twenty novel covert channels are identified in QUIC, with an accent on their transmission rate, undetectability, and robustness, suggested countermeasures, and one implemented covert channel as a proof-of-concept. Hanan Hafiz and Maher Alharby from Saudi Arabia introduce in their work a study that aims to develop efficient machine learning models for detecting DDoS attacks in cloud environments by addressing challenges related to multi-tenant traffic patterns and virtualized infrastructure constraints. The main contributions of this study include binary and multiclass DDoS classification with feature selection, evaluation of model performance and computational efficiency, and mitigation of data imbalance using oversampling techniques. Kausthav Pratim Kalita, Debojit Boro, and Dhruba Kumar Bhattacharyya from India investigate in their research the issue that big data platforms face limitations in centralized access control despite their distributed architecture and propose integrating blockchain technology using smart contracts to enable secure and controlled access to cluster resources. Through Ethereum-based simulations, the study demonstrates that appropriate indexing and hashing mechanisms can effectively enforce access control while maintaining acceptable execution cost and execution time. Gamze Cabadag, Ali Degirmenci, and Omer Karal from Türkiye research in their work FFT-based radar frequency estimation errors arising from non-integer FFT bin alignment and evaluate twelve interpolation techniques under Gaussian and Laplace noise over varying SNRs and bandwidths. Monte Carlo analyses combined with FLOPs-based complexity evaluation show that the improved Quinn method achieves the highest estimation accuracy for both noise types, while simpler methods offer lower computational cost with reduced performance. Last but not least, Mashael M. Alsulami, Kholoud Althobaiti and Haneen Algethami from Saudi Arabia address in their paper the limitation of traditional job recommendation systems by introducing JobMatcher, a multi-layered framework that combines content-based filtering-KNN, and large language model–based evaluation to better capture career context and progression. The findings show that utilizing ChatGPT as a refinement layer improves alignment with expert judgments, resulting in more relevant and realistic job recommendations. Enjoy Reading! Best regards, Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria

Smartphone use has increased globally and has become essential in daily life. Although benefits exist, concerns arise about the negative effects of prolonged hyperconnectivity. The excessive use of smartphones combined with demographic and mental health related risk factors can lead to problematic smartphone use (PSU). PSU is characterized as the compulsive use of smartphones that disrupts an individual’s daily life, work, and relationships. Considering this scenario, the present paper proposes OntoKratos as an ontology designed to detect and prevent PSU. The ontology enables inferences, such as determining the individual’s mental health and PSU state, inferring context information, identifying PSU demographic and emotional risk factors, and suggesting interventions. OntoKratos includes 89 classes, 43 object properties, 35 data properties, and 1,113 axioms. Evaluations performed through a simulated dataset demonstrated the ontology’s effectiveness regarding PSU identification and interventions for PSU behaviors. Ontology’s rules allowed the definition of accurate axioms, improving the correct classification and inference of eight instantiated individuals. This study presents the first ontology for PSU identification and intervention suggestions on PSU behaviors. OntoKratos allows to identify and assist individuals by considering mental health and PSU status, inferring potential PSU risk factors, and providing tailored intervention suggestions to cope with PSU.