Editorial

Abstract

Dear Readers, It gives me great pleasure to announce the tenth issue of 2026. In this issue, 6 articles by 16 authors from 6 countries – Belgium, China, Czech Republic, South Korea, Türkiye, USA  – cover various topical and novel aspects of computer science. As always, I would like to thank all the authors for their sound research and the editorial board and guest reviewers for their extremely valuable review effort and suggestions for improvement. I also want to thank the readers for their interest in our articles, which is reflected in the increasing number of accesses and PDF downloads. These contributions, together with the generous financial support of the KOALA initiative, sustain the quality of our journal. In a continuous effort to further strengthen our journal, I would like to expand the editorial board: If you are a tenured associate professor or above with a strong publication record, you are welcome to apply to join our editorial board. We are also interested in high-quality proposals for special issues on new topics and trends.  In the tenth J.UCS issue, I am very pleased to introduce the following 6 accepted articles: Mikel Vandeloise from Belgium, motivated by the lack of formal, quantitative metrics to assess conceptual compatibility between programming languages and paradigms in polyglot software systems, presents a systematic literature review of 82 primary studies, combining thematic synthesis with a citation network analysis. The analysis reveals a profound structural fragmentation of the field, which explains why the building blocks of a Language Compatibility Metric identified in the literature have never been consolidated, and the paper concludes with an evidence-based research agenda to unify the field. Mohamed Bettaz from the Czech Republic addresses in the research work the structuring challenges inherent in large-scale IT ontologies. The author demonstrates how the OntoObject-Z language, enhanced with meta-schemas, provides a scalable solution for formalizing complex domains. The efficacy of the proposed approach is evaluated through a local area network case study. Jiabo Liu and Huaxiong Zhang from China focus on the problem of accurately modeling semantic relevance and temporal relationships in large-scale scientific literature for citation recommendation. Thus, this study addresses the issue by integrating SciBERT for text encoding with a Temporal-Aware Graph Attention Network (TGAT) to jointly capture content semantics, network structure, and temporal dynamics. Experiments on the DBLP dataset demonstrate that the proposed hybrid approach significantly outperforms existing baseline methods, providing an effective solution for improving citation recommendation performance.  Sertac Kaan Tokyay, Hasan Demir, and Atıl Emre Cosgun from Türkiye investigate in their research frequency-dependent piezoelectric energy harvesting using PVDF material through an experimental setup and applies four machine learning algorithms — ANN, KNN, SVM, and Random Forest — to predict harvested energy output with comprehensive performance evaluation, including Taylor diagram and Bland-Altman analysis. The results demonstrate that the Random Forest model achieves the best balance between accuracy, stability, and efficiency, providing a reliable predictive reference for feasibility studies in pulse-based piezoelectric energy harvesting applications such as powering CMOS circuits and low-power sensors. In a collaborative research between researchers from South Korea and the USA, Sooin Kim, Kyungtae Kim, Donghoon Kim and Doosung Hwang focus on the problem that existing deep hashing methods often struggle to effectively capture complex structural relationships in visual data. Their article addresses this limitation by integrating transfer-learned autoencoder embeddings with graph convolutional networks and dynamically constructed local graphs for efficient similarity search. Experimental results on STL-10, Stanford Cars, and Tiny ImageNet show that the proposed approach effectively captures both global and local data structures and achieves competitive or improved retrieval performance compared with conventional CNN- and GCN-based hashing methods. And last but not least, Beyza Kışla, Fehmi Bora Şimşek, Funda Kayatürk, Güzin Türkmen, and Arda Sezen from Turkiye introduce in their research a forecasting framework that integrates Sentinel-2 optical imagery, Sentinel-5P atmospheric measurements, and ground-based meteorological data within an LSTM model, supported by a regionally calibrated Simple Water Body Mapping (SWBM) technique, to address the crucial need for precisely monitoring lake water level dynamics amid environmental changes. The findings demonstrate that this multi-dimensional approach significantly enhances mapping robustness and predictive accuracy compared to traditional single-source methods, effectively mitigating atmospheric interference and achieving a high explained variance of up to 93.1%. Enjoy Reading! Best regards,  Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria