Dear Readers,

First of all, I would like to wish you all the best for the New Year! It is with great pleasure that I welcome you to our first regular issue in 2026.

Looking back on the past year, we have further increased our visibility and taken steps to fully comply with the Diamond Open Content Standard and align our journal with the KOALA requirements. Thanks to the combined efforts of the Pensoft team and the J.UCS publishing team, we are listed and indexed in more than 40 indexing services worldwide, including DOAJ, Web of Science, and Scopus. The increased visibility and presence on social media have also led to a further increase in page views and article downloads.

With around 340,000 unique views, reader interest in J.UCS publications increased by almost 200% compared to the previous year. We can also look back on an increasing number of submitted articles and special issue proposals. This interest is reflected in a notable impact factor with a Scopus Cite Score of 2.5 and a Web of Science Journal Impact Factor of 0.9, both metrics have improved slightly since last year. We proudly look back on a total of 14 issues – 12 regular and 2 special issues – with 70 articles by 247 authors from 34 countries on novel aspects of various topics in computer science. The acceptance rate has fallen to below 6 per cent.

These great achievements were only possible thanks to the commitment and interest of the community, the valuable support of the Editorial Board and the financial supporters of J.UCS through the KOALA Computer Science Cluster from TIB - Leibniz Information Centre for Science and Technology University Library, Germany.

In 2025, we welcomed 10 new members to the Editorial Board, bringing our total number of Editorial Board members to 222. We would also like to gratefully acknowledge the support of 78 guest reviewers over the past year.

In particular, I would like to thank Dr. Ulrike Krießmann from the Library of the Graz University of Technology, the TIB - Leibniz Information Centre for Science and Technology University Library in Germany, and the Institute of Human-Centred Computing (HCC) from TU Graz for their financial support.

I would also like to thank the J.UCS team, Johanna Zeisberg for taking care of the publication process, David Kerschbaumer for his social media support, and Sebastian Gürtl and Alexander Nussbaumer for their technical support, as well as Pensoft Publishers Ltd. for hosting our journal.

I look forward to continuing to work with our editors, editorial team and technical support to maintain the success of J.UCS. I would be very grateful for suggestions and feedback on how we can improve and develop J.UCS in the future. We also greatly appreciate the generous support of the J.UCS community, especially in promoting the journal and citing relevant articles in their research papers.

In this regular issue, I am very pleased to present 6 accepted articles by 34 authors from 8 different countries, namely Austria, China, France, Germany, India, Thailand, Türkiye, United Kingdom.

In a collaborative research effort from several research institutions from Austria, Thailand and Germany, Stefan Lengauer, Lin Shao, Hossein Miri, Michael Bedek, Cordula Kupfer, Maria Zangl, Bettina Kubicek, Barbara Dienstbier, Klaus Jeitler, Cornelia Krenn, Thomas Semlitsch, Carolin Zipp, Dietrich Albert, Andrea Siebenhofer, and Tobias Schreck present an advanced and innovative visual health information system - A+CHIS - based on adaptive document visualizations. Depending on the users’ information needs and preferences, the system displays its content at different levels of detail, aggregation, and visual granularity, addressing individual needs and preferences.

Jinghan Liu, Hui Zhao, Chenyang Lin, Dan Wang, and Shufan Li from China address in their research the issue that existing smart contract vulnerability detection methods overly rely on expert rules and struggle to adapt to complex application scenarios. The article proposes a vulnerability detection model called ESA based on the enhanced sequential algorithm. Experimental results demonstrate that ESA significantly outperforms cutting-edge methods, achieving detection accuracies of 89.09% for reentrancy vulnerabilities and 88.49% for timestamp dependency vulnerabilities.

Kadir Ileri, M. Şamil Balcı, and Adem Dalcalı from Türkiye focus in their research on predicting the harvested power of toroidal electromagnetic energy harvesters using machine learning models optimized with the Artificial Bee Colony algorithm. The findings show that the ABC-optimized XGBoost model provides highly accurate and robust predictions, outperforming the other evaluated approaches.

Banani Ghose and Zeenat Rehena from India propose in their research work a Genetic Algorithm-based lossless data compression technique to reduce the size of the time-series data while conserving energy of the sensor-based system. The proposed lossless data compression technique can not only compress the air quality time series data with a high compression ratio, but it is also energy efficient, both contributing finally towards greater efficiency and longer lifetime of the sensor network.

Hamdullah Karamollaoğlu, İbrahim Yücedağ, İbrahim Alper Doğru, Sinan Toklu and İsmail Atacak from Türkiye cover in their study the critical security challenge of DDoS attacks in resource-constrained IoT environments by proposing a novel hybrid deep learning model (CBM-IDS) that integrates Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a Multi-Head Attention Mechanism for robust intrusion detection. The proposed model, evaluated on the CICDDoS2019 benchmark dataset and enhanced through advanced feature reduction and data balancing techniques, achieved a detection accuracy of 99.93%, demonstrating its significant potential for real-world IoT security applications.

In a collaborative research effort between France and Türkiye, İbrahim Çakırlar, Sevcan Emek, Şebnem Bora, and Oğuz Dikenelli introduce RatKit, an advanced framework and methodology for verification, validation and testing of agent-based simulation models, which is based on the Boids model. The findings demonstrate that a test-driven approach can enhance model reliability and ensure that individual agent behaviors coalesce into realistic emergent phenomena.

Enjoy Reading!

Best wishes, 

Christian Gütl, Managing Editor-in-Chief Graz University of Technology, Graz, Austria