Editorial

Dear Readers, It is a great pleasure to announce the twelfth J.UCS issue of 2025. As always, 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 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 5 accepted papers by 18 authors from 5 countries: Algeria, Brazil, China, Spain, and Türkiye.  Irene Ruiz-Pozo, Juan Morales-García, Claudia Ximena Aguirre-Mejía, and Antonio Serrano from Spain explore in their study the application of generative AI tools such as ChatGPT and Gemini - in creating Instagram marketing content for eSports, comparing them with human-generated campaigns. Findings reveal that AI-generated content achieves comparable or higher engagement metrics, while hybrid strategies combining AI and human creativity maximize reach, authenticity, and audience growth.  Aluizio Haendchen Filho, Jonathan Nau, Hércules Antonio do Prado, and Edilson Ferneda from Brazil tackle in their research the scarcity of methodologies for argument mining in Brazilian Portuguese by analyzing student essays from the National High School Exam, and introducing a feature-engineering approach based on discourse markers. This method enhances automated essay scoring while providing a transparent and computationally efficient alternative to black-box transformer models. Findings show that a streamlined set of five argument-mining features significantly improves scoring accuracy for the competency of developing intervention proposals grounded in scientific concepts.  Samet Aymaz from Türkiye discusses in the article a histogram-based feature selection method with a custom LSTM model to improve early detection of coronary artery disease. The proposed approach achieves convincing classification results on benchmark datasets, offering a fast, accurate, and scalable diagnostic solution.  Zhichao Chen, Zixi Han, Bingqing Shen, Yuxin Zeng, Min Wang, Hongming Cai, and Minqi Wang from China address in their study the issue of complex Integrated Process Planning and Scheduling (IPPS) modeling that existing methods have difficulties in satisfying new modeling requirements for adequately representing the complex scenarios and settings in real-life production. This study proposed an enhanced modeling and computing framework for solving complex IPPS problems, and empirically demonstrated its effectiveness and efficiency in solving complex IPPS problems with the key demanding features.  Fouzi Lezzar and Seif Eddine Mili from Algeria address in their research the lack of real-time guidance in home-based physical rehabilitation by proposing a Temporal Conditional Generative Adversarial Network (TCGAN) that generates patient-specific skeletal motion sequences for exercise execution. The system demonstrates high accuracy and realism, achieving strong alignment between generated and real movements, and significantly improves the precision of rehabilitation exercises compared to existing methods. Enjoy Reading! Cordially, Christian Gütl, Editor-in-Chief