In the rapidly evolving software development landscape, the integration of Open Source Software (OSS) has become commonplace, providing developers with extensive libraries and tools that enhance productivity and accelerate project timelines. However, the use of OSS comes with significant legal responsibilities, particularly regarding compliance with various Open Source Software Licenses (OSSL). An initial framework was designed to ensure OSS compliance, centering on automated creation of Software Bill of Materials (SBOMs) and a “License Playbook”. Automated checks were executed with tools such as Maven and Nexus, verifying license acceptability and required source-code inclusion.

In follow-up work, OSS notice lists were automated, domain-driven design was applied to improve communication, and Java-based tools for Maven were introduced to structure compliance data and reduce errors.

Over time, it became clear that the original framework no longer aligns with evolving requirements, especially as various web projects with focus on OSSL gained in importance. The existing license-management tool encounters challenges in handling large dependency sets, and post-release adjustments in Maven repositories remain difficult to perform. Consequently, alternative software suites are being evaluated to determine whether the proprietary tool should be adapted or replaced to meet evolving needs and strengthen the overall OSS compliance strategy.

Collaborative group projects are integral to computer science education, fostering teamwork, problem-solving, and industry-relevant skills. However, assessing individual contributions within group settings is a long-standing challenge. Traditional assessment strategies, such as equal distribution of grades or subjective peer assessments, fall short in terms of fairness, objectivity, and scalability, especially in large classrooms. This paper introduces a semi-automated, AI-assisted grading system that evaluates both project quality and individual effort using repository mining, communication analytics, and machine learning models. The system comprises modules for project evaluation, contribution analysis, and grade computation, integrating seamlessly with platforms like GitHub. A pilot deployment in a senior-level course demonstrated high alignment with instructor assessments, increased student satisfaction, and reduced instructor grading effort. We conclude by discussing implementation considerations, ethical implications, and proposed enhancements to broaden applicability.

Digitalization has become an integral part of both private and business life, fundamentally transforming all sectors of society. In recent years, the rapid advancement of Artificial Intelligence (AI) has further reshaped the landscape of entrepreneurship by enhancing operational efficiency, streamlining customer interactions, and enabling more informed decision-making. These technological developments offer significant benefits, particularly for small and medium-sized enterprises (SMEs) seeking to remain competitive in a dynamic digital environment. At the same time, these advances introduce new and increasingly complex risks - most notably, the rising threat of cyberattacks. SMEs, which typically operate with constrained financial and human resources, often face significant difficulties in developing and maintaining robust cybersecurity systems. This lack of preparedness makes them particularly attractive targets for cybercriminals, especially in the context of AI-driven operations that introduce novel vulnerabilities.

To address these challenges, this paper proposes a matrix-based approach that systematically links critical SME assets to specific categories of cyber risks. Building on the authors’ previous research, the study identifies and classifies essential assets and major threat types, integrating them into a comprehensive matrix framework. The proposed model serves as a practical tool for assessing vulnerability, prioritizing protective actions, and ultimately supporting SMEs in enhancing their overall cyber resilience.

To better understand the nature of art, this study investigated how different immersive environments influence viewers' physiological responses during art appreciation. We constructed two immersive spaces with distinct spatial characteristics: Immersive Space 1, which incorporates mirror displays to create a sense of infinite reflection, and Immersive Space 2, which is surrounded by large LED displays. While participants viewed a video artwork created by one of the authors, we recorded and analyzed their electrocardiographic (ECG) data.

The results revealed that in Immersive Space 1, both sympathetic and parasympathetic activities were suppressed during art viewing, suggesting a state of heightened arousal and reduced physiological relaxation. In contrast, in Immersive Space 2, parasympathetic activity was dominant, suggesting a more relaxed, emotionally stable physiological state. These findings underscore the significance of spatial context in shaping the embodied aesthetic experience.

The application of artificial intelligence in animal husbandry and veterinary medicine is gaining increasing attention. Using computer vision systems for assessing animal welfare seems promising in the latter field. The Istituto Zooprofilattico Sperimentale delle Venezie (IZSVe) is developing systems for assessing animal welfare based on innovative technological tools leveraging deep learning algorithms for complex computer vision tasks. These tools enable the automation of data processing, significantly increasing efficiency and scalability. By replacing labor-intensive manual analysis, the system allows for the rapid processing of large volumes of data, ensuring the extraction of critical information that would otherwise be lost or impractical to obtain through conventional methods.

In times of war and crisis, higher education institutions (HEIs) face unprecedented challenges requiring transdisciplinary adaptability, resilience, and innovative leadership. Digital transformation plays a crucial role in sustaining transdisciplinary academic processes, institutional governance, and crisis management.

This study aims to examine the transdisciplinary strategies deployed by Ukrainian universities, in navigating wartime impediments while fostering digital institutional leadership, ensuring academic sustainability, and strengthening governance frameworks.

Drawing from the Universities’ experience in educational leadership, strategic management, and crisis adaptation, the study explores digital governance, AI-enhanced institutional resilience, and leadership frameworks rooted in servant leadership philosophy. The paper highlights key institutional responses, including the integration of digitalized administrative workflows, crisis management systems, and AI-powered strategic decision-making to support academic operations during wartime uncertainty.

Applied trans-disciplinary lens contributes to the solution of holistic modeling of processes and results of updating models and mechanisms of the highly dynamic communication system of education in the digital environment as a whole and its individual formats in the emergency digitization measures of different types.

Artificial Intelligence (AI) is a rapidly growing field and deals with simulating human behaviors and decision making with the use of computer. AI is rapidly becoming a transformative force in almost all aspects of our society. Higher education is no exception, and AI is reshaping the landscape of teaching, learning, research, and management in higher education institutions around the world. As the demands of the digital environment around us continue to evolve, higher education institutions are adapting to use AI as a tool for higher efficiency and increased productivity. In this paper, we discuss the scope of AI’s impact on higher education. The impact of AI is divided into three sections; aspects of AI that are perceived to be positive, aspects of AI that are perceived to be negative, and aspects of AI that are perceived to be neutral.

This study evaluates the effectiveness of a gradient boosting regression model in forecasting concrete strength by comparing three hyperparameter configurations: default settings, manual tuning, and automated Grid Search CV. A publicly available dataset of 1030 concrete mixes, featuring cement, slag, fly ash, water, superplasticiser, coarse and fine aggregates, and concrete age, was divided into an 80-20 train-test split. Model performance was assessed using mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and the coefficient of determination (R²). The default model achieved R² = 0.89, while targeted adjustments to the parameters, such as the number of estimators, raised R² to 0.93. Manual fine-tuning of all hyperparameters simultaneously produced the best results of R² = 0.94, marginally outperforming Grid Search CV 3- and 5-fold of R² = 0.93. The number of estimators was identified as the most influential parameter. Although exhaustive grid search offers systematic optimisation with high runtimes, manual finetuning can yield superior accuracy within a constrained parameter space.

Transformative potential of the knowledge economy of the XXI century, establishment of networked society, emergency digitization due to the pandemic and wartime measures have imposed elaborate interdisciplinary and interoperable demands on the marketability of Liberal Arts skills and competences, upon entering the workforce.

This study examines the gap between transdisciplinary future sk?lls h?ghl?ghted ?n the World Econom?c Forum’s (WEF) "Future of Jobs" reports and those sought by learners ?n Coursera’s Global Sk?lls ?ndex. The emphasis lies on the role of Core skills combined with the Inner Development Goals (IDGs) framework in bridging these gaps. The proposed strategy roadmap links IDGs with the demands for future skills and Humanity-focused Higher Education (HiEd), besides, it provides actionable recommendations for HiEd staff, business schools and policymakers. By combining Inner Development with Leadership Skills and Digital Skills Programs in HiEd we may have a hope to stimulate employability for the AI age both for individuals' inner growth and collaboration/co-creation skills in teams and larger communities in a turbulent job market of 2025-2050.

The study results disclose the comprehensive review of dynamics of the digital skills development and application to construe interdisciplinary, AI-interoperable competencies of students and educators in Europe through the span of educational activities in the time-frame of the pandemic emergency digitization measures of 2020-2021 and wartime emergency digitization measures of 2022-2024 in Ukraine (including AI-enhanced communication as a staple of transdisciplinary education as of 2023).

The paper introduces a model of AI-interoperable digital skills for education and professional application in different social spheres. The survey analysis is used to evaluate the dimensions of interdisciplinarity, informed by the interoperability of soft skills, professional communication skills, and digital skills across contrasting frameworks of e-competence, Inner Development Goals, professional digital communication, and professional training.