International Institute of
A Critical Retrospect of OSS License Compliance: Lessons Learned and Next Steps
Dyck, Sergius; Haferkorn, Daniel (Germany)
https://doi.org/10.54808/WMSCI2025.01.408
ABSTRACT:
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.
A Framework for Engaging and Attracting Generation Z Employees via Effective Job Offers
Kamola, Liga; Orinska, Ieva; Reinvalde, Iveta (Latvia)
https://doi.org/10.54808/WMSCI2025.01.205
ABSTRACT:
Currently, the labour market is entering Generation Z, who have grown up in a digital age and an environment of socio-political change that has significantly impacted their work values and perceptions of their careers. This generation is characterised by a desire for flexible working environments, work-life balance and a greater importance of social responsibility and organisational values in work choices. The results reveal that 64% of Generation Z would be willing to work for lower pay if provided with a meaningful benefits package, underlining the importance of personalised benefits offers in attracting and retaining employees. Organisations that can create an environment where respect, transparency and development opportunities prevail have an advantage in attracting talent and strengthening organisational sustainability in a rapidly changing labour market. A mixed approach was used: literature analysis, expert interviews (n=5) and a survey (n=177). The results show that Generation Z values flexibility, work-life balance, meaningful work and personal development. Based on the findings, a framework for attracting and retaining Generation Z has been developed, which can also serve as a guideline for organisations working with multi-generational teams.
A Fundamental Study on Driving Safety Estimation and Gaze Guidance Based on Eye Movement Behavior
Tokita, Junon; Hishida, Hirotoshi (Japan)
https://doi.org/10.54808/WMSCI2025.01.134
ABSTRACT:
As a measure to prevent car accidents, a method of observing and analyzing the driver's gaze has been proposed. Most of the information a driver receives while driving is visual [1]. However, vision is highly localized and incomplete. The authors reasoned that when a driver's safety level falls below the limit of that, the risk of an accident increases significantly. They also consider a system, probably using artificial intelligence (AI), that can judge the safety level from the driver's eye movements. For example, the driver's fixation duration, eye movement frequency, and gaze area would be important information for estimating safety level. If the driver's safety level can be estimated based on the driver's gaze, a system which appropriately supports the driver based on the safety level would be devises. It would be that the accidents on the expressway are reduced.
A Fundamental Study on the Risk of Earphone-Induced Hearing Loss - The Analysis of Frequency Response Using Multiple Types of Earphones and Various Music Data -
Sasaki, Yuki; Hishida, Hirotoshi (Japan)
https://doi.org/10.54808/WMSCI2025.01.147
ABSTRACT:
Earphone hearing loss is a global problem that has been brought to attention by the WHO. Regarding the issue of earphone-induced hearing loss, the effects of the music listened to and the earphones used are investigated. The investigation is carried out to determine the power spectrum and the equivalent energy. The results show that each earphone emphasizes certain frequency bands beyond the impact of music. Earphones which emphasize high-frequency sounds can damage the ears of young people who can hear high-frequency sounds. In the future, it would be desirable to have a high-frequency sound reduction method which considers the music being listened to and which earphones are being used. It turns out that English songs are more likely to damage the ears than Japanese songs, and female songs are more likely than male songs.
A Model to Promote Well-Being for Remote Employees
Aleidzane, Viktorija; Kamola, Liga; Orinska, Ieva (Latvia)
https://doi.org/10.54808/WMSCI2025.01.152
ABSTRACT:
The rapid pace of the transition to remote working after COVID-19 has significantly changed the working environment, while creating new demands for the well-being of employees. This can increase burnout, disrupt work-life rhythms, and reduce emotional security without appropriate solutions. On the other hand, psychological strain often comes from unclear boundaries, a lack of informal support, and feeling alone. This work looks at the health and well-being of remote workers in Latvia by combining the Job Demands–Resources (JD-R) model, Work–Life Flow (WLF) theory, and the idea of psychological safety. The research results show that autonomy, empathy from leaders, and a clear communication structure are all important for maintaining well-being. The paper suggests a valuable model that includes structural, relational, and rhythmic interventions to help create long-lasting remote work cultures. Also, the paper adds to both theory and practical advice for institutions trying to figure out how to work in a world after the pandemic.
A Results-Driven Curriculum for Engineering Education
Mosia, Ngaka; Ramdass, Kemlall; Ilunga, Masengo; Maduna, Lusiwe (South Africa)
https://doi.org/10.54808/WMSCI2025.01.334
ABSTRACT:
The field of engineering education faces an ongoing challenge of effectively preparing students for the demands of the rapidly evolving technological landscape. Traditional engineering curricula often focus more on theoretical knowledge rather than practical application and outcomes. As a result, engineering graduates are not equipped with the necessary skills to excel in industry, leading to a gap between industry requirements and graduate competencies. To bridge this gap, a results-driven curriculum for engineering education has been proposed to provide students with the knowledge, skills, and abilities required to succeed in industry. To address the question, “Where do most curriculum programs fall short?” It is noted that curriculum programs focus on delivering content rather than delivering experiences that support and enable change in teaching and learning. When curriculum design is driven by content, it ends up with numerous blurred boundaries regarding the scope, audience, and the applicability of what is contained in the curriculum. Consequently, the curriculum may result in education that falls short in producing graduates required by current and future markets and industry. In an e-learning and distance education environment, a curriculum might look well designed and meet all the set criteria, but it might ultimately match the wrong objectives. The result of this mismatch is that students after completing the curriculum depart with a lot of information in their heads but with no practical skills that they can implement in the workplace. This indicates that the graduate attributes required by Engineering Council of South Africa (ECSA) and industry were not attained by the student at completion of the enrolled curriculum. The research adopts a qualitative case study approach to explore and explain the steps, activities, and tools that can be used to design a results driven and engineering-focused curriculum solution, that has a clear goal tied to well-articulated pedagogy strategy.
A Study on the Divergence Between Psychological Evaluation and Physiological Indices During Art Viewing in Immersive Spaces
Nakatsu, Ryohei; Tosa, Naoko; Ueda, Yoshiyuki; Nomura, Michio; Uraoka, Yasuyuki; Kitagawa, Akane; Murata, Koichi; Munaka, Tatsuya; Furuta, Masafumi (Japan)
https://doi.org/10.54808/WMSCI2025.01.120
ABSTRACT:
As an approach to the question of “what art is,” this study investigates the divergence between psychological evaluation and physiological responses during art appreciation. Two immersive environments were constructed: Immersive Space 1, which utilized mirror displays to create infinite visual reflections, and Immersive Space 2, a newly developed environment that employed large LED displays to eliminate visual self-reflection. Participants viewed a digital artwork in these spaces while both psychological responses and electrocardiographic (ECG) data were collected.
Psychological evaluations showed no significant differences between the two spaces. However, ECG data revealed a clear difference: Immersive Space 2 induced higher parasympathetic activity and lower sympathetic arousal, suggesting a more relaxed physiological state.
The findings suggest a dual-layered nature of art experience, offering new insight into the relationship between perception, cognition, and physical state.
A SWOT Analysis of the Communication of Trans- and Inter-Disciplinary Research in Education and Society
Nikolarea, Ekaterini (Greece)
https://doi.org/10.54808/WMSCI2025.01.513
ABSTRACT:
This presentation is a SWOT analysis to clearly and systematically examine the challenges and opportunities in trans- and inter-disciplinary research communication. Drawing on 30-year experience in different parts of the world (Canada,
the USA and Greece), the author of this study takes a reflective that there is a gap between theory and practice, that is, between inter-disciplinary research done in academia and society (job market included), using examples from academic practice in order to show how aspects of academic culture, like departmentalization and poor communication, hinder the interaction between academic research and society.1 Second, she will stress the need for research to be accessible and relevant to non-specialists (i.e. trans-disciplinary communication), so that the gap between academia and the job market can be bridged. Third, being both realistic about threats and optimistic about opportunities, the author will advocate for clarity, ethics, and simplicity in research communication, inspired by Steve Jobs’ emphasis on clarity, hoping that what she meditates upon can be a thoughtful, practical, and forward-
looking roadmap for making academic research more impactful and accessible to a wider society.
Adaptive Open Innovation Systems: Bridging Strategic Foresight and Resilience in Innovation Ecosystems
Banga, Kristaps; Gaile-Sarkane, Elīna (Latvia)
https://doi.org/10.54808/WMSCI2025.01.166
ABSTRACT:
The accelerating complexity and unpredictability of today’s business environments require innovation ecosystems to become more adaptive, resilient, and foresighted. This paper introduces the Adaptive Open Innovation Systems (AOIS) framework, which synthesizes Complex Adaptive Systems (CAS) theory with Open Innovation (OI) principles to empower innovation ecosystems to navigate uncertainty and disruption effectively. By fostering collaborative knowledge sharing, strategic integration, and adaptive learning, AOIS equips organizations and their ecosystems with the capabilities to anticipate emerging challenges, adapt to evolving conditions, and sustain long-term innovation success.
At the heart of this study lies the question: how can the AOIS framework enhance strategic foresight and resilience within innovation ecosystems to manage complexity and rapid change better? This research explores this by bridging theoretical insights from CAS and OI with the practice of scenario planning, thus connecting foresight methodologies with adaptive innovation management. Through illustrative examples from digital transformation, healthcare, and automotive sectors, the framework’s practical relevance is demonstrated. This contribution offers both scholars and practitioners a robust conceptual and actionable blueprint for designing and governing innovation ecosystems that are capable of continuous learning, flexibility, and proactive change management. As a result, AOIS serves as a catalyst for transforming uncertainty into opportunity by aligning diverse ecosystem actors toward shared strategic goals.
Advancing Digital Sustainability KPIs in FinTech: Innovation, Ethics, and Integration in the European Payments Sector
Kalmane-Pivkina, Evita; Gaile-Sarkane, Elina (Latvia)
https://doi.org/10.54808/WMSCI2025.01.186
ABSTRACT:
FinTech innovations in the European Economic Area (EEA) are increasingly expected to align with sustainability imperatives. This paper examines how artificial intelligence (AI) can enable the design, implementation, and monitoring of digital sustainability key performance indicators (KPIs) in the FinTech sector, with a special focus on the Baltic states (Estonia, Latvia, Lithuania). AI techniques including machine learning, natural language processing (NLP), and predictive analytics are highlighted as powerful tools to advance environmental, social, and governance (ESG) goals in finance. We review EU policy frameworks such as the Corporate Sustainability Reporting Directive (CSRD), the Markets in Crypto-Assets Regulation (MiCA), and the forthcoming AI Act, analysing their intersection with AI-driven sustainability metrics. Case studies from the Baltics illustrate both conceptual frameworks and real-world applications, while also revealing implementation challenges like data quality and ethical constraints. Finally, we propose strategies to align AI use in FinTech with sustainability objectives and regulatory compliance, ensuring that technological innovation supports transparent, responsible, and impactful ESG performance tracking.
AI-Enhanced Transdisciplinary Data Encoding for LLMs Training
Makhachashvili, Rusudan; Bober, Natalia (Ukraine)
https://doi.org/10.54808/WMSCI2025.01.327
ABSTRACT:
The rapid advancement of artificial intelligence (AI) has reshaped linguistic data encoding, particularly for Large Language Models (LLMs). AI-driven annotation techniques enable efficient lexical processing, semantic disambiguation, and automated neology tagging, refining computational language modeling across transdisciplinary domains.
This study explores AI-enhanced methodologies for encoding linguistic data for LLM training. AI-assisted lexicographic workflows enable LLMs to dynamically adjust to linguistic evolution while ensuring scalable annotation across diverse transdisciplinary corpora. LLMs trained on transdisciplinary lexicons can generate cross-modal language interpretations, refining machine-generated discourse across domains. The inquiry objective is the investigation of the innovative philosophic aspects cyberspace through the lenses of the language development processes as it informs AI models elaboration, LLMs training, and digital communication. The study design is the disclosure of cyberspace as an ontology model and as a logosphere model. Two data encoding projects, developed by the authors, serve as foundational elements for this investigation.
A methodology and AI-augmented, AI-performed protocols of computer vocabulary innovative elements phenomenological features identification is introduced supplying the template for a new study field – phenomenological, AI-enhanced digital neology, neography and neosemiotics. Transdisciplinary educational applications of these approaches to data encoding, include: training AI-enhanced NLP models for transdisciplinary communication; developing standardized linguistic annotation protocols, ensuring interoperability across AI-driven lexicographic systems; integrating transdisciplinary discourse structures into machine-learning lexicons, refining AI adaptive language comprehension.
Aligning SME Critical Assets with Cyber Risks Using a Matrix Model to Develop a Cyber Resilience Framework
Bahmanova, Alona; Lace, Natalja (Latvia)
https://doi.org/10.54808/WMSCI2025.01.436
ABSTRACT:
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.
Analysis and Merging of Images from RGB and Thermal Camera Using the ResNet Neural Network, RoI and Super-Resolution
Kozik, Jarosław; Nawrat, Aleksander M.; Daniec, Krzysztof (Poland)
https://doi.org/10.54808/WMSCI2025.01.138
ABSTRACT:
The paper presents a comparison of the effectiveness of object recognition on input signals from two different cameras and a signal fusion model was created for the analysis. The aim of the paper is to analyze the training parameters of the ResNet network for the RGB camera, the thermal imaging camera and the signal combination, their normalization and summary analysis. The paper also analyzes the selection of the most effective method of object recognition in the aspect of different networks and Super-Resolution technology. The recording system was placed on a drone, connected to efficient processing units and Jetson NANO microcomputers. This project is dedicated to military, border, customs, fire brigade and police applications. The aim of the software implementation is to increase security in border areas through continuous observation and image analysis, as well as monitoring threats and their dynamics. Combining RGB and thermal images offers many advantages, especially in tasks related to object detection, monitoring and analysis of the environment. The project was built using the HIKVISION camera: DS2CD2055FWD-I, camera software version 5.5.51_180314, 1027x768 and the FLIR VUE Pro thermal camera with a resolution of 640x512 pixels.
The essence of the project is to check whether adding an additional source of image from a thermal imaging camera will improve the detection parameters of specific objects.
To combine two signals from different sources and different input sizes, software written in Python, a *.py script was used, it combines the signal from two cameras at the input into a four-channel tensor, then the normalized signal is converted to one channel, which is then analyzed by ResNet.
During the data analysis during training of the module, different effectiveness was observed for both types of cameras. After combining the signals, an increase in the accuracy, sensitivity (recall) and precision (precision) indicators was observed. Image fusion allows [1] the ResNet model to see both visual details from the RGB camera and thermal details.
Artificial Intelligence as Narcissus in the Culture of Immediate Gratification
Marinov, Bilian (Bulgaria)
https://doi.org/10.54808/WMSCI2025.01.472
ABSTRACT:
This study develops a conceptual framework for examining how AI technologies, by aligning with the dynamics of narcissism and instant gratification, reshape human subjectivity. Drawing on an interdisciplinary synthesis of psychoanalysis and philosophy of technology, it argues that AI systems not only mirror but actively reinforce narcissistic interaction patterns. The research concludes by proposing principles for designing AI that foster emotional resilience and ecological consciousness, rather than perpetuating fragile self-concepts.
Assessing SVM and Logistic Regression Models for Live Birth Prediction in IVF: A Barbadian Case Study
Cumberbatch, Steven *; Als, Adrian **; Chami, Peter **; Skinner, Juliet ** (* Canada, ** Barbados)
https://doi.org/10.54808/WMSCI2025.01.66
ABSTRACT:
The success rates of in vitro fertilization (IVF) have significantly improved over recent decades due to advancements in both clinical practice and biomedical technologies. Clinicians rely on the analysis of large volumes of patient data to inform treatment decisions. Aggregated longitudinal data from multiple patients may reveal latent patterns that can further enhance IVF outcomes. In this study, three machine learning models — Linear Kernel SVM, RBF-Kernel SVM and Logistic Regression — were developed and implemented to predict live-births from IVF clinical and demographic data, and their performances were compared. Results show that the linear SVM achieved the highest global discrimination (ROC-AUC = 0.72) and the strongest cross-validated F1-score (0.56). Logistic regression followed closely in global discrimination (ROC-AUC = 0.69), but its cross-validation recall for the minority class was notably low (0.26). The RBF SVM demonstrated a higher recall for the minority class compared to the linear SVM (0.45 vs 0.36), yet its overall discriminative performance was weaker, as reflected by a lower ROC-AUC of 0.63. This research serves as an initial exploration of machine learning applications in IVF within developing countries in the Eastern Caribbean, such as Barbados. The findings may contribute to improved clinical decision-making, reduced treatment cycles, and lower healthcare costs in resource-constrained settings.
Assessing the Potential of Digitising Project Management Processes for Local Government Functions
Samausks, Dagnis; Kamols, Uldis; Auzins, Armands (Latvia)
https://doi.org/10.54808/WMSCI2025.01.254
ABSTRACT:
Unchanging values in changing times. These words are relevant in modern project management. A project consists of coordinated and controlled implementation of activities with a set start and end date, with a specific goal that must be achieved within the allocated time, expenses and resources.
Exploring Latvia's situation at the local municipal level, the lack of a holistic systems approach and insufficiently used techniques and tools to support project management has been identified.
The study aims to develop proposals for evaluating and improving project management in local governments by examining the project management techniques and digitalisation tools.
The study focuses on methods for improving project management in an organisation, including traditional ones and modern or agile methods.
For this study, digitalisation tools are considered various software, including those with artificially integrated functions, which are used in the organisation and supervision of the daily work of project management. The study assesses the potential of digitising the project management process and proposes a flowchart to implement project management methods in local governments effectively.
Assessing Tsallis Entropy Associated with First Year Undergraduate Students' Enrolments in South African Public Universities
Mathenjwa, Samukelisiwe; Ilunga, Masengo; Mazibuko, Meshack (South Africa)
https://doi.org/10.54808/WMSCI2025.01.370
ABSTRACT:
In this study, Tsallis entropy has been explored to estimate the degree of uncertainty contained in the students’ enrolments, as a variable of investigation, in the public higher education of South Africa. The study focuses on the twenty-six public universities contributing significantly to the education system, job market and to the society at large. Findings revealed that the Tsallis entropy fits for the education system which is currently in a state of non-equilibrium for the above-mentioned variable. The study showed that entropy was always the highest for the q- information value of 0.5, with a huge gap created when q changed to 2 and beyond. It was established that the maximum entropy was a function of the q-information parameter and decreased when this parameter increased. This situation implies a state of reduction in uncertainty of the system. Generally, the standardized entropy (with values over 0.80 fraction of a unit and above) suggested to the contrary that the enrolments tend to a quasi-uniform distribution in institutions, when the q-information increases. Despite these high values of standardised entropy, the South African education system has demonstrated resilience in its management and operations. Therefore, sustainable enrollment plans should be put forward to avoid the chaotic situation emanating from enrolment. Further investments would be needed to build more universities (equipped with physical and virtual campuses) to reduce the uncertainty currently associated with the higher education system.
Assessment of the Impact of Artificial Intelligence Tools on Consumer Behaviour in the FinTech Industry
Meldere, Marta; Andersone, Ieva (Latvia)
https://doi.org/10.54808/WMSCI2025.01.220
ABSTRACT:
The research aims to investigate the impact of MI tools on consumer satisfaction in the FinTech industry, assess the relationship between satisfaction and loyalty, and develop recommendations and conclusions for developing MI solutions and their impact on consumer satisfaction and loyalty. Research includes a literature review of FinTech, MI industry issues, loyalty and satisfaction theories, concepts, measurement methods, and factors influencing satisfaction and loyalty. The study's novelty derives from its empirical approach, which analyses the impact of AI tools on consumer loyalty and satisfaction in the Fintech sector, considering individual differences such as age, digital skills, and level of trust. Thus, a people-centred and action-oriented perspective is added to the existing innovation-oriented scientific literature, contributing theoretically and practically to the evolution of FinTech today.
Autonomy as a Driver of Engagement: Examining the Role of Flexible Work in Motivating Office-Based Employees
Pentjusa, Elina; Gaile-Sarkane, Elina (Latvia)
https://doi.org/10.54808/WMSCI2025.01.242
ABSTRACT:
Autonomy in flexible work arrangements has emerged as a critical driver of employee engagement in the modern workplace. This study examines how giving employees’ greater control over when, where, and how they work can boost motivation and wellbeing while identifying the conditions that shape success. It integrates four complementary frameworks to analyze psychological mechanisms linking autonomy to engagement, including affective commitment, cognitive overload, and motivational regulation. It also compares European cultural approaches, examines the impact of AI and algorithmic monitoring, and reviews labor policies such as the right to disconnect. Case studies from e-commerce and tech sectors illustrate lessons learned. Findings highlight that flexible work autonomy enhances engagement when implemented with supportive leadership, balanced expectations, and clear boundaries.
Basic Study on the Development of a Stress Detection System by Voice Using Machine Learning
Miura, Yoshiki; Hishida, Hirotoshi; Kurono, Akihiko (Japan)
https://doi.org/10.54808/WMSCI2025.01.142
ABSTRACT:
Stress significantly affects both mental and physical health, with stress-related disorders rising in Japan. Traditional self-report questionnaires are subjective and unsuitable for real-time monitoring. This study explores an objective, non-invasive approach to stress detection using speech analysis. In a preliminary experiment, a single speaker uttered the same Japanese sentence under three emotional states: neutral, anger, and happiness. Acoustic features—fundamental frequency (F0), formant frequencies (F1–F3), Mel-frequency cepstral coefficients (MFCC), jitter, and root mean square (RMS) energy—were extracted using Python-based libraries. Statistical analysis (ANOVA) revealed significant differences (p < 0.05) in most features. Notably, anger showed increased F0 and energy, while happiness displayed unique MFCC and formant patterns. These findings suggest that emotional arousal affects vocal characteristics measurably. Acoustic features may thus be promising indicators of emotional or stress states. As a next step, we will examine feature changes (Δ features) before and after stress-inducing tasks. Our goal is to develop a personalized, interpretable, and ethically sound model for estimating stress from voice. Future work also includes addressing interface usability and ethical considerations to ensure practical implementation.
Brains, Minds, and Science
Vieira Kritz, Maurício (United Kingdom/Brazil)
https://doi.org/10.54808/WMSCI2025.01.351
ABSTRACT:
To understand our inefficiencies when scientifically dealing with the dazzling natural and social challenges facing us, I have previously proposed the scientific milieu itself as a primary subject of study, due its social nature and intellectual characteristics. This draft paper describes the first step of a many-step modelling process that aims to develop a model of the scientific milieu deploying its many levels of aggregation and individuation, while also highlighting channels, procedures, and phenomenological characteristics that allow our brains to interfere in the process of knowledge production. It is part of a wider effort related to doing science while being aware of the tricks our own brains can throw on our thinking. It extends previous modelling efforts by sketching models that separate brains, minds, highlighting their interplay, by discussing points deserving attention to complete a more encompassing model, and by clarifying how agreement about elementary cross-disciplinary communication tokens is initially achieved.
Bridging the Gap: How Integrated Management Systems Drive Corporate Sustainability
Grinberga, Sāra; Pīlēna-Dālberga, Arta; Lapiņa, Inga (Latvia)
https://doi.org/10.54808/WMSCI2025.01.177
ABSTRACT:
Corporate sustainability is becoming a significant part of companies' strategic development, promoting efficient resource use, public trust, and competitiveness. The implementation of sustainability principles in management systems helps organisations adapt to evolving regulatory requirements and ESG (Environmental, Social, and Governance) challenges. Integrated management systems can serve as an effective tool for achieving sustainability goals and strengthening a company’s competitiveness. The aim of this paper is to analyse how integrated management systems support the implementation of corporate sustainability by ensuring compliance with ESG requirements and promoting sustainable business practices. The study employs a literature review and qualitative content analysis. Based on theoretical research, the authors identify how integrated management systems contribute to corporate sustainability and how these two concepts interact.
Case Study on Understanding the Power of Retrieval Augmented Generation (RAG)
Batthula, Venkata Jaipal Reddy; Segall, Richard S.; Sivasubramony, Sreejith (United States)
https://doi.org/10.54808/WMSCI2025.01.57
ABSTRACT:
This paper explores how Generative AI is changing with the use of Retrieval-Augmented Generation (RAG). RAG helps improve Artificial Intelligence (AI) systems by making them more capable, efficient and accurate. The paper explains how to build the Retrieval Augmented Generation model, covering important steps like preparing the data, creating embeddings, and setting up the retrieval system. Through a case study, we look at the main components of RAG, how it works with Large Language Models (LLMs), and why it is important in everyday digital tools. One of the goals is to compare different strategies for RAG, including choices for embeddings, similarity metrics and language models to find an optimal approach that can be generalized to work best. This helps us to understand how these factors affect performance and gives us ideas for building better and more efficient systems.
Challenges of Human-Centric, AI-Driven Direct Marketing Under a Regulated Environment in the European Union
Kulinskis, Andis; Caune, Janis (Latvia)
https://doi.org/10.54808/WMSCI2025.01.214
ABSTRACT:
The rapid development of the digital economy, combined with shifting consumer expectations, is driving the need for humancentric, artificial intelligence (AI)-driven direct marketing solutions. These solutions provide significant opportunities to personalize communication, enhance customer engagement, and optimize marketing costs through behavioral segmentation and data analytics. However, these opportunities also pose major challenges, such as ensuring user trust in data processing, adhering to ethical principles in data utilization, and developing explainable and transparent AI systems that comply with the strict regulatory requirements of the European Union.
This paper analyzes the theoretical aspects of human-centric AI applications in direct marketing under the current regulatory framework, identifying key risks and prerequisites for the sustainable and trustworthy development of such systems. It proposes a theoretical framework that seeks to balance marketing campaign effectiveness, message personalization, and digital trust, while respecting users’ autonomy and emotional wellbeing.
Further research would benefit from empirically examining how companies practically implement human-centric AI solutions to balance customer expectations with regulatory requirements. Another relevant research direction would be to empirically investigate the scalability of this framework across diverse business contexts.
Comparative Evaluation of Two Immersive Art Spaces Using ECG Data
Nakatsu, Ryohei; Tosa, Naoko; Ueda, Yoshiyuki; Nomura, Michio; Uraoka, Yasuyuki; Kitagawa, Akane; Murata, Koichi; Munaka, Tatsuya; Furuta, Masafumi (Japan)
https://doi.org/10.54808/WMSCI2025.01.127
ABSTRACT:
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, indicating a more relaxed and emotionally stable physiological state. These findings underscore the significance of spatial context in shaping the embodied aesthetic experience.
Comparing Assignment Description Intent with AI-Generated Results: Implications for Designing Effective Writing Prompts
Lipuma, James; Leon, Cristo (United States)
https://doi.org/10.54808/WMSCI2025.01.1
ABSTRACT:
This study investigated how students in higher education utilize Artificial Intelligence Systems (AIS) to interpret and complete writing assignments, focusing on the implications for instructional design and educational integrity. In 2022, the authors introduced the Disclosure of Support Statement (DSS), a tool designed to promote transparency by prompting students to reflect on their use of human and technological support, including generative AI. By 2024, this tool was refined into the Refined DSS (RDSS) to capture nuanced patterns of AI-supported learning across the stages of researching/formulating, compositing, and proofing stages.
Analysis of 216 RDSS responses from senior seminar students revealed that 96% used AI at some point in the writing process, with common uses including clarifying prompts, generating outlines, summarizing sources, and refining tone or grammar. However, repeated misuse of AI-generated definitions, especially for class-specific frameworks such as CANOE, indicated a lack of authentic engagement. Several students submitted generic AI outputs for their disclosures, raising concerns about the effectiveness of traditional assignments in an AI-integrated context.
To further examine this issue, the authors conducted a comparative analysis by processing the same assignment prompt through multiple ChatGPT and Google Gemini versions. The resulting outputs were evaluated based on the quality of reasoning, alignment with instructional intent, factual accuracy, originality, and educational coherence. The findings revealed that while generative AI could produce structurally acceptable responses, it often misinterpreted class-specific concepts and failed to meet deeper learning goals without additional prompting or contextual material.
This paper argues for redesigning writing prompts and disclosure protocols to support cognitive integrity and metacognitive transparency. The results underscore the importance of transforming instructional design not to resist AI use but to scaffold student reasoning, reflection, and value-added contributions in an age of machine collaboration.
Competencies of the Citizen of the Future and Their Link to University Education, Digital Culture, and Smart Technologies
Cabanilla Guerra, Galo; Cabanilla Guerra, Mara; Carrasquero Ferrer, Sedolfo; Vaca Suárez, Gabriel (Ecuador)
https://doi.org/10.54808/WMSCI2025.01.288
ABSTRACT:
Digital transformation has redefined the competencies required for the society of the future, driving higher education to adapt in order to shape citizens capable of thriving in technological and globalized environments. This research analyzes how the Universidad Tecnológica Empresarial de Guayaquil in Ecuador is integrating digital culture and smart technologies into university education. The study follows a documentary-analytical methodology, allowing for a systematic examination of institutional practices, academic programs, and their alignment with future competency frameworks. Four key competency dimensions were identified: technological and digital, socio-emotional and ethical, environmental and sustainability, and innovation and entrepreneurship. Through active methodologies and digital tools, the aim is to foster critical thinking, creativity, and social responsibility among students. This emerging research lays the foundation for designing innovative educational models that prepare future professionals to face the challenges and opportunities of the digital era.
Computer Vision Techniques to Support Animal Welfare and Veterinary Public Health
Urbani, Rachele; Bergamasco, Tommaso; Nalesso, Giacomo; Tregnaghi, Vittoria; Menegon, Francesca; Bassan, Massimiano; Manca, Grazia; Di Martino, Guido (Italy)
https://doi.org/10.54808/WMSCI2025.01.39
ABSTRACT:
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.
Containerized Security for ICS/SCADA Systems: From PLC Simulation to Kubernetes-Based Containment
Hall, Janne; Tanner, April L. (United States)
https://doi.org/10.54808/WMSCI2025.01.73
ABSTRACT:
Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) networks continue to face increasing cybersecurity risks due to their historically flat architectures and limited isolation capabilities. This paper presents a container-native containment validation framework for modern ICS/SCADA environments, leveraging Docker and Kubernetes to simulate adversarial behaviors, including privilege escalation attempts and unauthorized API access, within a segmented, microservice-based infrastructure. Through controlled experiments, we demonstrate how containerization confines potential attacker activity to isolated namespaces, enforces Role-Based Access Control (RBAC), and limits lateral movement, even under permissive runtime conditions. Unlike prior work, this study uniquely integrates Kubernetes-native RBAC enforcement, namespace isolation, and adversarial pod injection with TensorFlow-based telemetry filtering to empirically validate containment strategies in ICS/SCADA workloads. These simulations demonstrate that unlike traditional ICS systems where initial compromise often escalates, container-native architectures offer measurable resilience by preserving operational boundaries and reducing lateral exposure.
Control of a Mobile Platform for Collaborative Robots in Healthcare
Valchkova, Nina; Cvetkov, Vasil; Zahariev, Roman (Bulgaria)
https://doi.org/10.54808/WMSCI2025.01.43
ABSTRACT:
This project work presents an investigation of some aspects of the intelligent decision-making process as part of Service Collaborative Robots’ control and navigation on the base of sensor information. An example of a Graphical User Interface (GUI) is developed and closely considered because it is a crucial factor for the control of the robot and the successful implementation of its task. A robot system is described, as an example of the application of Robot powered by a Hydrogen Fuel Cell with the principles of modularity. It is used also for the performance of experiments for the intelligent making of decisions. In recent years, the Raspberry Pi 4B has established itself as a versatile and powerful tool for a wide range of applications, especially in the field of data collection for sensor systems. Whether used in academic research, industrial environments, the Raspberry Pi has proven to be an effective solution for collecting, processing, and analyzing data from various sensors. This paper discusses why Raspberry Pi 4B is an excellent choice for data collection in sensor systems, focusing on its affordability, computational power, flexibility, and extensive software support.
Cultural Branding and Digital Market Strategies for Modest Fashion in the GCC
AlMarzooqi, Alreem; Hojeij, Zeina (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.284
ABSTRACT:
This literature review synthesizes peer-reviewed research articles and industry reports on digital and market entry strategies for modest fashion brands entering the Gulf Cooperation Council (GCC). Drawing on empirical studies, theoretical models, and case analyses, the review examines digital branding and social commerce practices, influencer engagement models, culturally anchored trust signals, and identified research gaps. Findings inform practical recommendations for future strategy development.
Curricular Integration of Autonomous Driving Systems and Competencies in Automotive Engineering Career in Ecuador
Tapia, Pablo; Cordero, Yoskira Naylett (Ecuador)
https://doi.org/10.54808/WMSCI2025.01.524
ABSTRACT:
The automotive industry is evolving rapidly, integrating a broader range of engineering disciplines. In the wake of autonomous-driving technologies, a key question arises: how well are Automotive Engineering programmes in Ecuador preparing students for current and future technical demands? This study combines educational data mining, student surveys, and faculty interviews to evaluate curricular gaps. The analysis reveals limited exposure to autonomous-driving systems alongside an enduring emphasis on conventional topics such as vehicle manufacturing and maintenance. This misalignment between industry needs and curricular content underscores the urgency of introducing practice-oriented courses that address emerging technologies. Revising the curriculum to include hands-on modules in autonomous driving would strengthen graduate competencies, foster innovation, and better position Ecuador’s automotive sector to compete in an increasingly dynamic global market.
Detecting AI-Generated Text: A Comparative Study of Machine Learning Algorithms
Chang, Li-jing Arthur (United States)
https://doi.org/10.54808/WMSCI2025.01.294
ABSTRACT:
As ChatGPT and other large-language-model (LLM) tools have made the generation of text via AI much easier than before, there is an increasing need to determine if humans indeed write the text we are reading. The study used six machine learning and deep learning algorithms to detect AI-created text. Using a balanced sample of AI-generated and human-written text, the results showed that deep learning algorithms outperformed their machine learning counterparts. A hybrid deep learning algorithm achieved the top accuracy rate of 0.974 (or 97.4%). Post hoc analysis showed that with a small fraction, such as 10%, of the full sample used in the present study, the hybrid algorithm achieved 0.928.
Digital Transformation for E-governance in Ukraine: International Curriculum and Best Practices
Morze, Nataliia *; Makhachashvili, Rusudan *; Zvonar, Viktor **; Ilich, Liudmyla *; Boiko, Mariia * (* Ukraine, ** Poland)
https://doi.org/10.54808/WMSCI2025.01.10
ABSTRACT:
The study tackles the limits of understanding of EU e-governance principles and practices in Ukraine, since strong EU aspirations of the country are challenged by the warfare threatening the nation existence. The struggle of Ukraine against Russian invasion revealed the benefits of previous digitalization efforts in the public sector. However, civil servants and citizens in the country still feel the urgent need of enhancing digital competence. The public sector developed a clear understanding that further reforms must be aligned with EU experiences and expectations, and a proper expertise is called for. Thus, the research objective is to highlight and disseminate EU experience and best practices of the transition to e-governance. The research project e-DEBUT helps promote EU values of transparency, participatory democracy, and inclusiveness through strengthening the digital community in Ukraine. The study aims to develop an innovative curriculum to enhance skills and competencies of civil servants, enrolled in master’s programs, needed for effective rendering of public e-services in war-time, and transferring knowledge of the tech trends and best e- governance practices of EU countries. The project's meaningful results are: a course syllabus, summer schools’ curricula, and a workshop on the facets of development of e-governance in EU countries and in Ukraine; open digital educational resources and analytical materials; a manual for civil servants on the use of e- governance tools under martial law and through post-war reconstruction of Ukraine. The centerpiece of the study is the development of the study module, covering EU lens on concepts of e-governance and digital state, EU technological trends for e- governance, EU best practices in rendering e-governance for business and citizens, as well as the investigation of the adaptation of EU experience in the use of artificial intelligence and smart city infrastructures to the managerial needs of the country at war.
Dynamic Partnership Between Industry Stakeholders and Academia to Sustain a Resilient Framework for 21st-Century Higher Education in STEM
Kamola, Liga; Lice, Anita; Lapina, Inga (Latvia)
https://doi.org/10.54808/WMSCI2025.01.196
ABSTRACT:
Science, technology, engineering, and mathematics (STEM) fields in Latvia stand out with an exceptionally high labour market demand for qualified specialists. At the same time, employers are not completely satisfied with the quality of student learning outcomes. The research is based on a literature review on the role of stakeholders’ engagement in addressing higher education challenges and the industry stakeholder engagement mechanisms, as well as expert interviews to investigate the existing collaboration mechanisms and opportunities for collaboration within the real-life context. The research describes an overall framework between higher education institutions (HEIs) and industry stakeholders to address the key challenges in STEM fields, and the priority areas for collaboration are identified. The paper provides conceptual insights and practical guidance for policymakers, academics, and industry partners who wish to strengthen the attraction of STEM talent to higher education and the labour market. This research is based on empirical data and insights from the Erasmus+ KA2 programme project “Building an Ecosystem for 21st Century Skills Education in STEM”.
Embracing Transdisciplinary Communication: Redefining Digital Education Through Multimodality, Postdigital Humanism and Generative AI
Makhachashvili, Rusudan; Semenist, Ivan (Ukraine)
https://doi.org/10.54808/WMSCI2025.01.319
ABSTRACT:
In the study we explore the evolving landscape of digital multimodality and its implications for transdisciplinary communication in education. It is examined how digital literacy integrates multidisciplinary and multimodal approaches, fostering embodied transdisciplinarity that transcends traditional boundaries in the age of AI.
As a product of modern civilization, the digital reality has become an independent format of being. Accordingly, electronic media act not only as a means of transmitting information, but also reveal their own world-creating, meaning-making and, as a consequence, communicative potential. The global digital realm and AI models stand as an integral environment, demanding new cognition and perception ways via complex philosophic, cultural, social, linguistic approaches, providing unlimited opportunities for human intellect, communicative development and research.
Transdisciplinary communication in digital education represents a transformative trend for humanity, reshaping the way disciplines interact and collaborate. The core concept of transdisciplinarity hinges on dialogue—bridging disciplinary divides to create new frameworks for knowledge transfer. This evolution moves beyond digital humanism and digital humanities, progressing toward post-humanity and post-disciplinarity, where rigid disciplinary boundaries dissolve in favor of interconnected knowledge systems.
The context of the erupted military intervention in Ukraine and the ensuing information warfare in various digital ambients (social media, news coverage, digital communications), the specific value is allocated to the enhanced role of digital humanism as a tool of the internationally broadcast strife for freedom and sovereignty.
Enhancing Virtual Try-On Platforms for the UAE's E-Commerce Market
Taleb, Shahad; AlKhoori, Mariam; ElSayary, Areej (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.107
ABSTRACT:
This study aimed to explore how a unified Virtual Try-On (VTO) platform could be effectively marketed within the UAE’s online clothing market by evaluating both the usability of existing VTO technologies and local consumer expectations. A mixed-methods approach was used, combining a survey of 101 female participants aged 18–44 and focus group interviews with 10 individuals representing both online and in-store shopping preferences. The quantitative results showed that users value features such as fit accuracy (90.4%) and realistic visuals (84.6%), but privacy concerns (86.6%) and the lack of cultural representation—such as modest clothing and Arabic language support—pose significant barriers. Focus group insights corroborated these concerns, with dissatisfaction regarding avatar realism and a strong desire for mix-and-match, personalization, and traditional attire options. These findings suggest that for VTO platforms to succeed in the UAE, they must be culturally tailored, ethically designed, and user-centered. This research contributes to the literature by addressing the behavioral, ethical, and cultural factors influencing technology adoption, offering insights for developers, marketers, and policymakers.
Estimation of Runoff Using Geospatial Computation Intelligence: Case of Selected Quaternary Catchments in the Lower Crocodile River System, City of Mbombela Local Municipality of South Africa
Phahlamohlaka, Shimane Phemelo; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/WMSCI2025.01.376
ABSTRACT:
This preliminary study assesses the application of Google Earth Engine (GEE) to develop a rainfall-runoff model applying the soil conservation services curve number (SCS-CN) method within quaternary catchments (X24A & X24B) in the tertiary catchment X24 of the Lower Crocodile River system in the City of Mbombela Local Municipality, South Africa. The study leverages GEE’s cloud computing capabilities to integrate remote sensing data (CHIRPS daily, MODIS LULC, and iSDA soil texture), developing a rainfall-runoff model to dynamically estimate rainfall-runoff under varying antecedent moisture conditions (AMC). The validation of the runoff results against the Water Resources (WR2012) runoff data reveals GEE’s ability to conduct high-resolution runoff estimations whilst also noting the discrepancies of results arising because of data limitations. The study accentuates GEE’s potential to model rainfall runoff within semi-arid catchments, providing insights for the development of water management and flood risk management strategies.
Factors Influencing the Economic Value of NGOs
Alksne, Krista; Straujuma, Anita; Gaile-Sarkane, Elīna (Latvia)
https://doi.org/10.54808/WMSCI2025.01.160
ABSTRACT:
This paper, as a literature review, explores the key factors influencing the economic contribution of non-governmental organizations (NGOs) within national economies. Using qualitative content analysis of literature, the study identifies four main dimensions: political and institutional environment, economic and market conditions, social and community engagement, and internal organizational capacity. The findings highlight the interdependence of these factors and the need for a more nuanced and updated approach to NGO sector evaluation. The previously developed NGO sector assessment model is based on factors influencing NGO activities that have changed significantly since the model was developed. The research supports calls for a redefinition of NGOs’ economic roles in response to changing social, political, and technological contexts.
Fostering Transdisciplinary Digital Institutional Leadership in Higher Education During Wartime
Makhachashvili, Rusudan; Vinnikova, Nataliia; Semenist, Ivan; Tupakhina, Olena (Ukraine)
https://doi.org/10.54808/WMSCI2025.01.311
ABSTRACT:
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.
From Seat Maps to Learning Networks: Bridging Educational Practice and Complex-Systems Simulation
Yamazaki, Yusuke; Kikuchi, Takamasa; Yoshikawa, Atsushi (Japan)
https://doi.org/10.54808/WMSCI2025.01.531
ABSTRACT:
The student learning environment in educational science can be understood as a “Socio-Technical Learning System” (STLS), where physical spaces, social relationships, and digital data interact dynamically. This study conceptualizes “changing seats,” a common classroom management practice, as an “educational micro-intervention” within the STLS framework, aiming to explore its effects on student networks and learning outcomes.
To analyze these dynamics, we introduce the Network-Aware Adaptive Seating Model (NASM), a novel human-centered AI framework that extends Axelrod’s cultural dissemination model to educational contexts. NASM enables a systematic investigation of how seat changes influence both academic performance and the structure of student interactions.
Our simulation analysis across 4,000 runs revealed differential effect sizes across achievement tiers, with middle-high groups showing small, but potentially meaningful effects (Cohen's d = 0.19). Statistical analysis indicated no significant differences between tiers (F(3,15996) = 0.585, p = 0.624, η2 < 0.001), suggesting the uniform applicability of seat change interventions across academic levels, a finding with important implications for equitable classroom management.
The study's primary contribution lies in its methodological innovation and transdisciplinary integration, providing a replicable framework for analyzing educational micro-interventions through complex systems principles. These findings advance both the theoretical understanding of learning networks and their practical application in adaptive classroom design.
Fundamental Study on Driving Safety Evaluation Using Driver Facial Recognition
Kajiya, Togo; Hishida, Hirotoshi (Japan)
https://doi.org/10.54808/WMSCI2025.01.114
ABSTRACT:
Drowsy driving remains a major cause of serious traffic accidents in modern transportation society, particularly on highways and during long-distance journeys. It has been reported that drowsiness-related accidents tend to result in severe damage and high fatality rates. To address this issue, non-invasive and real-time drowsiness detection systems are increasingly demanded, especially for commercial drivers and in the context of semi-autonomous driving systems, where human supervision is still required. This study aims to establish a foundational approach for estimating driver drowsiness through facial indicators. Specifically, the objective indicators PERCLOS (Percentage of Eyelid Closure) and response time are examined alongside the subjective Karolinska Sleepiness Scale (KSS). A driving simulator is used to collect data from over 100 participants during a university event. Despite collecting a large dataset, the multiple regression analysis reveals no statistically significant correlation between response time and the other indicators. The lack of significant findings is attributed to the experimental environment and limitations in data acquisition, particularly regarding facial measurements. Nevertheless, this study provides preliminary insights into the challenges of real-time drowsiness estimation based on facial features. It highlights the need for improved measurement methods and suggests directions for future integration with driver monitoring systems in semi-autonomous vehicles.
Gender Imbalances in Georgia: Reality and Challenges
Abesadze, Nino *; Abesadze, Otar *; Kinkladze, Rusudan *; Paresashvili, Nino *; Robitashvili, Natalia *; Chitaladze, Ketevan *; Edzgveradze, Teona ** (* Georgia, ** Germany)
https://doi.org/10.54808/WMSCI2025.01.429
ABSTRACT:
The paper provides an analysis of gender imbalances in the Georgian labor market. The employment and wage rates of women and men are characterized by sectors and activities. Conclusions are drawn. Therefore, the aim of the paper was to identify and analyze gender inequality trends in Georgia.
Gender inequality in Georgia is differentiated by type and sector of economic activity; gender inequality is especially noticeable in a regional context.
Gender inequality is high in the construction sector, where employed men are 21.4 times more likely to be employed than women in the same sector. The number of men employed in the transport and warehousing sectors is 7.6 times higher than the number of women, while the number of women employed in education, healthcare and social services is 5.2 and 4.0 times higher than the number of men, respectively.
One of the most important manifestations of gender inequality in the labor market is the gender imbalance in wages. In all economic activities, men's wages exceed women's wages. The highest gender imbalance is in finance and insurance activities, as well as in the information and communications sector. One of the lowest-paid activities is education, where the majority of employees are women. Today, women in Georgia earn 67.9% of men's wages.
Geospatial Intelligence for Flood Monitoring and Damage Assessment: Case of the uThukela District Municipality, South Africa
Magagula, Khawulani Niclas; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/WMSCI2025.01.361
ABSTRACT:
The increasing frequency of global flood events has raised serious concerns about their social and environmental impacts, particularly in vulnerable regions such as Ladysmith, located within South Africa’s uThukela watershed. This area has experienced recurrent flooding for over 170 years, primarily due to its geographical setting.
This study employs geospatial intelligence, specifically Google Earth Engine (GEE), to analyze flood trends, identify flood-prone zones, assess property damage, and evaluate the impacts on agriculture, urban infrastructure, and population density. Results indicate fluctuating but rising flood levels, pointing to an escalating risk in the district.
The analysis underscores the severe consequences of flooding on livelihoods, infrastructure, and farmland. Geospatial techniques demonstrated significant value in monitoring floods and assessing associated damage, offering critical insights for sustainable flood management planning.
The study emphasizes the need for proactive disaster risk reduction strategies that integrate community-based knowledge and socio-economic considerations to enhance early warning systems and build resilience in flood-prone communities.
Identification and Assessment of Factors Influencing Financial Fraud in Latvian Companies
Saldana, Viktorija; Ciemleja, Guna (Latvia)
https://doi.org/10.54808/WMSCI2025.01.480
ABSTRACT:
The paper analyzes financial fraud risks in Latvian companies, evaluate detection and prevention methods, and develop recommendations for more effective fraud risk management based on empirical research data. The theoretical research of financial fraud is Web of Science-indexed research papers and a bibliometric analysis were used to assess, of financial fraud and identifying the associated risks. A survey among Latvian businessmen was carried out to assess the practical influence of the specified factors on small and medium-sized companies. The survey involved 206 companies of various sectors and sizes. Qualitative research method - expert interview, provide view on financial fraud prevention practices, are characterized specified risks and deeper views of the influence of the factors.
Improving Structural Image Quality Using OpenAI Models and Hybrid AI on Microsoft Azure: A Scalable Framework for Cost-Efficient Predictive Analytics
Mateev, Mihail (Bulgaria)
https://doi.org/10.54808/WMSCI2025.01.86
ABSTRACT:
This paper presents a novel architecture that integrates OpenAI's vision models with Microsoft Azure's hybrid AI capabilities to enhance structural image quality for predictive analytics in civil engineering. By leveraging generative AI and explainable hybrid mechanisms, we aim to overcome limitations of image degradation in construction scenarios. Using a curated dataset of 10,000 steel and concrete infrastructure images, the framework enables high-resolution enhancement through cloud APIs (e.g., GPT-4V, DALL·E) and hybrid edge deployment (Azure Arc, Azure ML). The system supports semantic fidelity, scalability, and cost efficiency while preserving measurable features vital for structural integrity analysis. Comparative results demonstrate the technical and economic superiority of specific AI models under varied deployment conditions. Further analysis interprets image quality metrics such as PSNR and SSIM to validate model efficacy.
Integrating Large Language Models Into Web Design Study: AI-Assisted Code Optimization in Higher Education
Kopishynska, Olena; Utkin, Yurii; Sliusar, Ihor; Pysarenko, Volodymyr; Galych, Oleksandr; Flehantov, Leonid; Zahrebelna, Iryna; Pysarenko, Svitlana (Ukraine)
https://doi.org/10.54808/WMSCI2025.01.497
ABSTRACT:
This study explores the potential and effectiveness of using large language models (LLM) of artificial intelligence (AI) based on GPT for enhancing, optimizing, and extending website code in during the development and technical auditing stages. The research is grounded in experiments conducted with commercial-purpose websites developed by university students. General characteristics and a list of essential elements and functions of the websites are provided; these sites were built using HTML, CSS, and JavaScript technologies, based on original projects and designs. Both ChatGPT and GitHub Copilot demonstrated high performance in completing the assigned tasks, with ChatGPT showing broader capabilities, especially in the role of a tutor. AI proved useful in analyzing website structure and design, offering suggestions for technical and SEO optimization, and proposing innovative solutions that significantly improved functionality. The findings support the integration of AI into the educational process for training specialists in programming and web design in higher education, positioning it as an additional tool for code development, optimization, and refactoring. Particular emphasis is placed on the importance of a critical approach to AI-generated recommendations and the ability to conduct productive dialogue.
Investigating Climatic Variability and Change Using Rainfall Data in the Metropolitan City of Johannesburg, South Africa
Seroka, Khutso; Ilunga, Masengo; Chabalala, Dumisani; Bodunrin, Idowu (South Africa)
https://doi.org/10.54808/WMSCI2025.01.387
ABSTRACT:
This study investigates the impacts of climate change and rainfall variability across four weather stations, namely Roodepoort Kloofendal, JHB Bot Tuine, Lanseria WO and Walter Sisulu National Botanical Gardens within the Metropolitan City of Johannesburg. Historical rainfall data were obtained from the South African Weather Services covering a 23-year period (2000-2023) were analysed. Descriptive statistics were employed to examine rainfall and climatic variability characteristics, while the Mann-Kendal and Sen’s slope estimator were applied to detect trends and quantify their magnitude. The results indicated a gradual decrease in rainfall over the study area. Trend analysis revealed statistically significant declining rainfall patterns at the selected stations, highlighting the potential impacts of climate change on water resource availability and urban resilience in Johannesburg.
Knowledge Representation in EAP Classrooms: Case Studies
Nikolarea, Ekaterini (Greece)
https://doi.org/10.54808/WMSCI2025.01.17
ABSTRACT:
This study reports meta-cognitive observations of an EAP1 teacher at a non-English-language University, and shows how knowledge representation is achieved by, or fails to be achieved by the teacher and students in EAP classrooms. After having contextualized and defined knowledge representation in EAP classrooms at such University, the EAP teacher and author of this paper looks at how previous knowledge can be either a helping hand or stumbling block for non-English-speaking students who move between two, at least, different linguistic environments to try to communicate their views and local research in English (the lingua franca or means of international communication) in an ever-globalizing world. Presenting specific case studies (i.e. essay writing, CV, cover letter), the author of this study shows how non-English-speaking students can acquire new knowledge, learn new ways of data mining and, at the same time, unlearn things that were poor practice and become aware of culture-bound issues. In such a way, students can become comfortable and have competitive advantage in ever globalizing market and academic environment, while the EAP teacher can learn from her own pitfalls in knowledge representation by improving her teaching methodologies with the help of her own students.
Knowledge Transfer Gaps in Organizations: A Conceptual Framework
Ozolina-Ugore, Simona; Straujuma, Anita; Gaile-Sarkane, Elina (Latvia)
https://doi.org/10.54808/WMSCI2025.01.236
ABSTRACT:
This paper presents a conceptual framework for understanding knowledge transfer (KT) gaps within organizational settings. Drawing on a qualitative content analysis of 35 peer-reviewed academic sources, the study identifies and systematizes three main types of KT gaps: institutional, individual, and technological. Each gap type reflects specific yet interrelated barriers that hinder effective knowledge circulation, including limited strategic leadership engagement, employee resistance or low motivation, and insufficient digital infrastructure.
By mapping how these gaps manifest and interact, the study provides a structured analytical lens for diagnosing discontinuities in organizational learning processes. The findings emphasize that successful knowledge integration requires more than technological solutions—it depends on the alignment of institutional structures and human dynamics. These insights are particularly relevant for organizations embedded in innovation ecosystems, where effective knowledge flow is essential for long-term adaptability and innovation-driven growth.
Mobile Application to Stimulate Continuous Monitoring in Patients with Hypertension in Lima, Peru
Pineda, Aaron; Subauste, Daniel; Molina, Leonardo (Peru)
https://doi.org/10.54808/WMSCI2025.01.32
ABSTRACT:
Hypertension is a significant public health issue, which continues to grow alarmingly within the global and Peruvian population. This disease requires active patient participation to maintain controlled blood pressure levels in a daily basis. However, the lack of consistency in this monitoring process by patients is often a reason for instability in their health condition. To address this, the present study proposes an mHealth mobile application model that employs selected gamification techniques (challenges, achievements, leaderboards) and the implementation of complementary tools such as wearables to motivate patients to continuously monitor their condition. The results showed a positive acceptance of the implemented challenges (80%) and consistent daily blood pressure measurement recordings among the surveyed users.
Modeling the Dynamics of Changes in EEG Fractal Dimension During Problem Solving
Gorbunov, Ivan; Morozova, Svetlana; Chukanov, Andrey; Sosnina, Iuliia (Russian Federation)
https://doi.org/10.54808/WMSCI2025.01.487
ABSTRACT:
Understanding the relationship between energy cost, cognitive activity, and performance is a key problem in psychophysiology. Prior studies have shown that solving cognitive tasks is accompanied by changes in the fractal dimension of EEG signals. In the present study, EEG was recorded from 68 participants solving a task in which participants matched graphs and tables. Participants identified which of three tables matched the numerical data presented in a graph. EEG signals were analyzed using the Higuchi fractal dimension algorithm. The results showed a short-term increase followed by a gradual decrease in EEG fractal dimension (F(4, 7656) = 36.594, p < 0.0001). We modeled this effect in the dynamics of neural network training. The first model was trained to classify 2 input values in the range from -π/2 to π/2. The target function was sinusoidal with further classification into two states: 0 or 1. At each training step, we summed the activations across all network neurons and computed the fractal dimension, which briefly increased and then gradually declined. The second model, based on ResNet18 and modified to solve the task of matching graphs and tables, showed a monotonic decrease in fractal dimension.
Off the Grid, on the Map: Designing Integrated Platforms for Off-Road Safety and Community in the UAE Desert
AlGhamdi, Abdulaziz; Khalifa, Majed; AlShamsi, Saif; Hojeij, Zeina (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.273
ABSTRACT:
Off-roading is a popular cultural and recreational activity in the United Arab Emirates (UAE), yet it presents persistent safety, logistical, and communication challenges. Despite increasing participation, off-roaders often rely on fragmented digital tools ranging from general-purpose GPS apps to informal WhatsApp groups that are not designed for the desert’s shifting terrain, lack of cellular coverage, and convoy-based driving style. This literature review examines existing practices, safety risks, and the limitations of current mobile platforms used in UAE off-roading contexts. It identifies significant gaps in emergency response coordination, real-time location tracking, and knowledge sharing, particularly for newcomers. Studies on outdoor and adventure platforms such as Gaia GPS and AllTrails highlight key design features like offline maps and user-contributed content, but these tools lack cultural and environmental localization. The review also draws on research in user-centered design to argue for a mobile solution that integrates live tracking, safety alerts, and social features tailored to the UAE's off-roading community. By analyzing both global best practices and local user behavior, the study lays the groundwork for a unified, region-specific platform that supports safer and more connected off-road experiences. This approach aligns with national goals for smart mobility and digital inclusion in recreational spaces.
Overcoming Entrepreneurial Barriers: Exploring Technology-Based Solutions for Business Success in the UAE
AlAmeri, Nouf; ElSayary, Areej (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.102
ABSTRACT:
This study examines the challenges entrepreneurs encounter when starting or managing a business, focusing on common barriers and proposing technology-driven solutions. It utilizes an observational design for quantitative research through surveys distributed to business owners and aspiring entrepreneurs. The findings reveal that various obstacles limit growth, and suggest that technology platforms can offer tools to help entrepreneurs overcome these challenges. The study provides valuable insights into the UAE’s growing entrepreneurial ecosystem, highlighting structural issues that impede progress by evaluating initial barriers. A key takeaway is the role of digital technologies, particularly AI and mobile applications, in identifying and addressing these barriers. Although many entrepreneurs have yet to implement technology-based solutions, there is openness to adopting customized tools.
Predicting Motorcycle Accident Severity in Thailand Using Machine Learning
Tanawongsuwan, Patrawadee (Thailand)
https://doi.org/10.54808/WMSCI2025.01.518
ABSTRACT:
Road traffic accidents are one of the leading causes of death worldwide. Using machine learning algorithms, this research aims to predict motorcycle traffic accident severity and identify the factors linked to fatality cases. In a previous study on predicting road traffic accident severity, which classifies an accident as either a fatality or an injury, the results show that the leading indicator by far is whether a motorcycle is involved. This study therefore focuses exclusively on the motorcycle accident cases.
The dataset comprises about twenty-five thousand cases. The study compares Decision Tree, Bootstrap Aggregating, Random Forest and Multilayer Perceptron, in the severity prediction as either a fatality or non-fatality. Different from the previous research, the techniques chosen for feature selection and class imbalance handling are ReliefF and resampling, respectively. As shown by multiple performance measures, Random Forest gives the best performance. In comparison with the previous study, the motorcycle-only models in this study outperform the previous all-vehicle-type models in predicting fatality cases. However, in non-fatality cases, the previous models still prevail. The key factors associated with the fatality prediction are found to be province, region, time, day, and month. The study results can be useful for road traffic safety improvement.
Predicting Strength of High-Performance Concrete Using Gradient Boosting Machine Learning: A Comparative Analysis Between Manual and Grid Search Cross-Validation Hyperparameter Tuning
Tyler, Ryan; Ilunga, Masengo; Ikotun, Bolanle; Zimbili, Omphemetse (South Africa)
https://doi.org/10.54808/WMSCI2025.01.396
ABSTRACT:
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.
Resilience Management Framework for Complex Systems Using Explainable Artificial Intelligence
Triantafellou, Eustathios *; Gegov, Alexander ***; Ichtev, Alexandar **; Kanta, Aikaterini *; Khusainov, Rinat * (* United Kingdom, ** Bulgaria, *** United Kingdom/Bulgaria)
https://doi.org/10.54808/WMSCI2025.01.94
ABSTRACT:
In volatile environments, traditional decision-making frameworks face challenges as they depend on cognitive patterns that may not be universally applicable. Identifying situational dependencies and interpreting disruption subtleties and their cadence is crucial for improving predictive robustness. This position paper examines the importance of cross-sector interdependencies in building resilience within complex systems. The paper emphasizes the role of Explainable Artificial Intelligence (XAI) in enhancing resilience strategies by augmenting human intuition and decision-making. By incorporating transparency and interpretability into AI systems, XAI fosters trust and usability—essential for making impactful, cost-effective, and timely decisions.
The increasing frequency of supply chain disruptions accentuates the necessity to design systems that facilitate effective monitoring and proactive planning for early identification and mitigation of risks. To address these challenges, the paper proposes a synergistic model that leverages AI-powered decision support to enhance cross-sector horizon scanning. This model complements expert judgment and human instinct and empowers the development of resilience strategies that are interpretable, justifiable, traceable, and accessible to an array of actors across disciplines.
Furthermore, the paper advocates for interdisciplinary collaboration as essential for optimizing resilience strategies, emphasizing the integration of diverse perspectives and knowledge systems to create adaptive and effective responses to complex systemic risks.
Rethinking Systems Thinking for Systems Changes Learning: Philosophizing, Theorizing, and Methodizing
Ing, David (Anguilla/Canada)
https://doi.org/10.54808/WMSCI2025.01.412
ABSTRACT:
Systems Change Learning proposes a rethinking of systems thinking, moving beyond the dominant Western premise of stability and its unfreezing-moving-refreezing paradigm. By foregrounding change, this approach challenges the adequacy of 20th-century systems philosophies, sciences, and methods. Grounded in constructivist metaphilosophy, (con)texturalism-dyadicism is introduced as a new world hypothesis. Theorizing through multiparadigm inquiry employs the appreciative systems framework. Methodizing praxis is framed using a process hub with four process spokes. A case study demonstrates preliminary acceptance of an (con)textural action learning cycle in practice. The mindset shift from Western philosophical and scientific presumptions continues to develop imagery that better reflects unfolding change.
Short-Term Retail Access in the UAE: Rethinking Tools for Small Business Growth
AlHarsi, Dana; Hojeij, Zeina (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.277
ABSTRACT:
This paper examines how artificial intelligence (AI) can support small business owners in the United Arab Emirates (UAE) who are seeking short-term physical retail space. Although national strategies encourage entrepreneurship, many local businesses face practical barriers when moving from online to physical stores. These include high rent, rigid lease terms, and limited access to useful property data. Internationally, platforms such as Storefront and Appear Here use AI tools to match businesses with available retail spaces based on timing, customer traffic, and price. However, there is no similar system tailored to the UAE’s legal, economic, or cultural context. Based on a review of recent studies and regional data, this paper argues that adapting AI-based matching tools to local needs could give small business owners more realistic ways to test and grow in physical locations.
Smart Agricultural Systems: Data-Driven Approaches to Monitoring and Decision Support
Kopishynska, Olena; Utkin, Yurii; Sliusar, Igor; Kalashnyk, Olena; Moroz, Svitlana; Liashenko, Viktor; Fedorchenko, Mark; Kovpak, Serhii (Ukraine)
https://doi.org/10.54808/WMSCI2025.01.505
ABSTRACT:
This paper presents practical case studies for conducting digital analysis of weather conditions using open data sources (e.g., Visual Crossing Weather) and methods for assessing their impact on crop yield. Data processing is demonstrated through the use of data analysis tools and statistical functions (such as COUNTIF, CORREL, IF, etc.) in Google Sheets. A separate focus is placed on a multiple linear regression model as one of the methods for crop yield forecasting over specific periods, using the LINEST function. The effectiveness of the model is evaluated, and recommendations are provided for its further use in agricultural enterprises. Special attention is given to agroscouting – a comprehensive method of crop monitoring that employs modern technologies to collect and analyze data on the condition of agricultural crops. The advantages of implementing agroscouting within the Soft.Farm information system, in combination with drone imagery, are demonstrated. The obtained results may be valuable not only for practicing agronomists seeking effective decision-making tools, but also for training future specialists in universities.
Smart Living: Integrating AI, IoT, and VR for Inclusive Interior Design in the United Arab Emirates
AlMahri, Mahra; Hojeij, Zeina (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.281
ABSTRACT:
This literature review examines how digital interior design tools—particularly systems combining data-driven intelligence, connected devices, and immersive visual platforms—can enhance home accessibility and adaptability. The discussion focuses on the development of an interactive consultation environment that incorporates personalized layout recommendations, real-time environmental input, and virtual visualization, all tailored to user needs. Emphasis is placed on the significance of inclusive design, especially for individuals with limited digital skills or physical impairments. Drawing from regional planning goals such as the UAE’s Vision 2031, the review highlights the growing demand for flexible and culturally responsive home solutions. Key studies are analyzed to understand current shortcomings in smart design platforms, including complexity, cost, and limited consideration of user diversity. The paper also explores how connected technologies can work together to support sustainable living, energy use monitoring, and improved user experience. Existing theoretical models like the Technology Acceptance Model (TAM) provide a framework for assessing user reception. The review ultimately identifies the importance of designing adaptable, easy-to-use environments that can evolve with household needs and expectations, especially within rapidly developing urban contexts.
Social Responsibility Regarding Masculinity Depictions in Advertising
Kreicbergs, Toms (Latvia)
https://doi.org/10.54808/WMSCI2025.01.305
ABSTRACT:
The study aims to explore the idea that advertisers have a social responsibility when depicting masculinity in advertising because advertising plays a vital role in giving men clues about how a man should behave. Therefore, stakeholder theory is particularly important for this research since advertisers have a responsibility toward the public when considering their advertising message. To get deeper insights into consumer perceptions when it comes to masculinity in advertising, the author conducted three focus groups. The findings of focus groups prove that masculinity in advertising is an issue that evokes emotions in consumers, especially men, and it makes people think and talk about the advertisement. The study also shows that the issue of masculinity and rejecting gender stereotypes are important for society and that advertisers, as suggested by the stakeholder theory, have a social responsibility to talk about what is important for their stakeholders, especially society.
Spatiotemporal Assessment of Urban Heat Island Phenomenon in Sekhukhune District Municipality Using Cloud Computing
Ledwaba, Matome Mirelda; Ilunga, Masengo (South Africa)
https://doi.org/10.54808/WMSCI2025.01.355
ABSTRACT:
This study evaluates the spatiotemporal variability of the land surface temperature and urban heat in the Sekhukhune District Municipality, South Africa, utilising geospatial computational intelligence that combines Google Earth Engine (GEE) and ArcGIS. This study confirmed that rising temperatures due to urbanisation lead to the urban heat island (UHI) effect, specifically in an African semi-arid area, between 2015 and 2019. The study revealed a linear correlation between LST and UHI, implying that increased surface temperatures result in a rise in UHI during this time interval. This could signal the adoption of sustainable urban planning strategies in the Municipality.
Stewardship and Firm Performance: A Concise State of the Art
Bancroft, Justin; Lace, Natalja; Oganisjana, Karine (Latvia)
https://doi.org/10.54808/WMSCI2025.01.445
ABSTRACT:
Stewardship has become a popular term of late and has gained prominence in corporate governance literature as a counterpoint to agency-based models, emphasizing long-term responsibility, relational accountability, and prosocial motivation. However, its modern application - particularly in regulatory and investment contexts - often dilutes these ethical foundations, reframing stewardship as compliance, monitoring, and disclosure. This paper provides a succinct review of the literature on stewardship and firm performance, blending contemporary and classical sources, while focusing on publicly listed and non-family-controlled firms. It synthesizes academic and institutional research to assess how stewardship is conceptualized, implemented, and empirically measured across governance systems. While agency-based governance mechanisms have been rigorously linked to firm performance through structural proxies, stewardship-oriented practices remain largely under-theorized and under-tested. A large body of empirical studies focuses on family firms or uses indirect indicators such as CEO tenure or founder control, limiting generalizability. Institutional investor adoption of stewardship codes has similarly emphasized procedural duties over behavioral change. This review identifies a persistent disconnect between the normative ideals of stewardship and their operational realization. It concludes by calling for more robust research into the behavioral dimensions of stewardship and their potential performance implications, urging greater conceptual clarity and empirical rigor in assessing stewardship’s contribution to corporate governance.
Technological Optimism: A Conceptual Framework for Sustainable Development
Marinov, Bilian (Bulgaria)
https://doi.org/10.54808/WMSCI2025.01.464
ABSTRACT:
This study introduces the concept of “techno-optimism”, emphasizing care as an ontological and ethical principle within the context of the identity crisis and ecological instability characteristic of the digital age. Through an interdisciplinary approach, it is established that technological progress can be guided by transcendental values such as truth, goodness, and beauty. The analysis reveals the dual nature of technology, both as a potential source of human flourishing and as a threat to identity and autonomy, and proposes a framework for innovation that prioritizes relationality, cognitive resilience, and ecological sustainability. Ultimately, it envisions a technological future aligned with deeper human values, promoting ethical integrity and shared meaning in a fragmented world.
Technology Marathons as Catalysts for SME Growth: Insights from the Latvian Innovation Ecosystem
Erina, Jana; Hiršfelde, Madara (Latvia)
https://doi.org/10.54808/WMSCI2025.01.452
ABSTRACT:
This research aims to assess the role of Latvian hackathons in creating and developing startups by analyzing the factors that influence the sustainable development of ideas after the event. The study used a mixed method, combining literature review, document archive research, survey, and SWOT analysis, within which the AHP method was used to determine priorities. The survey was conducted among Latvian students who had participated in at least one hackathon, obtaining 74 completed questionnaires. The results indicate that most participants gained valuable skills, contacts, and motivation for further activities; however, only a small part of the teams continued the development of ideas after the event. The AHP analysis revealed that the most significant factors for the sustainability of ideas are related to the experimentation of the hackathon format, the decline of student interest, and competition from other events. The conclusions indicate the need to improve post-event support mechanisms and create a structured path to commercialize ideas.
The Impact of Social Media on Adolescents' Emotional Regulation
Al Memari, Mouza; Al Remeithi, Maytha; Al Zaabi, Ayesha; ElSayary, Areej (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.267
ABSTRACT:
This study explores the impact of social media, particularly Instagram and TikTok, on adolescents' emotional regulation in the UAE, with a focus on parental observations. Utilizing an explanatory sequential mixed-methods design, the study combines survey data from 30 parents with in-depth interviews from five selected participants. The study examines the relationship between social media usage patterns, including daily screen time, and emotional regulation. Results indicate that social media provides adolescents with emotional fulfillment through entertainment, socializing, and stress management, but excessive use leads to emotional exhaustion, irritability, withdrawal, and disrupted sleep. The Social Learning Theory and Uses and Gratifications Theory frame the analysis, showing that adolescents model behaviors from influencers and seek gratification through social media. The study emphasizes the importance of mindful content consumption, the role of parents in regulating screen time, and the need for digital well-being programs to support healthy emotional development.
The Impact of Virtual Reality on Cultural Tourism and Heritage Preservation in the United Arab Emirates
AlMarzooqi, Hessa K.; ElSayary, Areej (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.51
ABSTRACT:
This study explores the potential of virtual reality (VR) to enhance cultural tourism and support heritage preservation in the United Arab Emirates (UAE). As the UAE diversifies its economy and modernizes, cultural tourism plays a crucial role in preserving and showcasing its rich heritage. The integration of VR in museums offers immersive experiences that can engage visitors, foster deeper understanding of Emirati culture, and make cultural sites more accessible. The study investigates how VR can transform cultural tourism by assessing its impact on visitor perceptions of Emirati culture and examining the challenges of implementing VR technology in cultural institutions. The findings aim to provide insights into the potential of VR to promote cultural preservation and contribute to the UAE’s broader tourism goals.
The Influence of Current AI Tools in Teaching and Learning
Jenq, John (United States)
https://doi.org/10.54808/WMSCI2025.01.493
ABSTRACT:
The development of AI chatbot technologies can be traced back to 1964 when ELIZA was first developed at MIT. It has taken several decades for the primitive chatbot to evolve into a smart chatbot. The real great breakthrough of chatbot technology did not happen until OpenAI introduced ChatGPT and it became viral in December 2022. The recent progress of AI, from 2023 until the present day, has grown exponentially. In less than three years, AI growth has been both quantitative and qualitative. During this three-year period, it has evolved from the original Gen AI to become Agentic AI. Agentic AI improves and enhances user experiences of using AI agents. We are fortunate to witness this great AI explosion in AI evolution. In this report, we conduct an experiment to observe how the evolution of AI might affect student assessment. We also discuss how the influence of recent AI development may affect or even change traditional teaching methods, learning, education and beyond.
Three-Dimensional Models as Teaching Tools in University Education
Giraldo de López, Marisela; Domenech Polo, Nayade V.; Carrasquero Ferrer, Sedolfo J.; Bravo Acosta, Olga M. (Ecuador)
https://doi.org/10.54808/WMSCI2025.01.300
ABSTRACT:
This research analyzes the use of three-dimensional (3D) models as teaching tools in university education, evaluating their impact on the teaching-learning process. These innovative tools have transformed educational paradigms, fostered autonomous and collaborative learning and redefined the roles of teachers and students. 3D models facilitate the understanding of complex concepts, especially in areas representing the Science, Technology, Engineering, and Mathematics (STEM) disciplines, by integrating advanced digital technologies. A descriptive design with a documentary approach was employed, utilizing content analysis and the hermeneutic-dialectical circle for a systematic and reflective interpretation of the data. The results indicate that 3D models are versatile and improve the understanding of abstract concepts thanks to their interactivity and accessibility, in addition to promoting a more immersive educational environment. However, challenges remain, such as the lack of digital competence among teachers and the absence of systematized methodologies. In conclusion, the use of 3D models significantly enhances educational processes by stimulating independent and interactive learning, increasing student motivation and engagement. It is recommended that they be integrated as essential teaching aids in various university disciplines and that research on their pedagogical application be strengthened to meet the demands of a digitalized society.
Transdisciplinary Competencies for the Future: Bridging the Gap Between Emotional Intelligence, Digital Literacy, Inner Development Goals, and Employability
Shtaltovna, Yuliya *; Makhachashvili, Rusudan ** (* Germany/Ukraine, ** Ukraine)
https://doi.org/10.54808/WMSCI2025.01.341
ABSTRACT:
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 skіlls hіghlіghted іn the World Economіc Forum’s (WEF) "Future of Jobs" reports and those sought by learners іn Coursera’s Global Skіlls іndex. 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.
Triggers of Conflicts and Mechanisms of Conflict Prevention in Organizations: Analysis of Factors and Classification
Ozolina, Jana; Saitere, Sanita; Gaile-Sarkane, Elina (Latvia)
https://doi.org/10.54808/WMSCI2025.01.228
ABSTRACT:
Workplace conflict emerges from the interaction of interpersonal, structural, and cultural dimensions, often manifesting through personality clashes, procedural ambiguity, value differences, hierarchical friction, and language barriers. This study explores how this conflict triggers interrelate, how their frequency varies across generational cohorts, and what coping strategies employees employ in response. Quantitative analysis revealed that personality, process, and value conflicts frequently co-occur, while proactive conflict attitudes aligned with constructive resolution behaviors. Generational patterns showed Gen X and Gen Y report the highest levels of personality and process conflicts, whereas Gen Z showed heightened sensitivity to language-related tension. Qualitative insights revealed a distinct archetype - the so-called "Employee Devil" - who, despite high performance, undermines cohesion through manipulation of informal power structures. Together, the findings support a two-level prevention framework that combines structural interventions (e.g., process clarification) with relational strategies (e.g., inclusive leadership, early detection of toxic informal influence). The study contributes to organizational conflict scholarship by demonstrating how formal systems and hidden dynamics jointly shape team cohesion and by proposing practical tools for addressing conflict complexity.
Utilizing Computational Tools in Biomedical Education to Promote Sustainable Healthcare Solutions: A Multidisciplinary Approach
ElSayary, Areej; Ragab, Jumana K.; Abuali, Reem M. (United Arab Emirates)
https://doi.org/10.54808/WMSCI2025.01.24
ABSTRACT:
The rapid evolution of computational tools, such as artificial intelligence (AI) and machine learning, presents transformative opportunities for biomedical education. This paper explores the integration of these technologies into educational curricula to promote sustainable healthcare solutions. It highlights the potential of computational tools in optimizing resource allocation, enhancing patient outcomes, and fostering interdisciplinary collaboration among educators, healthcare professionals, and tech experts. Through virtual laboratories, adaptive learning systems, and predictive analytics, these tools offer innovative approaches to medical training, ensuring that future healthcare professionals are equipped to address sustainability challenges. The paper further examines how computational technologies can mitigate environmental impacts by reducing resource consumption and waste in healthcare practices. It emphasizes the need for equitable access to these tools, especially in underserved regions, and discusses the challenges and opportunities in integrating technology without compromising humanistic aspects of patient care. Ultimately, the paper advocates for the inclusion of computational tools in biomedical education as a means of developing a sustainable and inclusive healthcare workforce.
Weapon Recognition Through Deep Learning
Acevedo, Elena; Acevedo, Marco; Gomez, Sandra (Mexico)
https://doi.org/10.54808/WMSCI2025.01.263
ABSTRACT:
The level of crime in recent times has increased and has resulted in a more significant number of fatalities. Automatic weapons recognition is proposed as a possible solution to this problem. This work proposes recognizing two types of weapons, short and long, using the YOLOv8 pre-trained network. The results show that the confidence level for correct recognition is 0.5, as long as the image quality is good. If the image quality deteriorates, lowering the confidence level to recognize the weapons is necessary. Recognition performance is also affected when the gun is mistaken for another object.
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