Search Results (749)

Digital public services are increasingly recognised as important instruments of digital public governance, supporting administrative efficiency, citizen-centred service delivery, and broader sustainability transitions in the public sector. This study examines how authentication mechanisms, data protection, perceived sustainability benefits, and user experience influence trust and satisfaction with digital public services in Slovenia. A mixed-methods research design was applied, combining quantitative survey data (N = 96) with qualitative insights from 12 anonymous interviews involving users, public-sector employees, and managerial representatives. Quantitative data were analysed using independent-samples t-tests, Spearman correlation analysis, and multiple linear regression, while qualitative data were analysed thematically. The findings support all five hypotheses. Users of services requiring SI-PASS or qualified digital certificates reported significantly higher perceived security. GDPR compliance was positively associated with trust and more favourable service evaluations. Perceived sustainability benefits, particularly reduced paper-based documentation, were strongly linked to positive evaluations of digital public services. User satisfaction was positively associated with accessibility, ease of use, and time savings, while user experience emerged as the strongest predictor of overall satisfaction. The study extends digital governance literature by demonstrating that digital public services function as strategic enablers of sustainable, resource-efficient, and citizen-centred public-sector transformation. Full article

Rapid urbanization across global South megacities has accelerated the overexploitation of urban aquifers, creating complex socio-hydrological crises that threaten long-term water resilience for a vast population. Conventional urban-water management frequently relies on uniform, city-wide regulatory mandates that fail to account for localized hydrogeological heterogeneities and the socio-economic drivers of private extraction. This study performed a seasonal assessment (post- and pre-monsoon) of groundwater levels (GWLs), across the National Capital Territory of Delhi, India, using a 13-year archival dataset (2010–2022) of 77 ‘common’ wells, with a sequential spatial–statistical framework. No statistically significant ‘seasonality’ was found in GWLs, except for isolated years. About 13% of the observations appeared as ‘deep outliers’, the call for more process-level hydrogeologic investigations. Spatial interpolation via the Inverse Distance Weighting (IDW) interpolation technique, alongside Global Moran’s I, Local Indicators of Spatial Association (LISA), and spatially Constrained Hierarchical Cluster Analysis (sHCA), revealed a recurrent spatial pattern: persistent, deep GWLs, within the fracture-dominated, low-yielding Alwar Quartzite (Delhi Ridge) of South and Southeast Delhi. The spatial clustering demonstrates the migration of the deep-GWL hotspots toward the unconfined alluvial aquifers of the Yamuna River floodplains to the east, threatening future baseflow stability. These spatial drawdown patterns represent a structural response to municipal Drinking Water Services Framework (DWSF) deficits, where intermittent supply and informal water markets incentivize the growth of more unregulated and unrestricted private pumping of groundwater. Achieving sustainable urban groundwater governance requires replacing blanket administrative mandates with a more data-driven, micro-zoned socio-hydrological framework across the city—combining area-specific extraction caps, economic instruments for geologically targeted aquifer storage and recovery, informal market regulation, and facilitating more participatory, community-based (Water users Associations, WUA) initiatives in the future to protect groundwater resources in Delhi. However, it requires specialized monitoring data, which is still largely lacking, and detailed investigations involving the aquifer hydrogeology and groundwater pumping patterns. Full article

Future transportation networks may require aerial communication platforms capable of providing flexible and reliable services to vehicular terminals. In conventional unmanned aerial vehicle (UAV) communication systems, the antenna geometry is commonly treated as fixed, which limits the attainable directional gain when the relative geometry between the UAV and users changes significantly. This work considered a UAV equipped with a mechanically reconfigurable antenna array and studied its joint motion and transmission control under finite-blocklength communication. A sequential optimization problem was formulated to maximize the accumulated user throughput by jointly optimizing the UAV trajectory, the array orientations, and the transmit beamforming vectors, subject to the UAV kinematic constraints, the UPA orientation constraints, and the transmission energy budget. The resulting problem involves nonlinear coupling among platform motion, antenna pointing, beamforming, and finite-blocklength rate expressions, making conventional optimization computationally demanding. To obtain an adaptive control policy, a soft actor–critic-based deep reinforcement learning method was developed. The simulation results showed that jointly controlling the UAV mobility, array orientation, and beamforming improves the achievable finite-blocklength transmission performance compared with benchmark schemes, demonstrating the effectiveness of the proposed framework in enhancing reliable data delivery for UAV-assisted transportation infrastructure applications. Full article

Background: The involvement of people with lived experience in psychiatric research is gaining momentum internationally, yet forensic psychiatry remains largely absent from this development—particularly in Germany. Highly regulated settings, stigma, and institutional barriers make meaningful service user involvement both rare and challenging. The PART Advisory Board serves as a best practice model for conducting research not merely about service users, but genuinely with them—as co-researchers throughout the entire research process. Methods: The PART Advisory Board brings together researchers and individuals with lived experience, including current inpatients and formerly detained persons. Involvement spans all phases of research—from topic identification and study design to recruitment and data collection. Examples include structured project presentations with guideline-based feedback aimed at achieving active participation within entire projects—such as serving as an expert panel for guidelines on VR use in forensic settings. Board members also revise patient information materials, co-develop flyers, and present them as co-speakers at conferences. Results: This multi-level involvement improves research quality, relevance, and acceptance. Researchers gain practice-oriented perspectives that refine methods and strengthen interpretation. Board members experience empowerment through recognition of their expertise. Initial analyses confirm that participatory structures are feasible in highly regulated forensic contexts when roles are clear, attitudes are respectful, and communication is continuous. Conclusions: The PART Advisory Board demonstrates that participatory research in forensic psychiatry is both achievable and enriching. Successful implementation requires tailored processes, flexible structures, and individual support throughout the research journey—offering a transferable model for embedding service user involvement as a permanent part of forensic research and practice. Full article

Walking tourism is increasingly supported by a wide range of digital technologies that are crucial for mitigating environmental impacts and promoting sustainable territorial development, yet their adoption remains fragmented and rarely interpreted within a unified infrastructure perspective. While systemic infrastructure perspectives are well-established in smart city and transport literature, existing studies on walking trails still tend to focus on individual tools or user-oriented applications. Drawing upon broader smart mobility concepts, this paper proposes a system-level framework, the SmartTrail Framework (STF), for the classification, functional interpretation and integration assessment of digital technologies in walking trail infrastructures. The framework is conceived as a transferable analytical and operational instrument applicable both to academic literature and to real-world trail systems, supporting infrastructure assessment, planning and digital maturity evaluation. Following an initial bibliometric analysis of a broader Scopus dataset comprising 1697 records, a structured literature review of 253 publications was conducted to operationalise and illustrate the framework through the systematic classification of digital technologies, the mapping of functional requirements, and the assessment of Integration Levels (IL). The STF integrates three analytical dimensions: a technology domain taxonomy (six categories), a functional requirement model (five infrastructure functions) and an IL scheme (IL0–IL3) that characterises the degree of systemic coherence among digital components. The results reveal a critical functional imbalance in current digitalisation: while user-oriented navigation and information services are widely adopted, backend infrastructure functions, particularly safety and operational logistics, remain severely underdeveloped, keeping most trail systems trapped in low-integration configurations. Applied to real-world trail systems, the framework enables the identification of architectural gaps, the prioritisation of integration investments and the definition of development pathways towards intelligent trail ecosystems. The paper aims to contribute to field research by providing a structured interpretative framework for understanding digital technologies as infrastructural components of walking trails. This supports a shift from tool-based thinking to system-based infrastructure design and offers practical implications for planners, managers and policymakers involved in the development of smart, resilient and environmentally sustainable trail infrastructures. Full article

Real-time multilingual communication (RTMC) is increasingly important in academic, governmental, defense, and clinical settings, where cloud-based speech processing may raise privacy, data sovereignty, connectivity, and cost concerns. This study presents Yapzek Stream, a fully offline multilingual broadcasting and speech-translation platform that performs speech recognition, translation, and text-to-speech synthesis within institution-owned infrastructure. The system integrates Web Real-Time Communication (WebRTC)-based broadcasting with locally hosted artificial intelligence (AI) services and provides multilingual audio delivery, browser-based access, recording, transcript and subtitle export, and institutional authentication. The platform was evaluated through an implementation-oriented analysis and a university pilot involving two live sessions with 10 and 50 participants and 12 semi-structured interviews. Estimated end-to-end processing latency was 1.8–4.1 s, while pilot observations ranged from approximately 2 s for short utterances to 4.5–5 s for longer speech. The results indicate the feasibility of offline multilingual broadcasting for privacy-preserving and institution-controlled communication. Deployment-specific accuracy and translation evaluation, controlled scalability and GPU-utilization measurements, robustness testing, and larger-scale user-acceptance studies remain for future work. Full article

The growing demand for scalable and cost-effective digital learning environments has increased interest in lightweight cloud-based learning management solutions that can support instructional delivery without the complexity and infrastructure requirements of conventional Learning Management Systems (LMSs). While enterprise LMS platforms provide extensive functionality, their deployment and maintenance often require substantial technical, financial, and administrative resources. This study proposes a lightweight cloud-based LMS architecture using low-code web technologies as an alternative framework for educational content management, learner engagement, resource distribution, and instructional support. The proposed architecture integrates four functional system layers: course management, performance management, content delivery, and productivity support. These components are designed to operate within a cloud-hosted environment that leverages web-based content management, embedded digital resources, collaborative productivity tools, and centralized storage services. The architecture emphasizes accessibility, modularity, low deployment overhead, and cross-platform compatibility, enabling rapid implementation in resource-constrained educational settings. To evaluate the feasibility of the proposed architecture, a large-scale deployment was conducted involving 1765 end users interacting with the platform within an educational environment. System functionality was assessed through feature-level evaluation across the core LMS components and supported by user capability indicators related to digital engagement and platform utilization. Analytical results demonstrated strong functional performance across all architectural modules, with course management and content delivery components exhibiting the highest operational effectiveness. Findings further indicated that the cloud-based architecture successfully supported essential LMS functions through integrated web services and low-code platform technologies. The study contributes a replicable systems architecture and deployment framework for lightweight learning management environments. The proposed model offers a practical foundation for the development of cloud-based educational platforms that support scalable content delivery, learner interaction, instructional management, and future integration with learning analytics, adaptive learning engines, and intelligent educational support systems. The framework provides an engineering-oriented approach for designing accessible and sustainable digital learning infrastructures using low-code technologies. Full article

Unwanted telemarketing calls are a persistent source of consumer frustration and a legally regulated issue in Germany. At first glance, the idea of addressing this problem with AI-based voice technology appears contradictory: why should an automated caller help restore trust in a communication channel that has been damaged by aggressive outbound practices? This design-oriented case and prototype study argues that the paradox can be resolved through a different design logic. Rather than using AI to intensify persuasion, we present a role-based voice-agent architecture that constrains conversational behavior through narrow task boundaries, explicit escalation rules, and auditable data handling. The paper reports a transfer project involving FH Aachen students, Capgemini, and Fairdient GmbH. Methodologically, the work is positioned as a design-oriented case study with a prototype artifact. The contribution is threefold: first, we describe a three-agent architecture for outbound screening, consent-aware explanation, and inbound service; second, we derive governance principles for legally and ethically sensitive telephony, including transparency, bounded knowledge, privacy-preserving deployment, and human fallback; and third, we propose an evaluation framework covering conversion, compliance, hallucination control, user trust, and cost per validated outcome. The prototype does not yet claim large-scale field effectiveness. Instead, it offers a structured and empirically testable design for trustworthy voice automation in a domain where misuse, opacity, and user distrust are especially pronounced. Full article

Preparing training data for domain-specific medical Named Entity Recognition (NER) involves a trade-off between annotation quality, expert effort, and data privacy: manual annotation is costly, whereas cloud-based Large Language Models (LLMs) raise concerns about the control of sensitive clinical text. This article introduces NERFlow, a workflow-driven subsystem of the FIT4NER project whose contribution is an abstraction layer that makes rule-based, model-based, and LLM-based annotation interchangeable and comparable within one configurable workflow environment. Open-source LLMs are integrated as exchangeable annotation services, deployable locally or in cloud-agnostic infrastructures via Kubernetes, and embedded into the KM-EP knowledge management system. NERFlow was evaluated qualitatively, through a cognitive walkthrough, an IEEE 1028 technical review, and a user-centered survey with 18 participants, and quantitatively on the CRAFT corpus with seven open-source and hosted LLMs run through an identical pipeline. The results support its use as LLM-assisted pre-annotation with expert correction, with locally deployable open-source models as the more reliable basis for reproducible operation. Full article

This study investigated how pre-service teachers’ frequency of artificial intelligence (AI) tool use is associated with their perceived workload and perceived task performance in AI-supported pedagogical design tasks, grounded in mental workload theory. Using a quantitative cross-sectional survey design, data were collected online from 464 pre-service teachers through a structured questionnaire covering AI use profiles, purposes of AI use, perceived usefulness of AI functionalities, and NASA-TLX-based workload ratings. Perceived workload was assessed using the raw NASA-TLX after participants completed an AI-supported lesson planning task involving the design of an instructional activity and teaching materials. Descriptive findings showed that AI use was widespread, with most participants reporting weekly or daily engagement. One-way ANOVA results indicated significant differences across AI use-frequency groups on all NASA-TLX dimensions. Infrequent users reported higher mental demand, temporal demand, effort, and frustration, as well as lower perceived task performance. However, polynomial trend analyses showed that the association was not strictly linear across all dimensions. Some workload dimensions were lowest among occasional users, whereas frustration decreased and perceived task performance increased as AI use frequency increased. These findings suggest that the workload implications of AI engagement may vary depending on the specific dimension of task demand considered. Full article

Social activity is considered a primary factor in prolonging health and independence in later life, alongside physical activity. While population ageing reflects increasing life expectancy, the risk of living longer in poor health remains. In response to this risk, the United Kingdom developed extra-care schemes, introducing a housing and care model that combines social activities with flexible support to promote healthy and independent living in later life. Despite these potential benefits, observations that male tenants engage less with social opportunities than female tenants highlight a gap in understanding the social needs of men in care environments. This study explores how male social engagement is understood and influenced in extra-care schemes through a dual-perspective approach, combining service–user and service–provider viewpoints. Semi-structured interviews were conducted with six male tenants (mean age = 84 years) and five scheme managers across four extra-care schemes. Reflexive Thematic Analysis revealed barriers to male social engagement related to gender imbalanced environments, poor health, social identity disruption, and widowhood. Conversely, group membership and reliable family involvement function as protective resources that sustain identity roles and support networks for men living in a care environment. Developing male-oriented community links and peer support initiatives within extra-care, alongside appropriate training and psychoeducation for staff and tenants, may be effective when supporting men in care settings. Future research would benefit from broader multi-stakeholder samples to address the limitations of this study. Full article

This exploratory study compares the conceptual performance of human respondents and ChatGPT on the Force Concept Inventory (FCI), a widely used assessment designed to diagnose alternative conceptions in Newtonian mechanics. Rather than relying exclusively on overall performance scores, the study investigates whether item-level analyses reveal qualitative differences between human and AI responses. The dataset comprised responses from fourteen Belgian pre-service physics teachers and eleven independent ChatGPT sessions generated through different user accounts. A Rasch model was employed to estimate respondent proficiency and item difficulty, complemented by an item-by-item comparison of success rates and analyses of Item Characteristic Curves (ICCs). The findings indicate that although ChatGPT achieved an overall level of performance comparable to that of several human participants, substantial discrepancies emerged on conceptually demanding items requiring the coordination of multiple physical relationships. These discrepancies are interpreted through Vergnaud’s Theory of Conceptual Fields as suggesting that successful human performance may involve richer coordination of operational invariants than can be inferred from ChatGPT response patterns. Because of the exploratory design, the small sample size, and the asymmetry between human and AI testing conditions, these interpretations should be regarded as hypothesis-generating rather than confirmatory. The study illustrates how item-level analyses, combined with a conceptual framework grounded in physics education research, may provide a more informative perspective on human–AI comparisons than global scores alone. Full article

This paper presents an evaluation of AI-assisted human–computer interaction for mathematical problem solving within a multimodal mathematical editing environment. The proposed architecture integrates a visual equation editor, voice-based interaction, REST communication services, and externally hosted generative AI large language models (LLMs) to support the creation, interpretation, and solution of mathematical expressions. The study was conducted using the Equation Wizard environment, which provides multimodal mathematical content editing based on both proprietary and standard formula representations, including MathML and LATEX. A conversational AI interaction layer enables users to communicate with selected LLMs using natural language voice commands. The main objective of the study was to determine whether contemporary LLM-based services can reliably support mathematical problem solving within an AI-enhanced equation editing environment. To address this objective, seven contemporary GenAI LLMs were evaluated using a benchmark consisting of representative high school mathematics problems covering algebra, limits, trigonometry, logarithms, inequalities, and parameterized expressions. The evaluation focused on mathematical correctness, output interpretability and visualization quality, response latency, and compliance with mathematical encoding formats within the complete interaction workflow. The study also compares representative model families with respect to correctness, syntactic compliance, and responsiveness within the complete interaction workflow. Unlike conventional LLM benchmarks that assess models in isolation, this work evaluates end-to-end AI-assisted mathematical interaction involving the editor, communication infrastructure, and language models. The study demonstrates a practical approach to assessing the usefulness of multimodal AI-enhanced mathematical problem solving in realistic usage scenarios. The results show that, within the evaluated interaction workflow, current LLMs achieve high levels of mathematical problem-solving performance, although substantial differences were observed in response latency and output-format compliance. An important finding is the discrepancy between the models’ strong semantic understanding of custom-encoded mathematical input and their weaker ability to generate syntactically compliant encoded outputs, highlighting a key challenge in the integration of LLMs with structured mathematical software environments. Full article

Current Digital Twins of the Ocean rely predominantly on two-dimensional geoportal interfaces that constrain the spatial comprehension of complex marine environments. This paper presents Immersive Ocean, a Virtual Twin platform developed under the EU-ILIAD Digital Twins of the Ocean initiative that procedurally transforms standardised European geospatial data (EMODnet, Copernicus Marine Service) into interactive three-dimensional maritime environments via Unreal Engine 5 (Epic Games, Cary, NC, USA). The platform supports both desktop and immersive virtual reality modes, enabling users to visualise and manipulate spatial scenarios involving offshore wind farms, shipping corridors, aquaculture installations, and species distributions. System performance testing confirmed stable rendering across PC and VR configurations (61 FPS and 42 FPS, respectively). A user evaluation with 31 participants across three hardware configurations revealed that core geovisualisation capabilities—ease of use, immersion, and procedural generation utility—remained robust, regardless of hardware, whilst feedback responsiveness (H = 5.99, p = 0.04995) and perceived realism (H = 6.31, p = 0.04258) were significantly affected. These findings inform deployment strategies for immersive geospatial tools: minimum specification systems preserve functional access, whilst recommended hardware enhances the perceptual fidelity critical for spatial decision support. This evaluation establishes usability among digitally proficient users; efficacy with domain stakeholders in authentic planning contexts remains for future work. Full article

Chatbots are becoming increasingly significant entry points to digital services and are used in areas including job support, education, healthcare, and customer services. On the other hand, less is known about how chatbots affect people individually, in groups, and in society. Moreover, several obstacles must be overcome before chatbots can realize their full potential. As a result, chatbots have become a significant research topic in recent years. We propose a research agenda outlining future directions and issues to advance knowledge in chatbot research and education. This research involves the quantitative analysis of the impact of these chatbot tools on academic staff, with a count of 40, and students, with a count of 300, at Al Zahra College for Women (ZCW). The links are uploaded electronically in bilingual form for both staff and students, and the responses are retrieved from them. This analysis is conducted in IBM SPSS Statistics version 29, and a comparative report is also prepared based on the questionnaire responses from students and staff members of ZCW. The study investigates the effects of artificial intelligence (AI) chatbots on the faculty and students at ZCW. The use of AI-powered tools in educational settings is examined, along with their effects on administrative, instructional, and learning procedures. This will enable us to identify the advantages of using AI tools within E-Learning systems. The results demonstrate how well AI chatbots can streamline administrative duties, enhance student involvement, and provide academic help. However, there are drawbacks as well, such as user adjustment, privacy issues, and technological constraints. The study offers helpful suggestions for enhancing chatbot integration at Al Zahra College to enhance learning results and operational effectiveness. Full article