Search Results (1,292)

The growing global demand for critical raw materials (CRMs) essential to renewable energy, electromobility, and digital technologies has accelerated the need for advanced exploration methods capable of operating in increasingly challenging geological environments. Traditional drilling systems, designed primarily for shallow mineral and hydrocarbon exploration, face limitations in heterogeneous and consolidated formations where rock heterogeneity, variable mechanical strength, and borehole instability restrict operational efficiency. This review bridges geological science and robotic engineering by analyzing the evolution of next-generation autonomous drilling technologies integrating sensor systems, artificial intelligence (AI), and real-time geotechnical feedback. The current work explores how robotic drilling systems can autonomously adapt to variable lithologies, optimize penetration rates, and ensure borehole stability through intelligent sensing and control. The paper reviews the geological, geomechanical and ore deposit characteristics of CRMs, discusses state-of-the-art drilling optimization strategies, and highlights advances in measurement while drilling (MWD), logging while drilling (LWD), and geochemical analysis techniques. It also suggests a list of sensor techniques for possible future integration in autonomous subsurface robotic systems. It concludes by emphasizing the need for integration between subsurface geological modeling and intelligent drilling robotics as a pathway toward sustainable and efficient CRM exploration. Full article

Background: Home-based digital healthcare interventions are increasingly used to support people living with dementia (PLWD) and their family caregivers. However, evidence regarding their effectiveness across patient and caregiver outcomes remains heterogeneous. Methods: This systematic review followed PRISMA 2020 guidelines and was prospectively registered in PROSPERO (CRD420261302166). Six databases (PubMed, Embase, CINAHL, PsycINFO, Web of Science, and Scopus) were searched from January 2000 to October 2025. Randomized and quasi-experimental quantitative studies evaluating home-based or remotely delivered digital interventions for PLWD and/or informal caregivers were included. Risk of bias was assessed using RoB 2 and ROBINS-I. Due to heterogeneity, findings were synthesized narratively. Results: Fourteen studies met the inclusion criteria. Interventions included web-based psychoeducation, telecoaching, digital cognitive training, assistive technologies, and multicomponent programs. Caregiver outcomes demonstrated the most consistent benefits, including reduced burden and stress, improved self-efficacy, and improved sleep efficiency in technology-supported trials. For PLWD, small-to-moderate improvements were observed in global cognition and selected neuropsychiatric symptoms, particularly in interactive and personalized programs. Multicomponent interventions combining caregiver education with patient activation and professional feedback showed more durable effects. Conclusions: Home-based digital interventions appear feasible and beneficial, particularly for caregiver outcomes. Future large-scale trials with longer follow-up and standardized outcome measures are needed to confirm durability, equity, and cost-effectiveness. Full article

Effective Parkinson’s disease (PD) management is hindered by the intermittent nature of clinical snapshots and the discomfort of rigid monitoring hardware. This review critically evaluates the synergy between flexible bioelectronics and artificial intelligence (AI) for continuous remote monitoring. Our analysis reveals that while material innovations have achieved milligram-level sensitivity, a significant ‘translational gap’ persists due to limited validation in real-world environments and small cohort sizes. We conclude that multimodal fusion architectures are essential for accurately mapping digital biomarkers to clinical gold standards such as MDS-UPDRS. By leveraging edge AI for privacy and closed-loop feedback for intervention, this integration facilitates the transition from reactive clinical visits to proactive, personalized digital home-care ecosystems. Full article

The increasing demand for scalable and fair assessment of open-form responses in digital education shows the need for intelligent grading systems capable of balancing syntactic precision with semantic understanding. This study proposes a hybrid semantic–syntactic NLP framework for automated grading of short-answer and cloze-type questions. The framework integrates a rule-based matcher for syntactic accuracy, MPNet (Masked and Permuted Pre-trained Network) embeddings for semantic similarity, and a fine-tuned DeBERTa (Decoding-enhanced Bidirectional Encoder Representations from Transformer with Disentangled Attention) regressor for continuous score prediction, while a T5-small model provides pedagogically aligned feedback generation. Evaluations were conducted using benchmark corpora, synthetic cloze datasets, and a domain-specific short-answer corpus. Results demonstrate that the hybrid system outperforms traditional baselines, achieving 91% accuracy, a 0.89 F1 score, a mean absolute error of 0.36, and strong inter-rater agreement (κ = 0.87), aligning closely with human graders. Qualitative analyses show that the framework successfully recognizes paraphrased responses, assigns partial credit, and generates meaningful feedback. Ablation studies further validate the necessity of each subsystem, with performance significantly declining when components were removed. The findings confirm that the proposed framework is both computationally robust and pedagogically valuable, establishing a foundation for scalable, interpretable, and fair automated grading in contemporary educational environments. Full article

Background: Hearing impairment affects a significant portion of the global population. The development of assistive technologies, particularly wearable devices, has been pivotal in mitigating these challenges. Methods: We present a systematic literature review on wearable assistive technologies for individuals with hearing impairment, analyzing 106 scientific articles identified from diverse sources (IEEE Xplore, ACM Digital Library, and Web of Science). Our comprehensive analysis is structured around device types, body locations, user study methodologies, sensory modalities, and application domains. Results: Findings reveal a strong emphasis on auditory and visual feedback, a mix of traditional hearing aids complemented by smart wearable devices, and experimental evaluations focusing on speech comprehension and usability. Visual analysis highlights a significant anatomical shift towards body-worn and wrist-worn haptic devices. While speech accuracy is rigorously reported, user-centric metrics like comfort and battery life are frequently neglected. Conclusions: Addressing these disparities, we propose the HEAR framework (Hybrid Architectures, Engaging Experiences, Adaptive Systems, Real-world Validation). This strategic roadmap advocates for a diversification of sensory outputs, more extensive longitudinal user studies, and the development of adaptive, multi-modal solutions that seamlessly integrate into users’ everyday lives. Full article

The objective of this paper is to propose a sociological and interdisciplinary framework for analyzing the digitization of emotions in adolescence. This contribution aims to promote theoretical reflection and inform educational and political interventions in the digital age, framing adolescents’ digital experiences as emotionally embodied and socially integrated processes. These aspects are of paramount importance due to the rapid proliferation of digital technologies and artificial intelligence, which have precipitated a profound transformation in the emotional, relational, and educational experiences of adolescents. The role of digital and AI-based environments in mediating communication is expanding beyond the scope of simple facilitation. These environments are increasingly implicated in the production, modulation, and regulation of emotions, thereby influencing developmental trajectories and identity formation processes. This phenomenon is theorized as a socio-technical process, wherein emotions are embodied, narrated, and governed within digital environments. The article introduces the concept of digital emotional embodiment, drawing on the sociology of emotions, theories of embodiment, and critical perspectives on artificial intelligence. Specifically, the concept refers to the manner in which adolescents experience and express emotions through avatars, images, emojis, algorithmic feedback, and AI-mediated interactions. Therefore, it is imperative to underscore the evolution of empathy, which is progressively configured as a virtualized and datafied process, diverging from the tradition established by Hume and characterized by sympathy. In contemporary processes, shaped by the logic of platforms, recommendation systems, and emotionally reactive technologies, conventional emotional concepts have undergone deconstruction, and digital constructs are undergoing a gradual restructuring. In this context, AI systems do not merely reflect adolescents’ emotions but rather actively contribute to the construction of emotional narratives, influencing emotional regulation, social connection, and future orientation. Digital environments have been shown to encourage emotional expressiveness, experimentation, and inclusivity. Conversely, they have the capacity to encourage emotional standardization, dependency, and forms of affective vulnerability, particularly during a sensitive developmental stage such as adolescence. Full article

Digital trade rules have proliferated rapidly, yet the literature still treats institutional environments and firm behavior in a comparative-static manner, overlooking the feedback loops and stock-like accumulation dynamics through which regulatory openness shapes firm capabilities over time. Drawing on general systems theory and system dynamics, this paper models the digital trade rule regime as an “institutional system” and the overseas subsidiary network of digital MNEs as an “enterprise system,” linked through three capability stocks (market, production, knowledge), cross-subsystem coupling, absorptive capacity modulation, and five internal feedback loops. We derive a reduced-form dynamic panel equation mapping structural parameters onto estimable coefficients, and test its static counterpart using data on 6850 subsidiaries of UNCTAD’s top 100 digital MNEs (2000–2024) matched with the TAPED database. Three findings emerge. First, institutional openness—measured by rule depth and breadth—exerts a positive causal effect on subsidiary ROA, surviving IV estimation and multiple robustness checks. Second, the effect transmits through market expansion, production efficiency, and knowledge accumulation channels, confirmed by Baron–Kenny mediation with Sobel tests. Third, the New Digital Economy (NDE) module displays point estimates 4–8 times larger than other modules, and joint Wald tests reject coefficient equality, providing qualified support for Meadows’ leverage-point hierarchy. Our contribution lies in bridging system dynamics modeling with econometric causal identification, and in unifying transaction cost theory, the OLI paradigm, and the knowledge-based view within a single open-system framework. Full article

Italian hand gestures constitute a distinctive and widely recognized form of nonverbal communication, deeply embedded in everyday interaction and cultural identity. Despite their prominence, these gestures are rarely formalized or systematically taught, posing challenges for foreign speakers and visitors seeking to interpret their meaning and pragmatic use. Moreover, their ephemeral and embodied nature complicates traditional preservation and transmission approaches, positioning them within the broader domain of intangible cultural heritage. This paper introduces a machine learning–based framework for recognizing iconic Italian hand gestures, designed to support cultural learning and engagement among foreign speakers and visitors. The approach combines RGB–D sensing with depth-enhanced geometric feature extraction, employing interpretable classification models trained on a purpose-built dataset. The recognition system is integrated into a non-immersive virtual reality application simulating an interactive digital totem conceived for public arrival spaces, providing tutorial content, real-time gesture recognition, and immediate feedback within a playful and accessible learning environment. Three supervised machine learning pipelines were evaluated, and Random Forest achieved the best overall performance. Its integration with an Isolation Forest module was further considered for deployment, achieving a macro-averaged accuracy and F1-score of 0.82 under a 5-fold cross-validation protocol. An experimental user study was conducted with 25 subjects to evaluate the proposed interactive system in terms of usability, user engagement, and learning effectiveness, obtaining favorable results and demonstrating its potential as a practical tool for cultural education and intercultural communication. Full article

Indoor Environmental Quality (IEQ) plays a vital role in occupant health and productivity. However, current Building Management Systems (BMS) often struggle in sustaining optimal IEQ levels due to limitations in data management and lack of occupant-centric feedback loops. To address these gaps, this research synthesizes the state-of-the-art methods for IEQ monitoring, assessment, and control within Building Automation Systems (BAS), identifying both technological and methodological advancements, as well as highlighting the challenges and potential opportunities for future innovations. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, this multi-stage literature review analyzes 176 publications from 1997 to 2024, with a focus on the decade of rapid technological evolution from 2014 to 2024. The review focuses on high-impact journals indexed in Scopus to ensure quality while acknowledging the potential bias inherent in a single-database search. The synthesis reveals a methodological shift in monitoring from sparse, zone-level sensing towards dense, multi-modal systems that incorporate physiological data via wearables and behavioral recognition through computer vision. Assessment techniques are evolving from static models such as the Predicted Mean Vote (PMV) towards adaptive, personalized frameworks supported by Digital Twins and integrated simulations. Furthermore, control logic is transitioning toward Reinforcement Learning and Model Predictive Control to proactively manage occupancy surges and environmental variables. This evolution of monitoring approaches, assessment techniques, and control strategies is represented within the study’s Three-Tiered Developmental Trajectory, providing a novel Body of Knowledge (BOK) for mapping the transition of building systems from reactive tools to autonomous, occupant-centric agents. This study also introduces a Cross-Modal Interaction Matrix to systematically analyze the systemic trade-offs between IEQ domains. Furthermore, by establishing the “Implementation Frontier,” this work identifies the specific technical and ethical bottlenecks, such as “false vacancy” sensing errors, fragmented data silos, and the ethical complexities of high-resolution data collection that prevent academic innovations from becoming industry standards. To bridge these gaps, we conclude that the next generation of “cognitive buildings” must prioritize three pillars: resolving binary sensing limitations, harmonizing data via vendor-neutral APIs, and adopting privacy-preserving architectures to ensure scalable, interoperable, and occupant-centric optimization. Full article

The rapid transition to Industry 4.0/5.0 has widened the gap between graduates’ skill sets and labor market expectations; this study aimed to profile student competencies and align academic pathways with inclusive and adaptive AI-driven learning. A quantitative design was applied: an online survey of n = 126 students (engineering and economics, February–March 2025), expert evaluations from 5 faculty and 5 employers on a 5-point scale, framed by T-shaped competencies, 4C skills, and Bloom’s taxonomy. Analysis was performed in Python 3.11; future demand until 2035 was forecasted using ARIMA and Prophet models trained on publicly available labor market data (OECD, WEF, Eurostat 2015–2024); competency prioritization employed K-Means clustering and Random Forest models. Strengths included cooperation 4.2, critical thinking 3.9, communication 3.8, and creativity 3.6. Deficits were programming 2.8, project management 3.2, and solution development 3.2; employers rated programming at 2.5 (−0.7 compared to faculty). Forecast 2025–2035 showed growth in demand for programming +56% (3.2 → 5.0), data analytics +39% (3.6 → 5.0), project management +34% (3.2 → 4.3), digital literacy +30% (3.7 → 4.8), and critical thinking +15% (3.9 → 4.5). Clustering identified critical (programming, analytics, project management), supporting (creativity, communication, teamwork), and optional (narrow theoretical depth) competencies. Curriculum adjustment with practice-oriented modules, AI-enabled adaptive learning, and systematic university–employer feedback is essential; the proposed AI-supported profiling model is scalable and enhances inclusiveness. Full article

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article

The integration of Artificial Intelligence (AI) into the design, manufacturing, and lifecycle management of Composite Overwrapped Pressure Vessels (COPVs) is transforming the pathway toward autonomous and adaptive composite systems. This paper presents a comprehensive review and roadmap for AI-enabled COPVs development, bridging materials design, process optimisation, and predictive maintenance. The study synthesises over a decade of research on data-driven composite manufacturing, combining technology intelligence, PESTEL-SWOT environmental assessment, and cross-sectoral analysis of industrial and academic advances. A unified workflow is proposed to illustrate AI integration across the COPVs lifecycle, highlighting data feedback loops for continuous optimisation through digital twins and intelligent process control. Structural Health Monitoring (SHM) plays a central role in this ecosystem by providing real-time high-fidelity data on damage evolution and environmental interactions in COPVs. Through embedded sensing technologies such as fibre optic sensors and acoustic emission systems, SHM enhances digital twin fidelity, supports AI-based anomaly detection, and strengthens model validation in safety-critical hydrogen storage applications. Critical challenges are identified, including limited hydrogen-exposure datasets, lack of real-time adaptability, explainability in safety-critical design, and sustainability of AI-intensive workflows. These challenges highlight the need for tighter SHM-AI integration to enable reliable condition assessment and prognostics under multi-physics loading conditions. Based on these findings, the paper outlines actionable research directions to enable reliable, transparent, and sustainable AI adoption in composite manufacturing under the Industry 4.0 and hydrogen-economy paradigms. Full article

Accurate and fast junction-temperature estimation in Silicon Carbide (SiC) power modules is crucial for reliable operation, health monitoring and predictive control of power electronic converters in different applications. However, direct temperature measurement inside the module is difficult and high-fidelity thermal models are often very computationally expensive for real-time implementation. This paper proposes a digital twin development approach for fast and accurate temperature estimation in all three dimensions of a SiC MOSFET power module by a combination of finite element method (FEM) modelling and neural networks. The work is especially relevant in thermal monitoring and managing power electronics converters such as renewable energy systems, energy storage systems, Electric Vehicles (EV), etc. The model incorporates a neural network trained on data generated from an FEM model built in COMSOL Multiphysics. The developed digital twin can estimate the temperature distribution, including the ten junction temperatures of the Wolfspeed EAB450M12XM3 module, with an average estimation time of 0.063 s, enabling predictive control. In order to improve practical applicability and model synchronization with the physical system, NTC-based feedback techniques are discussed (single-Temperature Coefficient (NTC) and double-NTC approaches). The proposed framework is investigated in terms of prediction accuracy and computational performance related to the FEM-generated reference data. The approach improves model reliability by adjusting the parameters of the critical digital and physical modules. The combination of FEM-based modelling and machine learning can provide a foundation for accurate, real-time thermal monitoring in power electronic modules. Full article

Adolescents increasingly engage with digital technologies, yet understanding patterns of smartphone use and fostering reflective awareness remain challenging. Traditional assessments in clinical or school settings may limit participation and self-reflection. This study evaluated the feasibility and impact of a Computer-Assisted Web Interviewing (CAWI) approach to monitor smartphone use, provide immediate individualized feedback, and support peer-mediated dissemination in a public science engagement context. Across three editions of the European Researchers’ Night in Rome (2023–2025), 807 adolescents aged 10–19 completed the SAS-SV questionnaire via on-site tablets or personal devices using QR codes. Smartphone use was categorized into Low Involvement, At-Risk, or Problematic. Participants were encouraged to share the survey link with peers, enabling snowball-mediated recruitment. Participant acceptance was assessed through the Net Promoter Score (NPS). Snowball participation accounted for the majority of responses, highlighting the effectiveness of peer-mediated diffusion. SAS-SV categorization indicated 46% Low Involvement, 39% At-Risk, and 15% Problematic use, with minimal gender differences. NPS values ranged from +69 to +79, with snowball participants reporting slightly higher satisfaction than on-site attendees. These results underscore high engagement, perceived value, and the role of peer networks in promoting reflective digital behavior. Integrating CAWI assessment, immediate feedback, and peer-mediated diffusion created a socially situated environment supporting self-reflection and voluntary dissemination. Peer networks extended both the temporal and social reach of the initiative beyond the public event, demonstrating a scalable and non-stigmatizing model. CAWI-based monitoring combined with feedback and peer-driven diffusion is feasible and effective for adolescent digital wellbeing interventions. This approach fosters reflective digital citizenship, supports self-awareness, and leverages social networks to enhance the reach and impact of youth-centered health promotion initiatives. Full article

Cognitive Learning Analytics (CLA) is an interdisciplinary domain that combines cognitive science and learning analytics to interpret and enhance human learning through theoretically grounded data analysis. It integrates learning analytics with models of cognition to support theoretically grounded interpretation of learner data. Learning analytics, since its inception in 2011, has developed as a research field and applied practice, focusing on “the measurement, collection, analysis, and reporting of data about learners and their contexts.” It focuses on understanding and optimizing learning processes and environments by leveraging large-scale, multimodal educational data. Cognitive science, in parallel, provides established theories of human learning, memory, attention, and metacognition. CLA links observable behaviors with theoretically defined cognitive mechanisms. Through the integration of cognitive theories and computational techniques, CLA models how learners process information, make decisions, and construct knowledge in digital learning environments. CLA employs diverse data sources—including clickstream logs, eye tracking, biometric signals, and linguistic traces—to infer learners’ cognitive and affective states. These inferences inform adaptive learning systems, personalized feedback mechanisms, and intelligent tutoring tools that respond dynamically to the learner’s mental workload, engagement, or metacognitive strategies. Full article