Search Results (1,115)

The notion of “AI natives” is increasingly used to describe young learners’ presumed ease with generative artificial intelligence (GenAI), but the label often turns age and exposure into proxies for competence. This study re-examines that assumption through an exploratory secondary analysis of two open student datasets and proposes a practice-based relational framework for AI nativeness. In this framework, AI nativeness is not a generational identity, but a testable configuration of competence foundations, GenAI use practices, access conditions, and institutional mediation. Because the two datasets do not measure all four dimensions within the same learners, the framework is motivated rather than fully tested here. The results show that basic operational skills do not, on their own, explain AI readiness; critical information literacy is the most consistent positive predictor in the regression models. Student GenAI use and access conditions are also heterogeneous, forming four interpretable GenAI use/access profiles: low-adoption learners, high-intensity multi-taskers, mobile-dependent moderate users, and balanced cognitive adopters. These findings do not establish who is or is not an AI native. They show why the age-based label is analytically insufficient and why future research should examine AI nativeness through competence, practice, access, and institutional mediation together. Full article

Robotics serves as a key enabler in disability support by offering new pathways to augment social work practice and enhance autonomy, safety, and inclusion for persons with disabilities (PWDs). In this study, an interdisciplinary team executed a socio-technical investigation that integrated robotics engineering, artificial intelligence, rehabilitation sciences, and social work. It was examined how assistive and socially interactive robots, including mobility robots, cognitive-assistive systems, exoskeletons, telepresence units, and socially assistive humanoids, must be embedded within disability services to improve functional independence, strengthen care continuity, and address increasing workforce demands. A comprehensive research design was adopted by combining a systematic literature review and technical benchmarking of robot capabilities with qualitative inputs gathered from co-codesign workshops involving PWDs, caregivers, and social workers. To test these applications, the team evaluated three pilot domains: home-based independent living support, community-based rehabilitation, and social-work-led remote engagement utilizing telepresence robotics. The results demonstrate that these robotic interventions improved independent task completion by 22–41% and reduced caregiver burden by 18–34%. Furthermore, the data revealed significant gains in communication and emotional engagement for individuals with cognitive or speech impairments. While robotics cannot replace social workers, these technologies meaningfully complement care delivery when practitioners develop them through ethical, participatory, and contextually sensitive frameworks. Ultimately, this paper highlights clear pathways toward scalable, inclusive robotic support systems that align engineering innovation with person-centered social work values. Full article

In the central nervous system, the neuropeptide oxytocin stimulates neural networks that regulate social behaviors, including social attachment, aggression, and complex social cognition. Perturbations in oxytocin and/or oxytocin receptor expression results in social behavioral deficits and are associated with a number of psychopathologies including autism spectrum disorder, schizophrenia, anxiety, and depression. In rodent models of autism spectrum disorder, oxytocin is effective at improving social behavior. However, limited translatability between animal models and human physiology has hindered successful translation of these findings into human therapeutics. Oxytocin variant-induced cellular signaling pathways and G-protein coupling have largely been investigated in HEK and CHO heterologous expression systems; however, cellular context is crucial, and these profiles likely differ from intact neurons. This project assessed oxytocin variants in in vitro human-derived neuron-like SH-SY5Y cells that endogenously express the oxytocin receptor. Results demonstrated that the naturally occurring oxytocin variants Leu⁸-OT, Pro⁸-OT and Val³-Pro⁸-OT activated intracellular signaling pathways and promoted neuronal differentiation-associated responses, including calcium mobilization, membrane hyperpolarization, and neurite outgrowth in a concentration-dependent manner. Knowledge of how oxytocin variants alter cellular structure and function has the potential to both identify mechanisms that produce social dysfunction and to inform the development of therapeutic agents. Full article

This paper aims to evaluate the usability and user experience of ad hoc augmented reality systems in the learning experience of mechanical engineering students, focusing on fluid machinery education. Three mobile augmented reality applications were developed to support teachers in explaining the main parts of an impeller blade and its fluid interaction, integrating computer-aided design (CAD) models with velocity and pressure maps derived from computational fluid dynamics (CFD) simulations. By providing multiple means of representation, these tools were developed to support the explanation of complex 2D concepts without requiring specialized hardware. The obtained results revealed that the usability of the developed applications, assessed through the System Usability Scale (SUS), was remarkably effective. Furthermore, the User Experience Questionnaire (UEQ) showed that the average scores for each evaluation criterion were highly positive, especially in the “Stimulation” and “Novelty” areas. Accurate statistical analyses revealed that students’ feedback was not influenced by users’ familiarity with virtual and augmented reality tools. In conclusion, since no objective learning gains were evaluated, this investigation’s outcomes indicate that the developed applications provide an engaging tool with high usability and positive user experience, framing inclusive education as a fundamental design rationale rather than an empirically demonstrated outcome and laying the groundwork for future studies to objectively measure cognitive impact. Full article

This study examined how environmental expectation gaps, defined as discrepancies between older adults’ expectations before admission and their actual experiences after admission, are associated with residential adaptation in nursing homes. Survey data from 613 residents of six public and private inclusive nursing homes in Changchun, China, were analyzed using structural equation modeling and bias-corrected bootstrapping. Expectation gaps in safety and mobility convenience, privacy and autonomy, environmental comfort, and spatial cognition and familiarity were significantly associated with emotional adaptation, with spatial cognition and familiarity showing the strongest relationship. Support for activities and social interaction was positively associated with relationship formation but negatively associated with communal-living adaptation. Residential adaptation followed a sequential process from emotional adaptation to relationship formation, acceptance of institutional living, and adaptation to communal living. Bootstrap analysis further showed that several environmental expectation gaps were indirectly associated with later adaptation outcomes through this sequential process, with some direct and indirect relationships operating in opposite directions. These findings demonstrate that environmental expectation gaps are associated with residential adaptation through differentiated and interconnected pathways, extending nursing home evaluation beyond facility conditions and satisfaction alone. Full article

Active jamming in complex electromagnetic environments can severely degrade radar remote sensing imaging and target detection, especially when deceptive and suppressive jamming components coexist. Existing deep learning methods usually formulate jamming recognition as a closed set classification task, which provides limited information about component superposition, time-frequency localization, and physical jamming parameters. To address these limitations, this paper proposes a prior-guided lightweight dual-task network for structured composite active jamming cognition. The proposed framework extracts multi-domain handcrafted features and decision tree based coarse priors from the received signal, and fuses them with short-time Fourier transform (STFT) time-frequency images through a confidence-gated MobileViT_CA-based network. The recognition branch predicts the jamming family, fine-grained class, and composite attributes, while the segmentation branch estimates component masks for copy, convolution, and noise components. A FiLM-conditioned mask refinement module further improves mask continuity and boundary quality, enabling the extraction of physical parameters such as bandwidth, center frequency, coverage duration, delay, slice width, and repetition interval. Experiments on a 22-class active jamming dataset show that the proposed method, built on a 1.92 M-parameter MobileViT_CA backbone, achieves 93.78% overall classification accuracy, 96.49% jamming family accuracy, and 89.17% accuracy on the 12 composite classes. The FiLM-conditioned mask refinement module improves the test-set mIoU from 67.55% to 82.93%, and most representative physical parameters are estimated with relative errors below 15%. Full article

Background/Objectives: To investigate the prevalence of the Motoric Cognitive Risk (MCR) Syndrome among older adults in long-term care facilities and its associated risk factors. Methods: From January to December 2024, older adults were recruited from five long-term care facilities in Southwest China via convenience sampling. All participants underwent a series of standardized assessments, including a self-designed demographic questionnaire, the 15-item Geriatric Depression Scale (GDS-15), the Timed Up and Go (TUG) test, the Fried frailty phenotype scale, sarcopenia assessment based on the SARC-F sarcopenia screening scale, the Basic Activities of Daily Living (BADL) scale, the Lawton Instrumental Activities of Daily Living (IADL, Lawton) scale, and the Mini-Mental State Examination (MMSE). The 4 m walk test was performed to measure participants’ gait speed. This cross-sectional study aimed to explore the prevalence of MCR syndrome and its associated influencing factors among institutionalized older adults. Results: The overall MCR prevalence was 8.6%. Univariate tests detected intergroup differences in BMI, depressive symptoms, hearing loss, multimorbidity, mobility impairment, frailty and several chronic diseases (all p < 0.05). Multivariate logistic regression analysis further demonstrated that, after adjustment for confounders, higher depressive symptom scores (OR = 1.169, 95% CI: 1.079–1.265, p < 0.001), mild mobility impairment (OR = 4.725, 95% CI: 1.340–16.663, p = 0.016), moderate mobility impairment (OR = 3.921, 95% CI: 1.146–13.420, p = 0.029), and more than three chronic comorbidities were independent risk factors for MCR among institutional older adults residing in long-term care facilities (OR = 2.789, 95% CI: 1.465–5.309, p = 0.002). Conclusions: Depressive symptoms, mild-to-moderate mobility impairment and multimorbidity were independent correlates of MCR among institutional older adults. Distinct risk-factor patterns existed for SG and SCCs, highlighting the separable nature of motor and subjective-cognitive dimensions. Integrated screening for emotional, physical and chronic disease risk and personalized activity interventions are warranted for long-term care residents. Full article

Background/Objectives: Toileting-related falls may arise from interacting urinary symptoms, impaired mobility and balance, cognitive or psychological factors, environmental hazards, and caregiver limitations that are often managed separately. This study aims to evaluate the implementation, safety, and 6-month clinical outcomes of an integrated multidisciplinary prevention, treatment, and rehabilitation pathway for older adults and people with disabilities at risk of toileting-related falls. Methods: This multicenter, prospective, single-arm implementation study will enroll 300 adults aged ≥18 years across 15 centers. Participants will undergo standardized assessment of urinary function, gait and balance, physical function, psychological/cognitive factors, activities of daily living, fall history, and toileting-related environmental risks. Prespecified risk domain criteria will guide individualized urinary, rehabilitation, psychological/cognitive, caregiver, and environmental interventions, with reassessment and adjustment during follow-up. The primary outcome will be the 6-month cumulative incidence of at least one toileting-related fall. The secondary outcomes will include nighttime and total falls, urinary symptoms, nocturia, mobility, and functional independence. The implementation will be evaluated using RE-AIM and intervention fidelity measures. Expected Results: The study will characterize pathway reach, adoption, delivery, fidelity, safety, follow-up performance, data completeness, clinical event rates, and within-participant change. Conclusions: The findings may support a reproducible multidisciplinary care pathway and inform subsequent comparative evaluations of effectiveness, sustainability, component contributions, and resource use. Full article

Mobile self-reports provide subjective and contextual information that passive sensors cannot recover, but frequent prompts compete with everyday activities and are often answered late. Here, we show that situational context and recent response history predict whether an ecological momentary assessment response begins within 15 min. We analyzed 70,375 records from 170 participants in the Italian arm of DiversityOne; 43.19% met the operational timeliness criterion. Participant-clustered generalized estimating equations identified associations with activity, social setting, location, mood, weekday, hour and survey day, with corrected Cramér’s V values of 0.044–0.175. In leakage-resistant evaluation, gc-Forest achieved an accuracy of 0.719 for personalized forward prediction, whereas history-augmented LightGBM achieved an accuracy of 0.704 and area under the receiver operating characteristic curve of 0.779 when participants were held out. Retrospectively ranking ten candidate moments increased the participant-balanced timely-response rate from 44.60% to 51.43%. These results establish prompt timeliness as a learnable scheduling outcome, while distinguishing it from content accuracy or cognitive effort. Full article

Urban fragmentation is often approached through residential patterns, spatial distance and the uneven distribution of urban resources. However, in Southern European medium-sized cities, where socially contrasting neighbourhoods may be located in close physical proximity, fragmentation may operate through less visible everyday mechanisms. This article examines how selective spatial practices reflect and may reproduce unequal access to places, activities and opportunities, and how this perspective may inform debates on spatial governance and urban planning. Drawing on GPS tracking, in-depth interviews and mental maps, it analyses daily mobility, activity spaces and lived spatial boundaries in two socially contrasting neighbourhoods of Lleida, Spain. The article contributes to debates on urban inequality, understood as the structurally conditioned unequal distribution of resources, opportunities, capacities and recognition by conceptualising differentiated everyday geographies as both manifestations of this inequality and mechanisms through which it may be reproduced. The findings show how spatial practices and cognitive representations structure urban experience beyond residential segregation: physical proximity does not necessarily translate into shared urban experience: as some participants develop broader and socially legitimised relations with the city, whereas others display more restricted and symbolically bounded patterns of urban engagement. The article therefore advances practised accessibility as an analytical and planning perspective that incorporates the temporal, modal, cognitive and symbolic conditions that shape everyday access to urban opportunities beyond their informal availability. Full article

As a potential contributor to urban sustainability, the commercialization of Urban Air Mobility depends on social license rather than mere technical feasibility. To address the limitations of the Technology Acceptance Model (TAM) in high-risk contexts, this study integrates trust, perceived risk, personal innovativeness, and governance expectation to construct an extended acceptance model tailored to China’s policy-driven institutional environment. Based on 567 valid samples analyzed via Structural Equation Modeling (SEM) and Multi-Group Analysis (MGA), the results reveal three core mechanisms. First, perceived risk positively enhances perceived usefulness (β = 0.765, p < 0.001) via risk-induced cognitive reframing, indicating that the public rationalizes threats by amplifying the technology’s functional value. Second, trust exhibits a strong compensatory effect on perceived ease of use (β = 0.885, p < 0.001) and directly drives behavioral intention (β = 0.549, p < 0.001), serving as a heuristic to reduce perceived complexity in risky decisions. Third, governance expectation directly drives behavioral intention (β = 0.225, p = 0.004) and attitude (β = 0.201, p = 0.007), confirming the primacy of institutional trust in China. The model explains 50.5% of the variance in behavioral intention (R² = 0.505) and 48.6% in attitude (R² = 0.486). Notably, the direct effects of perceived ease of use on intention were non-significant, redefining TAM boundaries where safety supersedes efficiency. Finally, gender moderates the covariance between innovativeness and governance expectation (female β = 0.765 vs. male β = 0.720, C.R. = 3.196, p = 0.001), revealing higher institutional dependency among females. These findings elucidate the risk–trust institution nexus in UAM, offering empirical evidence for differentiated sustainable transport policies and marketing strategies. Full article

Balance impairment threatens mobility and independence in older adults with cardiovascular disease, yet cardiac rehabilitation (CR) has traditionally prioritised aerobic capacity and cardiovascular outcomes. This narrative review examines the mechanisms and assessment of balance impairment, balance recovery within CR, and implications for clinical practice. Balance impairment reflects interactions among musculoskeletal, sensory, cognitive–motor, and cardiovascular constraints that affect different domains of postural control to varying extents. Standardised clinical measures do not capture these domains equally, and single scores or completion times can obscure the deficits underlying poor performance. Instrumented and wearable technologies extend clinical assessment by quantifying postural sway and gait, helping to distinguish broader mobility gains from recovery within specific balance domains. To translate this distinction into practice, this review recommends individualised, balance-focused CR, with assessment guiding task-specific training and virtual reality or exergaming providing graded practice and performance feedback to promote mobility and independence. Full article

The electromagnetic spectrum grows increasingly crowded with the rapid expansion of mobile, satellite and Internet of Things communications, making intelligent spectrum sensing and efficient management an urgent priority. Automatic modulation classification (AMC) serves as the core of cognitive radio and intelligent communication. Existing deep models often suffer from a large number of parameters and low storage efficiency. To overcome these limitations, we propose MDSCNet, a multi-scale depth-wise separable complex network. Built upon complex depth-wise separable convolution, the network makes full use of the phase information in in-phase and quadrature signals while naturally preserving the symmetric relationship between the in-phase and quadrature components (IQ). The asymmetric multi-scale structure combined with the embedded lightweight attention module jointly forms the overall feature extraction process. The overall parameter count is kept extremely low, at only 47.739 k. Experiments on the RML2016.10a and RML2016.10b datasets show that MDSCNet delivers recognition performance under low signal-to-noise ratios (SNR), reaching 63.42% and 66.71% respectively. More importantly, it outperforms mainstream methods in both parameter count and storage efficiency. Full article

Background: Mobile health interventions offer a potential solution to adherence challenges, yet evidence regarding their collective efficacy in schizophrenia spectrum disorders has not been formally synthesized. This systematic review evaluates the impact of mobile health (mHealth) interventions on medication adherence as a primary outcome and on daily functioning and psychotic symptoms as secondary outcomes in individuals with schizophrenia spectrum disorders. Methods: Using the Population, Intervention, Comparison, Outcome (PICO) framework, a systematic search was conducted across multiple databases to identify relevant randomized controlled trials (RCTs) evaluating mHealth strategies for medication adherence in adults with schizophrenia spectrum disorders. The PubMed, CINAHL, PsycINFO, EMBASE, and JBI databases were searched from inception until 24 February 2026, using combinations of search terms such as “Schizo” OR “Psychos” AND “mHealth” OR “Digital Health” AND “Medication Adherence”. Data extraction was conducted using a standardized data extraction table and narratively synthesized. This review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring structured and comprehensive reporting of the findings. Results: Fourteen randomized controlled trials (RCTs) with 1717 participants were included in this review. Four studies evaluated text messaging interventions, four employed phone call interventions, three used electronic medication monitoring systems, and three used mobile applications. Nine of the fourteen studies reported statistically significant improvements in medication adherence. For secondary outcomes, the results were highly inconsistent: only three studies demonstrated significant reductions in psychotic symptoms, and none showed benefits for daily functioning. While various theoretical frameworks, such as the Health Belief Model and Cognitive Behavioral Therapy and intervention modalities, were utilized, the overall evidence was limited by high clinical heterogeneity and a lack of robust long-term data. Conclusions: mHealth interventions, particularly text messaging and mobile applications, demonstrate clear potential to improve medication adherence in individuals with schizophrenia spectrum disorders. Given the high heterogeneity and lack of long-term evidence, future research should prioritize standardized outcome measurements, rigorous designs, and extended follow-up periods to confirm clinical utility. Full article

This article presents a practice-oriented methodological proposal for designing inclusive multimodal learning content with Generative AI (GAI) in migrant adult literacy. It does not report an experimental intervention or a statistical evaluation of learning outcomes. Its contribution lies in formalizing a context-sensitive design pathway for early preA1–A2 literacy and language-learning provision in Italian CPIA settings, where learner profiles are highly heterogeneous, attendance may be discontinuous, and written language is both a learning goal and a barrier to participation. Unlike generic AI-supported instructional design frameworks, the proposed approach starts from recurrent communicative needs in adult migrant education and translates them into short, modular and reusable learning artifacts that coordinate textual, visual, audio-oral and interactive layers. The framework distinguishes multimodal design, understood as the pedagogical coordination of different semiotic modes, from the mere use of multiple media. It also integrates accessibility as a set of concrete design criteria, including linguistic readability, visual clarity, audio quality, layout, font size, contrast, cognitive load and usability in print or mobile formats. The article outlines a sequence of design operations: mapping learner profiles, selecting situated communicative scenarios, generating and revising textual material, developing visual and audio scaffolds, structuring guided interaction, and applying pedagogical, cultural and ethical review. An illustrative micro-unit on asking for information at a municipal office shows how this pathway can support dialog, visual glossary, audio practice, role-play and formative assessment. The proposal is intended for CPIA educators, adult literacy professionals, instructional designers and researchers in multimedia learning and educational technology. Its educational implication is that GAI can support inclusive material design only when its outputs are treated as provisional resources to be selected, adapted and validated through human pedagogical judgment. Full article