Search Results (1,655)

Simulation-based learning (SBL) is recognized as a transformative methodology in teacher education, yet the social–emotional processes through which pre- and in-service teachers navigate role dissonance remain underexplored. Drawing on the Social–Emotional Learning (SEL) framework proposed by CASEL, this study conceptualizes simulation as a cyclical SEL loop across three phases, pre-briefing, simulation, and debriefing, each activating distinct competencies: self-awareness, self-management, social awareness, relationship skills, and responsible decision-making. This qualitative phenomenological inquiry examines the lived social–emotional experiences of pre- and in-service teachers participating in The School of Valtance, a large-scale, multi-role international simulation embedded in a postgraduate teacher qualification program. Participants from 9 international faculties were deliberately assigned to roles, such as pedagogue, head of studies, and parent, divergent from their professional backgrounds, generating the cognitive–emotional disruption known as the “valley of despair”. Data were gathered through semi-structured interviews, reflective journals, and facilitator field notes across all three simulation phases and subjected to thematic analysis. The findings reveal that role dissonance disrupts participants’ emotional equilibrium, activating self-awareness and social awareness competencies that precede and enable metacognitive growth. The debriefing phase emerges as the space in which the SEL loop closes, as participants articulate and consolidate both the emotional turbulence and relational learning generated during the simulation, transforming affective experience into transferable social–emotional competence. These findings offer practical guidance for simulation designers seeking to embed SEL intentionally across all phases of the pedagogical cycle in intercultural teacher training contexts. Full article

On behalf of the Canadian Association of Psychosocial Oncology, we are pleased to present the abstracts from the 2026 Annual Conference, titled “A New Horizon: Expanding the Access and Impact of Psychosocial Oncology”. The 41st Annual CAPO Conference was held in St. John’s, Newfoundland from 8 June 2026 to 9 June 2026. As we stand at a new horizon in psychosocial oncology, we recognize the unprecedented opportunities to expand both the access to and the impact of comprehensive cancer care. This conference will explore innovative strategies for breaking down traditional barriers that have historically limited access to psychosocial support, including geographic isolation, resource constraints, cultural disparities, and systemic inequities in healthcare delivery. This expansion of reach and influence represents not merely growth in service numbers, but a fundamental transformation in how we conceptualize, design, and implement patient-centered psychosocial care across diverse communities and care settings. We will explore scalable solutions that amplify impact while maintaining the deeply personal, human-centered approach that defines excellence in psychosocial oncology. From telehealth innovations and peer support networks to community-based interventions and integrated care models, this conference will showcase evidence-based strategies that expand our collective ability to support individuals and families navigating the cancer journey, wherever they may be. This conference brought together key stakeholders including multidisciplinary professionals from nursing, psychology, psychiatry, social work, spiritual care, nutrition, medicine, rehabilitation medicine, occupational health and radiation therapy for both adult and pediatric populations. Participants included clinicians, researchers, educators in cancer care, community-based organizations and patient representatives. Patients, caregivers and family members presented abstracts that speak to their role in managing cancer experiences and care. Over one-hundred and fifty (150) abstracts were submitted for presentation as symposia, 20 min oral presentations, 10 min oral presentations, 90 min workshops and poster presentations. We congratulate all the presenters on their research work and contributions. Full article

Background/Objectives: Hispanic adults in the United States seek and access professional mental health services at a disproportionately lower rate than non-Hispanic White adults, despite having similar rates of mental health challenges. Community focus groups held in El Paso, Texas, in 2022 revealed both systemic and individual-level factors that serve as barriers to help-seeking and access. Participant recommendations for overcoming these barriers included increasing mental health education and improving the cultural responsiveness and navigability of mental health services. Stemming from these findings and informed by the literature on trauma-informed practice, culturally responsive interventions, and evidence-informed practices, we developed and pilot-tested a 4-week closed group community-grounded mental health engagement program called Pensamientos y Pláticas. Methods: The program was offered to 82 participants. Paired-sample t-tests were used to analyze differences between pre- and post-intervention data for the outcome variables assessed: stigma and emotional well-being. Analysis was limited to the 39 participants who completed at least half of the modules and had intact pre- and post-intervention data. Results: Data analysis revealed a statistically significant reduction in stigma related to help-seeking with a small-to-moderate effect size, while no statistically significant increase in emotional well-being was supported by the data. Conclusions: Findings from this pilot study reflect preliminary evidence of feasibility, acceptability, and potential benefit at reducing stigma related to seeking help in Hispanic communities and support Pensamientos y Pláticas as a promising culturally responsive stigma-reduction intervention. Full article

The transition to kindergarten is a critical developmental milestone within the broader continuum of early intervention and early childhood special education, particularly for children with disabilities who must navigate new instructional expectations, service changes, and social environments. This systematic review synthesized 12 peer-reviewed studies to examine parents’ perspectives on this process and the extent to which evidence-based transition practices are reflected in research and practice. Guided by PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) procedures and appraised using the Mixed Methods Appraisal Tool, the review identified seven themes: communication and collaboration, transition practices, parental concerns, inclusion priorities, formal and informal supports, transition programs, and implementation gaps. Parents consistently emphasized the importance of early and sustained communication, continuity of services, and culturally responsive engagement while also describing stress, inequities, and gaps in implementation that undermined confidence in schools. Findings further indicate that many practices valued by families align with established evidence-based and recommended practices; however, their implementation remains inconsistent across settings. Although high-intensity, individualized transition practices were valued, they were inconsistently available, and low-intensity events were viewed as insufficient. Emerging evidence on structured transition programs and family-focused interventions suggests promising, yet underutilized, approaches for improving child and family outcomes. Findings highlight that parents serve as central advocates, decision-makers, and partners, underscoring the need for relationship-based, equitable transition systems. Implications for research, practice, and policy include the need to strengthen implementation fidelity, expand access to evidence-based supports, and explore innovative approaches, including technology-enhanced strategies, to improve transition experiences. Future research should prioritize more inclusive and intersectional approaches and evaluate structured interventions that strengthen family–school collaboration. Full article

Background: Family caregivers of children with autism spectrum disorder (ASD) increasingly utilize large language models (LLMs) for health information. This study presents a systematic comparative evaluation of three widely used LLMs as localized ASD health information tools in Saudi Arabia. Methods: Twenty-four clinically validated, caregiver-oriented questions were posed to Google Gemini 1.5 Pro, OpenAI ChatGPT (GPT-4o), and DeepSeek-V3 using a standardized prompt. Three expert raters independently evaluated responses across four dimensions: scientific accuracy, PEMAT-P understandability, PEMAT-P actionability, and neurodiversity (ND)-affirming language. Readability was assessed via Flesch–Kincaid Grade Level (FKGL) and SMOG indices. Non-parametric Kruskal–Wallis tests with post hoc Mann–Whitney U comparisons and one-sample t-tests were applied. Results: Gemini achieved the highest mean accuracy (2.96/3.00), significantly outperforming DeepSeek (p = 0.003, r = −0.37). Accuracy failures across all LLMs clustered on regional epidemiological, genetic risk, and financial inquiries. ChatGPT achieved significantly higher understandability than Gemini (p < 0.001, r = 0.55), while DeepSeek achieved significantly higher actionability than Gemini (p < 0.001, r = 0.59). However, all three LLM scores fell short of the Agency for Healthcare Research and Quality (AHRQ) 80% actionability benchmark (all p < 0.001). All LLMs exceeded patient education readability benchmarks (FKGL ≤ 6, SMOG ≤ 8; all p < 0.001); ChatGPT was the most readable (FKGL = 7.86; SMOG = 9.97) and Gemini the most complex. No model differed significantly on ND-affirming language, defaulting to a mixed medical-affirming register. Conclusions: Evaluated LLMs demonstrated distinct, specialized strengths: Gemini was the most accurate, ChatGPT the most readable, and DeepSeek the most actionable. Importantly, all models failed to meet established consumer education standards for readability and actionability. LLMs require extensive plain-language adaptation and cultural customization. Clinicians must guide families on navigating LLM outputs, particularly concerning country-specific epidemiological, economic, and healthcare service queries. Full article

Autonomous Cyber–Physical Systems (CPS) must jointly satisfy energy efficiency, accuracy, and real-time constraints, which are typically treated separately in existing methods. This paper proposes a verifiable service-oriented CPS framework for energy-aware autonomous navigation using a high-fidelity cyber–physical twin. The approach integrates physics-based Model Predictive Control (MPC) with explicit power modeling ($P=F·v$) and Dubins curve-based trajectory generation under the 5C (connection, conversion, cyber, cognition, and configuration) architecture using CARLA for synchronized cyber–physical interaction. The proposed method achieves 30.7% reduction in mean power consumption and 12.5% reduction in total energy usage while maintaining sub-centimeter tracking error (<0.05 m). Mission duration increases by 26.3% with only 7% computational overhead, confirming real-time feasibility. The framework provides a verifiable CPS methodology that unifies physics-based control, digital twin synchronization, and service-oriented design for energy-aware autonomous navigation. Full article

This article examines how pilgrimage to sacred sites is being reconfigured through digital navigation systems and how these transformations intersect with the digital literacy challenges faced by ageing pilgrims. Situating the study within broader discussions on digital religion and pilgrimage, it explores how access to holy spaces is increasingly mediated by platforms that regulate verification, mobility, and ritual logistics prior to physical arrival. Focusing on Nusuk as a case study, the article examines Saudi Arabia’s official digital platform for Hajj and Umrah services at the holiest sites of Islam, through which pilgrims access registration, package selection, permits, booking, payment, guidance, transportation coordination, and various related pilgrimage services. The article draws on interdisciplinary analysis of existing research on pilgrimage, digital Islam and the larger digital divide. It argues that contemporary pilgrimage now begins within digitally governed environments, where participation depends on the ability to navigate interfaces, authentication systems, and algorithmic processes. While these technologies are framed as enhancing efficiency and safety, they simultaneously presuppose forms of access, literacy, and adaptability that many older pilgrims do not hold, thereby producing new dependencies and, in some cases, barriers to active participation. The study demonstrates that digital mediation reshapes both agency and sacred space, transitioning authority from embodied and communal practices to device-based guidance and automated systems. It concludes that contemporary holy cities must be understood as ethical infrastructures in which questions of access, care, and inclusion are central and proposes a “digital theology of pilgrimage” and ageing that calls for greater responsibility among religious institutions, communities, and platform designers to address the digital divide. Full article

Urban service robots require route planners that are not only collision-free but also consistent with the energetic behavior of differential drive locomotion. Conventional grid planners such as A* are efficient and reliable for geometric navigation, but their usual cost structure prioritizes path length and does not explicitly account for heading changes, concentrated turns, or localization risk near obstacles. This study presents an energy-aware route-planning formulation for differential-drive mobile robots operating in dense polygonal urban environments. The path is encoded through twelve continuous internal waypoints and is evaluated using an interpretable energy proxy that combines translational distance, cumulative absolute rotation, squared rotation, and a clearance-dependent localization risk term. Collision avoidance, boundary compliance, and maximum turn feasibility are handled through a feasibility-first dominance rule, and the resulting constrained problem is solved using a Genetic Algorithm and Particle Swarm Optimization. A* with clearance inflated occupancy grids is included as a deterministic baseline. The final experiments used a dense urban scenario with sixteen polygonal obstacles, an A* grid resolution of 0.10 m, a robot radius of 0.20 m, a safety clearance of 0.10 m, 80 GA individuals, 80 PSO particles, 100 iterations, and 10 independent runs. All methods achieved a 100% feasibility rate. PSO obtained the lowest average energy proxy, 18.505, compared with 18.645 for A* and 18.645 for GA, and reduced average rotation from 4.136 rad to 4.079 rad. However, A* remained much faster, 5.860 s on average, compared with 174.574 s for GA and 175.869 s for PSO. The ranking analysis shows that GA produced the best aggregate score when route quality, time, clearance, and feasibility were weighted equally, while PSO produced the best route quality. External perturbation tests indicate that open-loop execution in narrow corridors is sensitive to bias and waypoint noise, which motivates closed-loop tracking, online replanning, and physical validation in future work. Full article

Large language models (LLMs) are increasingly discussed in digital government research, but existing studies remain fragmented across application opportunities, technical performance, and governance risks, with limited synthesis of how they support service provision within digital government service processes. Using the concept of interpretive support, this study examines: (1) what forms of interpretive support LLMs provide; (2) what task- and service-level effects are reported; and (3) what governance conditions shape responsible integration. Drawing on a socio-technical systems perspective, the study conducts a PRISMA-informed systematic review and thematic synthesis of 60 studies from the Web of Science Core Collection and Scopus. The findings show a shift from stand-alone question answering to broader forms of interpretive support, including rule explanation, service navigation, complaint interpretation, issue routing, and back-office knowledge structuring. The strongest evidence concerns efficiency, responsiveness, and accessibility, whereas claims about trust, accountability, and wider public value remain less well supported. The review concludes that public value is conditional rather than automatic, depending on reliability, legal boundaries, responsibility allocation, data security and privacy, and organizational capacity. Future research should examine whether these service-level gains persist in routine service environments and how governance mechanisms affect service quality, equity, and accountability. Full article

Accurate long-term prediction of GNSS satellite clock errors is essential for autonomous navigation, real-time precise point positioning (PPP), and continuous positioning, navigation, and timing (PNT) services when real-time clock products are unavailable or delayed. However, conventional methods, such as quadratic polynomial fitting and Kalman filtering, have limited capability in modeling nonlinear and non-stationary clock behaviors over long prediction intervals, especially under abnormal onboard atomic clock conditions. To address this issue, an inter-satellite comparison data (ISCD)-based radial basis function neural network (RBFNN) model is proposed for long-term satellite clock error prediction. Through correlation analysis, reference satellite clocks closely related to the target satellite are selected, and both ISCD and satellite-ground comparison data are integrated to establish a nonlinear prediction model. Experiments using GPS and Galileo satellite clock datasets demonstrate that the proposed method significantly improves long-term prediction accuracy. For 180-day prediction, the proposed model reduces the RMSE by more than 60% for GPS satellites and approximately 99% for Galileo satellites with abnormal clock behavior compared with conventional methods. Rolling prediction experiments further verify the robustness and stability of the proposed model. Full article

The growing emphasis on 21st-century skills underscores the importance of multiliteracies for pre-service English language teachers in navigating diverse, digital, and multimodal classrooms. However, research on their perceived multiliteracies and associations with demographic factors remains limited. Thus, this study bridges this gap by determining how pre-service English language teachers perceived their multiliteracies using Teacher Multiliteracies Scale (TMS) encompassing four key dimensions: multimodal literacy (ML), digital literacy (DL), critical literacy (CL), socio-cultural literacy (SCL), and analyzing the variations based on demographic characteristics involving a total of 393 pre-service English language teachers. The result showed that the participants generally perceived themselves as moderately competent across all the dimensions, with DL dimension scoring the highest, followed by SCL, ML, and CL dimensions. Interestingly, no significant differences were found for gender, age, or academic level. Further regression analysis confirmed that teaching experience was modestly associated with ML, CL, and SCL dimensions, whereas DL showed no significant variations across the demographic variables among pre-service English language teachers. These findings suggest that teacher education programs may consider providing more authentic teaching experiences and practice-based learning opportunities to support multiliteracies development. Full article

Visually impaired persons (VIPs), including blind people, face persistent barriers when shopping in conventional retail environments, such as store navigation, product identification, price verification, and checkout. Although the number of assistive technologies addressing these challenges is increasing, their usability and their ability to support the entire shopping experience remain limited. This review analyses peer-reviewed literature published between 2003 and 2025 on assistive solutions for in-store shopping by VIPs. The systems reviewed are classified according to their dominant technological approach, including tag-based solutions such as RFID and NFC; computer vision marker-based systems using barcodes, QR codes, or AR markers; computer vision non-marker-based systems; and hybrid solutions. Beyond technical functionality, the review examines supported shopping tasks, interaction demands, infrastructural dependence, usability, and validation with end users. The analysis shows that current solutions often support only isolated parts of the shopping journey, depend on modified store infrastructure or reliable product databases, and are insufficiently evaluated with VIP users in real retail environments. Human–computer interaction factors, including cognitive load, trust, discretion, feedback modality, and compatibility with familiar devices, emerge as critical for adoption and practical usefulness. Future research should therefore prioritise scalable, user-centred, discreet, and low-burden solutions that require minimal environmental modification while supporting the shopping process more holistically. Such approaches are essential for improving autonomy, dignity, and equitable access to everyday retail services for VIPs. Full article

Unmanned Aerial Vehicles (UAVs) increasingly operate in missions requiring the simultaneous satisfaction of multiple objectives: reaching task locations, performing the correct service, and preserving sufficient onboard energy for continuous operation. Mission efficiency depends not only on task completion but also on managing the trade-off between service duration and battery recharging. This work proposes a Double Deep Q-Network (DDQN) policy for energy-aware UAV navigation on graph maps. The UAV must first collect the appropriate servicing tool from a depot node and then deliver it to the active failure node. At the same time, it autonomously decides when to interrupt the mission for recharging so as to ensure sufficient battery reserve throughout continuous operations, while minimizing task-servicing duration. The key contribution is an energy-aware reward based on a Dynamic Battery Threshold (DBT) computed from graph shortest-path distances to the nearest charging station, enabling a topology-aware recharge policy that is safer yet less conservative than a per-map tuned safety margin. Extensive Monte Carlo tests on increasingly complex graphs show that the proposed policy achieves a 100% task completion rate with always sufficient final battery to reach a charging node from the task node, while degrading less with map complexity and exhibiting greater robustness to stochastic battery dynamics than a pseudo-optimal baseline. Full article

Educational environments, particularly those with limited resources, require affordable mobile robots capable of combining human–robot interaction, autonomous assistance, and academic support without continuous dependence on cloud services. This work presents a low-cost ROS2-based mobile robot implemented on a Raspberry Pi 4B to provide educational assistance in Spanish within controlled classroom environments. The system integrates voice interaction, text-to-speech synthesis, YOLOv8n-based object perception, a specialized door detection model, ultrasonic and inertial sensing, differential-drive control, and a hybrid natural language processing architecture based on semantic caching, local inference, and optional cloud connectivity. Two task-dependent operating modes, education and navigation, selectively activate ROS2 nodes to reduce computational load and energy consumption. Experimental tests conducted in a university classroom evaluated speech recognition, vision models, natural language processing alternatives, sensor behavior, and battery life. The speech recognition module achieved 98% accuracy under both quiet and noisy conditions. YOLOv8n achieved an F1-score of 0.975 for common classroom objects, while the specialized door detector achieved 100% recall with 58.7% precision. The semantic cache correctly resolved recurrent academic queries in the exact-match evaluation, with an average latency of 3.8 s, reducing the need for external language models in known-question scenarios. The robot operated for 96 min in education mode and 75.6 min in navigation mode. These results demonstrate that Spanish voice interaction, reactive navigation, academic question answering, and resource-aware operation can be integrated into a single low-cost edge robotic platform for educational environments. Full article

With the rapid development of artificial intelligence and Internet of Things technologies, smart libraries increasingly require low-latency and energy-efficient computing support for heterogeneous services such as access control, intelligent recommendation, indoor navigation, and book localization. To address the limitations of cloud-only processing, this paper investigates task-offloading optimization in a cloud-assisted mobile edge computing environment for smart library services. A three-tier cloud–edge–device collaborative architecture is first established, and the task-offloading problem is formulated as a multi-objective optimization problem that jointly minimizes task-completion delay and user-side energy consumption under latency, resource-capacity, and coverage constraints. To solve the dynamic decision-making problem, a preference-adaptive dueling double deep Q-network algorithm, termed PA-DDQN, is proposed by integrating preference conditioning, multi-head attention, a dueling architecture, and double Q-learning. Simulation results show that PA-DDQN achieves better performance than fixed offloading strategies and representative reinforcement-learning baselines. Under the heaviest task load, PA-DDQN reduces the average task-completion delay by 23.1% and 31.0% compared with D3QN and DDQN, respectively, while reducing energy consumption by 5.8% and 9.9%. It also improves the task success rate by 14.8% and 21.7%, demonstrating its effectiveness in enhancing service responsiveness, energy efficiency, and reliability in smart library MEC systems. Full article