Search Results (1,476)

Effective laboratory training is essential in engineering education, yet conventional on-site instruction is often constrained by time, accessibility, and safety considerations. To address these challenges, this study presents the design, implementation, and evaluation of a web-based virtual reality (WebVR) representation of a large-scale engineering laboratory constructed from massive colorized point cloud data. This study proposes a novel WebVR approach that integrates Unity and Potree for high-fidelity point-cloud visualization combined with advanced interactive capabilities in a browser-based virtual laboratory. It supports immersive first-person exploration, guided navigation, interactive hotspots conveying equipment and safety information, and emergency evacuation simulations. The usability, usefulness, and acceptance of the virtual laboratory were evaluated through an anonymous questionnaire administered to students and laboratory staff. User evaluation results indicated consistently positive feedback, with 100% of respondents rating the interface/navigation and visual/interactive content as good or excellent, 88.6% identifying scene realism as the biggest system strength (the most frequently selected), 74.3% reporting significantly higher engagement compared with traditional online laboratory training, and 82.9% indicating they would definitely recommend the system as a learning resource. In addition, a thematic analysis of qualitative feedback was performed to inform future enhancements of the WebVR environment. Overall, the findings demonstrate that the WebVR-based virtual laboratory can effectively complement conventional on-site laboratory instruction, offering a scalable, accessible, and low-risk platform that enhances learning experiences in engineering education. Full article

Autonomous ground vehicle navigation in dynamic real-world environments demands path planning systems that simultaneously accommodate real-time environmental hazards and diverse user-defined objectives requirements that classical algorithms, with their static, single-objective cost functions, cannot fulfil. This paper presents the Semantic Personalised Path Planning (SPPP) system, centred on a novel Semantic Personalised Cost (SPC) algorithm that augments the A* search framework with a dynamically computed personalised cost term. The SPC function integrates eight real-time semantic obstacle categories including traffic congestion, weather severity, road surface conditions, and construction activity with eight user-defined preference dimensions spanning safety, travel time, emergency response, comfort, and battery efficiency. An adaptive scaling mechanism amplifies obstacle penalties near the goal, and a gradient-based weight evolution rule refines preference weights iteratively over successive route segments. The user-defined preference activation directly personalises the routing objective to individual operational needs, with the gradient-based evolution further refining preference alignment over successive route segments. Experiments were conducted in two phases: 500 randomised obstacle configurations on a controlled 8 × 8 grid, and a real 847-node road graph extracted from OpenStreetMap around SRM Institute of Science and Technology, Kattankulathur, representing a single 1.4 km urban corridor, with obstacle scores derived from live Mapbox Traffic and OpenWeatherMap application programming interface data. Under the full emergency preference scenario, SPPP achieves 94.3% obstacle avoidance versus 31.7% for the Euclidean distance threshold A* baseline, a difference statistically significant at p < 0.001 under the Wilcoxon signed-rank test with Cohen’s d ≈ 18.9. Real-world computation time of 1.91 ms on a standard laptop and 3.76 ms on a Raspberry Pi 4 confirms deployability on embedded autonomous vehicle hardware. Full article

Economic challenges are a latent reality in emerging economies such as Peru, and the growth capacity of entrepreneurs depends largely on certain factors, such as education and financial inclusion. To delve deeper into these factors, this study aims to analyze the association between micro-business performance, education, and financial inclusion, as well as to evaluate the mediating role of financial inclusion in the association between financial education and micro-business performance. The study was of an explanatory design. The research focused on owners, business owners, general managers, and other administrators of micro-businesses who could provide information on the performance of the companies. The results showed a statistically significant positive association between micro-business performance, education, and financial inclusion. It was also proven that financial inclusion is positively associated with micro-business performance, and it was also proven that financial inclusion has a mediating role in the association between financial education and micro-business performance. While these relationships are meaningful, the moderate explanatory power of the model (R² = 0.370–0.488) suggests that financial education and financial inclusion are important but partial contributors to business outcomes in this context. In conclusion, entrepreneurs with stronger financial knowledge appear to be better positioned to navigate business challenges and leverage financial systems, which may contribute to improved micro-business performance indicators. Full article

Background: Endometrial cancer outcomes differ among Black women when examined by nativity, and timely evaluation of postmenopausal bleeding (PMB), the most common presenting symptom, may contribute to these disparities. Methods: This qualitative study explored cultural, societal, and behavioral factors shaping PMB appraisal and anticipated care-seeking among US-born Black, Caribbean-born Black, and Haitian Creole-speaking women in South Florida, guided by the Safer–Andersen Model of Total Patient Delay. Ten focus groups were conducted with 55 Black women aged ≥50 years recruited through purposive and snowball sampling. Discussions were held in English or Haitian Creole, audio-recorded, professionally transcribed, translated when needed, and analyzed thematically using a hybrid deductive–inductive approach. Reporting followed the Consolidated Criteria for Reporting Qualitative Research. Results: Three themes emerged: limited awareness and information-seeking regarding menopause and PMB; cultural and societal influences, including faith-based coping, traditional remedies, and limited family discussion of health history; and healthcare system barriers, including cost, lack of insurance, distrust, and communication challenges with providers. Subgroup differences were noted in preferred information sources, perceived susceptibility, and the role of religion in care-seeking. Conclusions: Findings suggest that PMB appraisal and anticipated care-seeking vary by nativity and language among Black women. Nativity- and language-tailored community education and navigation strategies may improve symptom recognition and support timely evaluation, but future quantitative studies are needed to test whether these approaches reduce pre-diagnostic intervals for endometrial cancer. Full article

Artificial Intelligence (AI) is emerging as a transformative enabler in aviation, with applications spanning Guidance, Navigation and Control (GNC), Air Traffic Management (ATM), and predictive maintenance. However, the adoption of AI in safety-critical domains remains constrained by the absence of established certification guidance. Traditional standards such as Aerospace Recommended Practice (ARP), ARP4754B, ARP4761A, DO-178C, and DO-254 assume deterministic behaviour and verifiable logic, whereas AI exhibits adaptive and non-deterministic characteristics. Regulatory initiatives, including the European Union Artificial Intelligence Act, the European Union Aviation Safety Agency (EASA) AI Roadmap 2.0, the Federal Aviation Administration (FAA) AI Safety Assurance Roadmap, and ISO/IEC Technical Report (TR) 5469:2024, signal progress but remain fragmented, exploratory, and often limited to low-level autonomous use cases. This study adopts a qualitative approach combining literature and standards analysis with expert interviews to identify gaps in post-deployment assurance, data governance, explainability, and accountability. A conceptual life cycle-oriented framework is proposed that embeds AI-specific assurance activities such as dataset validation, iterative verification, drift detection, and retraining oversight into established certification processes. The framework extends classical and emerging verification and validation models into operational service, linking machine learning constituents to system-level safety arguments and regulatory expectations to support the development of trustworthy and certifiable AI-enabled aviation systems. Full article

The Northern Sea Route (NSR) is becoming increasingly accessible as Arctic sea ice declines, motivating data-driven forecasts of seasonal navigability. We compiled a 13-year (2013–2025) monthly dataset of AMSR2 sea ice concentration (SIC) and ERA5 atmospheric reanalysis variables over the NSR corridor (68–80° N, 30–180° E) and benchmarked a hierarchy of forecasting models for 1-, 3-, and 6-month lead times. Baselines (climatology, persistence, anomaly persistence, SARIMA, ridge regression) were compared with compact deep learning architectures (LSTM, Transformer; 10,000–70,000 parameters) trained on 12-month sequences with anomaly targets and five-seed ensembles. Three findings emerge. First, the seasonal cycle explains 98.0% of the monthly SIC variance, so climatology alone yields RMSE = 4.56% and three-class navigability accuracy of 87.5%. Second, SARIMA, the compact LSTM ensemble, random forest, and MLP_small all yield small positive skill scores over climatology: SARIMA achieves the lowest 1-month RMSE (3.98%, skill score +0.239), while the compact LSTM ensemble shows positive skill at all horizons (mean skill score +0.038); however, the bootstrap confidence intervals overlap and these differences are not statistically distinguishable from climatology. Third, all skilful models converge to identical classification metrics (accuracy 0.875, macro-F1 0.78, κ = 0.76); McNemar tests and overlapping bootstrap confidence intervals show no statistically significant differences. Permutation importance confirms that AMSR2 ice-state features dominate, whereas the high raw correlations of ERA5 radiation variables collapse after detrending. These results indicate that compact statistical and deep learning models are equivalent for NSR seasonal navigability prediction and that honest baseline comparison is essential when seasonal cycles dominate. Full article

Frequent reservoir navigation and the deployment of coastal refueling stations pose a potential risk of sudden petroleum contamination to the raw water in reservoirs, necessitating effective containment strategies. This study uses a river flow model and computational fluid dynamics (CFD) with a three-phase volume of fluid (VOF) approach to investigate vortex generation and synergistic oil removal mechanisms for containment drums and nets under varying submersion depths, flow velocities, and layout configurations. The simulation identifies a critical flow velocity for oil droplet entrainment failure of 0.23 m/s. A drum submersion depth of 1.5 cm generates stable upstream and downstream vortices that maximize oil–drum contact, whereas increasing the depth to 3.0 cm causes downstream vortex detachment, reducing contact time and leading to failure. For containment nets, the vertical double-layer deployment creates a low-velocity storage zone between layers, forcing oil to breach two barriers, while vertical tiling generates a static wall effect that prolongs oil residence time. In the combined drum–net system, the favorable vortex areas generated by both devices can be fully utilized to improve oil spill control. These findings demonstrate that optimizing drum submersion depth and net configuration significantly enhances oil containment efficiency, providing guidance for emergency response in source water reservoirs. Full article

As modern power systems undergo a paradigm shift toward decentralization, driven by substantial investments in Distributed Energy Resources (DERs), Virtual Power Plants (VPPs) have emerged as the primary mechanism for their effective technical and commercial integration. This paper provides a seminal and comprehensive literature review, dissecting the VPP ecosystem through operational, infrastructural, and coordination strategy perspectives. By categorizing VPPs into distinct technical and commercial frameworks, this study critically evaluates their role in optimizing smart grid components, including demand response, multifaceted market structures, cooperative game-theoretic behaviors, and multi-carrier energy systems. The analysis transcends basic infrastructure, focusing on the resolution of fundamental challenges: mitigating carbon emissions and energy costs, characterizing generation uncertainty and asynchrony, and maintaining the dynamic equilibrium between supply and demand. Furthermore, the review explores advanced strategies for incentivizing prosumer engagement, enhancing market pricing transparency, and ensuring transaction integrity within rigorous operational constraints. A significant methodological evolution is identified, highlighting the transition toward advanced mathematical frameworks and data-driven optimization techniques designed to enhance system resilience and operational stability under multifaceted uncertainties. The synthesis reveals that VPP-led sector coupling integrating electricity, thermal, and hydrogen vectors provides a robust pathway for minimizing grid imbalances and diminishing the overall carbon footprint. By evaluating the subject through a multidimensional lens—technical, economic, environmental, and regulatory—this study serves as a critical reference and strategic roadmap for researchers, planners, and policymakers aiming to navigate the complexities of future smart grids and build a sustainable energy ecosystem. Full article

Background: Infants discharged home from a neonatal intensive care unit (NICU) often have multiple ongoing medical needs. The Coached, Coordinated, Enhanced Neonatal Transition (CCENT) program provides nurse navigator-led support for caregivers of high-risk infants through their first year after transitioning from the NICU to home. The objective was to compare health care resource use and costs between CCENT and standard care control groups post-discharge. Methods: Resource use and costs were collected at 4 months and 12 months post-discharge from families enrolled in the CCENT randomized controlled trial across Canada. Infant healthcare utilization and parent mental health service use and costs were analyzed from public health care system and family payer perspectives and were compared statistically between groups and within groups over time. Results: A total of 97 and 105 infants were randomized to the intervention and control groups, respectively. Significant reductions in use of medications and equipment were observed over time in both groups while use of allied health professionals decreased and emergency department (ED) visits increased for CCENT. Annual total healthcare costs per child to the public payer were $4135 (95% CI $2825, $5709) for the CCENT group and $4578 (95% CI $2246, $8356) for controls. The cost of delivering CCENT was $669 per family (SD $362). The average annual out-of-pocket cost per family was $724 (95% CI $467, $1024) for CCENT and $728 (95% CI $479, $1007) for controls. Conclusions: This study indicates the importance of considering patterns of healthcare utilization, program costs and costs to families when implementing NICU to home care interventions. Excluding the cost of a nurse navigator, costs to the healthcare system were not increased in the intervention group. Such a program may help families access appropriate care. Full article

This study examines the relationship between climate policy uncertainty (CPU) and residential housing prices across U.S. metropolitan areas using the U.S. CPU index developed by Gavriilidis in 2021 and monthly S&P CoreLogic Case-Shiller Home Price Indices, covering January 1991 to May 2024. Employing a Fourier-augmented Toda–Yamamoto causality framework that accounts for both abrupt and gradual structural breaks, we document significant CPU → housing prices transmission in multiple metropolitan markets, with bidirectional transmission dynamics emerging in Los Angeles, New York, San Diego, and San Francisco, as well as at the U.S. national level. The results reveal substantial spatial heterogeneity across various market types. Coastal high-exposure markets exhibit strong CPU sensitivity, which may reflect the influence of physical climate risks and regulatory uncertainty; inland growth markets display housing prices → CPU feedback, likely operating through political economy channels; Midwest extreme-weather markets show persistent transmission despite their non-coastal locations; recession-sensitive markets become CPU-responsive following the Great Recession; and insulated markets show no significant transmission. The findings indicate that CPU operates as a priced systematic risk factor requiring integration into housing finance oversight, macroprudential frameworks, and investment strategies. These results have important implications for financial stability monitoring, mortgage credit risk assessment, and climate policy design as markets navigate transition risks in a low-carbon economy. Full article

This study advances understanding of how Catholics and Protestants in the United States reconcile capitalist ideology, religious commitment, and sustainability orientation, a triadic relationship largely unexplored in existing scholarship. By integrating System Justification Theory with multidimensional approaches to religiosity, we demonstrate that Economic System Justification and Fair Market Ideology operate through distinct cognitive and motivational logics with divergent implications for sustainability. Religiosity emerges as a demographically contingent moderator, reshaping how market ideology translates into sustainability attitudes differently across age cohorts and income strata. The study extends System Justification Theory by establishing the theoretical independence of defensive system-preserving motivations from proactive market beliefs, while reconceptualizing religious commitment as a conditional mechanism activated under specific biographical and material circumstances rather than a uniform force. Crucially, spiritual resources retain capacity to reorient believers’ navigation of economic participation and sustainability responsibility, illuminating how moral frameworks rooted in religious tradition counterbalance market logic’s encroachment upon value systems. These insights offer pathways for faith communities, sustainability practitioners, and policymakers seeking to foster sustainable attitudes through demographically calibrated interventions that leverage the ethical scaffolding religious commitment provides. Full article

Depression has emerged as a prevalent psychological disease among older people, causing severe disruption to patients and society. Lighting in the built environment plays a powerful role in relieving depression. However, the varied impacts of different lighting effects and specific areas have not been clearly clarified or effectively implemented. This study established a Lighting Effects–Depression Model and investigated relationships between lighting effects in areas of care and attention homes and depression in older people. A quantitative survey was conducted through face-to-face interviews with older residents from care and attention homes. On-site observations were used to investigate lighting conditions and installations. Lighting effects (uniformity, navigation, colour, glare) of bedrooms, corridors, and dining rooms on depression (lack of energy, sleep disorders, task performance decline, low life satisfaction, low self-satisfaction, negative feelings) were analyzed. The results indicated significant influences of colour improving task performance, while glare induced sleep disorders across all areas examined. Furthermore, navigation and uniformity in bedrooms positively impacted depression via self-satisfaction and life satisfaction. Recommendations for utilizing lighting effects were proposed to relieve depressive symptoms in older people in care and attention homes. Full article

Mine disasters require urgent lifeline setup in confined tunnels, but manual rescue in unstable accident zones carries huge safety risks. Coal mine rescue robots (CMRRs) have become key equipment to replace manual rescue. However, traditional remote-controlled CMRRs suffer from low autonomy and weak environmental perception capability, which have become critical bottlenecks for field application. As an emerging technology in the mining field, digital twin enables high-precision virtual-real mapping and on-site operation guidance, providing a novel solution to the above problems. To realize autonomous navigation and digital twin visualization of the CMRR, this paper first carries out targeted hardware retrofits on the CMRR platform, upgrades environmental perception, communication transmission and motion control modules, and lays a solid hardware foundation for subsequent algorithm design and system implementation. Aiming at the complex post-disaster underground environment, a digital twin-integrated CMRR system is constructed. For intelligent autonomous navigation, this study investigates a 3D point cloud–based autonomous navigation framework and proposes a slope-fitting method as well as a maximum arrival probability obstacle avoidance method based on Bézier curve trajectories. For environmental visualization, a digital twin interactive interface is built to monitor gas and other environmental parameters in real time, and accurately reconstruct underground roadway structures based on point cloud data. This design not only ensures the robot’s autonomous obstacle avoidance but also helps rescuers grasp underground conditions in advance. Field tests in a simulated post-disaster mine with complex terrain show that the system can stably complete autonomous navigation tasks, maintain stable motion control under dynamic interference, and provide accurate and reliable environmental data for rescue decisions, verifying its feasibility and effectiveness in harsh mine rescue scenarios. Full article

Equity in gifted education remains a persistent international challenge, particularly regarding the “excellence gap” in advanced achievement and long-term attainment. This study investigates the supports and barriers shaping the talent development of socio-economically disadvantaged gifted students in Taiwan. Using a retrospective narrative inquiry, we analyzed the life stories of 25 alumni from the “Bright Minds Award Program,” a long-term initiative providing financial aid, mentorship, and enrichment opportunities for high-ability learners from low-income households. Findings indicate that participants often displayed early academic promise, yet their developmental trajectories were continuously negotiated under structural constraints (limited material and cultural resources, restricted access to domain-specific cultivation, and opportunity gaps across educational transitions) and the psychosocial burden of poverty (shame, stigma management, and identity strain). Drawing on the Actiotope Model of Giftedness, we identify how exogenous educational capital (e.g., scholarships, information brokerage, mentoring networks) and endogenous learning capital (e.g., resilience, self-regulation, goal persistence) interact to stabilize—or destabilize—developmental pathways. A novel contribution is the emergence of “Acting Middle Class” as a coping mechanism through which participants navigated social stigma and the hidden curriculum of elite educational settings. We argue that effective intervention requires not only resource provision but sustained “educational scaffolding” that is psychologically safe and institutionally stigma-sensitive. Implications are discussed for talent development research, school practice, and equity-oriented policy designs aimed at preventing talent attrition and promoting developmental justice. Full article

In complex marine environments, achieving low-cost, highly reliable, and continuous navigation is crucial for the intelligent and autonomous operation of unmanned surface vehicles (USVs). Currently, the integrated Global Navigation Satellite System and Strapdown Inertial Navigation System (GNSS/SINS) serves as the primary navigation architecture for USVs. While the cost of high-performance GNSS receivers has steadily decreased, high-precision SINS remains prohibitively expensive. Consequently, micro-electromechanical system (MEMS)-based SINS has emerged as a preferred alternative due to its favorable balance of cost, power consumption, and size. However, significant inertial sensor errors make it difficult to maintain high-precision positioning during GNSS outages. To address this limitation, the single-axis rotational inertial navigation system (SRINS) has been introduced. Nevertheless, constrained by the single-axis mechanical structure and complex sea state disturbances, the system still struggles to effectively modulate random errors and azimuth gyroscope drift, rendering it insufficient for highly demanding navigation tasks. To overcome these bottlenecks, this article systematically reviews four core technologies: (1) Comprehensive denoising and temperature drift compensation techniques for MEMS gyroscopes; (2) rapid moving-base initial alignment models under high sea state disturbances; (3) fast online calibration methods for azimuth gyroscope drift; and (4) adaptive and robust GNSS/SINS integration architectures capable of accommodating high-dynamic conditions and non-Gaussian interference. Finally, this article discusses the engineering conflict between deploying high-precision algorithms and the limited onboard computational capacity of USVs. It concludes by highlighting a highly promising navigation paradigm for future research: the integration of factor graph optimization with physics-informed deep learning. Full article