Search Results (2,789)

Arthrogenic muscle inhibition (AMI), neurophysiological suppression of voluntary quadriceps activation triggered by joint effusion and inflammation, is consistently initiated within hours of any form of knee surgery. If not actively counteracted during the first two postoperative weeks, AMI may drive a cascade of neuromuscular, morphological, and biomechanical deficits that can persist for years, substantially increasing the risk of post-traumatic osteoarthritis, reinjury, and long-term functional disability. Emerging evidence indicates that preoperative patient-related factors, including baseline quadriceps strength, age, body mass index, and physical fitness, further modulate the rehabilitation response and should be considered in planning early postoperative protocols. This narrative review, which was not designed as a systematic review or meta-analysis and therefore does not include formal quality assessment or pooled statistical analysis, evaluates evidence for seven early-phase (0–2 weeks postoperative) knee muscle activation interventions: neuromuscular electrical stimulation (NMES), isometric quadriceps exercise, blood flow restriction (BFR) training, electromyographic (EMG) biofeedback, open and closed kinetic chain (OKC/CKC) exercise, cryotherapy, and continuous passive motion (CPM). Findings are synthesized against six clinically relevant dimensions, safety in the 0–2 week window, home-based usability, capacity to overcome AMI, requirement for volitional effort, objective monitoring capability, and progressive resistance, to characterize a consistent pattern: no single existing modality simultaneously meets all combined requirements for home deployment, volitional engagement, objective monitoring, and progressive resistance from postoperative day one. This collective unmet need provides direction for future device development and clinical research. Full article

The transition to electric bus (EB) fleets is a critical step towards sustainable urban transportation, offering substantial reductions in greenhouse gas and pollutant emissions relative to diesel buses. However, transit authorities face multifaceted challenges in this transition, including limited driving ranges of EBs, the need for widespread charging infrastructure, and potential strain on the electric grid, alongside opportunities such as governmental subsidies and increased fare revenues. This paper proposes a comprehensive multi-period mixed-integer programming model seeking to optimize long-term EB fleet transition plans in urban contexts while jointly accounting for all inherent financial, technical, and operational factors impacting such a transition. The model is operationalized using real data acquired from Dubai’s Roads & Transport Authority (RTA), encompassing 71 bus routes and a 25-year planning horizon to meet a 100% electrification target by 2050. A scenario-based analysis evaluates the robustness of the transition plans under variations in key operational parameters. The results illustrate that optimized long-term planning yields substantial cost savings and emissions reductions, where the incorporation of environmental and social externalities and revenue shifts causes profit maximization to emerge as a more appropriate objective. In addition, it turns out that adequate dwell time is crucial for cost containment and full fleet electrification feasibility. While RTA targets 100% electrification by 2050, the base case is deliberately relaxed to 90% as certain routes, notably double-decker lines, are incompatible with currently available EB configurations. Nevertheless, full electrification is restored under the minimum dwell scenario. Also, a policy of purchasing only EBs accelerates full fleet electrification by roughly a decade with only a marginal increase in total cost, unlike imposing strict interim electrification targets. The optimized transition plans provide actionable insights for transit authorities balancing economic efficiency with sustainability goals. Full article

The fifth annual pan-tumour Early Age Onset Cancer (EAOC) Symposium, held in November 2025 and organized by the Colorectal Cancer Resource & Action Network (CCRAN), convened clinicians, researchers, policymakers, patients, and caregivers to address the rising incidence of cancers in individuals under 50 years. In addition to discussions around diagnostic and therapeutic advances for patients with late-stage disease, content centered on addressing critical gaps along the EAOC care continuum, including (i) diagnostic delays related to limited awareness and suboptimal primary care pathways, (ii) screening eligibility criteria for colorectal cancer (CRC) that no longer reflect current disease epidemiology, and (iii) insufficient age-appropriate infrastructure to meet the EAOC population’s unique unmet needs with respect to psychosocial support, fertility counseling, financial navigation, and survivorship planning. The symposium generated consensus recommendations such as the embedding of EAOC education into medical training curricula to increase the index of suspicion of EAOC in primary care, lowering the CRC screening age to 45 years to match this population’s rising disease incidence, and expanding multidisciplinary adolescent and young adult (AYA) and EAOC programs—including through the use of virtual models—to ensure that patients receive coordinated, comprehensive, equitable and age-appropriate care across the country. Full article

Background/Objectives: The rising prevalence of multimorbid and complex chronically ill young and middle-aged adults necessitates the implementation of innovative care models and the creation of roles that can meet the complex healthcare needs of this patient group. Advanced Practice Nurses (APNs) can play a crucial role in the care of multimorbid and complex chronically ill young and middle-aged adults in APN-led clinics; however, in Switzerland, these roles are still evolving. The aim of this study was to explore APNs’ perspectives on the planned development of their roles in an APN-led clinic. Methods: To gain insights into the experiences of APNs in caring for this patient group, a qualitative study design was chosen. Data were collected through interviews with APNs from Switzerland, the USA, and Canada. In total, 19 APNs (12 from Switzerland and 7 from the United States and Canada) participated in the study. The data were collected through semi-structured online interviews. These data were analyzed using reflective thematic analysis in accordance with the approach presented by Braun and Clarke. Results: The analysis identified 10 themes that describe the competencies, components, and framework conditions required for the work of APNs in an APN-led clinic for multimorbid and complex chronically ill young and middle-aged adults within the Swiss clinical context. Required competencies include direct clinical practice, guidance and coaching, collaboration, and psychosocial support. Essential components include person-centered care, transitional care, and continuity of care. Key framework conditions include regulations of the legal and regulatory framework and eligibility for reimbursement of services, resources, and extended competencies and scope of practice. Conclusions: The perspectives of the APNs involved in this study show that multimorbid and complexly chronically ill young and middle-aged adults require complex and long-term care that extends beyond the hospital setting. The findings of this study show that Swiss APNs may be well positioned to contribute to this role. Full article

Robotic belt grinding is an effective and widely adopted finishing method for superalloys, offering notable advantages such as high material removal capability, low heat input, and reduced workpiece damage. In addition, robots can readily integrate multiple sensors—such as infrared radiation cameras, force sensors, and high-speed cameras—which facilitate real-time monitoring of the grinding process and thereby enhance grinding quality control. With the establishment and continuous advancement of large-scale artificial intelligence (AI) data models, new breakthroughs have emerged in the optimization of robotic grinding processes. Owing to its dexterous workspace and advantages in high flexibility and cost-effectiveness, robotic belt grinding has become a critical process for the precision forming of complex curved components such as aero-engine blades and blisks. However, factors such as the limited absolute accuracy of industrial robots, time-varying grinding contact states, and significant transient boundary effects make it difficult for the current constant-parameter open-loop machining mode to simultaneously meet the demands for high material removal efficiency and high surface integrity on complex profiles. This paper systematically reviews the technologies for precision control and process optimization of robotic belt grinding aimed at pointwise precise material removal. First, the structural composition of the robotic belt grinding system and the material removal mechanism are analyzed. Then, centered on the compensation concept, a hierarchical progressive technical framework is outlined, covering geometric calibration compensation, force/position hybrid online compensation, transient entry boundary compensation, and system-level comprehensive compensation of multi-source errors, with a comparison of the applicable scenarios and the effects on shape and property control at each level. Furthermore, under the support of effective compensation, the collaborative optimization methods of material removal modeling, multi-objective optimization of process parameters, force-constrained trajectory planning, and intelligent adaptive processes are elaborated. Finally, current technical bottlenecks are summarized, and future trends in next-generation adaptive grinding technology driven by digital twins and embodied intelligence are envisioned. This review aims to provide a systematic theoretical reference for the high-precision and intelligent upgrading of robotic precision grinding systems. Full article

Oil-dependent urban regions face persistent ecological and societal issues following extraction, including land degradation and infrastructural neglect. Despite the discourse on environmental justice and extractivism, a research gap exists regarding the transition of post-extractive cities from recognizing environmental harm to implementing territorial rehabilitation strategies. This study examines Poza Rica, Mexico, a critical city in the oil industry, as a case study for restorative justice and urban transition after extraction. Utilizing a qualitative case study approach with planning documents, technical reports, environmental regulations, spatial data, and community input, the research evaluates the territorial impacts of seventy years of oil extraction and explores restoration pathways. The results indicate a landscape characterized by abandoned wells, environmental liabilities, and the integration of former extraction zones into urban areas. In the Tampico–Misantla Basin, 49.5% of wells remain inactive, with only 2.7% meeting contemporary closure standards. In Poza Rica, nearly 98% of urban growth from 1997 to 2016 occurred in regions previously linked to oil extraction. The article posits that restorative justice in post-extractive cities necessitates more than mere financial restitution. It advocates for a territorial restitution framework centred on remediation, economic transformation, and community governance, illustrating how former extraction sites can evolve into assets for urban resilience and sustainable development. Full article

University students often intend to exercise regularly but fail to translate intention into action. The present study tested which post-intentional processes distinguish successful from unsuccessful intenders in self-reported regular leisure-time moderate-to-vigorous physical activity (MVPA) enactment. Chinese undergraduates from 10 universities completed a three-wave survey administered at roughly two-week intervals. Habit automaticity was assessed at Wave 1, intention together with action planning and coping planning at Wave 2, and self-reported regular-exercise status at Wave 3. Because the dependent variable was assessed using a single stage-based self-report item, the findings should be interpreted as explaining self-reported regular-exercise status rather than objectively measured MVPA volume, frequency, or intensity. Primary analyses focused on students classified as intenders under the prespecified threshold (n = 1119 of N = 1670) and used hierarchical logistic regression to predict Wave 3 active versus inactive status. Under the primary threshold, 43.23% of participants were successful intenders and 23.77% were unsuccessful intenders, yielding an intention-behavior gap of 35.48% among intenders. Confirmatory factor analyses supported treating action planning and coping planning as distinct constructs. Among intenders, stronger action planning, stronger habit automaticity, and stronger intention strength independently predicted greater odds of meeting the regular-exercise criterion at follow-up. Coping planning did not show unique predictive value once action planning, habit automaticity, and intention strength were considered simultaneously, and no planning × habit interaction was supported. The pattern was robust across three alternative intention thresholds. These findings suggest that, among already motivated university students, successful exercise enactment depends less on coping planning alone than on a combination of commitment, concrete scheduling, and emerging behavioral automaticity. Interventions for student physical activity may therefore benefit from emphasizing detailed action planning and repeated performance in stable contexts. Full article

In the ongoing data-rich era, intelligent cognition is playing an increasingly important role in advancing remote sensing applications. However, traditional intelligent methods for remote sensing processing no longer fully meet the growing demands of this era and still suffer from several limitations, such as passive data-dependent processing, predefined-task execution, and lack of closed-loop optimization. As a customized GeoAI innovation for remote sensing, Remote Sensing Agent has entered an early stage of research explosion. This paper focuses on its paradigm-shifting role in reshaping remote sensing information processing, clarifies the “4+1” core characteristics including multimodal spatial perception, goal-driven spatial mission planning, geoscientific knowledge reference, geospatial workflow execution, and feedback loop. It elaborates the threefold reshaping of remote sensing information processing from initiation mode, execution mode, and evaluation criterion, namely shifting from passive data processing to active task-driven, from predefined-task processing to multi-agent collaboration, and from result-oriented output to full-process closed-loop optimization. Future prospects of Remote Sensing Agent in geoscientific knowledge base optimization, multi-agent collaboration efficiency, and complex-scenario adaptability are discussed. This paper provides targeted and forward-looking perspectives for intelligent innovation research in remote sensing. Full article

In heavy-haul railway systems, effective empty railcar distribution (ERD) can optimize composition planning and meet empty railcar requirements (ERRs) at all loading ends, thereby improving the efficiency of train operations. To solve practical challenges such as the imbalanced supply–demand of empty trains, redundant loading and unloading cycles, and prolonged waiting times, this study establishes a multi-objective and 0–1 integer programming model for ERD at the loading end of a heavy-haul railway. The model can simultaneously maximize the fulfilment of all ERRs, minimize the ERD delay time, and reduce the waiting time in the heavy-train combination problem under complex constraints, including the passing capacity of sections, combination capacity of stations, and ERR at the loading end. While traditional optimization methods such as mathematical programming or heuristic algorithms partially address these issues, they are ineffective under dynamic constraints and state-space explosion. Furthermore, traditional reinforcement learning-based methods, such as Q-learning, exhibit limitations in railway scheduling due to the state-space explosion problem and inadequate model generalization. To overcome these limitations, this study proposes an innovative framework; the ERD at the loading end of the heavy-haul railway is formalized as a Markov decision process and optimized using deep Q-network (DQN) reinforcement learning. In addition, this study proposes an experience data fusion mechanism that integrates the empirical rules of the dispatchers through a modular architecture, achieving real-time constraint compliance while maintaining scalability for practical implementation. The NSGA-II genetic algorithm for multi-objective problems is used in this study to evaluate the performance of the DQN algorithm. The experimental results demonstrate that the DQN algorithm can fully meet ERRs with zero delay and produce optimal schemes for train combinations. Meanwhile, NSGA-II presents superior performance in minimizing the combination waiting time and same-destination train combinations. Meanwhile, the DQN algorithm can identify superior ERD strategies in the expanded-action and state spaces, enabling the effective handling of complex constraint-based ERD. Full article

Hydrogen (H₂) is regarded as a promising option for sustainable energy systems; however, its large-scale use in electricity supply remains limited. This study develops a stochastic network optimization model to examine the applicability of H₂-based electricity generation. The proposed Hydrogen Supply Chain (HSC) model evaluates cost and emission performance under uncertainty by considering disaster conditions, transmission losses, depreciation, and the time value of money. The Marmara Region of Türkiye is divided into 24 grid nodes, and a single-period model for 2023 is solved using Mixed-Integer Linear Programming (MILP). The HSC is allowed to meet 10–40% of electricity demand and to replace collapsed grid lines by supplying critical public centers (CPCs) during disasters. The results show that the HSC can meet 24.82% of demand, although at costs approximately 3.9 times higher than power grid (PG) electricity, while producing 3.44 MtCO₂/year compared to 65.96 MtCO₂/year from the PG. The model is then extended to a multi-period structure (2023–2053) and solved by Variable Neighborhood Search (VNS). Over time, H₂ costs decline, and their share rises from 19% to 35%, while electricity costs decrease from 408 USD/MWh to 170 USD/MWh. These findings suggest that H₂-based electricity supply can support long-term sustainability and resilience objectives in regional energy planning. Full article

To address the problems of state estimation bias, dynamic threat response lag, and insufficient safety margin in formation coordination caused by the mismatch between the three-dimensional continuous motion model and the discrete sampling characteristics of sensors in UAV multi-aircraft collaborative inspection missions under complex dynamic environments, this paper studies a trajectory planning method that integrates model predictive control and multi-constraint optimization. By constructing a three-dimensional continuous motion model of the UAV and discretizing it using the Euler integral method, the mapping deviation between the continuous motion characteristics and the discrete working mechanism of the airborne system is solved. Based on the model predictive control method, a patrol trajectory tracking planning model is designed, and state increment and integral augmentation strategies are introduced to transform global reference trajectory tracking into a constrained quadratic programming problem in the rolling time domain, achieving high-precision closed-loop tracking. Furthermore, a dynamic environment model coupling static terrain height field and sudden spherical threat is constructed to systematically characterize the static obstacles and random dynamic threats faced by the UAV in complex scenarios such as mountains and hills. On this basis, multiple constraints such as flight altitude, pitch angle, horizontal turning angle, terrain safety margin, and multi-aircraft collision avoidance are integrated to establish a comprehensive objective function that includes range cost, attitude penalty, and safety cost. Through a collaborative mechanism of global optimization and local online correction, a reference trajectory that meets the requirements of formation safety and flight efficiency is generated and used as the input command for the tracking planning model, forming a closed-loop architecture of global optimization generation, local closed-loop tracking, and dynamic real-time correction for trajectory planning. Experimental results show that the success rate of dynamic obstacle avoidance in complex dynamic environments is always higher than 99.9%, and the mean square error of trajectory tracking is stable in the range of 0.02–0.04 km, which verifies its significant advantages in dynamic adaptability, tracking accuracy and formation safety. Full article

Technical higher education in the Philippines is a government priority, as it equips learners with the technical knowledge and practical skills necessary to develop industry and technology-ready human capital. In parts of Mindanao, the Philippines, where socio-economic and technological challenges are significant, Industrial Technology Education (ITE) learners must be equipped with relevant, comprehensive knowledge of industry trends. A sequential explanatory mixed-methods design was employed in this research, combining quantitative surveys with qualitative interviews to provide a comprehensive analysis. Five hundred twenty-six learners participated in the survey, and six were selected for in-depth interviews. The findings highlight the significant impact of familial and peer support on fostering confidence, encouraging academic perseverance, and improving mental health. However, financial hardships and inadequate emotional support remain significant obstacles. The study emphasizes the importance of cultivating an inclusive campus atmosphere via awareness efforts, accessible services, and mentorship programs to guarantee fair educational opportunities. For this reason, the implementation of personalized education plans, flexible learning, digital access, academic and educational support, and an institutional support system is highly encouraged to address learners’ essential needs. The research findings also suggest integrating mentorship programs, adopting inclusive learning practices, developing an adaptable curriculum, and providing mental health support services for learners, particularly those with disabilities. By aligning the curriculum with industry specifications and standards and providing well-planned support frameworks, higher education institutions in Mindanao can produce graduates who are professionally qualified, highly skilled, well-mannered, and career-prepared, fully equipped to meet the demands of the dynamic workforce. Full article

With the rapid development of offshore wind energy toward deep water, the structural safety of truss-type legs for jack-up wind turbine installation vessels (WTIVs) under complex operational and environmental loads has become a key concern. This study focuses on the directional coupling effect between crane slewing angles and wave directions, which has rarely been systematically investigated in previous research. A finite element model of a 1500 t-class WTIV truss leg is established using SESAM, and its reliability is verified by mesh convergence analysis and literature comparison. The influences of crane slewing angle, wave direction, and their coupling on structural displacement and stress are analyzed quantitatively, and strength evaluation is carried out under typical working conditions in accordance with classification society rules. The results show that the structural response presents significant directional dependence and stiffness anisotropy. The peak displacement and stress occur at a crane slewing angle of 270°, with the maximum displacement approximately 33% higher than the minimum value. Obvious response amplification is observed when the crane slewing angle and wave direction are aligned within 225–270°, which constitutes the most unfavorable loading combination. The strength assessment demonstrates that all conditions meet the specification requirements, and the survival condition is the most critical, with a maximum stress of 289.66 MPa and a maximum displacement of 338.6 mm. This study reveals the coupling mechanism between operational loads and environmental loads and identifies the critical dangerous angle sector. The research findings can provide reasonable references for offshore lifting operation management and operational planning of marine truss leg structures. Full article

Achieving deep decarbonization of the residential building sector is essential for meeting Canada’s climate commitments and Net Zero targets. However, large-scale residential retrofit planning is often constrained by the time, cost, and expertise required for detailed building energy modelling. This study evaluates the applicability of a typology-based retrofit planning tool developed by Homes to Zero (HTZ) as a simplified alternative to conventional simulation-based analysis. Two representative Canadian residential archetypes—a detached bungalow and a two-storey semi-detached home located in Toronto—were analyzed using both the HTZ platform and detailed hourly energy simulations conducted in eQuest (DOE-2.2 engine). Baseline energy consumption and greenhouse gas (GHG) emissions were first compared across the two modelling approaches. Results show strong agreement for the bungalow case, with differences of less than 1% for electricity and natural gas consumption and approximately 4% for total emissions. For the two-storey dwelling, baseline electricity estimates were identical while natural gas consumption differed by approximately 17%, highlighting the sensitivity of physics-based simulations to envelope and operational assumptions. Retrofit scenarios were then compared using single-measure GHG reductions derived from HTZ and incremental simulation results from eQuest. While both tools identified electrification through air-source heat pumps as the dominant emission-reduction strategy, differences were observed in the magnitude of savings for envelope upgrades and secondary measures. The HTZ platform also provides approximate retrofit cost estimates, enabling order-of-magnitude budgeting, whereas eQuest requires separate costing analysis. This study is framed as a screening-level benchmark rather than a full validation exercise, highlighting the trade-off between scalability and modelling fidelity in residential retrofit planning. The results suggest that typology-based tools can provide credible screening-level guidance for residential retrofit planning and large-scale policy analysis, while detailed simulation remains valuable for evaluating integrated retrofit packages and design-level decisions. Full article

Fragmented transit governance across multiple agencies makes measuring service inequality in large metropolitan regions notoriously difficult. This paper maps schedule-aware transit service intensity—an origin-side, supply-focused component of accessibility—across the Greater Toronto Area (GTA) by integrating General Transit Feed Specification (GTFS) data from six providers within an H3 hierarchical hexagonal grid. The measure does not capture destination access, travel time, transfers, fares, reliability, or crowding, and is therefore framed throughout as a service-intensity indicator rather than a full accessibility evaluation. We operationalize the indicator as the number of cumulative scheduled departures per hour reachable within an 800 m walking catchment for three distinct time windows: weekday peak, weekday midday, and Saturday midday. Across 9635 hexagons and 23,026 stops, our results reveal a sharply hierarchical regional network. When weighted by population, 16.4% of GTA residents have no scheduled service within walking distance during the weekday morning peak; the corresponding area-weighted share, reflecting the extensive greenbelt and agricultural fringe, is 70.6%. Only 22.6% of hexagons reach at least 12 departures per hour, while 75.5% of residents meet that threshold. Median service intensity drops from 234.25 departures per hour in the Urban Core to zero beyond the Inner Suburban Ring, and service thins out on weekends, with retention in the outer rings dropping to roughly 75% of weekday levels. Spearman correlations show that service intensity is concentrated in denser, more diverse, and lower-income census-tract contexts, with population density emerging as the strongest hex-level correlate ($\rho =0.69$); after Clifford–Richardson correction for spatial autocorrelation (effective $n\approx 745$), the principal CT-level correlations remain statistically significant ($p<{10}^{-15}$), and partial correlations controlling for density indicate that socioeconomic composition retains an independent, if attenuated, association. Under one-tract-one-observation aggregation ($n=1144$ unique tracts), the income gradient strengthens to $\rho =-0.74$ and becomes co-equal in magnitude with population density ($\rho =0.74$), confirming that the hex-level coefficients are not artifacts of pseudo-replication. A population-weighted Gini coefficient of 0.60 confirms substantial distributional inequality. Sensitivity analyses confirm that the Inner-to-Outer Suburban break is robust to alternative ring thresholds (10/25/40 and 20/35/50 km), to exclusion of the four Halton municipalities affected by incomplete local-feed coverage, to H3 resolution at the municipal level, and—in a representative shortest-path network sub-analysis for Pickering (not a full GTA-wide network-distance test)—to use of network rather than Euclidean walking distance. These patterns suggest that a substantial gap exists between where suburban residential growth has occurred and where frequent transit service is available, a pattern with historical roots in the 1996–2006 service–need alignment, though the 2006–2023 trajectory is not directly measured here. The results suggest that the transition zone between the inner and outer suburbs may warrant further investigation as a planning focus, and that cross-agency weekend service coordination merits further analysis as a potential equity dimension. This multi-agency H3 framework establishes a reproducible baseline for monitoring schedule-aware service intensity in polycentric metropolitan areas. Full article