Search Results (124)

Person-centered navigation (PCN) in healthcare refers to a proactive collaboration among professionals, researchers, patients, and their families to guide individuals toward timely access to screening, treatment, follow-up, and psychosocial support. PCN—which includes professional, peer, and virtual guidance, is particularly crucial for rare cancers, where affected individuals face uncertainty, limited support, financial strain, and difficulties accessing relevant information, testing, and other services. The Canadian Cholangiocarcinoma Collaborative (C3) prioritizes PCN implementation to address these challenges in the context of Biliary Tract Cancers (BTCs). C3 uses a virtual PCN model and staffs a “C3 Research Navigator” who provides clinical and research navigation such as personalized guidance and support, facilitating access to molecular testing, clinical trials, and case reviews through national multidisciplinary rounds. C3 also supports a national network of BTC experts, a patient research registry, and advocacy activities. C3’s implementation strategies include co-design, timely delivery of support, and optimal outcomes across its many initiatives. Future priorities include expanding the C3 network, enhancing user engagement, and further integrating its innovative approach into routine care. Full article

Small towns in mountainous regions face significant challenges in formulating effective landscape zoning strategies due to pronounced landscape fragmentation, which is driven by both the dominance of large-scale forest resources and the lack of coordination between administrative planning departments. To tackle this problem, this study focused on Yuqian, a quintessential small mountainous town in Hangzhou, Zhejiang Province. The town’s layout was divided into a grid network measuring 70 m × 70 m. A two-step cluster process was employed using ArcGIS and SPSS software to analyze five landscape variables: altitude, slope, land use, heritage density, and visual visibility. Further, eCognition software’s semi-automated segmentation technique, complemented by manual adjustments, helped delineate landscape character types and areas. The overlay analysis integrated these areas with administrative village units, identifying four landscape character types across 35 character areas, which were recategorized into four planning and management zones: urban comprehensive service areas, agricultural and cultural tourism development areas, industrial development growth areas, and mountain forest ecological conservation areas. This result optimizes the current zoning types. These zones closely match governmental sustainable development zoning requirements. Based on these findings, we propose integrated landscape management and conservation strategies, including the cautious expansion of urban areas, leveraging agricultural and cultural tourism, ensuring industrial activities do not impact the natural and village environment adversely, and prioritizing ecological conservation in sensitive areas. This approach integrates spatial and administrative dimensions to enhance landscape connectivity and resource sustainability, providing key guidance for small town development in mountainous regions with unique environmental and cultural contexts. Full article

Background/Objectives: BRIGHTLIGHT was the national evaluation of adolescent and young adult (AYA) cancer services in England. BRIGHTLIGHT results were not available when the most recent healthcare policy (NHSE service specifications for AYA Cancer) for AYA was drafted and therefore did not consider BRIGHTLIGHT findings and recommendations. We describe the co-development and delivery of a Policy Lab to expedite the implementation of the new service specification in the context of BRIGHTLIGHT results, examining the roles of multi-stakeholders to ensure service delivery is optimised to benefit AYA patients. We address the key question, “What is the roadmap for empowering different stakeholders to shape how the AYA service specifications are implemented?”. Methods: A 1-day face-to-face policy lab was facilitated, utilising a unique, user-centric engagement approach by bringing diverse AYA stakeholders together to co-design strategies to translate BRIGHTLIGHT evidence into policy and impact. This was accompanied by an online workshop and prioritisation survey, individual interviews, and an AYA patient workshop. Workshop outputs were analysed thematically and survey data quantitatively. Results: Eighteen professionals and five AYAs attended the face-to-face Policy Lab, 16 surveys were completed, 13 attended the online workshop, three professionals were interviewed, and three AYAs attended the patient workshop. The Policy Lab generated eight national and six local recommendations, which were prioritised into three national priorities: 1. Launching the service specification supported by compelling communication; 2. Harnessing the ideas of young people; and 3. Evaluation of AYA patient outcomes/experiences and establishing a national dashboard of AYA cancer network performance. An animation was created by AYAs to inform local hospitals what matters to them most in the service specification. Conclusions: Policy and research evidence are not always aligned, so when emerging evidence does not support current guidance, further exploration is required. We have shown through multi-stakeholder involvement including young people that it was possible to gain a different interpretation based on current knowledge and context. This additional insight enabled practical recommendations to be identified to support the implementation of the service specification. Full article

ZigBee, a wireless technology standard for the Internet of Things (IoT) devices based on IEEE 802.15.4, faces significant security challenges that threaten the confidentiality, integrity, and availability of its networks. Despite using 128-bit Advanced Encryption Standard (AES) with symmetric keys for node authentication and data confidentiality, ZigBee’s design constraints, such as low cost and low power, have allowed security issues to persist. While ZigBee 3.0 introduces enhanced security features such as install codes and trust centre link key updates, there remains a lack of empirical research evaluating their effectiveness in real-world deployments. This research addresses the gap by conducting a comprehensive, hardware-based analysis of ZigBee 3.0 networks using XBee 3 radio modules and ZigBee-compatible devices. We investigate the following three core security issues: (a) the security of symmetric keys, focusing on vulnerabilities that could allow attackers to obtain these keys; (b) the impact of compromised symmetric keys on network confidentiality; and (c) susceptibility to Denial-of-Service (DoS) attacks due to insufficient protection mechanisms. Our experiments simulate realistic attack scenarios under both Centralised and Distributed Security Models to assess the protocol’s resilience. The findings reveal that while ZigBee 3.0 improves upon earlier versions, certain vulnerabilities remain exploitable. We also propose practical security controls and best practices to mitigate these attacks and enhance network security. This work contributes novel insights into the operational security of ZigBee 3.0, offering guidance for secure IoT deployments and advancing the understanding of protocol-level defences in constrained environments. Full article

Under the continuous disturbance of ecosystems driven by urbanization, landscape fragmentation and the disruption of ecological processes and functions are key challenges in optimizing ecological networks (EN). This study aims to examine the spatiotemporal evolution of topological patterns, ecological processes, and ecosystem services (ES) in Wuhan from the “pattern–process–function” perspective. To overcome the lag in research concerning the coupling of ecological processes, functions, and spatial patterns, we explore the long-term dynamic evolution of ecosystem structure, process, and function by integrating multi-source data, including remote sensing, enabling comprehensive spatiotemporal analysis from 2000 to 2020. Addressing limitations in current EN optimization approaches, we integrate morphological spatial pattern analysis (MSPA), use circuit theory to identify EN components, and conduct spatial optimization accurately. We further assess the effectiveness of two scenario types: “pattern–function” and “pattern–process”. The results reveal a distinct “increase-then-decrease” trend in EN structural attributes: from 2000 to 2020, source areas declined from 39 (900 km²) to 37 (725 km²), while corridor numbers fluctuated before stabilizing at 89. Ecological processes and functions exhibited phased fluctuations. Among water-related indicators, water conservation (as a core function), and modified normalized difference water index (MNDWI, as a key process) predominantly drive positive correlations under the “pattern–function” and “pattern–process” scenarios, respectively. The “pattern–function” scenario strengthens core area connectivity (24% and 4% slower degradation under targeted/random attacks, respectively), enhancing resistance to general disturbances, whereas the “pattern–process” scenario increases redundancy in edge transition zones (21% slower degradation under targeted attacks), improving resilience to targeted disruptions. This complementary design results in a gradient EN structure characterized by core stability and peripheral resilience. This study pioneers an EN optimization framework that systematically integrates identification, assessment, optimization, and validation into a closed-loop workflow. Notably, it establishes a quantifiable, multi-objective decision basis for EN optimization, offering transferable guidance for green infrastructure planning and ecological restoration from a pattern–process–function perspective. Full article

Ecological security patterns (ESPs) are fundamental to safeguarding regional ecological integrity and enhancing human well-being. Consequently, research on conservation and restoration in critical regions is vital for ensuring ecological security and optimizing territorial ecological spatial configurations. Focusing on the Henan section of the Yellow River Basin, this study established the regional ESP and conservation–restoration framework through an integrated approach: (1) assessing four key ecosystem services—soil conservation, water retention, carbon sequestration, and habitat quality; (2) identifying ecological sources based on ecosystem service importance classification; (3) calculating a comprehensive resistance surface using the entropy weight method, incorporating key factors (land cover type, NDVI, topographic relief, and slope); (4) delineating ecological corridors and nodes using Linkage Mapper and the minimum cumulative resistance (MCR) theory; and (5) integrating ecological functional zoning to synthesize the final spatial conservation and restoration strategy. Key findings reveal: (1) 20 ecological sources, totaling 8947 km² (20.9% of the study area), and 43 ecological corridors, spanning 778.24 km, were delineated within the basin. Nineteen ecological barriers (predominantly located in farmland, bare land, construction land, and low-coverage grassland) and twenty-one ecological pinch points (primarily clustered in forestland, grassland, water bodies, and wetlands) were identified. Collectively, these elements form the Henan section’s Ecological Security Pattern (ESP), integrating source areas, a corridor network, and key regional nodes for ecological conservation and restoration. (2) Building upon the ESP and the ecological baseline, and informed by ecological functional zoning, we identified a spatial framework for conservation and restoration characterized by “one axis, two cores, and multiple zones”. Tailored conservation and restoration strategies were subsequently proposed. This study provides critical data support for reconciling ecological security and economic development in the Henan Yellow River Basin, offering a scientific foundation and practical guidance for regional territorial spatial ecological restoration planning and implementation. Full article

Against the backdrop of the energy crisis and a rapidly advancing digital economy, electricity–computing synergy has become a strategic pathway to resolve energy constraints in computing power center and overcome renewable energy consumption challenges. This study breaks through the existing single-factor fragmented analysis method and systematically constructs a vertically progressive and horizontally coupled electricity–computing synergy planning model to deconstruct the core elements of computing power center construction and reconstruct the path of electricity–computing value co-creation. It proposes a multi-objective site selection decision-making method for computing power center based on linear weighting and the principle of reusability and universality, effectively avoiding the problem of overall system efficiency loss caused by single-objective optimization. Based on the empirical results from data from 31 provinces in China, this study classifies the endowments for computing power center construction, conducts targeted analyses of each province’s situation, and finds three major contradictions facing China’s computing power center: a spatial mismatch between green energy resources and service demand, a dynamic imbalance between electricity price advantages and comprehensive costs, and structural contradictions between talent reserves and sustainable development. Finally, a multi-dimensional integrated strategy was systematically constructed, encompassing demand-driven initiatives, electricity price adjustments, talent innovation, natural cold-source activation, and network upgrades, to provide guidance and policy toolkits for the government planning of computing power infrastructure development. Full article

The realization of forest ecological functions value is an important path for implementing the “Two Mountains” theory. Improving the efficiency of forest ecological functions and benefits value realization faces several challenges, such as an underdeveloped value evaluation system that makes it difficult to quantify ecological value, a weak policy system lacking effective incentive mechanisms, and unclear ecological property rights leading to unfair benefits distribution. Forest new quality productive drivers are a key factor in promoting high-quality forestry development, and can effectively address several issues hindering the efficiency of forest ecological functions and benefits value realization. Forest ecological functions and benefits are divided into tangible forest products and intangible ecological services, with the efficiency of realizing their economic and welfare values reflecting the input–output status of forest ecological value. This paper constructs an indicator system for assessing the modern productive capacity in forestry and the efficiency of forest ecological value realization, and uses a two-stage network DEA model and a double fixed effects model for empirical analysis. The study finds that the advanced drivers of forestry productivity significantly enhance the efficiency of forest ecological economic value realization but constrain the efficiency of ecological welfare value realization, with significant regional differences. As a moderating variable, enhancing the resilience of the industry chain can significantly deepen the effect throughout the process, while improving the informatization level of residents can weaken the constraints of forest new quality productive drivers on the efficiency of forest ecological welfare value realization. Therefore, this paper offers targeted recommendations aimed at providing theoretical support and practical guidance for optimizing the efficiency of forest ecological value realization. Full article

Weeds compete with crops for resources, posing multiple negative impacts for agricultural production systems and triggering degradation of ecosystem services (e.g., alterations in the soil microbial community structure). Under the guidance of green plant protection, the development of efficient biocontrol strains with environmentally friendly characteristics has become a crucial research direction for sustainable agriculture. This study aimed to develop a fungal bioherbicide by isolating and purifying a pathogenic fungal strain (JZ-5) from infected redroot pigweed (Amaranthus retroflexus L.). The strain exhibited pathogenicity rates ranging from 23.46% to 86.25% against six weed species, with the most pronounced control efficacy observed against henbit deadnettle (Lamium amplexicaule L.), achieving a pathogenicity rate of 86.25%. Through comprehensive characterization of cultural features, morphological observations, and molecular biological identification, the strain was taxonomically classified as Fusarium oxysporum. Scanning electron microscopy revealed that seven days post-inoculation, F. oxysporum JZ-5 formed dense mycelial networks on the leaf surfaces of cluster mallow (Malva verticillata L.), causing severe tissue damage. Safety assessments demonstrated that the spore suspension (10⁴ spores/mL) had no adverse effects on three crops: hulless barley (Hordeum vulgare var. coeleste L.), wheat (Triticum aestivum L.), and potato (Solanum tuberosum L.). These findings suggest that F. oxysporum strain JZ-5 warrants further investigation as a potential bioherbicide for controlling three problematic weed species—Chenopodium album L. (common lambsquarters), Elsholtzia densa Benth. (dense-flowered elsholtzia), and Lamium amplexicaule L. (henbit deadnettle)—in cultivated fields of hulless barley (Hordeum vulgare var. coeleste L.), wheat (Triticum aestivum L.), and potato (Solanum tuberosum L.). This discovery provides valuable fungal resources for ecologically sustainable weed management strategies, contributing significantly to the advancement of sustainable agricultural practices. Full article

Over the past decade, the rapid growth of supply chain finance (SCF) in developing countries has made it a key profit driver for commercial banks and financial firms. In parallel, financial risk control in SCF has attracted more and more attention from financial service providers and has gained research momentum in recent years. This study analyzes the contagion mechanism of SCF-related risks faced by commercial banks through examining SCF network topology. First, this study uses complex network theory to integrate an SEIR epidemic model (Susceptible–Exposed–Infectious–Recovered) into financial risk management. The model simulates how financial risks spread in supply chain finance (SCF) under banks’ strategic, tactical, or operational interventions. Then, some key points for financial risk control from the perspective of commercial banks are obtained by investigating the risk stability threshold of the financial network of SCF and its stability. Numerical simulations show that effective interventions—such as strengthening loan guarantees to reduce the number of exposed firms—significantly curb risk transmission by restricting its scope and shortening its duration. This research provides commercial banks with a quantitative framework to analyze risk propagation and actionable strategies to optimize SCF risk control, enhancing financial system stability and offering practical guidance for preventing systemic risks. Full article

Designing a sustainable supply chain network for perishable products is challenging due to their short shelf life and sensitivity to environmental conditions. These factors necessitate strict quality control and efficient logistics. The emergence of Internet of Things (IoT) technology has significantly improved supply chain operations by enabling real-time monitoring of environmental conditions. This helps maintain product quality and ensures timely deliveries. Additionally, using mixed fleets—comprising both electric and conventional vehicles—can reduce carbon emissions without compromising operational reliability. While previous studies have explored the application of IoT to enhance delivery efficiency and the use of mixed fleets to address environmental concerns, few have examined both technologies within a unified modeling framework. This study proposes a sustainable multi-period supply chain network for perishable products that integrates IoT technology and mixed fleets into an optimization framework. We develop a multi-objective location-inventory-routing model. The first objective minimizes total costs, including production, facility operation, inventory, transportation, carbon emissions, IoT deployment, and energy use. The second objective aims to maximize service levels, which are measured by product quality and on-time delivery. The model is solved using the Non-Dominated Sorting Genetic Algorithm II (NSGA-II). A case study based on real-world data demonstrates the model’s effectiveness. Sensitivity analysis indicates that balancing the emphasis on quality and delivery reliability leads to improved cost and service performance. Furthermore, while total costs steadily increase with higher demand, service levels remain stable, showcasing the model’s robustness. These results provide practical guidance for managing sustainable supply chains for perishable products. Full article

The value network of the high-tech park constitutes a value co-creation system where multiple entities facilitate knowledge transformation through interaction, thereby achieving collaborative innovation. The reasonable distribution of collaborative innovation benefits among various innovation entities is a critical factor in maintaining the motivation for innovation within the value network. This study examines the co-creation mechanism of the value network in high-tech parks from the perspective of knowledge innovation, with the aim of enhancing the efficiency of knowledge transfer and spillover among entities. Additionally, it seeks to establish a fairer and more rational benefit distribution framework to promote collaborative innovation and ensure the stable operation of the value network. Firstly, we identify the entities involved in value co-creation within the high-tech park. Subsequently, we analyze the roles and interrelationships of these entities within the value co-creation network. We determine the knowledge flow pathways by employing the shortest path method, and innovatively construct an MMPP/M/C queuing model to depict the processes of knowledge transfer and spillover among the entities engaged in value co-creation. We optimize and solve the queuing model using the matrix geometric method, deriving metrics such as the average queue length, average arrival rate, average waiting time, and service intensity under the steady state of the system, and verify the applicability and effectiveness of the model in the application of the high-tech park through empirical data. Finally, by integrating the improved Shapley value method, a benefit distribution model is constructed that incorporates five types of factors: contribution level, resource input, knowledge spillover effect, effort level, and risk undertaking. The rationality and operability of this model are validated through computational examples. Research findings indicate that the optimized queuing model enhances the efficiency of knowledge transfer and spillover among entities, while the refined benefit distribution mechanism effectively compensates entities with high contribution levels, substantial resource inputs, significant knowledge spillover effects, elevated effort levels, and high risk assumption levels. This provides both theoretical support and practical guidance for sustaining the long-term stable operation of the value network. Full article

Pooled ridesharing offers on-demand, one-way, cost-effective transportation for passengers traveling in similar directions via a shared vehicle ride with others they do not know. Despite its potential benefits, the adoption of pooled rideshare remains low in the United States. This exploratory study aims to evaluate potential service improvements and features that may increase users’ willingness to adopt the service. The study analyzed transportation behaviors, rideshare preferences, and willingness to adopt pooled rideshare services among 8296 U.S. participants in 2025, building on findings from a 2021 nationwide survey of 5385 U.S. participants. The study incorporated 77 actionable items developed from the results of the 2021 survey to assess whether addressing specific user-generated topics such as safety, reliability, convenience, and privacy can improve pooled rideshare use. A side-by-side comparison of the 2021 and 2025 data revealed shifts in transportation behavior, with personal rideshare usage increasing from 22% to 28%, public transportation from 21% to 27%, and pooled rideshare from 6% to 8%, while personal vehicle (79%) use remained dominant. Participants rated features such as driver verification (94%), vehicle information (93%), peak time reliability (93%), and saving time and money (92–93%) as most important for improving rideshare services. A pre-to-post analysis of willingness to use pooled rideshare utilizing the actionable items as per respondents’ preferences showed improvement: “definitely will” increased from 15.9% to 20.1% and “probably will” rose from 35.6% to 47.7%. These results suggest that well-targeted service improvements may meaningfully enhance pooled rideshare acceptance. This study offers practical guidance for Transportation Network Companies (TNCs) and policymakers aiming to improve pooled rideshare as well as potential future research opportunities. Full article

Shield tunnels in operation are often affected by complex geological conditions, environmental factors, and structural aging, leading to cumulative damage in the segments and, consequently, increased deformation that compromises structural safety. To investigate the deformation behavior of tunnel linings under random damage conditions, this study integrates finite element numerical simulation with deep learning techniques to analyze and predict the deformation of shield tunnel segments. First, a refined three-dimensional finite element model was established, and a random damage modeling method was developed to simulate the deformation evolution of tunnel segments under different damage ratios. Additionally, a statistical analysis was conducted to assess the uncertainty in deformation caused by random damage. Furthermore, this study introduces a convolutional neural network (CNN) surrogate model to enable the rapid prediction of shield tunnel deformation under random damage conditions. The results indicate that as the damage ratio increases, both the mean deformation and its variability progressively rise, leading to increased deformation instability, demonstrating the cumulative effect of damage on segment deformation. Moreover, the 1D-CNN surrogate model was trained using finite element computation results, and predictions on the test dataset showed excellent agreement with FEM calculations. The surrogate model achieved a correlation coefficient (R²) exceeding 0.95 and an RMSE below 0.016 mm, confirming its ability to accurately predict the deformation of tunnel segments across different damage conditions. To the best of our knowledge, the finite-element–deep-learning hybrid approach proposed in this study provides a valuable theoretical foundation for predicting the deformation of in-service shield tunnels and assessing structural safety, offering scientific guidance for tunnel safety evaluation and damage repair strategies. Full article

A road network is an important spatial carrier for the efficient and reliable operation of urban services and material flows. In recent years, the “high road density, small block size” trend has become a major focus in urban planning practices. However, whether high-density road networks are highly resilient lacks quantitative evidence. This study presents a multi-scale analytical framework for measuring road network resilience from a topological perspective. We abstract 186 ideal orthogonal grid density models from an actual urban road network, quantifying resilience under two disturbance scenarios: random failures and intentional attacks. The results indicate that road network density indeed has a significant impact on resilience, with both scenarios showing a trend where higher densities correlate with greater resilience. However, the increase in resilience value under the intentional attack scenario is significantly higher than that under the random failure scenario. The findings indicate that network density plays a decisive role in determining resilience levels when critical edges fail. This is attributed to the greater presence of loops in denser networks, which helps maintain connectivity even under intentional disruption. In the random failure scenario, network resilience depends on the combined effects of the node degree and density. This study offers quantitative insights into the design of resilient urban forms in the face of disruptive events, establishing reference benchmarks for road network spacing at both meso- and micro-scales. The results provide practical guidance for resilient city planning in both newly developed and existing urban areas, supporting informed decision-making in urban morphology and disaster risk management. Full article