Search Results (6,151)

Urban tunnel portals constitute critical safety zones for autonomous vehicles, where abrupt luminance transitions, shortened sight distances, and densely distributed structural and traffic elements pose considerable challenges to perception reliability. Existing driving scenario datasets are rarely tailored to tunnel environments and have not quantitatively evaluated how specific infrastructure components influence perception latency in autonomous systems. This study develops a requirement-driven framework for the identification and sensitivity ranking of information perception elements within urban tunnel portals. Based on expert evaluations and a combined function–safety scoring system, nine key elements—including road surfaces, tunnel portals, lane markings, and vehicles—were identified as perception-critical. A “mandatory–optional” combination rule was then applied to generate 48 logical scene types, and 376 images after brightness (30–220 px), blur (Laplacian variance ≥ 100), and occlusion filtering (≤0.5% pixel error) were obtained after luminance and occlusion screening. A ResNet50–PSPNet convolutional neural network was trained to perform pixel-level segmentation, with inference rate adopted as a quantitative proxy for perceptual sensitivity. Field experiments across ten urban tunnels in China indicate that the model consistently recognized road surfaces, lane markings, cars, and motorcycles with the shortest inference times (<6.5 ms), whereas portal structures and vegetation required longer recognition times (>7.5 ms). This sensitivity ranking is statistically stable under clear, daytime conditions (p < 0.01). The findings provide engineering insights for optimizing tunnel lighting design, signage placement, and V2X configuration, and offers a pilot dataset to support perception-oriented design and evaluation of urban tunnel portals in semi-enclosed environments. Unlike generic segmentation datasets, this study quantifies element-specific CNN latency at tunnel portals for the first time. Full article

Accurate imputation of missing pavement-condition data is critical for proactive infrastructure management, yet it is complicated by spatial non-stationarity—deterioration patterns and data quality vary markedly across regions. This study proposes a Spatially Gated Mixture-of-Experts (SG-MoE) imputation model that explicitly encodes spatial heterogeneity by (i) clustering road segments using geographic coordinates and (ii) supervising a gating network to route each sample to region-specialized expert regressors. Using a large-scale national pavement management database, we benchmark SG-MoE against a strong baseline under controlled missingness mechanisms (MCAR: missing completely at random; MAR: missing at random; MNAR: missing not at random) and missing rates (10–50%). Across scenarios, SG-MoE consistently matches or improves upon the baseline; the largest gains occur under MCAR and the challenging MNAR setting, where spatial specialization reduces systematic underestimation of high crack-rate sections. The results provide practical guidance on when spatially aware ensembling is most beneficial for infrastructure imputation at scale. We additionally report comparative results under three missingness mechanisms. Across five random seeds, SG-MoE is comparable to the single LightGBM baseline under MCAR/MAR and achieves its largest gains under MNAR (e.g., sMAPE improves by 0.82 points at 10% MNAR missingness). Full article

The global literature underscores a set of human wellbeing challenges and opportunities for forestland exploitation, albeit the lack of region-specific evidence. This concerns the Congo Basin, the second-largest forest ecosystem in the world. This study uses the case of the Campo Ma’an Landscape to: (i) analyze the challenges linked to the exploitation of forestland resources, and (ii) explore forest resource exploitation opportunities in the landscape. The study employed a random sample of 200 natural resource-dependent households drawn from four study zones—Niete, Campo, Ma’an and Akom II. This was complemented by focus group discussions (n = 4), key informant (n = 6) and expert (n = 6) interviews. The descriptive and inferential analyses led to the following results: First, economic, technical, socio-cultural and institutional challenges affect the sustainable exploitation of forestland resources in the Campo Ma’an Landscape. The economic challenges of forest (B = −0.389, p = 0.01) and land resource exploitation (B = −0.423, p = 0.006) significantly affect sustainable exploitation compared to other challenges, leading to biodiversity loss and deforestation. These constitute a threat to planetary health systems. Almost all households rely on forestland resources for their livelihoods and development, with opportunities for land resource exploitation outweighing those in forest resource exploitation. Protected area management and agriculture are affected owing to competing interests among farmers, conservationists and other land users. Thus, short-term economic gains are prioritized over long-term sustainability, putting the resource landscape at risk of degradation and future uncertainties. Integrated stakeholder engagement, capacity building, and policy revision could enhance the planetary health approach by linking the social, economic and environmental dimensions of forestland resource management. Full article

With the rapid deployment of the Internet of Things (IoT) in critical domains such as power and industrial systems, the number of IoT devices has surged, accompanied by increasingly severe network security risks. IoT networks face diverse threats, including distributed denial-of-service attacks, advanced persistent threats, and data theft or tampering, while traditional detection and defense, lacking deep feature analysis, struggle with complex and unknown attacks, degrading security threat event detection. To this end, this paper proposes an IoT network security threat detection algorithm that integrates symmetric linear routing with a sparse mixture-of-experts model. The algorithm consists of a ConvNeXt feature extractor and a sparse BiLSTM expert layer, with symmetric linear routing embedded in the gating module. ConvNeXt provides refined global and local representations, Top-K gated BiLSTM experts for the module sequence-level dependencies among ordered features, and symmetric linear routing suppresses routing bias, enabling efficient and robust detection of IoT security threats. Experimental results on the CIC-IDS2018, TON-IoT, and BoT-IoT datasets indicate that the proposed IoT network security threat detection algorithm achieves accuracies of 94.08%, $99.99\pm 0.01$%, and 99.78%, respectively. Comparative experiments show the proposed algorithm outperforms baseline and state-of-the-art models, while the ablation and Top-K studies confirm module effectiveness for IoT intrusion detection. Full article

The present paper analyses the extent to which European Union regulatory frameworks induce the development of circular agriculture within the European Union. In order to evaluate the progress towards circular agriculture within the European Union, a composite Agricultural Circularity Index (ACI) was developed for all EU-27 Member States for the period of 2014–2023. An expert interview and a TOPSIS multi-criteria decision-making technique were employed for the construction of the ACI. Results indicate only marginal improvements in the development of circular agriculture on the aggregate EU level, although a pronounced cross-country divergence towards achieving circularity in agriculture was observed. The following four distinct trajectories in the evolution of the circular economy within the EU were distinguished: structurally advancing promoters, short-term breakthrough cases, high baseline, but eroding systems, and mixed or stagnating countries. Indicator decomposition analysis reveals that durable circularity gains in agriculture arise when increased material recirculation coincides with verifiable bandwidth, whereas intensifying input use frequently negates the progress. The findings underscore that regulatory ambition alone is insufficient: implementation design, uncompromised enforcement, and market integration determine whether initial initiatives towards circular agriculture materialize into sustainable practices or remain transitory. From a policy perspective, the ACI functions as a diagnostic tool to locate structural bottlenecks and to target CAP-style interventions where circular flows can be scaled most effectively. Full article

The nexus of rapid demographic transition and underdeveloped geriatric infrastructure poses a critical, yet understudied challenge in Sub-Saharan Africa (SSA). As global life expectancies rise, SSA’s older population is projected to triple by 2050, intensifying the need for sustainable age-friendly environments (AFEs) and robust healthy ageing interventions. Informal or family caregiving structures, while vital, are under strain from rapid urbanisation and shifting social dynamics, creating a compelling gap between need and provision. This expert viewpoint draws on the authors’ professional and scholarly experience regarding population ageing, AFEs, and healthy ageing to provide a comprehensive outlook on these issues in SSA. Selective literature searches were conducted in Google Scholar, Scopus and PubMed using targeted keywords and MESH terms, including “ageing in Africa”, “ageing in Sub-Saharan Africa”, “healthy ageing in Africa”, “healthy ageing in Sub-Saharan Africa”, “population ageing in Africa”, “population ageing in Sub-Saharan Africa”, “age-friendly environment in Africa”, and “age-friendly environment in Sub-Saharan Africa.” The authors argue that rapid population ageing in SSA is outpacing existing informal care arrangements, necessitating a strategic shift towards the development of age-friendly environments and more coordinated healthy ageing interventions to bridge the widening gap between demographic change and geriatric support systems. This paper underscores the necessity of proactive, evidence-based policy implementation to secure the well-being of SSA’s burgeoning older population. Full article

This paper examines how county-level government in China formulates and implements solar photovoltaic (PV) policies through an adaptive-governance lens, using Lin’an District (Hangzhou) as a case study. Drawing on multi-level policy document analysis and 30 semi-structured interviews with government officials, developers, grid actors and experts, we identify three stages of local PV development (rooftop diffusion; rapid utility-scale expansion; and market-oriented regulatory adjustment). Key governance innovations include a district PV task force, an industry alliance, and a dual acceptance safety mechanism that together accelerated deployment while managing technical and political risks. We show how adaptive governance operates within an authoritarian, hierarchical system by combining top-down targets with bottom-up development and stakeholder coordination. The findings illuminate practical trade-offs between market liberalization and regulatory control, and provide transferable lessons for other developing countries pursuing decentralized renewable energy transitions. Full article

Elevators are critical vertical transportation systems in modern urban infrastructure; however, their intricate mechanical and electrical configurations render them highly susceptible to safety-critical failures. Although various automated diagnostic techniques have been proposed, many data-driven approaches exhibit limited generalizability due to their insufficient consideration of physical fault mechanisms and strong dependence on facility-specific training data. To overcome these limitations, this study presents a rule-based automated diagnostic framework for elevator state recognition that prioritizes reliability, real-time performance, and interpretability. The proposed approach explicitly integrates physically meaningful fault characteristics and dominant frequency components into the diagnostic process, and employs predefined expert rules derived from established standards to classify fault states in an automated manner. The effectiveness of the proposed method is verified using real operational data collected from an in-service elevator, demonstrating improved diagnostic accuracy and computational efficiency compared to conventional manual inspection procedures. The proposed framework provides a practical and scalable solution for intelligent elevator condition monitoring and is expected to serve as a foundational technology for future smart maintenance and preventive safety systems. Full article

Efficient and intuitive programming strategies are essential for enabling robots to adapt to small-batch, high-mix production scenarios. Mixed reality (MR) and programming by demonstration (PbD) have shown great potential to lower the programming barrier and enhance human–robot interaction by leveraging natural human guidance. However, traditional offline programming methods, while capable of generating industrial-grade trajectories, remain time-consuming, costly to debug, and heavily dependent on expert knowledge. Conversely, existing MR-based PbD approaches primarily focus on improving intuitiveness but often suffer from low trajectory quality due to hand jitter and the lack of refinement mechanisms. To address these limitations, this paper introduces a coarse-to-fine human–robot collaborative programming paradigm. In this paradigm, the operator’s role is elevated from a low-level “trajectory drawer” to a high-level “task guider”. By leveraging sparse key points as guidance, the paradigm decouples high-level human task intent from machine-level trajectory planning, enabling their effective integration. The feasibility of the proposed system is validated through two industrial case studies and comparative quantitative experiments against conventional programming methods. The results demonstrate that the coarse-to-fine paradigm significantly improves programming efficiency and usability while reducing operator cognitive load. Crucially, it achieves this without compromising the final output, automatically generating smooth, high-fidelity trajectories from simple user inputs. This work provides an effective pathway toward reconciling programming intuitiveness with final trajectory quality. Full article

This study presents the development and implementation of a functional system that utilizes large language models (LLMs) to improve the identification, organization, and retrieval of mobility interoperability resources. The established framework assists novice and experienced implementers of mobility services such as planning organizations and multimodal transportation agencies to efficiently access interoperability resources, such as standards and case studies, which are often dispersed and difficult to navigate. The web-based system includes a backend that generates abstracts and tags and a frontend that supports manual or chatbot-based search. A prompt-refinement mechanism suggests improved queries within the context of mobility interoperability when no matches are found. To validate the quality of LLM-generated abstracts and tags, subject matter experts reviewed outputs from multiple prompt iterations to assess accuracy and clarity. Of the 82 resources evaluated, 72% of abstracts met expert expectations for relevance, while 91% of the tags were considered appropriate. A comprehensive case study of 330 representative user queries was also conducted to evaluate the chatbot’s output. Overall, the presented framework aims to reduce cataloging effort, improve classification consistency, and improve accessibility to relevant information. With minimal setup costs, the system offers a scalable and cost-effective solution for managing large, uncatalogued repositories. Full article

Background/Objectives: Advanced-stage mycosis fungoides (MF) and Sézary syndrome (SS) are aggressive forms of cutaneous T-cell lymphoma (CTCL) for which treatment options are limited and prognosis is poor. Brentuximab vedotin (BV), an anti-CD30 antibody–drug conjugate, has demonstrated high response rates in patients with CD30 expression ≥ 10%. However, data on its efficacy in cases with low CD30 expression (<10%) remain scarce. Methods: This retrospective analysis evaluated the real-world efficacy of BV in patients with advanced-stage MF/SS and low CD30 expression. A retrospective analysis was conducted on 32 patients across 11 German CTCL expert centers. All patients had advanced-stage MF or SS with CD30 expression < 10% and received BV at the standard dose. Treatment response was assessed using EORTC-ISCL criteria. Results: All patients had received prior systemic therapies (median: 3) with 36% having undergone prior mono- or polychemotherapy. The study population included 30 MF (stage IIB) and two SS cases. The overall response rate (ORR) in this population was 53.1% (17/32). A complete response (CR) was achieved in 12.5% (4/32), a partial response (PR) was achieved in 40.6% (13/32), stable disease (SD) was seen in 18.8% (6/32), and progressive disease (PD) was seen in 28.1% (9/32). The median progression-free survival (PFS) was 4.0 months (arithmetic mean: 6.38; range: 0.5–15.5), and the median time to next treatment (TTNT) was 7.25 months (arithmetic mean: 7.30; range: 2.00–15.5). Conclusions: BV demonstrated encouraging activity in heavily pretreated advanced MF/SS with low CD30 expression, achieving an ORR comparable to that observed in patients with higher CD30 levels. While response rates were similar, PFS was shorter. These findings suggest that BV remains a potential therapeutic option in this patient population and merits further prospective investigation. Full article

With the rapid development of cross-sea infrastructure, the immersed tube method has been increasingly applied to deep-water immersed-tube tunnel construction. However, when the construction depth reaches the scale of one hundred meters, issues such as high hydrostatic pressure, complex hydrological conditions, and limited construction windows significantly elevate project risks. Against this backdrop, this study systematically reviews relevant domestic and international research findings in the context of 100-m-deep water environments and constructs a comprehensive risk index system covering the construction processes of the WBS breakdown system based on the WBS-RBS decomposition method within the HSE framework. A risk index weighting analysis combines quantitative and qualitative analysis, categorizing the indicators into qualitative and quantitative categories. Quantitative analysis employs threshold determination and the LEC method; qualitative analysis utilizes expert surveys and the G1 method. Ultimately, a model that combines multiple methods for a 100-m-deep water environment, integrating subjective expertise and objective data, is developed. On this basis, multi-level prevention and control measures are proposed for hundred-meter-deep water-immersed tube construction. The results demonstrate that the proposed system can effectively identify key risk sources under deep-water conditions and provide practical countermeasures, offering significant guidance for ensuring construction safety and engineering quality in hundred-meter immersed-tube tunnel projects. Full article

The rapid expansion of Internet of Things (IoT) applications requires robust mechanisms to ensure the security, reliability, and maintainability of embedded software throughout its lifecycle. Over-the-Air (OTA) update systems play a central role in enabling the continuous evolution of IoT deployments. Despite their importance, OTA solutions are often designed in an ad hoc manner, supported by fragmented guidelines that lack a structured basis for selecting mechanisms and techniques aligned with the quality needs of IoT systems. This work presents a consolidated catalog for designing OTA update systems in IoT environments, developed through a review of academic and industrial literature. The catalog comprises 34 techniques organized into six mechanisms, each with representative use cases and a mapping to relevant quality attributes that make beneficial and adverse impacts explicit. The catalog was evaluated through a controlled industrial experiment involving 10 engineers, balanced between novices and experts, who designed an OTA update system for a real application scenario using either their prior knowledge and experience or the catalog. This work offers four contributions: (1) a catalog of 34 OTA techniques structured into six mechanisms; (2) clarified architectural definitions of technique and mechanism; (3) a controlled industrial experiment evaluating the catalog in a realistic setting; and (4) a quality-attribute trade-off analysis for each technique. Together, these contributions establish a coherent foundation for systematic and quality-aware OTA update system design. Full article

The 2025 Canadian Breast Cancer Symposium (CBCS) brought together patients, clinicians and researchers from across Canada to discuss advances shaping personalized breast cancer care. Key updates in systemic therapy highlighted expanding treatment options, including CDK4/6 inhibitors, oral SERDs, PI3K/AKT-targeted therapies, and antibody–drug conjugates across early and metastatic settings. Radiation oncology sessions emphasized treatment de-escalation, featuring evidence for ultra-hypofractionation, selective omission of nodal irradiation, and stereotactic strategies to manage oligoprogression. Surgical presentations focused on reducing morbidity through tailored axillary management and emerging techniques to prevent lymphedema. Advances in the management of central nervous system metastases underscored the growing synergy between stereotactic radiotherapy and CNS-active systemic therapies. Informed by patient testimony and advocacy perspectives, experts reflected on persistent gaps in diagnosis, access, and survivorship that shape priorities for future improvements. Together, these insights outline key directions that help to refine clinical practice and guide future research. Full article

Accurate classification of wafer map defect patterns is crucial for enhancing yield in semiconductor manufacturing. To address the problem of deep learning model over-fitting to label noise present in real industrial data, this study proposes a fuzzy logic-based framework for identifying both single and composite-type defect patterns. To demonstrate the robustness of our approach, we utilized the public dataset WM-811K and developed a Fuzzy Inference System (FIS) that leverages quantitative metrics such as the Center Zone Density (CZD). Data quality was also improved through preprocessing steps, including resolving class imbalances and refining labels via expert review. The performance of the proposed FIS was evaluated against a quantitative feature-based neural network, an FIS-neural network hybrid, and a CNN model. Experimental results showed that in single-pattern classification, the proposed FIS model achieved the highest accuracy of 99.20%, followed by the feature-based neural network (91.63%), the FIS-neural network hybrid model (88.55%), and the CNN (81.06%). These results prove that the proposed FIS approach maintains high classification accuracy while offering the advantages of interpretability and rule-based adjustability. This framework presents a practical solution that can effectively integrate domain knowledge to reduce the risk of overfitting in data environments with imperfect labels. Full article