Search Results (16,932)

Street seating serves as a critical medium for stimulating spatial vitality and holds substantial design value in the refined planning of commercial upgrading and quality enhancement in aging districts. As urban regeneration and the optimization of existing built environments have become dominant paradigms in global urban development, the improvement of street quality—given its role as the primary setting for everyday public life—has increasingly depended on the fine-grained design and precise regulation of micro-scale environmental elements. This study takes Pingdeng Street in Zhengzhou, China, and its 33 seating installations as an empirical case. A multi-level analytical framework—comprising the seating ontology level, the seating space level, and the street environment level—was developed to quantitatively examine the relationships between multi-level spatial elements and street vitality intensity. Through correlation and regression analyses, the study systematically investigated the mechanisms by which seating-related elements at different levels influence street vitality. The results indicate that the Green View Index (GVI) is the core driver of street vitality, with the most significant enhancement observed when GVI ranges between 28% and 35%. The synergistic coupling of multi-level seating elements is essential for maximizing street vitality, while optimization pathways vary across different functional seating types. In design practice, high-comfort seating with backrests is recommended, with seating continuity controlled within 0.63–0.90. Seating spaces should adopt moderately enclosed spatial forms, such as eave-covered areas, and be supplemented with adequate lighting facilities. At the street environment level, a GVI of 28–35% and spatial openness of 9–18% should be maintained. The multi-level analytical framework and quantified indicator thresholds established in this study offer a new perspective on the mechanisms linking seating and street vitality. The findings provide a scientific theoretical basis and offer context-sensitive design guidance for the refined renewal of aging urban districts under comparable conditions. Full article

Large-scale composite equipment manufacturing imposes stringent requirements on the lean management of multi-specification fiber prepreg sheet storage, while existing optimization methods suffer from poor process adaptability, insufficient multi-objective collaborative optimization capability, and low space utilization of static layouts. This study constructs a symmetric optimization framework for multi-objective composite sheet storage to address these critical bottlenecks. Specifically, the multi-dimensional process value of fiber sheets is quantified, and the layered storage optimization problem is transformed into a 0–1 knapsack problem with symmetric constraints. An improved Dynamic Programming–Backtracking (DP-BT) material selection algorithm and an adaptive dynamic programming iterative space optimization algorithm are proposed to achieve a symmetric balance of inter-layer space utilization and global optimization. Experimental validation with actual production data of 17 fiber sheet types verifies that the proposed method enables space optimization for specified layer counts to maximize average space utilization, with the rate rising from 79.4% (initial 4-layer layout) to 95.7% (3-layer) and 99.9% (2-layer), and a peak single-layer utilization of 100%. This framework achieves favorable optimization performance in the target production scenario and provides a referenceable symmetric optimization approach for the lean storage management of similar fiber sheet storage scenarios in composite manufacturing. Full article

High-resolution optical satellite data have become fundamental for acquiring global accurate remote sensing information (e.g., object geometric and spectral characteristics). However, due to the difficulty in obtaining accurate ground control points on a global scale, achieving accurate global positioning of satellite imagery remains a technical challenge. To realize global positioning optimization without relying on accurate control points, this paper leverages open-source data such as Google Earth orthophoto maps (GE maps) and FABDEM, and proposes the Coarse-to-Fine Open-Source Basemap Integration (CFBI) Method. The core idea of this method is to effectively eliminate gross errors in coarse control points by leveraging the differential projection offsets of roofs between single-view satellite images and multi-source orthophotos. On this basis, an iterative weight-selection adjustment strategy is adopted to achieve accurate positioning results. Experiments conducted in three regions, Jacksonville, New York, and Boston, demonstrate that the proposed algorithm significantly improves the positioning accuracy of satellite imagery, with an average enhancement of 62.92%, and accuracy in most areas reaching within 2 m. Full article

Long-span bridges are critical components of transportation infrastructure because they promote efficient connectivity between agricultural production centers, tourist destinations, and major urban areas. To construct these structures, the balanced cantilever method is widely used; however, the lack of rigid longitudinal connections between the pylons and the deck often allows for large displacement demands during seismic activities. Fluid viscous dampers (FVDs) are employed to mitigate these effects. This study investigates the impact of using FVDs at the abutments of the Hisgaura cable-stayed bridge located on the Curos-Malaga corridor in the department of Santander, Colombia. A nonlinear response history analysis was conducted using seismic records from crustal sources, scaled to the local seismic hazard, and performed in SAP2000©. The results indicate that the presence of FVDs does not adversely affect the axial forces in the stay cables under the Extreme Event Limit State I. Furthermore, demand reductions were observed at the pylon closest to the abutment (Pylon 4). Under critical seismic records, reductions of up to 81.95% in relative deck-pylon displacement, 62.17% in bending moment, and 58.46% in base shear were achieved. These findings demonstrate an improved global structural behavior under severe seismic loading conditions. Full article

Generating high-precision normalized digital surface models (nDSMs) from a single remote sensing image remains a challenging and ill-posed problem due to the absence of reliable geometric constraints. In this work, we show that monocular depth provides structurally stable cues of local geometry but lacks the global scale and vertical reference required for absolute height recovery. This intrinsic mismatch limits direct depth-to-height regression, particularly when transferring across heterogeneous terrains, land-cover compositions, and imaging conditions. Building on this idea, we propose the Relative Depth–Absolute Height Prediction Network (RDAH-Net), a framework that exploits relative depth as a geometry-aware prior while learning terrain-dependent height mappings from image appearance to absolute height. As the backbone, we employ a lightweight MobileNetV2 enhanced with a Convolutional Block Attention Module (CBAM), and further incorporate a cross-modal bidirectional attention fusion scheme with positional encoding to achieve a deep and effective fusion of image appearance and depth prior cues. Finally, a PixelShuffle-based upsampling strategy is used to sharpen prediction details and mitigate typical upsampling artifacts. Extensive experiments across diverse regions demonstrate that RDAH-Net achieves robust and generalizable height estimation, providing a practical alternative for large-scale mapping and rapid update scenarios. Full article

In the 21st century, rapid urban development during global urbanization has led to high-density environments. These settings have become a significant cause of stress-related health problems for residents. Healing street design plays an important role in helping address mental health challenges caused by this process. Current research often focuses on healing elements and methods from only a single field. As a result, it lacks the integration of multidisciplinary and multi-stakeholder perspectives. To address this gap, this paper formed a Delphi expert panel with multidisciplinary scholars, urban managers, and practicing designers. The panel developed a quantitative evaluation model. This model covers four core dimensions: Safety (0.3210), Attractiveness (0.1080), Friendliness (0.2155), and Comfort (0.3553). It also includes eleven healing elements, such as Pedestrian Right-of-Way (0.4131), Night Lighting (0.3209), Visual Landscape (0.759), Street Furniture (0.4000), and Street Scale (0.3274). Using this model, the healing potential of Jingliu Road in Zhengzhou was assessed. The analysis identified the overall healing potential, core healing dimensions, and shortcomings of the street. This finding provides a clear direction for future healing-oriented street design. This paper builds a healing system for pedestrian spaces in high-density urban streets in China. It thus offers an evidence-based scientific tool for environmental design. Healing environments have expanded from less accessible spaces, such as squares and parks, to interactive and accessible streets. This transition enhances urban spaces’ capacity to address residents’ mental health concerns and promotes public health. Additionally, this paper offers specific recommendations for planners and policymakers to prioritize healing elements in urban renewal projects. Full article

Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full. Full article

Satellite remote sensing plays an essential role in wildfire monitoring due to its large-scale observation capability. However, fire targets in satellite imagery are typically extremely small, sparsely distributed, and embedded in complex backgrounds, making accurate segmentation highly challenging for existing methods. To address these challenges, this paper proposes a multi-scale Mamba-based network for tiny fire segmentation, named FireMambaNet. The network adopts a nested U-shaped encoder-decoder architecture, primarily consisting of three modules: the Cross-layer Gated Residual U-shaped module (CG-RSU), the Fire-aware Directional Context Modulation module (FDCM), and the Multi-scale Mamba Attention Module (M2AM). The CG-RSU, as the core building block, adaptively suppresses background redundancy and enhances weak fire responses by extracting multi-scale features through cross-layer gating. The FDCM explicitly enhances the network’s ability to perceive anisotropic expansion features of fire points, such as those along the wind direction and terrain orientation, by modeling multi-directional context. The M2AM model employs a Mamba state-space model to suppress background interference through global context modeling during cross-scale feature fusion, while enhancing consistency among sparsely distributed tiny fire targets. In addition, experimental validation is conducted using two subsets from the Active Fire dataset, which have significant pixel-level sparse features: Oceania and Asia4. The results show that the proposed method significantly outperforms various mainstream CNN, Transformer, and Mamba baseline models on both datasets. It achieves an IoU of 88.51% and F1 score of 93.76% on the Oceania dataset, and an IoU of 85.65% and F1 score of 92.26% on the Asia4 dataset. Compared to the best-performing CNN baseline model, the IoU is improved by 1.81% and 2.07%, respectively. Overall, the FireMambaNet demonstrates significant advantages in detecting tiny fire points in complex backgrounds. Full article

Unsupervised change detection (CD) in heterogeneous remote sensing images is intrinsically difficult due to severe sensor-specific discrepancies. In the absence of ground truth, these discrepancies result in ambiguous optimization objectives that make it difficult for models to distinguish true land-cover changes from modality-driven pseudo-changes. To address these challenges, we propose MaskUCD, a novel unsupervised framework that reformulates heterogeneous CD as a dynamic mask-driven constraint scheduling problem. Fundamentally distinct from conventional strategies that enforce selective feature alignment, MaskUCD employs a spatially adaptive optimization mechanism. Specifically, the iteratively refined mask serves as a geometric reference to guide optimization. It enforces strict feature alignment in mask-unchanged regions to suppress modality-induced discrepancies, while simultaneously promoting feature divergence in mask-changed regions to emphasize semantic inconsistencies. In this way, explicit optimization objectives are established, together with an intrinsic interpretability constraint that guides the CD process. This strategy treats the mask as a structural guide for representation learning rather than a ground-truth reference, thereby avoiding error accumulation caused by directly using inaccurate masks as supervisory signals. To facilitate this optimization, we design a specialized asymmetric autoencoder with a hybrid encoder architecture, utilizing multi-scale frequency analysis and global context modeling to enhance feature representation capabilities. Consequently, this design enables the generation of refined and semantically consistent masks, which provide increasingly precise structural guidance, yielding converged and discriminative difference maps. Extensive experiments demonstrate that MaskUCD achieves state-of-the-art performance and superior robustness compared to existing advanced methods. Full article

Serious games and negotiation simulations such as the Phosphorus Negotiation Game (P-Game) are increasingly used to support sustainability-oriented education. To broaden accessibility, a smartphone-based version of the face-to-face P-Game was developed and is presented here. A comparative design integrating quantitative pre–post survey measures with analysis of open-ended responses was employed to examine self-reported knowledge gains and learning experiences among participants who completed the P-Game in face-to-face workshops and those who played the virtual version. Both formats were associated with significant increases in participants’ perceived understanding of phosphorus science and negotiation science/practice. Self-reported knowledge of phosphorus science increased by 92.3% (global face-to-face), 70.7% (Hungarian face-to-face), and 88.4% (online), with comparable gains observed in negotiation science and practice across groups. Qualitative findings complemented these results, indicating that while learning gains were broadly similar, the modes differed in experiential emphasis: face-to-face delivery elicited performance-oriented and socially embedded reflections, whereas the online format was more frequently described in terms of structured participation and reflective processing. User satisfaction with the virtual P-Game was high, reflected by a System Usability Scale (SUS) score above 80. Overall, the findings suggest that the virtual P-Game represents a viable and accessible complement to traditional face-to-face implementation, maintaining educational impact while extending reach. Further research with larger and more diverse participant samples is recommended to strengthen generalizability and explore long-term learning outcomes in sustainability contexts. Full article

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this paper proposes an improved RTDETR-based detection framework, termed CEA-DETR, for wind turbine blade surface defect inspection. First, a Cross-Scale Multi-Edge feature Extraction (CSME) backbone is designed by integrating multi-scale pooling and edge-enhancement units with a dual-domain feature selection mechanism, enabling effective extraction of fine-grained texture and edge features across different scales. Second, an Efficient Multi-Scale Feature Fusion Network (EMSFFN) is constructed to facilitate deep cross-level feature interaction through adaptive weighted fusion and multi-scale convolutional structures, thereby enhancing the representation of multi-scale defects. Furthermore, an adaptive sparse self-attention mechanism is introduced to reconstruct the AIFI module, strengthening global dependency modeling and guiding the network to focus on critical defect regions under complex background conditions. Experimental results demonstrate that CEA-DETR achieves mAP50 and mAP50:95 of 89.4% and 68.9%, respectively, representing improvements of 3.1% and 6.5% over the RT-DETR-r18 baseline. Meanwhile, the proposed model reduces computational cost (GFLOPs) by 20.1% and parameter count by 8.1%. These advantages make CEA-DETR more suitable for deployment on resource-constrained unmanned aerial vehicles (UAVs), enabling efficient and real-time autonomous inspection of wind turbine blades. Full article

Bone marrow cell classification and nucleus segmentation in microscopic images are fundamental tasks for computer-aided diagnosis of lymphocytic leukemia. However, bone marrow cells from different subtypes exhibit high morphological similarity, and structural information is often constrained under optical microscopic imaging, posing challenges for stable and effective feature representation. To address this issue, we propose MTLL (Multitask Model on Lymphocytic Leukemia), a novel multitask approach that performs cell classification and nucleus segmentation within a unified network to exploit their complementary information. The model constructs a hybrid backbone for shared feature representation based on a CNN-Transformer architecture, in which Fuse-MBConv modules are tightly integrated with multilayer multi-scale transformers to enable deep fusion of local texture and global semantic information. For the segmentation branch, we design an AM (Atrous Multilayer Perceptron) decoder that combines atrous spatial pyramid pooling with multilayer perceptrons to fuse multi-scale information and accurately delineate nucleus boundaries. The classification branch incorporates prior knowledge of cell nuclei structures to capture subtle variations in cellular morphology and texture, thereby enhancing the model’s ability to distinguish between leukemia subtypes. Experimental results demonstrate that the MTLL model significantly outperforms existing advanced single-task and multi-task models in both lymphocytic leukemia classification and cell nucleus segmentation. These results validate the effectiveness of the multi-task feature-sharing strategy for lymphocytic leukemia diagnosis using bone marrow microscopic images. Full article

Cervical cancer is one of the most prevalent and easily contracted diseases among women, significantly impacting their daily lives. Computer vision-based cervical cell morphology diagnosis technology can offer robust support for cervical cell analysis at a lower cost. However, the presence of a substantial number of overlapping cells in cervical images renders existing cell segmentation methods less accurate, thereby complicating the guidance of medical diagnosis. In this paper, we introduce a tristage Progressive Refinement method (PRefiner) for overlapping cell segmentation that decouples the traditional end-to-end pipeline, with the final stage specifically correcting anomalous results to enhance precision. We achieve separable overlapping cervical cell segmentation results through a cell nucleus locator, a single-cell segmenter, and a Segmentation Result Mask Refiner. Specifically, we employ a hybrid U-Net as the primary network for the cell nucleus locator and single-cell segmenter, which determines the position of the cell nucleus and procures the initial coarse segmentation result. In the mask refiner, we incorporate a conditional generation framework to address the perception decision problem and design a local–global dual-scale discriminator to ensure that the segmentation result aligns with the prior of a single-cell mask. Experimental results on CCEDD and ISBI2015 demonstrate that PRefiner achieves optimal performance by effectively resolving abnormal segmentations. Notably, our method improves the Dice coefficient of abnormal results from five different models by an average of 2.62% (ranging from 1.0% to 5.1%). Full article

Background: Global immunization programs continue to rely on needle-based injections despite persistent concerns regarding sharps disposal, accidental injuries, and the technical skill required for accurate intradermal administration. Needle-free jet injectors (NFJIs) are an alternative delivery method in which narrow, high-velocity liquid jets penetrate the skin without a needle. Contemporary designs, ranging from single-use disposable-syringe injectors to digitally controlled electromechanical devices, address historical safety issues and meet current WHO and FDA device expectations. Methods: Evidence from engineering analyses, preclinical modeling, and clinical trials was reviewed to characterize how jet velocity, nozzle structure, and formulation rheology influence skin penetration and drug dispersion. Published vaccine studies were examined for antibody responses, seroconversion, and reactogenicity compared with needle–syringe injection. Field vaccination campaign data from national campaigns and operational reports were evaluated to describe implementation steps, acceptability, and implementation constraints. Results: Published studies evaluating vaccines, including inactivated influenza, hepatitis B, typhoid, rabies, and measles, report antibody titers and seroconversion rates after NFJI administration that are comparable to those achieved with conventional intramuscular or intradermal needle injection. Needle-free delivery was associated with operational advantages in several immunization programs, including reduced sharps waste and improved vaccination rate during high-volume immunization campaigns. Local and systemic reactogenicity follows expected patterns, with slightly higher injection-site responses in some NFJI studies. Imaging and mechanical data confirm that jet performance depends on nozzle geometry and controlled pressure pulses. At the same time, formulation stability remains a critical determinant of successful jet-based vaccine administration, particularly for protein antigens, adjuvanted formulations, and emerging mRNA vaccines that may experience transient shear stress during high-velocity injection. Evidence from vaccination campaigns further indicates that needle-free jet injectors reduce sharps waste, simplify vaccine handling and administration procedures, and support rapid vaccine delivery in large-scale immunization programs. Conclusions: Needle-free jet injectors are a practical alternative to traditional needle-based injections for some vaccines. Their main benefits include enabling intradermal dose-sparing strategies, reducing reliance on sharps disposal methods, and enabling the efficient vaccination of large groups without compromising immunogenicity. Future research should define the physicochemical stability limits of biologic formulations subjected to jet injection and evaluate digitally controlled injectors capable of precise pressure modulation and adjustable delivery parameters. In addition, needle-free jet injection eliminates needle penetration and sharps handling, which may reduce needle-associated anxiety and improve vaccine acceptability among individuals with needle aversion. Full article