Search Results (387)

Against the backdrop of urban stock development worldwide, resident-led urban regeneration and in-situ demolition-and-reconstruction models are crucial for advancing sustainable urban regeneration. However, these initiatives often stall due to collective action dilemmas arising from complex interactions among governments, residents, and contractors. To address this, we develop a tripartite evolutionary game model that incorporates a novel multi-dimensional policy lever system. This system integrates the following: (1) resource-allocation levers (area-expansion coefficient, w; expansion benefit-sharing coefficient, v), (2) cost-sharing levers (expansion-purchase coefficient, p; original-area reconstruction payment coefficient, q), and (3) behavioral-intervention levers (cost-burden perception coefficient, e; accident-risk perception coefficient, d), the latter quantifying behavioral economics principles like loss aversion and probability weighting. Through numerical simulations, we identify the nonlinear effects, critical thresholds, and interaction mechanisms of these levers. The results demonstrate that resource-allocation and cost-sharing levers exhibit critical ranges, whereas behavioral-intervention levers are characterized by perception thresholds and saturation effects. Crucially, coordinated optimization of all parameters—rather than one-sided incentives—is essential to steer the system towards the ideal cooperative equilibrium (government guidance, contractor participation, and resident engagement). This study provides a systematic theoretical framework and practical pathway for crafting targeted urban regeneration policies, emphasizing that aligning economic incentives with behavioral interventions can simultaneously enhance compactness, feasibility, and equity, thereby contributing to the achievement of Sustainable Development Goal 11. Full article

Background/Objectives: Free gracilis (GM) and latissimus dorsi muscle (LDM) flaps are reliable options for complex defect coverage, but long-term sensory outcomes remain underexplored. Sensory impairment, especially the loss of protective cutaneous sensation, increases the risk of injury, thermal damage, and ulceration in reconstructed areas. This study aimed to systematically assess multidimensional sensory recovery after free muscle flap (FMF) reconstruction. Methods: In a prospective single-center study, 94 patients (49 GM, 45 LDM) underwent standardized sensory testing following FMF transfer. Five modalities were evaluated: pressure detection (Semmes-Weinstein monofilaments), vibration perception, two-point discrimination (2PD), sharp–dull differentiation, and temperature differentiation. Measurements were compared to contralateral healthy skin (CHS). Subgroup analyses were performed by anatomical region (head, trunk, extremities). Results: All sensory modalities were significantly impaired in FMF compared to CHS (p < 0.0001). Mean pressure thresholds were markedly higher in FMF (248.8 g) versus CHS (46.8 g). Vibration perception scores were reduced (FMF 3.97 vs. CHS 5.31), and 2PD was significantly poorer (11.6 cm vs. 4.7 cm). Sharp–dull and thermal discrimination were largely absent in FMF (positivity rates < 20%), with 58.5% of patients demonstrating only deep pressure sensation (≥300 g). No significant differences were found between GM and LDM in most modalities, except for worse 2PD in GM. Subgroup analyses confirmed uniform deficits across all anatomical regions. Conclusions: FMFs without neurotization result in profound, persistent sensory deficits, particularly the loss of protective sensation. Clinically, fascio-cutaneous flaps with nerve coaptation should be considered in functionally critical regions. Future strategies should focus on neurotization techniques to enhance sensory recovery. Full article

Eight Views is a time-honored East Asian cultural-landscape paradigm in which eight emblematic natural—cultural scenes fuse regional character, historical memory, and aesthetic ideals into a coherent narrative. It encodes the collective memory and identity of a city (or garden/region), a premodern ‘mental map’ or proto- ‘city brand’. In China, the historic Urban Eight Views are rooted in local environments and traditions and constitute significant, high-value landscape heritage today. Yet rapid urbanization has inflicted severe physical damage on these ensembles. Coupled with insufficient holistic and systemic understanding among managers and the public, this has led, during development and conservation alike, to spatial insularization, fragmentation, and even disappearance, alongside widening divergences in cultural cognition and biases in value judgment. Taking Longmen Haoyue in Chongqing, one of the historic Urban Eight Views, as a case that manifests these issues, this study develops a holistic–relational approach for the urban, historical Eight Views and explores landscape-based pathways to protect the spatial structure and cultural connotations of the heritage that has been severely damaged and is in a state of disappearance or semi-disappearance amid modernization. Methodologically, we employ decomposition analysis to extract the historical information elements of Longmen Haoyue and its internal relational structure and corroborate its persistence through field surveys. We then apply the FAHP method to grade the conservation value and importance of elements within the Eight Views, quantitatively clarifying protection hierarchies and priorities. In parallel, a multidimensional corpus is constructed to analyze online dissemination and public perception, revealing multiple challenges in the evolution and reconstruction of Longmen Haoyue, including symbolic misreading and cultural decontextualization. In response, we propose an integrated strategy comprising graded element protection and intervention, reconstruction of relational structures, and the building of a coherent cultural-semantic and symbol system. This study provides a systematic theoretical basis and methodological support for the conservation of the urban historic Eight Views cultural landscapes, the place-making of distinctive spatial character, and the enhancement of cultural meanings. It develops an integrated research framework, element extraction, value assessment, perception analysis, and strategic response that is applicable not only to the Eight Views heritage in China but is also transferable to World Heritage properties with similar attributes worldwide, especially composite cultural landscapes composed of multiple natural and cultural elements, sustained by narrative traditions of place identity, and facing risks of symbolic weakening, decontextualization, or public misperception. Full article

Technology has historically served as a fundamental driver of human welfare and progress. Contemporary calls for temporary moratoria on technological development, motivated by concerns about existential threats to humanity, represent a misguided approach that may ultimately prove counterproductive to human flourishing. This paper argues that technology itself is not inherently problematic; rather, the issue lies in contemporary society’s fragmented ontological framework. Drawing on African philosophical traditions, particularly Kemetic cosmology and ubuntu philosophy, we examine how ancient Kemetic civilization exemplified transhumanist principles through its integration of technological advancement within a holistic worldview. The Kemetic understanding of Reality as a sacred, differentiated Whole, embodied in their conception of Atum as the self-developing divine principle, always connected to and guided by Shu (life) and Tefnut/Ma’at (order), provided a cosmological foundation that enabled beneficial coexistence with technology as a life-giving human contingency regulated by ma’at. Similarly, the ubuntu cosmo-philosophical vision in contemporary African thought emphasizes Reality as an interconnected totality, with technology being an independent yet connected excitation in this Reality. This study, therefore, contends that the fundamental challenge facing modern society today is not technological or AI development per se, but rather the need to reconstruct our fragmented perception of Reality. Within a properly integrated cosmological vision, technology functions not as a selfish instrument or an object readily available for our exploitative purposes but as an inherently life-affirming, sustaining, and enhancing force indispensable for the well-being of the Whole. The implications suggest that, rather than constraining technological advancement, which could be detrimental to our well-being due to our inherent reliance on it, as it relies on us, efforts should be directed toward cultivating a holistic yet relational understanding of technology, with the cosmos. Full article

Understanding the mechanisms that assemble diverse forest communities is a central goal in ecology. Phylogenetic analyses based on DNA barcodes have advanced this field, but their use of sequences evolving at constant rates may not capture adaptations to specific environmental drivers. Light is a critical factor shaping forest structure, particularly in the vertical dimension. This study introduces a novel phylogenetic approach using the blue-light receptor gene, cryptochrome (Cry), which is directly involved in plant light perception and adaptation. We reconstructed a Cry-based phylogeny for 96 tree species in a 20 ha subtropical forest dynamics plot and analyzed community structure using the net relatedness index (NRI) and nearest taxon index (NTI) across horizontal habitats, successional stages, and vertical canopy layers. Compared to traditional DNA barcoding, the Cry phylogeny revealed distinct patterns, showing consistent phylogenetic structure across different habitats—a finding indicative of convergent evolution in light-sensing systems. Furthermore, the Cry-based analysis demonstrated a stronger and more consistent signal in the forest’s vertical structure, with significant phylogenetic clustering in upper canopy layers, directly linking light adaptation to community stratification. Over time, both NRI and NTI values increased, suggesting succession leads to greater phylogenetic overdispersion and highlighting an increased role for environmental filtering among closely related taxa. Our results validate Cry as a powerful functional gene marker for phylogenetics, providing unique insights into how light environment filters species and shapes the vertical assembly of forest communities. Full article

Acquiring high-resolution digital elevation models (DEMs) over across extensive regions remains challenging due to high costs and insufficient detail, creating demand for super-resolution (SR) techniques. However, existing DEM SR methods still rely on limited data sources and often neglect essential terrain features. To address the issues, SAR data complements existing sources with its all-weather capability and strong penetration, and a Transformer-based Residual Attention Network combining SAR and Terrain Features (TRAN-ST) is proposed. The network incorporates intensity and coherence as SAR features to restore the details of the high-resolution DEMs, while slope and aspect constraints in the loss function enhance terrain consistency. Additionally, it combines the lightweight Transformer module with the residual feature aggregation module, which enhances the global perception capability while aggregating local residual features, thereby improving the reconstruction accuracy and training efficiency. Experiments were conducted on two DEMs in San Diego, USA, and the results show that compared with methods such as the bicubic, SRCNN, EDSR, RFAN, HNCT methods, the model reduces the mean absolute error (MAE) by 2–30%, the root mean square error (RMSE) by 1–31%, and the MAE of the slope by 2–13%, and it reduces the number of parameters effectively, which proves that TRAN-ST outperforms current typical methods. Full article

This paper proposes a novel image tone-mapping framework that incorporates meta-learning, a psychophysical model, Bayesian optimization, and light-field virtual diffraction. First, we formalize the virtual diffraction process as a mathematical operator defined in the frequency domain to reconstruct high-dynamic-range (HDR) images through phase modulation, enabling the precise control of image details and contrast. In parallel, we apply the Stevens power law to simulate the nonlinear luminance perception of the human visual system, thereby adjusting the overall brightness distribution of the HDR image and improving the visual experience. Unlike existing methods that primarily emphasize structural fidelity, the proposed method strikes a balance between perceptual fidelity and visual naturalness. Secondly, an adaptive parameter tuning system based on Bayesian optimization is developed to conduct optimization of the Tone Mapping Quality Index (TMQI), quantifying uncertainty using probabilistic models to approximate the global optimum with fewer evaluations. Furthermore, we propose a task-distribution-oriented meta-learning framework: a meta-feature space based on image statistics is constructed, and task clustering is combined with a gated meta-learner to rapidly predict initial parameters. This approach significantly enhances the robustness of the algorithm in generalizing to diverse HDR content and effectively mitigates the cold-start problem in the early stage of Bayesian optimization, thereby accelerating the convergence of the overall optimization process. Experimental results demonstrate that the proposed method substantially outperforms state-of-the-art tone-mapping algorithms across multiple benchmark datasets, with an average improvement of up to 27% in naturalness. Furthermore, the meta-learning-guided Bayesian optimization achieves two- to five-fold faster convergence. In the trade-off between computational time and performance, the proposed method consistently dominates the Pareto frontier, achieving high-quality results and efficient convergence with a low computational cost. Full article

Image restoration tasks such as deraining, deblurring, and dehazing are crucial for enhancing the environmental perception of autonomous vehicles and traffic systems, particularly for tasks like vehicle detection, pedestrian detection and lane line identification. While transformer-based models excel in these tasks, their prohibitive computational complexity hinders real-world deployment on resource-constrained platforms. To bridge this gap, this paper introduces a novel Soft Knowledge Distillation (SKD) framework, designed specifically for creating highly efficient yet powerful image restoration models. Our core innovation is twofold: first, we propose a Multi-dimensional Cross-Net Attention(MCA) mechanism that allows a compact student model to learn comprehensive attention relationships from a large teacher model across both spatial and channel dimensions, capturing fine-grained details essential for high-quality restoration. Second, we pioneer the use of a contrastive learning loss at the reconstruction level, treating the teacher’s outputs as positives and the degraded inputs as negatives, which significantly elevates the student’s reconstruction quality. Extensive experiments demonstrate that our method achieves a superior trade-off between performance and efficiency, notably enhancing downstream tasks like object detection. The primary contributions of this work lie in delivering a practical and compelling solution for real-time perceptual enhancement in autonomous systems, pushing the boundaries of efficient model design. Full article

Omnidirectional vision systems enable panoramic perception for autonomous navigation and large-scale mapping, but physical testbeds are costly, resource-intensive, and carry operational risks. We develop a virtual simulation platform for multi-view omnidirectional vision that supports flexible camera configuration and cross-platform data streaming for efficient processing. Building on this platform, we propose and validate a reconstruction and ranging method that fuses multi-view omnidirectional images with structured-light projection. The method achieves high-precision obstacle contour reconstruction and distance estimation without extensive physical calibration or rigid hardware setups. Experiments in simulation and the real world demonstrate distance errors within 8 mm and robust performance across diverse camera configurations, highlighting the practicality of the platform for omnidirectional vision research. Full article

In many disaster-prone regions, lower-income communities face disproportionate impacts due to the prevalence of informal housing. Informal housing, characterized by substandard construction and lack of adherence to building codes, exacerbates vulnerabilities during disasters, leading to widespread destruction and hampered recovery efforts. This study examines the multifaceted causes of informal housing in Puerto Rico using a qualitative content analysis of applicable literature. Seven interdisciplinary factors were derived from 42 relevant manuscripts with identifiable factors linked to informal housing in Puerto Rico: Knowledge, Perception, Government Dynamics, Institutional Support, Enforcement, Culture, and Resources. Despite post-disaster efforts advocating for building back better, systemic challenges perpetuate informal housing practices, reinforcing cycles of vulnerability. This research underscores the need for integrated decision making in pre-disaster preparation and post-disaster reconstruction efforts. This research presents a detailed understanding of the Informal Housing Cycle, demonstrates how interdisciplinary factors are barriers to safe and sustainable housing, and explores the complex relationships between these factors. This study aims to guide policy and practice to reduce future disaster impacts on Puerto Rico housing, thus breaking the cycle of vulnerability, empowering communities, and fostering sustainable resilience in post-disaster reconstruction efforts. Full article

The significance of human capital (HC) has been gaining attention worldwide. However, practices of human resource management (HRM) vary across countries. In Japan, these HRM practices have been shaped by top-down initiatives such as the Charter of Work–Life Balance, the Work Style (WS) Reform, and the mandatory disclosure of HC for publicly listed companies. This study examines the evolution of HRM perceptions in Japanese companies from 2008 to 2024, as well as the quantitative trends and semantic shifts in the mentions of a series of institutional reforms. This study performed text analysis on 51,666 narrative disclosure documents from 3970 listed Japanese companies. Results show that mandatory HC disclosure marked a semantic turning point, shifting the corporate focus from short-term productivity to long-term sustainability. Moreover, the sequential introduction of new concepts has fostered coexistence and semantic reconstruction, rather than competition or exclusion between HC and WS. This study empirically demonstrates that top-down institutional reforms can reshape corporate perceptions beyond mere compliance. It clarifies the dynamics of conceptual coexistence and semantic evolution when related ideas are introduced sequentially. In addition, the findings highlight the value of large-scale narrative disclosure documents in analyzing semantic change. Full article

Conventional audio and video codecs are designed for human perception, often discarding subtle spectral cues that are essential for machine-based analysis. To overcome this limitation, we propose a machine-oriented compression framework that reinterprets spectrograms as visual objects and applies Feature Coding for Machines (FCM) to anomalous sound detection (ASD). In our approach, audio signals are transformed log-mel spectrograms, from which intermediate feature maps are extracted, compressed, and reconstructed through the FCM pipeline. For comparison, we implement AAC-LC (Advanced Audio Coding Low Complexity) as a representative perceptual audio codec and VVC (Versatile Video Coding) as spectrogram-based video codec. Experiments were conducted on the DCASE (Detection and Classification of Acoustic Scenes and Events) 2023 Task 2 dataset, covering four machine types (fan, valve, toycar, slider), with anomaly detection performed using the official Autoencoder baseline model released in DCASE 2024. Detection scores were computed from reconstruction error and Mahalanobis distance. The results show that the proposed FCM-based ACoM (Audio Coding for Machines) achieves comparable or superior performance to AAC at less than half the bitrate, reliably preserving critical features even under ultra-low bitrate conditions (1.3–6.3 kbps). While VVC retains competitive performance only at high bitrates, it degrades sharply at low bitrates. These findings demonstrate that feature-based compression offers a promising direction for next-generation ACoM standardization, enabling efficient and robust ASD in bandwidth-constrained industrial environments. Full article

As vehicle-centric perception struggles with occlusion and dense traffic, vehicle-infrastructure cooperative perception (VICP) offers a viable route to extend sensing coverage and robustness. This study proposes a feature-level VICP framework that fuses vehicle- and roadside-derived visual features via V2X communication. The model integrates four components: regional feature reconstruction (RFR) for transferring region-specific roadside cues, context-driven channel attention (CDCA) for channel recalibration, uncertainty-weighted fusion (UWF) for confidence-guided weighting, and point sampling voxel fusion (PSVF) for efficient alignment. Evaluated on the DAIR-V2X-C benchmark, our method consistently outperforms state-of-the-art feature-level fusion baselines, achieving improved ${AP}_{3D}$ and ${AP}_{BEV}$ (reported settings: 16.31% and 21.49%, respectively). Ablations show RFR provides the largest single-module gain +3.27% ${AP}_{3D}$ and +3.85% ${AP}_{BEV}$, UWF yields substantial robustness gains, and CDCA offers modest calibration benefits. The framework enhances occlusion handling and cross-view detection while reducing dependence on explicit camera calibration, supporting more generalizable cooperative perception. Full article

To address the challenge of feature extraction and reconstruction in weak-texture environments, and to provide data support for environmental perception in mobile robots operating in such environments, a Feature Extraction and Reconstruction in Weak-Texture Environments solution is proposed. The solution enhances environmental features through laser-assisted marking and employs a two-step feature extraction strategy in conjunction with binocular vision. First, an improved SURF algorithm for feature point fast localization method (FLM) based on multi-constraints is proposed to quickly locate the initial positions of feature points. Then, the robust correction method (RCM) for feature points based on light strip grayscale consistency is proposed to calibrate and obtain the precise positions of the feature points. Finally, a sparse 3D (three-dimensional) point cloud is generated through feature matching and reconstruction. At a working distance of 1 m, the spatial modeling achieves an accuracy of ±0.5 mm, a relative error of 2‰, and an effective extraction rate exceeding 97%. While ensuring both efficiency and accuracy, the solution demonstrates strong robustness against interference. It effectively supports robots in performing tasks such as precise positioning, object grasping, and posture adjustment in dynamic, weak-texture environments. Full article

Specific emitter identification (SEI), which classifies radio transmitters by extracting hardware-intrinsic radio frequency fingerprints (RFFs), faces critical challenges in noise robustness, generalization under limited training data and class imbalance. To address these limitations, we propose adaptive deep ensemble learning (ADEL)—a framework that integrates heterogeneous neural networks including convolutional neural networks (CNN), multilayer perception (MLP) and transformer for hierarchical feature extraction. Crucially, ADEL also adopts adaptive weighted predictions of the three base classifiers based on reconstruction errors and hybrid losses for robust classification. The methodology employs (1) three heterogeneous neural networks for robust feature extraction; (2) the hybrid losses refine feature space structure and preserve feature integrity for better feature generalization; and (3) collaborative decision-making via adaptive weighted reconstruction errors of the base learners for precise inference. Extensive experiments are performed to validate the effectiveness of ADEL. The results indicate that the proposed method significantly outperforms other competing methods. ADEL establishes a new SEI paradigm through robust feature extraction and adaptive decision integrity, enabling potential deployment in space target identification and situational awareness under limited training samples and imbalanced classes conditions. Full article