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This study represents a significant advancement in structural health monitoring by integrating infrared thermography (IRT) with cutting-edge deep learning techniques, specifically through the use of the Mask R-CNN neural network. This approach targets the precise detection and segmentation of hidden defects within the interfacial layers of Fiber-Reinforced Polymer (FRP)-reinforced concrete structures. Employing a dual RGB and thermal camera setup, we captured and meticulously aligned image data, which were then annotated for semantic segmentation to train the deep learning model. The fusion of the RGB and thermal imaging significantly enhanced the model's capabilities, achieving an average accuracy of 96.28% across a 5-fold cross-validation. The model demonstrated robust performance, consistently identifying true negatives with an average specificity of 96.78% and maintaining high precision at 96.42% in accurately delineating damaged areas. It also showed a high recall rate of 96.91%, effectively recognizing almost all actual cases of damage, which is crucial for the maintenance of structural integrity. The balanced precision and recall culminated in an average F1-score of 96.78%, highlighting the model's effectiveness in comprehensive damage assessment. Overall, this synergistic approach of combining IRT and deep learning provides a powerful tool for the automated inspection and preservation of critical infrastructure components. [ABSTRACT FROM AUTHOR]

In the realm of sustainable city development, evaluating the spatial vitality of urban green spaces (UGS) has become increasingly pivotal for assessing public space quality. This study delves into the spatial heterogeneity of park vitality across diverse urban landscapes at a city scale, addressing limitations inherent in conventional approaches to understanding the dynamics of park vitality. Leveraging geotagged check-in data from 65 parks in the study case of Changsha City, a quantitative analysis was undertaken to assess spatial vitality. The investigation incorporated data concerning internal and external factors influencing park vitality, employing the Multi-scale Geographically Weighted Regression (MGWR) model to dissect nuanced spatial heterogeneity. The research uncovers notable spatial discrepancies in factors influencing park vitality across diverse urban areas, emphasizing the reliance on adjacent residential communities and internal commercial amenities provision. These dependencies correspond with economic development differences among urban locales, revealing distinct geographic trends. This study has a novel perspective and methodology for investigating urban park vitality, providing significant insights for urban green space planning and management. It emphasizes the necessity of acknowledging spatial diversity in urban park planning and design by incorporating the distinct socio-economic characteristics of each urban zone, which is crucial for both urban planners and policymakers. [ABSTRACT FROM AUTHOR]

Previous work has demonstrated the modest impact of environmental interventions that manipulate lighting, sound, or temperature on sleep inertia symptoms. The current study sought to expand on previous work and measure the impact of a multimodal intervention that collectively manipulated light, sound, and ambient temperature on sleep inertia. Participants slept in the lab for four nights and were awoken each morning by either a traditional alarm clock or the multimodal intervention. Feelings of sleep inertia were measured each morning through Psychomotor Vigilance Test (PVT) assessments and ratings of sleepiness and mood at five time-points. While there was little overall impact of the intervention, the participant's chronotype and the length of the lighting exposure on intervention mornings both influenced sleep inertia symptoms. Moderate evening types who received a shorter lighting exposure (≤15 min) demonstrated more lapses relative to the control condition, whereas intermediate types exhibited a better response speed and fewer lapses. Conversely, moderate evening types who experienced a longer light exposure (>15 min) during the intervention exhibited fewer false alarms over time. The results suggest that the length of the environmental intervention may play a role in mitigating feelings of sleep inertia, particularly for groups who might exhibit stronger feelings of sleep inertia, including evening types. [ABSTRACT FROM AUTHOR]

High-density communities have proliferated globally during rapid urbanization. They are characterized by a high population density and limited per capita public spaces, making them susceptible to infectious disease risks. The impact of infectious diseases in these communities, as evident during the COVID-19 pandemic, underscores their vulnerabilities. Yet, research on disease prevention in high-density areas remains limited. This study aims to investigate the relationship between the built environment and the transmission of infectious diseases in high-density urban communities, with a particular focus on the lessons learned during the COVID-19 pandemic. Utilizing Shenzhen city as a case study, this study collected data on the built environment and epidemic trends and involved a generalized linear regression analysis, aiming to understand the key built environment factors that affect epidemic spread in high-density areas. The results from the study revealed that high-density communities experience higher rates of infectious disease transmission compared to their medium- to low-density counterparts. The significant factors identified include land use mixture and walkability, with land use mixture showing the most substantial impact on infection rates. Through a combination of qualitative analysis and empirical research, we constructed a conceptual framework linking containment measures, non-pharmaceutical interventions, and the built environment. The findings emphasize the significance to focus on the health development of high-density communities and offer valuable insights for tailored urban planning and built environment design. These insights are crucial for promoting the healthy and sustainable transformation of existing high-density communities. [ABSTRACT FROM AUTHOR]