Search Results (1,716)

Early wildfire detection (EWD) is of the utmost importance to enable rapid response efforts and thus minimize the negative impacts of wildfire spreads. To this end, we present PyroNear${}_{2025}$ , a new dataset composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. The data is sourced from the following: (i) web-scraped videos of wildfires from public networks of cameras for wildfire detection in-the-wild, (ii) videos from our in-house network of cameras, and (iii) a small portion of synthetic and real images. This dataset includes around 150,000 manual annotations on 50,000 images, covering 640 wildfires; PyroNear${}_{2025}$ surpasses existing datasets in size and diversity. It includes data from France, Spain, Chile, and the United States. Finally, it is composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. We ran cross-dataset experiments using a lightweight state-of-the-art object detection model, similar to the ones used in real-world applications, and found that the proposed dataset is particularly challenging, with an F1 score of around 70%, but it is more stable than existing datasets. Finally, its use in concordance with other public datasets helps to reach higher results overall. Last but not least, the video part of the dataset enables another technical contribution, as it can be used to train a lightweight sequential model, improving global recall while maintaining precision for earlier detections. The output of this work has real-life implications, as it is used to automatically detect wildfires, with our models running on Raspberry Pi in several countries. We will make both our code and data available online. Full article

Background/Objectives: Mental health problems among university students represent a growing public health concern and are shaped by both psychological and socioeconomic determinants that may act independently and interactively. This systematic review aimed to evaluate the separate and combined effects of these determinants on depression, anxiety, stress, and psychological distress in higher education students. Methods: A structured and targeted search strategy using predefined keyword groups and Boolean combinations across PubMed, Scopus, Web of Science, and Google Scholar identified 99 records, of which 19 duplicates were removed. After screening 80 titles and 52 abstracts, 34 full-text articles were assessed for eligibility, and 30 studies were ultimately included in the final review. Data were extracted on study characteristics, mental health outcomes, psychological determinants, socioeconomic factors, and their interactions. Results: The included studies consistently showed that psychological factors, including resilience, coping strategies, loneliness, self-efficacy, and perceived control, were associated with mental health outcomes, with higher resilience and self-efficacy linked to lower levels of depression and anxiety, and maladaptive coping and loneliness associated with increased psychological distress. Socioeconomic determinants, including financial stress, low socioeconomic status, parental education, housing insecurity, and food insecurity also independently contributed to elevated risks of depression, anxiety, and stress. Importantly, several studies demonstrated an interaction between these domains, where socioeconomic disadvantage amplified the adverse effects of poor coping capacity, low resilience, and social isolation, whereas social support and adaptive coping mitigated these effects. Conclusions: Student mental health is influenced by both distinct and interacting psychological and socioeconomic mechanisms, emphasizing the need for integrated institutional strategies that address structural vulnerabilities alongside individual psychological resilience. Full article

Background/Objectives: In laparoscopic and robot-assisted surgery, bleeding may rapidly impair operative-field readability and procedural safety. In the broader Robin Heart teleoperation framework, interpretation of such events is relevant not only for scene understanding but also as a potential prerequisite for future safety-oriented supervisory functions under communication-degraded conditions. The aim of this study was to assess whether a deep learning model for blood segmentation could provide outputs suitable for preliminary image-level temporal characterization of visible blood-region behavior in laparoscopic video. Methods: A U-Net-based binary blood-segmentation model was implemented in-house in PyTorch and evaluated on three paired image–mask datasets: a simulated bleeding dataset prepared under controlled laboratory conditions, an internal operative laparoscopic dataset, and an external-domain subset derived from the public GynSurg dataset. Segmentation performance was assessed using 5-fold cross-validation and reported using the Dice coefficient and Intersection over Union (IoU). Training dynamics were analyzed using training and validation loss and Dice curves. Additional baseline comparisons were performed on the internal operative dataset using U-Net++ and DeepLabV3+. Temporal analysis was performed on selected video fragments, including a low-motion reference sequence without active bleeding progression, internal bleeding-related sequences, and external-domain sequences, using mask-derived descriptors and auxiliary optical-flow-based motion descriptors computed after camera-motion compensation within the detected blood-related ROI. Results: In 5-fold cross-validation, the U-Net-based model achieved Dice coefficient and IoU values of 0.915 ± 0.012 and 0.851 ± 0.019 on the simulated dataset, 0.856 ± 0.013 and 0.756 ± 0.025 on the internal operative dataset, and 0.707 ± 0.053 and 0.570 ± 0.056 on the external-domain GynSurg subset, respectively. On the internal operative dataset, the proposed model performed comparably to U-Net++ and slightly above DeepLabV3+ under the same cross-validation protocol. The temporal descriptor set differentiated low-motion reference behavior, more spatially coherent progression, rapid coherent expansion, and dynamic or motion-active progression profiles. Peak dA/dt reflected abrupt visible blood-area expansion, temporal IoU described mask stability over time, and optical-flow-based descriptors provided additional information on local motion activity within the detected blood-related ROI. Conclusions: The results support the feasibility of combining deep-learning-based blood segmentation with temporal and optical-flow-based descriptors for exploratory image-level characterization of visible blood-region behavior in laparoscopic video. Within the Robin Heart development pathway, such descriptors may, in the future, serve as candidate components of image-analysis support modules for safety-oriented teleoperative scenarios. At this stage, they should be interpreted as exploratory image-derived indicators rather than clinically validated markers of bleeding severity. Full article

Background: Dengue infection is an expanding public health threat in Indonesia, increasingly reported in semi-urban areas undergoing rapid demographic and environmental change, where household-level determinants remain poorly characterised. Methods: We conducted a case–control study in the Deli Serdang district, North Sumatra, evaluating sociodemographic and environmental risk factors for dengue. Patients admitted to the district referral hospital (July–September 2024) were screened via medical records. Laboratory-confirmed dengue cases were compared with non-dengue febrile controls. Housing conditions and sociodemographic characteristics were assessed using a validated electronic questionnaire with photographic documentation. Multivariable logistic regression identified independent risk factors. Results: Of 238 individuals screened, 39 dengue cases and 78 controls were enrolled. Male sex (aOR 6.7, 95% CI 1.3–33.7), student status (aOR 7.8, 95% CI 1.1–56.5), absence of window screens (aOR 12.9, 95% CI 3.1–53.8), and surrounding vegetation (aOR 7.3, 95% CI 1.7–31.9) were independently associated with dengue infection. Rural residence was overrepresented among cases, suggesting expansion beyond traditional urban boundaries. Conclusions: Dengue risk in a transitional setting is shaped by demographic exposure and modifiable structural vulnerabilities. Integrated prevention strategies, including window screening, covered water storage, environmental management, and school-based vector control, are needed in rapidly urbanising districts. Full article

This paper examines the spatial integration of post-socialist churches and parish complexes within the modernist housing estates of Novi Zagreb. Constructed after 1990 in neighbourhoods originally planned without sacral programs, these buildings represent a specific form of post-socialist urban intervention. The study employs a qualitative, comparative approach, analysing five case studies through the parameters of urban context, volumetry, spatial composition, program, and public space interface. The analysis identifies a limited set of recurring typologies that define patterns of spatial integration within the existing urban fabric. The findings indicate that these complexes do not function as dominant urban elements, but instead adapt to the open, functionally organized structure of modernist planning. Their impact on public space remains limited, as they rarely generate new centres or clearly articulated urban nodes. At the same time, the results reveal a shift from singular religious buildings toward programmatically expanded parish complexes that incorporate social and community functions. However, this transformation remains largely internal and does not lead to a significant reconfiguration of the urban structure. The paper contributes to the understanding of post-socialist urban transformation by identifying typological patterns and interpreting religious architecture as a context-dependent urban actor. Full article

In Burke County, North Carolina, a Hepatitis A outbreak among unsheltered residents exposed gaps in access to clinic-based care and prompted early, ad hoc “backpack medicine” outreach efforts to deliver care directly in nontraditional settings. While this approach addressed immediate needs, it highlighted the inadequacy of isolated interventions, prompting local partners to pursue more structured, coordinated, and community-driven approaches to homeless health system design. This project report describes how Burke County Public Health, in partnership with the University of North Carolina at Chapel Hill, applied systems thinking, community coalition building, and human-centered design to transition from reactive outreach to a structured, sustainable mobile health delivery model for people experiencing homelessness. Guided by Community Coalition Action Theory (CCAT), partners used human-centered design methods to engage over 40 community stakeholders and 10 individuals with lived experience of homelessness or housing instability. Through empathy mapping, iterative prototyping, and thematic analysis, the team identified priority service gaps, defined operational requirements, and developed prototype service models, while building cross-agency readiness for implementation. Full article

Improving the decoding accuracy and information transfer rate (ITR) of steady-state visual evoked potential brain–computer interface (SSVEP-BCI) systems, while enhancing cross-subject generalization and reducing calibration cost, is essential for practical deployment. This study proposes an end-to-end framework that integrates adaptive filtering with semi-supervised domain adaptation. The framework incorporates a Gabor adaptive filter bank (G-AFB) to optimize time–frequency representations and extract features matched to individual neural responses. It also introduces a three-stage semi-supervised domain-adversarial neural network (TriS-DANN), which combines unsupervised pre-alignment and supervised fine-tuning to align cross-subject feature distributions and enable lightweight calibration. On the 1.0 s public benchmark dataset, G-AFB-tCNN achieved 89.13% accuracy, a 4.63 percentage-point improvement over its conventional filter-bank counterpart. On the 0.4 s in-house dataset, G-AFB-tCNN achieved 91.85% accuracy, a 3.22 percentage-point improvement over the conventional fixed filter bank. In transfer learning, TriS-DANN reached 86.60% accuracy using 0.4 s segments extracted from the stimulation period and only 23.07% of the available target-domain training/calibration trials, demonstrating higher efficiency and stability than conventional fine-tuning. These results support the proposed framework as a feasible route toward reliable, low-calibration SSVEP-BCI systems. Full article

Atrophic fracture nonunion is a clinically challenging form of failed bone repair, particularly under inflammatory conditions, but the cell-intrinsic programs that impair the function of skeletal stem/progenitor cells (SSPCs) remain incompletely defined. Here, we integrated public and in-house single-cell RNA sequencing datasets from mouse periosteum, normal fracture healing, and inflammation-associated fracture nonunion models to characterize stromal cell fate changes. Trajectory inference, transcription factor network analysis, and intercellular communication modeling were combined with in vitro and in vivo validation experiments. SSPCs in the nonunion microenvironment were arrested in an undifferentiated state and acquired a pro-inflammatory and pro-ferroptotic phenotype, with enrichment of ferroptosis-related genes including Acsl4. Computational analyses nominated IL-1β as a candidate upstream inflammatory signal, with neutrophils representing a potential source, and linked this signal to NF-κB activation and increased Epas1 activity in SSPCs. In primary SSPCs, IL-1β induced lipid peroxidation, intracellular ferrous iron accumulation, ferroptosis-related protein expression, and impaired osteochondrogenic differentiation. Ferroptosis inhibitor treatment further attenuated IL-1β-induced ferroptosis-related protein changes, supporting pathway specificity. Pharmacological inhibition of EPAS1 with PT2385 attenuated IL-1β-induced ferroptotic stress and restored SSPC differentiation in vitro, while also improving IL-1β-impaired fracture repair in vivo. Mendelian randomization analysis provided additional genetic evidence supporting potential links among IL-1β, EPAS1, and human nonunion risk. Together, these findings suggest that an IL-1β/EPAS1-associated ferroptotic program contributes to SSPC dysfunction during inflammation-associated fracture nonunion and may represent a potential targetable mechanism for improving impaired bone repair. Full article

In the context of economic disparities, housing as a fundamental right highlights processes of social differentiation and stratification. From a complexity perspective, factors such as location, distance from development hubs, and designs that standardize needs exacerbate weaknesses in its conception. The new realities of living in housing prompt us to rethink design approaches that integrate housing and work. This research analyzes the Ciudad Alegría Social Housing Program, located in the city of Loja, Ecuador. The diagnostic method indicated that 24% of homes have commercial projections as a survival strategy. While these spatial patterns diminish the levels of habitability in the homes, they also provide benefits such as proximity between home and work, savings in transportation costs, interaction with neighbors, and mixed uses. These observations reveal gaps in the architectural design process, which fails to consider both service providers and users in decision-making related to the design of VIS programs, highlighting the need for this phenomenon to be elevated to public policy. Full article

Public spaces in informal settlements are often viewed as congested, unregulated, or residual areas, yet they play a central role in everyday urban life. This paper examines how public spaces are socially produced through everyday appropriation, interaction, and routine use in two informal settlements in Lahore, Pakistan. Using a qualitative comparative case-study design, the study draws on field observations, semi-structured interviews, questionnaires, activity mapping, photographic documentation, and spatial interpretation. The findings show that streets function as multifunctional public spaces rather than simple movement corridors. They support livelihood activities, children’s play, domestic extension, informal mobility, social gathering, and community visibility. The results also show that public space use varies by gender, age, time of day, and settlement morphology, with everyday practices shaped by the interaction between street layouts, housing forms, public–private thresholds, and local socio-cultural routines. The paper concludes that informal public spaces should not be understood only as signs of disorder or planning failure. They are adaptive socio-spatial systems that support livelihood, belonging, and everyday resilience. Recognizing these resident-led spatial practices can inform more sensitive upgrading approaches that improve physical conditions without erasing the social relations and everyday uses through which public space is produced. Full article

The necessity of brownfield remediation has drawn more attention to scholarly literature in recent years. This review aims to understand the application of brownfield regeneration to sustainable housing and infrastructure development. With an emphasis on deciding the obstacles to regeneration, success factors, and their effects on outcomes, including the environmental, social, and economic aspects of housing and infrastructure development, this paper employs a systematic literature review approach to analyse current perspectives on brownfield regeneration for housing and infrastructure. This paper employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach and frequency based meta-synthesis to systematically quantify how often specific teams, variables, barriers, or concepts appear in body of literature. This investigation thoroughly examined 88 publications from the Scopus database. The research also classified and highlighted institutional and legal, financial and economic, socio-political, and environmental challenges to brownfield regeneration. The main success criteria for brownfield regeneration are mature policy alignment, approval processes, grants, and access to financial incentives. The developing gap found in this study should be the focus of future research. To address financial obstacles, a strong business case model for brownfield sites that provide investors or stakeholders with a financial forecast is needed. Finally, it is essential to develop a sustainable framework for brownfield site regeneration that encompasses all sustainable dimensions related to housing and infrastructure development. Finally, the review contributes to theory and practice by giving a comprehensive overview of brownfield barriers, success factors, and their impact on academics and industry. Full article

Background/Objectives: Dynamic navigation and robot-assisted implant workflows depend on robust intraoral perception. Marker-based tracking introduces workflow complexity and is sensitive to occlusions, motivating markerless alternatives. This study evaluates whether a single-stage YOLO instance segmentation model (YOLO-seg) can provide a practical markerless perception layer for dental navigation, combining accurate per-tooth delineation with low, predictable inference latency. Methods: YOLO-seg was trained end to end on an intraoral RGB corpus of 400 training, 20 validation, and 100 testing images, combining a public source and a partner-hospital in-house set. A two-stage YOLO + SAM baseline was implemented for comparison. Segmentation quality was evaluated on a 50-image held-out clinical test set at three complementary levels (per-instance matching, per-class union, and global union), with paired Wilcoxon signed-rank tests, Cliff’s delta effect sizes, and 95% bootstrap confidence intervals. Runtime was assessed under matched inference-only and end-to-end conditions on N = 100 frames at a 640 × 640 resolution on an NVIDIA RTX A2000 GPU. Results: YOLO-seg significantly outperformed YOLO + SAM across all primary metrics, with very large effect sizes (Cliff’s delta: 0.76–0.94; Wilcoxon p < 10⁻⁸ on every metric except precision at IoU ≥ 0.5). YOLO-seg reached AP₅₀ = 0.716 and recall = 0.973 versus 0.383 and 0.398 for YOLO + SAM. Under matched inference-only timing, YOLO-seg ran at 27.08 ms per frame (36.9 FPS) versus 1302.78 ms (0.77 FPS), an approximately 48-fold latency gap intrinsic to the two-stage forward pass. Conclusions: YOLO-seg shows strong potential as a 2D perception module for dental navigation, balancing per-instance segmentation fidelity with real-time feasibility under the tested conditions. These results support its use as a 2D perception front-end for future integration with stereo-based 3D reconstruction and robot-assisted navigation; 3D registration accuracy, implant-placement error, and robotic execution remain outside the scope of the present study. Full article

First launched in Boston, MA in January 2018, Community Care in Reach^® is a mobile addiction model that uses mobile clinics to deliver harm reduction and clinical services to people at high risk of drug-related morbidity and mortality and who are unhoused or at risk of losing housing. Through a public/private partnership, the model has grown to include six programs across Massachusetts. Using the RE-AIM framework, this initial, descriptive evaluation for this multisite project included quantitative and qualitative methods to give insight into reach and performance. From 1 January 2022 to 30 June 2024, there were 17,887 harm reduction encounters and 16,117 clinical encounters providing care to 4645 individuals. Buprenorphine-based treatment (a key medication for opioid use disorder) was initiated among 1227 individuals, of whom 15% remained in buprenorphine-based treatment after 180 days. Evaluation across multiple organizations posed unique challenges; however, results demonstrated universal engagement of hard-to-reach individuals. Full article

Falls among older adults are a major public health concern. Older adults living in low-income housing (LIH) are at higher risk of falls due to disadvantages linked to social determinants of health, yet remain underrepresented in research. This study aimed to assess the feasibility of a program addressing fear of falling and its consequences on activity levels among older adults living in LIH in Côte-des-Neiges (Montreal, QC, Canada). A mixed-methods pilot feasibility study was conducted using Bowen et al.’s framework, drawing on data collected through questionnaires, observation grids, attendance records, and semi-structured interviews. The program consisted of six 90 min sessions designed to enhance participants’ confidence in preventing falls while promoting engagement in activities. Fourteen older adults (mean age = 75.5 years) were recruited. Outcome data on fall-related psychological factors, activity engagement, and knowledge about falls were collected before and after the program. Moreover, older participants’ perceived benefits were assessed following program completion. Post-program interviews with older adults, facilitators, and a community field worker were conducted to further explore program feasibility, including its acceptability, implementation, and integration. The program showed strong acceptability and a high attendance rate (95%). Although no statistically significant pre-post changes were observed in the outcome variables, older participants reported several benefits at post-test, including improved knowledge about fall prevention (100%) and greater confidence in their ability to avoid falls (85%). These findings support the feasibility of implementing this culturally adapted fall prevention program in similar LIH settings and provide valuable insights for its refinement and future research. Further investigation is warranted to examine the program’s feasibility across other LIH settings and linguistic groups, as well as to assess its effectiveness. Full article

In China, communities are the primary living environments for older adults, where the neighborhood environment is closely linked to their mental well-being (MW). Old urban residential communities commonly encounter problems including poor housing quality, inadequate public resources, and substandard living conditions. The association between such neighborhood environments and the MW of older adults is particularly worthy of examination. Therefore, based on empirical survey data from Nanjing, China, and from a subjective perception perspective, this study explores how the perceived neighborhood environment in old urban residential communities correlates with older adults’ MW. The findings indicate that both the perceived built environment (BE) and social environment (SE) are correlated with the MW of older adults. The BE has a stronger correlation with MW than the SE, which mediates the correlation between the BE and MW. The correlation between the neighborhood environment and MW is moderated by factors including age, residence type, and average monthly income. Among the component factors of neighborhood environment in old urban residential communities, housing quality, shopping convenience, neighborhood interaction, and community services show significant positive correlations with the MW of older adults. These findings provide valuable implications for the age-friendly renewal of old urban residential communities, the development of age-friendly communities, and the improvement in the subjective well-being of older adults in such communities. Full article