Search Results (2,765)

Cities worldwide are turning to green infrastructure (GI) as a potential decentralized pathway to stormwater management, heat abatement, and other climate adaptation and wellbeing benefits. As with environmental governance, public engagement in GI planning—from design to implementation and maintenance—is necessary to incorporate diverse perspectives, better understand the potential impact of environmental policies, and ensure fair and equitable outcomes. However, GI is different from broader environmental governance approaches in that it demands on-the-ground labor and long-term maintenance, which are crucial for ecosystem function. In this paper, we conduct a comprehensive literature review of 46 articles published between 2014 and 2024 to provide a more nuanced understanding of public engagement for GI in municipal settings. Results reveal diverse and innovative approaches to engagement that involve integrating social and environmental data, on-the-ground activities, and working groups. We further highlight four key characteristics of GI engagement: (1) the multifunctionality of GI, (2) the incorporation of public and private land, (3) effects on community-building and sense of place, and (4) environmental and social justice. By embracing the multifunctionality of GI and centering justice, engagement efforts are more likely to recruit diverse community members, maintain long-term engagement, and simultaneously address multiple social and environmental needs. Full article

Background and objectives: Artificial intelligence (AI)-based adaptive learning has the potential to strengthen clinical decision-making and enhance quality of care at primary health centers. The present study assessed the perceptions, barriers, and learning needs involved in AI-based adaptive learning among primary care physicians in Northern Saudi Arabia. Methods: We used a cross-sectional study design to obtain data from 285 primary care physicians of different cadres working in various primary health centers. A validated data collection tool was used to measure three domains: perceptions, barriers, and learning needs. A multivariable analysis was carried out to identify the factors associated with these three domains. Results: Among the studied participants, low perceptions were observed in 55.1% of physicians; they were higher among those aged >40 years (p = 0.019) and non-Saudi nationals (p = 0.003). High barriers were reported by 42.5% of respondents, and this was higher among those aged >40 years (p = 0.031). Learning needs were higher among non-Saudi nationals (p = 0.017) and those with >10 years of experience (p = 0.007). The perception and learning need scores were positively correlated, and barrier scores were negatively correlated with the other two domains (p < 0.001). Conclusions: The authorities concerned may consider implementing targeted measures for AI-based adaptive learning. Moreover, efforts should be made to reduce the barriers to AI-based adaptive learning at all levels. These measures could strengthen primary care practice and enhance patient care. Full article

Background/Objectives: Hepatitis E virus (HEV) infection is an increasingly recognized cause of acute hepatitis in Europe, but short-term in-hospital laboratory dynamics remain insufficiently described in hospitalized cohorts. We aimed to characterize admission biochemical abnormalities and paired admission-to-discharge laboratory changes in hospitalized patients with acute hepatitis E from Craiova, Romania, with exploratory sex- and age-stratified analyses. Methods: We conducted a single-center retrospective observational study including 40 consecutive hospitalized patients with acute hepatitis E during 2024–2025. Admission and discharge laboratory values were compared at the within-patient level, and exploratory subgroup analyses by sex and age class were performed. Given the limited sample size, multivariable analyses were restricted to parsimonious age-adjusted models for selected endpoints. Results: The cohort comprised 22 females (55%) and 18 males (45%), with a mean age of 53.05 ± 21.44 years; two in-hospital deaths occurred. At admission, marked transaminase elevation and frequent hyperbilirubinemia were observed, with 70% of patients having total bilirubin ≥ 2 mg/dL and 40% ≥ 10 mg/dL. During hospitalization, ALT and AST declined markedly, whereas total and direct bilirubin improved more modestly, indicating slower resolution of jaundice/cholestatic abnormalities. Platelets increased, while prothrombin index changes were heterogeneous. Male patients had higher bilirubin values at admission and discharge and more frequent clinically relevant hyperbilirubinemia thresholds; however, these findings should be interpreted cautiously given the small sample size, the retrospective design, and the absence of standardized clinical confounders and mechanistic data. Exploratory age-stratified analyses did not identify robust differences after multiplicity control. Conclusions: In hospitalized hepatitis E, hepatocellular injury markers improved rapidly during hospitalization, whereas cholestatic abnormalities resolved more slowly and often remained clinically relevant at discharge. The observed sex-related cholestatic pattern should be considered exploratory and requires confirmation in larger studies with standardized clinical covariates and longer follow-up. These findings support closer monitoring of bilirubin trajectories at discharge, particularly in male patients, and highlight the need for integrating laboratory dynamics into short-term clinical assessment of hospitalized HEV cases. Full article

Accurate prediction of hard disk drive (HDD) health states is critical for enabling proactive data maintenance and ensuring data reliability in large-scale data centers. However, conventional models often suffer from semantic entanglement among heterogeneous SMART attributes and from the masking of incipient failure signatures by stochastic noise. To address these challenges, we propose SDGR-Net, a spatiotemporally decoupled learning framework designed to model the complex degradation dynamics of HDDs. SDGR-Net introduces three synergistic innovations: (1) a spatiotemporally decoupled dual-branch encoder that disentangles longitudinal temporal evolution from cross-variable correlations via parameter-isolated branches, thereby reducing representational interference; (2) a parsimonious dual-view temporal extraction mechanism that captures early-stage anomalies through forward–reverse sequence concatenation, enabling high-fidelity preservation of non-stationary pre-failure patterns; and (3) a cross-branch dynamic gated residual fusion module that functions as an adaptive information bottleneck to emphasize failure-critical features while suppressing redundant noise. Extensive experiments conducted on three heterogeneous HDD datasets, ST4000DM000, HUH721212ALN604, and MG07ACA14TA, demonstrate that SDGR-Net consistently outperforms six state-of-the-art baselines. In particular, SDGR-Net achieves a peak fault detection rate (FDR) of 0.9898 and a 69.6% relative reduction in false alarm rate (FAR) under high-reliability operating conditions. These results, corroborated by comprehensive ablation studies, indicate that SDGR-Net effectively balances detection sensitivity and operational robustness, offering a practical solution for intelligent HDD health monitoring. Full article

Challenge-based learning (CBL) is a student-centered approach engaging learners in complex, open-ended problems. While such openness can foster deeper learning and creativity, it also demands high self-direction. Teachers must therefore provide sufficient scaffolding without undermining CBL’s authenticity. This study examines how secondary education teachers design and enact scaffolding in sustainability-oriented CBL projects, and how students experience this support. A multiple-case study of three projects distinguished soft scaffolding (adaptive, just-in-time support) from hard scaffolding (predefined tools and structures). Data included teacher interviews (n = 3), student focus groups (n = 18), and classroom observations. Findings showed the scaffolding type and function depended on project open-endedness, teacher readiness, and alignment with project focus. In the most open-ended project, the teacher mainly used soft scaffolding to guide thinking and decision-making, whereas more structured projects relied on hard scaffolds for procedural support. Students’ experiences varied: some thrived under autonomy, developing ownership and engagement, while others needed more explicit guidance. These results suggest that scaffolds should align with project goals, be explicit in their contribution to progress, match students’ readiness for self-directed learning, be enacted by teachers comfortable in a coaching role, and include affective support to help students manage frustration and uncertainty. Full article

In response to global climate change, low-carbon transition in the industrial sector has become essential for emission reduction. Industrial parks, as concentrated centers of production, are major sources of urban energy use and carbon emissions. Whether park-based policy interventions can generate broader decarbonization effects remains unclear. This study conceptualizes China’s National Low-Carbon Industrial Park Pilot Policy (NLCIPP) as a meso-level systemic intervention and examines its impact on urban carbon intensity (UCI). Using panel data for 282 Chinese cities from 2006 to 2020, causal effects are identified through a multi-period DID framework combined with a synthetic DID approach. The results show that the NLCIPP significantly reduces UCI, indicating that energy-oriented interventions at the industrial park level can induce broader decarbonization outcomes. The policy effect mainly works via reduced energy consumption and enhanced green technological capability, while the contribution of industrial structural upgrading is relatively limited. Stronger impacts appear in central regions, cities with stricter environmental regulation, and non-resource-based cities, highlighting the context-dependent effectiveness of energy transition policies. These findings provide empirical evidence for designing effective industrial energy policies to promote low-carbon transition. Full article

This paper presents robotic manipulation methods for rapid high-arc object transfer using dynamic, non-prehensile interactions. Two complementary techniques are introduced, two-fingered scoop-and-flick and one-fingered topple-and-flick, designed for objects with low and high centers of mass, respectively. Both methods enable a robot to retrieve objects resting on a surface and launch them into controlled projectile trajectories without requiring stable grasp formation. To support these maneuvers, we develop physics-based models of object acquisition and release, and combine them with a data-driven framework. While analytical modeling guides the acquisition phase, the highly nonlinear flicking dynamics are captured using learned predictive models that enable accurate selection of control parameters for desired trajectories. The proposed techniques enable dynamic object transfer, reduced grasp planning complexity, and adaptability to environmental constraints. Experiments conducted on a custom robotic platform demonstrate reliable and accurate high-arc object transfer, in which the majority of object displacement is achieved through projectile motion. Full article

Psoriasis patients face a significantly elevated risk of cardiovascular diseases (CVD), necessitating early and accurate risk prediction tools. This study developed and validated a machine learning model to predict CVD risk in psoriasis patients using clinical and biochemical data from 2685 individuals. After preprocessing and addressing class imbalance with SMOTE-NC, six machine learning models (Logistic Regression as baseline, XGBoost, LightGBM, CatBoost, GradientBoosting, AdaBoost) were evaluated using a completely leak-free nested cross-validation framework (outer k = 10, inner k = 3) with randomized hyperparameter search (n_iter = 50). Feature selection via the Boruta algorithm was performed separately within each training fold to prevent data leakage. The Boruta algorithm identified 21 key predictors, including age, systolic blood pressure (SBP), apolipoprotein B (apoB), fasting blood glucose (FBG), and complement C1q. CatBoost emerged as the top-performing model (OOF ROC-AUC = 0.908, 95% CI [0.892–0.924]; PR-AUC = 0.509, 95% CI [0.448–0.578]; F1 = 0.540; MCC = 0.498; Brier = 0.078), while the Logistic Regression baseline achieved ROC-AUC = 0.909 but was eliminated due to poor calibration (Brier = 0.114 > 0.10). All metrics were evaluated with 95% bootstrap confidence intervals (n = 1000 iterations). Explainable AI techniques (SHAP, LIME, Anchors) revealed that older age, elevated SBP, and metabolic dysregulation (e.g., high apoB, FBG) were the strongest CVD predictors. Local explanations were provided for five representative patients (high-risk, low-risk, and randomly selected), rather than a single instance, to better characterize model stability. Limitations include the single-center, retrospective design and lack of external validation. Future work should incorporate multi-ethnic cohorts and advanced biomarkers (e.g., genetic, imaging data) to enhance generalizability. This study demonstrates the potential of explainable AI to improve CVD risk stratification in psoriasis patients, offering a scalable tool for preventive cardiology. Full article

Translating heterogeneous user requirements (URs) into robust engineering specifications for public-access products is a critical challenge, often impeded by information uncertainty and fragmented design processes. To address this, we propose an integrated decision-making framework underpinned by Rough Set Theory (RST) as a unified mathematical language for uncertainty management. The framework systematically guides customer-driven product development by integrating a series of RST-based methods: a Kano model analysis to screen URs, a novel rough-Shapley value model to determine their interdependent weights, a rough-QFD approach to translate them into weighted design requirements (DRs), and the rough-VIKOR method to select the optimal design alternative. A case study on public-access faucets validates the framework’s efficacy. The results demonstrate its capability to identify critical URs, derive robust DRs by systematically resolving technical attribute conflicts, and select a superior design solution that optimally balances hygiene, durability, and user experience. The application of the framework successfully identified Alternative ${\mathrm{A}}_1$ (Push-Activated Spout) as the optimal solution, demonstrating superior performance in proactive hygiene and core functionality. The results prove that maintaining data integrity through a unified RST pipeline effectively resolves early-stage design conflicts. This research contributes a rigorous, data-driven decision support system that enhances objectivity and information fidelity, providing a transparent and auditable methodology for designing human-centered public infrastructure. Full article

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

Floods are among the extreme events associated with climate variability in the Lacramarca River basin, located in the department of Ancash, Peru. Meteorological phenomena such as El Niño during the periods 1982–1983 and 1997–1998, as well as the Coastal El Niño in 2017, constitute key reference events that motivated the development of the present study, based on a case study conducted in the area between the rural settlements of Santa Clemencia and Pampadura. This research is based on maximum precipitation data derived from historical climate records and from the climate scenarios ACCESS 1-3, HadGEM2-ES, and MPI-ESM-MR, as well as the median projected scenario for 2050, obtained from the National Meteorology and Hydrology Service of Peru (SENAMHI) data platform. This information was analyzed considering the spatial location of the basin and its position relative to the area of interest, using Intensity–Duration–Frequency (IDF) curves. To demonstrate the changes in the river hydrological behavior before and after the 2017 Coastal El Niño event, a Random Forest modeling approach was applied using Sentinel-2 satellite imagery. Design peak discharges for return periods of 50, 100, and 140 years were estimated using the HEC-HMS software. Hydraulic simulation of the Lacramarca River basin, carried out using HEC-RAS version 6.7 beta 3 and IBER version 3.3.1 software, made it possible to identify flood-prone areas affecting agricultural land and areas adjacent to population centers, covering 149,000 m² and 172,000 m² for return periods of 100 and 140 years, respectively, based on information from the historical scenario. In contrast, using data from the 2050 projection scenario, affected areas of 242,000 m² and 323,000 m² were estimated for the same return periods. Full article

Objectives: Design strategies to improve management, outcomes, and quality of life for people with heart failure (HF) in Spain through the identification of areas of improvement regarding diagnosis, treatment, comorbidities, progression of disease and healthcare coordination between specialists. Methods: IC-MMERSIVE project was developed by the Cardiology and Primary Care Integration Working Group of the Spanish Society of Cardiology. The project included a pre-session survey for participants, face-to-face sessions led by a clinical cardiologist, and post-session questionnaires for the moderator and for participants. A web platform was created to host program content and resources and electronic forms for data collection and analysis. Results: A total of 1186 physicians (80.5% cardiologists) participated in 144 face-to-face sessions throughout Spain. When patients are at risk for HF (HF stage B), 78.9% of respondents said they proactively search for HF. Only 38.0% were familiar with and applied the IC-BERG study questions designed to detect falsely stable patients. Specific protocols for optimizing and implementing the four pharmacologic pillars of treatment for HF were used by 51.6% of participants, 53.9% had protocols to reach the guideline-recommended target doses, and 25.6% reported no nursing involvement. Structured follow-up was conducted in 53.9% of cases. Even though 63.0% used shared single medical records, the connection between specialized HF consultations and healthcare centers was occasional in 72.1% of cases. Conclusions: There is marked room to improve HF management in daily clinical practice. These findings highlight specific actionable gaps in HF management and support the need for structured, multidisciplinary strategies to improve patient outcomes. Full article

Background: The Cystic Fibrosis Foundation (CFF) recognizes exercise as a critical part of managing cystic fibrosis (CF). This becomes even more important in the era of highly effective modulator therapy (HEMT) due to many people with cystic fibrosis (pwCF) having decreased symptom burden and a newfound ability to tolerate exercise better. Our single-center pilot study was designed to assess the implementation of a remotely delivered, individualized, and comprehensive exercise program for pwCF. We aimed to determine the feasibility, safety and acceptance of this intervention. Methods: PwCF ≥ 18 years old were recruited and consented at the University of Alabama in Birmingham in 2022 and 2023. Basic fitness was assessed for each participant, and an individualized exercise prescription was prepared for each participant, who was expected to exercise three times weekly on a remote basis with the exercise physiologist for 12 consecutive weeks. Subjects were reassessed at 4 and 7 months for post-exercise evaluation. Patient demographics and clinical parameters, including exacerbation rate, FEV₁ percent predicted, 6-min walk test (6MWT), and modified shuttle test (MST) were collected. Questionnaire data from the CFQ-R, PRAISE, and IPAQ were also recorded. The study was registered with ClinicalTrials.gov (NCT04680403) and was submitted on 17 December 2020. Results: Our goal was to enroll 12 participants over the 2-year study period. We were able to recruit nine people for the study, with four participants finishing the program. From the 36 sessions offered over the 12-week program, participants completed an average of 15 sessions. Clinical outcome data was observed, including lung function and exacerbation frequency, but not statistically analyzed due to the small sample size. Conclusions: Implementation of an individualized telehealth-based exercise program for pwCF was well received by participants, safe, and appreciated by the participants. Recruitment and adherence were challenging, which was partially due to the ongoing pandemic. Follow-up studies are needed to assess whether improvements in reducing the amount or supervision of weekly exercise sessions and/or extending the total time might help with adherence. Full article

Accurately reconstructing Mechanical, Electrical and Plumbing (MEP) systems from laser-scanned point clouds is often hindered by structural occlusions, sensor noise, and extreme scale imbalance between large pipes and small fittings. This study presents a hybrid framework, driven by both knowledge and data, for automated pipeline BIM updating. To tackle scale variance, we implement a coarse-to-fine segmentation strategy using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to isolate pipeline instances before segmentation with PointNeXt. Furthermore, a logic-based refinement module integrates geometric and topological priors from the design BIM to correct coordinate deviations in incomplete datasets. Finally, graph isomorphism analysis enables automated topological mapping between unstructured point cloud instances and structured BIM components. Experimental results from a dense shopping center case study demonstrate that the framework achieves a semantic segmentation mIoU of 74.45% and reduces the average spatial coordinate error to within 7 mm. Notably, the automated workflow compressed the modeling time from 3–5 days to approximately 3 h, offering a robust solution for digital twin-oriented facility management. Full article

Maritime Autonomous Surface Ships (MASS) are progressing from proof-of-concept to engineering test and initial application phases due to advancements in intelligent sensing, automatic control, and communication technologies. However, numerous studies have shown that the improvement of automation level does not linearly reduce human factor risks. Instead, it exhibits more complex evolutionary characteristics at the medium automation level. In particular, MASS Level 2 (MASS L2) features a “system-dominated, human-supervised” operational mode, and its human factor risks have become one of the key factors restricting the safe operation, large-scale application and sustainable long-term deployment of autonomous ships. This study employs a systematic literature review to analyze 89 core articles (2020–2025) and summarizes the theoretical basis, risk characteristics, and evolutionary trends of human factor risk research in MASS L2. The review results indicate that the current research consensus has gradually shifted from the traditional “human error”-centered explanatory paradigm to a systematic understanding of “information mismatches, opacity, and coupling failures in the human-machine-shore collaborative system”. Typical human factor risks in MASS L2 are mainly manifested as the degradation of supervisory cognition and situation awareness, imbalance in trust in automation, vulnerability in mode switching and takeover, skill degradation, and structural risks in ship-shore collaboration. Based on these findings, this study constructs a classification system and a comprehensive analysis framework for human factor risks in MASS L2, reveals the interaction relationships and dynamic evolution mechanisms among different risk types from a system-level perspective, and further discusses the limitations of existing research in terms of methods, data, and engineering applicability. Finally, considering the development trends of autonomous ship technology, this study proposes future research directions in human factor theoretical modeling, dynamic risk assessment, system design, and operation management. This study aims to provide a systematic knowledge framework for human factor risk research in MASS L2 and offer references for the safety design, safety management, and development of higher-level automation of autonomous ships, while supporting the sustainable and safe advancement of the global intelligent shipping industry. Full article