Search Results (10,089)

Urban services increasingly depend on interconnected sensing, communication, computing, decision-support, and response functions, yet technical performance alone does not establish service effectiveness. This structured integrative review examines WSNs, WBANs, V2X systems, 5G-enabled edge–cloud infrastructures, and prospective 6G capabilities across health, mobility, environmental monitoring, energy, water, infrastructure safety, and climate resilience. A five-database search covering January 2018–March 2025 was supplemented by citation tracing and a documented gap-directed update with a final cutoff of 1 July 2026. The analytical corpus comprised 40 peer-reviewed studies, five deployment cases, and one scope-boundary case. An outcome-mediated perception–network–edge/cloud–decision–response framework enabled categorical comparison of heterogeneous evidence without pooling non-comparable measures. Attribution was classified as T1 (comparatively evaluated downstream outcome), T2 (technical or bounded operational outcome), or T3 (conceptual linkage): three studies were T1, 34 T2, and three T3. Ten studies supported target-level SDG alignment, whereas none reached indicator-level correspondence. Evidence remained concentrated at communication, processing, decision-support, and bounded operational endpoints, with limited assessment of response availability and disruption–recovery conditions. Among the deployment cases, only SFpark supported T1 interpretation. These findings characterize the selected corpus and identify a persistent gap between technical performance and comparative, longitudinal, and distributionally assessed urban-service outcomes. Full article

Water treatment plants are essential urban infrastructure with direct implications for public health and everyday life. Data-driven management has received growing attention in drinking water treatment, particularly for optimizing chemical dosing to improve operational efficiency, reduce costs, and ease operator workload. AI-based prediction of coagulant dosage has therefore become an active research topic. Existing studies, however, have focused mainly on model architecture, with less attention to data validity and cost control. In practice, many plants face data-quality problems, including inconsistent dosing records under similar water-quality conditions. Conventional data cleaning may also remove large portions of the dataset, which can weaken model reliability. This study proposes an artificial intelligence (AI) modeling framework for coagulation dosing that handles anomalous data, emphasizes data quality assurance, and combines cost-oriented feedforward prediction with feedback control. A genetic algorithm-optimized backpropagation (GA-BP) neural network was first evaluated on controlled laboratory data and full-scale plant data using the same core model architecture, allowing the effects of model configuration to be separated from those of data quality. Historical plant records were subsequently cleaned through expert-guided validation, approximate time-delay alignment, and turbidity-based classification of operating conditions. Settled-water turbidity was then used as a feedback signal to dynamically adjust subsequent coagulant dosage and assess the resulting chemical savings. Changes in the input structure produced only modest improvements in full-scale prediction performance (R² = 0.53–0.72). In contrast, data cleaning and process-based data organization markedly improved predictive performance, with R² values increasing to 0.927–0.969. Standalone AI models achieved only moderate dosage reductions, while their integration with real-time turbidity feedback provided the best cost-control performance. The model-based control strategy reduced average coagulant consumption by 10.37%, with a maximum reduction of 21.33% at a settled-water turbidity target of 1.9 nephelometric turbidity units (NTU). Across the evaluated feedback-control scenarios, manual dosing was up to 32.83% higher than the corresponding feedback-controlled dosage. Overall, AI models can fit coagulation-dosing data and predict coagulant dosage with sufficient accuracy, but data quality assurance remains the main factor determining model performance. Effective cost control also requires real-time turbidity-based feedback regulation rather than model outputs alone. Full article

Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets. Full article

Long COVID, defined by the National Academies of Sciences as an infection-associated chronic condition persisting at least three months following SARS-CoV-2 infection, presents a growing public health challenge. Despite national prevalence estimates of 11 to 15 percent among U.S. adults who have had COVID-19, the general population estimates of approximately 5 to 7 percent population-level symptom burden in diverse metropolitan areas remain insufficiently characterized. A cross-sectional survey was administered to adults residing in Houston and Harris County. Participants self-reported infection history, persistent symptoms lasting at least 90 days, health status, access to care, and functional impacts. Descriptive statistics summarize infected and affected, symptom frequency, and functional limitations. Of 264 eligible respondents who tested positive for COVID and rated the worst symptoms, 92 (34.85 percent) met the operational definition of Long COVID, representing an estimated sample-based prevalence of 16.91 percent across all 544 survey respondents. Given the use of convenience sampling, this figure should be interpreted as descriptive of this sample rather than as a generalized population-level estimate. Fatigue was the most reported persistent symptom (69.32 percent), followed by brain fog (62.5 percent), shortness of breath (48.84 percent), headache or migraine (43.02 percent), and joint or muscle pain (41.38 percent). Cognitive and functional symptoms predominate over respiratory symptoms. These findings demonstrate a substantial burden of persistent, multisystem symptoms in a sample of community-dwelling urban adults, highlighting the need for longitudinal research and targeted public health strategies to address long-term consequences of SARS-CoV-2 infection in diverse metropolitan settings. Full article

Proper municipal solid waste (MSW) management is vital for mitigating environmental degradation and protecting public health. Landfill site selection remains a complex spatial decision-making challenge, balancing ecological, social, and economic parameters. This study presents a comprehensive systematic review of 175 peer-reviewed articles published between 2016 and 2026, evaluating the evolution of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) frameworks. The findings indicate that road accessibility (93.7%), surface and groundwater protection (91.4%), slope gradients (84.6%), and settlement buffer zones (78.9%) represent the most critical and universally applied siting criteria. Digital Elevation Models (81.9%) and geological maps (57.3%) serve as foundational geospatial datasets. While the Analytic Hierarchy Process (AHP) remains the dominant weighting technique (63.41%), recent trends show an increasing adoption of hybrid multi-criteria models and optimisation algorithms. Geographically, research output is led by India, Iran, and Turkey, peaking significantly in 2025. Crucially, this review exposes prominent methodological shortcomings, notably a heavy reliance on subjective expert validation (73.8%), whereas quantitative validation, sensitivity analysis, and uncertainty assessment remain critically underutilised. In contrast to earlier reviews, this review offers a thorough and critical synthesis of GIS- and MCDA-based approaches to landfill site selection by carefully evaluating methodological advancements, examining the advantages, disadvantages, and limitations of current approaches, and incorporating statistical trends with a structured methodological framework. This approach highlights important research gaps and offers evidence-based suggestions for creating more transparent, reliable, and sustainable techniques for landfill site selection by selecting, screening, and including relevant articles. To foster sustainable urban planning, future research must prioritise standardised evaluation frameworks, rigorous uncertainty quantification, and the integration of artificial intelligence and machine learning with spatial modelling. Full article

As critical biological components of freshwater ecosystems, aquatic insects are highly sensitive to cumulative anthropogenic disturbances and can effectively reflect long-term variations in river ecological quality, serving as ideal bioindicators for aquatic ecological monitoring and assessment. However, most existing benthic biotic integrity indices integrate diverse macroinvertebrate groups, which dilute the unique indicative value of aquatic insect communities, and few targeted evaluation systems have been independently developed for highly disturbed plain river basins in northern China. In this study, a field investigation of aquatic insect communities was conducted across 32 sampling sites in the Haihe River Basin from April to September 2023, aiming to construct a novel Aquatic Insect-based Index of Biotic Integrity (Ai-IBI) and systematically evaluate the basin’s river ecosystem health status. A total of 10,267 aquatic insect individuals belonging to eight orders and 43 families were identified, with Chironomidae dominating the community and predominantly distributing in midstream and downstream reaches. Based on ecological principles, discriminant ability analysis, and redundancy analysis, four core metrics were ultimately screened out to establish the Ai-IBI system, including total taxa richness, percentage of the top three dominant taxa, density of tolerant groups, and percentage of predators. Considering the widespread background ecological degradation of the Haihe River Basin, a localized reference-site selection framework integrating water quality standards, physical habitat assessment, and field ecological surveys was adopted to determine eight minimally disturbed reference sites, which effectively improved the regional applicability of the evaluation model. The health assessment results indicated that only a small proportion of river reaches maintained a healthy state, while most sites were classified as sub-healthy or fair, presenting a distinct spatial differentiation pattern wherein upstream mountainous reaches exhibited superior ecological integrity compared with downstream plain reaches. The constructed Ai-IBI showed high consistency with conventional physicochemical indicators and habitat evaluation results, verifying its excellent reliability and robustness. Different from traditional empirical IBI models suitable for near-natural watersheds, the Ai-IBI fully adapts to the unique ecological background of long-term water resource overexploitation, urbanization-induced habitat fragmentation, and nutrient enrichment in the Haihe River Basin. The Ai-IBI developed in this study provides a refined biological evaluation tool for quantitative diagnosis of river ecological degradation, offers empirical support for differentiated ecological protection and precise restoration strategies in the Haihe River Basin, and serves as a feasible methodological reference for ecological health assessment in other human-dominated plain river basins globally. Full article

Vernonia calvoana (VC), a commonly used medicinal plant in West Africa, has been shown by our research team to inhibit the proliferation of OVCAR-3 ovarian cancer cells through mechanisms involving oxidative stress, DNA damage, and S-phase cell cycle arrest. The objective of the current study was to elucidate the intrinsic apoptotic mechanisms triggered by VC fraction seven (VCF7). OVCAR-3 cells were treated with VCF7 (0, 8, 16, and 32 μg/mL) for a duration of 48 h. Apoptosis was assessed using Annexin V/Propidium Iodide (PI) staining followed by flow cytometry analysis. Mitochondrial membrane potential (ΔΨm) was assessed through JC-1 staining and confocal microscopy, while chromatin condensation was analyzed using DAPI staining. DNA fragmentation was examined by agarose gel electrophoresis. Caspase 3 activity was measured using flow cytometry. Protein expression levels of p53, Bcl-2, cytochrome c, caspase-9, and caspase-3 were determined by Western blot analysis, and mRNA expression levels of p53 and Bcl-2 were evaluated using qRT-PCR. VCF7 induced apoptosis in a concentration-dependent manner. Analysis using Annexin V/PI indicated an increase in apoptotic cell populations from 10.5% to 30%, along with a rise in necrotic cells from 7% to 50% across treatment concentrations. A modest, concentration-associated decrease in mitochondrial membrane potential was recorded (0.96-, 0.88-, and 0.85-fold at 8, 16, and 32 μg/mL, respectively; p < 0.05). DAPI staining validated the concentration-dependent chromatin condensation and nuclear fragmentation. The analysis of DNA fragmentation showed progressive internucleosomal degradation, appearing as a smear pattern with distinct fragments at elevated concentrations, indicative of concurrent apoptotic and necrotic cell death. The activation of caspase-3 reached a peak of 28% at 16 μg/mL. Western blot analysis indicated an upregulation of p53, a downregulation of Bcl-2, an increase in total cytochrome c protein levels, and an increased expression of caspase-9 and caspase-3 in a concentration-dependent manner. These findings were corroborated at the transcriptional level by qRT-PCR, which showed increased p53 mRNA and decreased Bcl-2 mRNA expression. Taken together, these results underscore the potential of VCF7 as a promising plant-derived anticancer agent and support the need for further preclinical and clinical studies in ovarian cancer. Full article

Background: Counterfeit alcoholic beverages pose a major public health threat in Nigeria, yet their infiltration of legitimate supply chains remains poorly understood, especially in Sub-Saharan African markets where informality and weak regulation create conditions overlooked by mainstream counterfeiting models. Methods: This study utilised a qualitative multiple case design, drawing on eight enforcement cases and fourteen semi-structured interviews with retailers, distributors, regulatory officers and police. Results: The findings revealed a contradiction to global models built on the model of upstream adulteration or transnational smuggling. The findings revealed that counterfeiting in Nigeria operates domestically, whereby the supply chain is vertically compressed network in which manufacturing, warehousing, distribution and retail are operated as whole within residential and commercial premises. Three operational clusters are identified: vertically integrated illicit hubs (Cluster A), covert urban micro-production sites (Cluster B), and peripheral component and distribution nodes (Cluster C). Conclusions: Theoretically, the study extends Transaction Cost Economics by showing illicit actors invert conventional governance logic to minimise detection rather than transaction costs; enriches Principal–Agent Theory by demonstrating dual-agency equilibria in which downstream intermediaries serve legitimate and illicit principals simultaneously; and integrates institutional voids theory to explain why this infiltration regime self-reinforces under fragmented licensing and sporadic enforcement. Full article

Accurate forecasting of nitrogen dioxide (${\mathrm{NO}}_2$) in high-traffic urban environments is a critical challenge for public health management and environmental policy. This study presents a hybrid physics-informed machine learning pipeline for ${\mathrm{NO}}_2$ dispersion modeling and short-term forecasting along Viale Regione Siciliana in Palermo, Italy, one of the highest traffic-density corridors in Europe, over six full years (2020–2025) of hourly data resolved at approximately 11 m. Station measurements and weather data are harmonized hourly over the OpenStreetMap road network, converted into emissions with COPERT-Italy (COmputer Program to calculate Emissions from Road Transport) factors, and dispersed with the SIRANE street-network model; the chain is also inverted to recover corridor traffic from the observed ${\mathrm{NO}}_2$. The resulting field is joined with the measured predictors in the machine learning stage, and the output is mapped in GIS Geographic Information System. The hourly scatter between observations and the SIRANE model built on 44,752 h is centered on the 1:1 line, with a Pearson correlation of $\mathit{r}=0.83$. Of two “memory-less” ensembles on an identical predictor set, XGBoost (eXtreme Gradient Boosting) returns the better $R^2$, mean absolute error, and RMSE Root Mean Square Error at all twelve recursive lead times, declining from $R^2=0.79$ at $t+1$ h to a plateau of 0.66 at $t+12$ h, compared to 0.76 to 0.59 for random forest; both reproduce the mean field almost exactly ($r\ge 0.97$), with the XGBoost residual staying between 1.0 and 1.7 $\mathsf{\mu }$g m⁻³ at every horizon and that of random forest growing to 4.3 $\mathsf{\mu }$g m⁻³ by $t+12$ h. The proposed pipeline offers a transferable methodology for urban air quality management in traffic-dominated environments. Full article

This paper examines whether the establishment of “Key Controlled Areas for Air Pollution Prevention and Control” and its associated policy package in China improved residents’ health. Based on data from the China Health and Retirement Longitudinal Study (CHARLS) 2011–2018, Our study finds that this command-and-control environmental regulation is associated with notable declines in residents’ morbidity from asthma, lung disease, and heart disease, while also producing preventive effects. Potential channel analysis indicates that these health improvements operate through reduced air pollution, heightened public environmental concern, and enhanced residents’ lung function. Heterogeneity analysis indicates that the respiratory benefits for asthma are larger among rural residents, males, and the in-service group; the benefits for lung disease are larger among rural residents, females, and the in-service group; the cardiovascular benefits for heart disease are larger among urban residents, females, and the retired group. Furthermore, this paper also finds that the establishment of this policy and its associated policy package is significantly associated with lower residents’ medical expenditures and improved subjective and mental health. Full article

Rapid urbanization and the increasing proportion of time spent indoors have intensified concerns regarding indoor air quality and public health. Fast and reliable prediction of indoor pollutant dispersion is essential for building design optimization and risk-informed emergency response. To enable efficient indoor pollutant dispersion prediction, this study proposes a graph neural network framework with dual-layer aggregation and graph augmentation to address several limitations of traditional GNNs, including restricted receptive fields, inefficient long-range information propagation, and limited ability to capture long-range dependencies. Graph data augmentation is employed to enrich the diversity of concentration field distributions, while a dual-layer aggregation mechanism is introduced to expand the receptive field and enhance message-passing efficiency. Specifically, node features are first aggregated from adjacent edges to capture local information and are then further aggregated across nodes to incorporate global contextual features, enabling the modeling of long-range physical dependencies. Validation across multiple building configurations demonstrates that the proposed model effectively captures physical characteristics of pollutant dispersion in distant rooms and accurately predicts toxic gas dispersion under different layouts, achieving a coefficient of determination $R^2$ of up to 0.97, while delivering computational speeds approximately one order of magnitude faster than conventional CFD solvers. Full article

Despite longstanding vaccine availability, human papillomavirus (HPV) remains the most common sexually transmitted infection in the United States and a leading cause of preventable cancers. Additionally, HPV vaccination rates remain below other routinely recommended adolescent immunizations, particularly in rural populations. This study aimed to identify barriers related to healthcare access, socioeconomic conditions, cultural beliefs, and provider–patient communication in rural communities. A bibliographical review of articles published in English from 2020 to 2025 and an analysis of national datasets were conducted to establish trends in HPV vaccination rates. State-level HPV vaccination data for adolescents aged 13–17 years were obtained from America’s Health Rankings and the Centers for Disease Control and Prevention National Immunization Survey-Teen. States were classified as predominantly rural or urban using Rural–Urban Continuum Codes, and mean vaccination completion rates were compared. The mean HPV vaccination completion rate was lower in rural states (60.55%) compared to urban states (67.31%); however, this difference did not meet the selected threshold for statistical significance (p = 0.025). Barriers identified in the literature included reduced access to healthcare, differences in provider communication, socioeconomic constraints, and limited health literacy in rural communities. Of the identified barriers, healthcare provider recommendations emerged as one of the strongest predictors of vaccine acceptance. These findings highlight multilevel determinants contributing to differences in HPV vaccine uptake and underscore the need for targeted, evidence-based strategies to improve vaccine access and coverage in underserved adolescent populations. Full article

Background/Objectives: While screening, brief intervention, and referral to treatment (SBIRT) is a widely recommended evidence-based approach for early detection and intervention for alcohol and other drug (AOD) use, limited guidance exists for implementing SBIRT among hospitalized adolescents with chronic medical conditions (A-CMCs). This exploratory qualitative study examined A-CMC and caregiver perspectives on factors that may shape the acceptability, feasibility, and equitable implementation of a proposed inpatient SBIRT approach for A-CMCs. Methods: Two separate focus groups were conducted in an urban pediatric hospital in 2023 with A-CMCs aged 13–18 (n = 7), who had a history of hospitalization for their medical condition, and their caregivers (n = 6). Data were coded using thematic analysis guided by the Consolidated Framework for Implementation Research (CFIR) and the Health Equity Implementation Framework (HEIF), which captured implementation and equity-relevant determinants, respectively. Results: Although A-CMCs and caregivers recognized the importance of SBIRT within hospital settings, its acceptability hinged on the conditions of its delivery. The timing, relevance to current health needs, and modality of screening shaped an A-CMC’s willingness to disclose AOD use. Clinician communication style, including the use of a nonjudgmental tone and clear parameters for confidentiality, were also indicated as crucial for SBIRT delivery. Broadly, participants noted the significant impact that the sociopolitical context (e.g., stigma) and structural factors (e.g., financial burden) had on a family’s ability to benefit from SBIRT. Conclusions: In this exploratory qualitative study, participants identified confidentiality-forward, patient-centered workflows, and accessible follow-up supports as potentially important considerations for inpatient SBIRT among A-CMCs. These findings generate hypotheses for future co-design and implementation research across diverse pediatric inpatient settings. Full article

Background: Urban tree canopy, the proportion of a city’s land surface covered by trees, has been linked to physical and mental health benefits. This association is understudied in the U.S. Deep South despite its high burden of poor health and environmental inequity. This study examined associations between census tract-level tree canopy and self-reported mental and physical health in urban Alabama. Methods: We linked tract-level tree canopy estimates for Alabama metropolitan statistical areas with self-reported health and contextual measures from publicly available data, retaining tree canopy as the exposure of interest. A bootstrap-stabilized penalized variable-selection approach identified covariates from socioeconomic, demographic, housing, transportation, environmental, and health-related measures; we then mapped spatial distributions and fit spatial error multivariable models. Results: A higher proportion of land surface covered by trees was associated with a lower age-adjusted prevalence of adults who report 14 or more days during the past 30 days during which their mental health was not good (estimate: −0.0110; p < 0.001). A higher proportion of land surface covered by trees was also associated with a lower age-adjusted prevalence of adults who report 14 or more days during the past 30 days during which their physical health was not good (estimate: −0.0045; p = 0.002). Spatial error parameters persisted in both models, indicating residual spatial patterning not fully explained by canopy or the selected covariates. Conclusions: The findings extend the urban tree canopy literature to an understudied setting and demonstrate how publicly available tract-level data, combined with machine learning-guided covariate selection and spatial modeling, can generate actionable hypotheses for environmental health and equity research. Full article

Urban heat islands in densely built cities pose growing risks to public health and energy demand. Urban parks are key nature-based solutions, yet empirical evidence on park cool island (PCI) effects intensity and extent in compact cities remains limited. This study evaluated PCI for 12 pocket-to-medium/large urban parks in central Taipei, Taiwan. Land surface temperature (LST), mean radiant temperature (MRT), and effective temperature (ET) were measured along transects from park interiors into surrounding areas and linked to local environmental conditions. Parks showed PCI intensity in LST with an average cooling of 4.23 °C and an average cooling of 0.3 °C in ET; MRT cooling was weaker and spatially inconsistent. The cooling of LST extended to about 60 m beyond the park’s edges, while the cooling of ET reached approximately 150 m. In addition, the results show that shade and relative humidity are significant factors affecting PCI, and pervious pavement and wind speed also contribute to cooling LST and ET. The field observations provide evidence that urban parks contribute cooling effects and might reduce the risk of heat hazards. Full article