Prior research has shown that, among normal hearing college students, Hispanic-identifying participants experience higher levels of environmental noise and lower signal to noise ratios as compared with White non-Hispanic participants. The primary objective of this study was to examine whether these differences extend to cochlear implant (CI) users by using CI datalogging to quantify characteristics of the listeners' auditory environments. The authors further examined whether differences in auditory environments between groups persisted after controlling for demographic and socioeconomic factors. The primary socioeconomic variable of interest was population density, as it strongly correlates with other socioeconomic factors (e.g., education and income) and is more likely to directly influence auditory environments. A retrospective chart review of CI patients at a tertiary medical center in New York City identified 80 adults (38 Hispanic, 42 White non-Hispanic) for further review. Demographic variables were compiled, and home addresses were used to obtain population-based socioeconomic data via the U.S. Census. Datalogging information extracted from the CI speech processor included hours of total use and time spent in different auditory environments, classified by the CI software into sound levels (in dBA) and sound scenes ("noise," "quiet," "speech in noise," "speech in quiet," "music," and "other"). Despite similar levels of device usage, there was a statistically significant group difference in the percentage of time spent in each scene: Hispanic-identifying participants spent more time in "speech in noise," "music," and "noise"; White non-Hispanics spent more time in "quiet" and "other." The Hispanic participants lived in census tracts with higher population density, which correlated with higher sound levels (>70 dBA) in the environment. Group differences in auditory environments remained statistically significant after controlling for age, CI experience, and population density (median daily level difference ~2.4 dB). Even after accounting for demographic and socioeconomic factors, the two groups showed distinct auditory environments, indicating a possible cultural contribution to these differences. Audiologists counseling CI patients regarding auditory environments should be conscious of their patients' cultural background and may consider the impact of listening preferences when advising on which environments to seek out or avoid.

Amidst rapid global urbanization, understanding the association between urban planning and mental health is crucial. This review synthesizes fragmented evidence on six core elements - walkability, green spaces, blue spaces, population density, public transport accessibility, and road design-to address gaps in causality and equity, providing timely insights for creating psychologically supportive urban environments. Evidence consistently indicates a moderate protective association between urban green space and mental health. Findings for walkability and population density are highly heterogeneous, often moderated by socioeconomic factors and air pollution. Research on blue spaces and road design remains limited, highlighting significant variability and context-dependence across planning elements. Methodological challenges, such as establishing causality, simplistic exposure metrics, and inadequate consideration of equity-constrain current evidence. Future research should prioritize longitudinal and natural experiment designs, dynamic multiexposure assessments, and explicit equity integration to generate actionable guidance for urban planning that supports mental well being equitably.

PM2.5 pollution remains a critical environmental and public health challenge in China despite post-2015 improvements. However, our understanding of the spatially heterogeneous and nonlinear associations of its driving factors remains limited. To fill this gap, we employ an explainable geospatial artificial intelligence (GeoAI) framework that integrates the geographical random forest (GRF) model and the Shapley additive explanations (SHAP) approach to examine the associations between 16 determinants and PM2.5 concentrations. Based on a nationwide and multi-year analysis across 288 cities selected from all 336 Chinese cities between 2015 and 2022, the results show that GRF achieves at least a 0.04 higher R² than baseline models. Our analysis reveals three findings. First, population density is the most influential factor in 52.39% of cities; combined with temperature, road density, and gas supply, these four dominate over 95% of cities. Second, drivers exhibit significant spatially varying and nonlinear associations. For instance, population density correlates positively with PM2.5 in the North China Plain but negatively in sparsely populated areas; and the association of temperature follows an inverted U-shaped pattern. Third, these spatial and nonlinear associations undergo temporal changes. These findings offer insights for future environmental management strategies to mitigate the negative impacts of various drivers.

The invasive nematode Angiostrongylus cantonensis (rat lungworm) can cause eosinophilic meningitis in humans. Once restricted to Southeast Asia, A. cantonensis nematodes are now widespread across the tropics and have been reported in Europe. Tenerife, in the Canary Islands, and the Mediterranean region are emerging hotspots. We surveyed gastropods, rats, and lizards across Tenerife and detected the parasite in all host groups at 2.4%-41.6% prevalence. Using species distribution models, we identified precipitation seasonality as the main driver of habitat suitability; tree cover and climatic variability primarily shaped prevalence patterns. Modeling showed suitable habitats in northeastern Tenerife and several western Canary Islands but limited overlap with areas of dense human population. Multivariate environmental similarity surface analysis comparison with another A. cantonensis hotspot, Hawaii, USA, revealed similar environments across the archipelago, except for the novel northeastern Tenerife area. Although no human infections have been reported, continued vigilance is warranted because A. cantonensis nematodes are established in Tenerife.

• Employ Bayes’ rules to infer carsharing travel purposes of 4.8 million trips across 58 cities. • Investigates distinctive characteristics of carsharing usage patterns in different cities. • Examine how urban and system characteristics influence usage under various trip purposes. • Support policy formulation for local governments and operational strategies for carsharing companies. Carsharing provides a convenient travel option and has attracted increasing attention in Chinese market in recent years. This study conducted an analysis of carsharing travel patterns and purposes across multiple Chinese cities. We collected over 4.8 million trips from 58 cities, employed Bayes’ rules to infer users’ trip purposes and analyzed their travel patterns within a comparative analytic framework. The analysis explores variations in carsharing usage and trip purposes across cities and examines how urban characteristics influence various trip purposes. The results reveal a strong preference for leisure-oriented travels using carsharing services. There are more work-related trips during daytime and leisure activities in the evenings on weekdays and predominantly leisure activities on weekends. Additionally, there is considerable variability in the distribution of trip purposes across cities. The megacity demonstrates a more balanced usage pattern, whereas the Super-Large City shows a higher demand for commuting trips. In Large City I and Large City II, carsharing is primarily used for leisure and recreational trips. Some carsharing system characteristics, including the number of carsharing parking spaces and shared vehicles, are associated positively with carsharing utilization for different trip purposes, while other factors, like population and road densities, show varying impacts. This study offers a systematic exploration of carsharing services in China and contributes to improving the efficiency of carsharing services in diverse urban contexts.

Comparative assessment of ovitraps for the collection of Aedes albopictus (Diptera: Culicidae) eggs in field studies in Attica Region, Greece.

Prehospital time efficiency in the Portuguese Stroke Fast-Track pathway: a nationwide observational study.

Effects of Weak Organic Acid Salts on Growth Kinetics of Spoilage-Associated Lactic Acid Bacteria in Cured Ready-to-Eat Turkey Breast.

When aquatic exposure ends but effects persist: metal-dependent recovery dynamics.

Forests are affected by novel biotic disturbance agents that require timely evaluation of their relevance and adequate response actions. Oak forests are of particular ecological and economical value and hold great potential for future Central European forests due to their high drought tolerance. Yet, pedunculate and sessile oak ( Quercus robur L.; Q. petraea (Matt.) Liebl.) suffer from recurrent declines. Predisposing, inciting, and contributing factors are manifold. Among the latter, the two-spotted oak borer, Agrilus biguttatus Fabricius, is commonly perceived as a secondary pest. Girdling of the hosts’ vascular tissues by larval feeding might be the proximate cause of death for previously weakened oaks. At high population densities, A. biguttatus might even colonize healthier oaks. Quantitative studies on the causality between A. biguttatus colonization and oak host mortality are still missing and prove challenging to conduct due to A. biguttatus ’ cryptic lifestyle, extended periods of its apparent absence, and its difficult experimental handling. Nonetheless, we perceive an urgent demand for advice from forest practitioners on how to handle A. biguttatus . Reliable early detection methods and action thresholds are deficient, and population control is thus far limited to sanitation felling of unvalidated efficacy. We aim to provide perspectives on the issue by briefly reviewing what is known about the species, evaluating its damage potential, outlining an integrated management, and prioritizing critical research questions. We advocate streamlined research efforts to enhance our understanding and foster progress toward a more sophisticated management. The case of A. biguttatus might ultimately serve as a blueprint for other emerging pests. • Empirical observations indicate A. biguttatus as a mortality agent for weakened oaks, but causality has not yet been proven. • Despite this uncertainty, we examine management options to meet foresters’ demands and prepare for future outbreaks. • We conclude by prioritization of relevant research needs to enhance decision-making and long-term maintenance of oak stands.

ABSTRACT Geographic Artificial Intelligence supports smart city land management, where modeling complex inter-parcel relationships and extracting effective features remain key challenges for accurate land use classification. Urban areas exhibit diverse relationships including spatial similarity between adjacent blocks, configurational similarity between non-adjacent blocks, and heterogeneous relationships among functional zones. However, existing research lacks comprehensive frameworks to fully describe these complex interaction systems. We propose a graph neural network framework based on higher-order Markov inference that integrates three types of complex relationships for urban land use identification. The framework utilizes social media check-in data to construct a third-order transition matrix, explicitly modeling population mobility’s chain influence mechanism. It employs hypergraph structures to fuse point-of-interest semantic features with remote sensing visual features, capturing similarities among spatially distant but functionally homogeneous areas. Finally, it integrates multi-source feature embeddings and block adjacency relationships through distance-weighted graph attention networks. Empirical studies using real data demonstrate superior performance compared to traditional machine learning methods. Higher-order activity type inference performs optimally in areas with high population density, monofunctional land use, and heterogeneous destination land use patterns for inter-regional travel. This model provides scientific modeling approaches and analytical tools for urban land use planning and smart city management.

How heritage cities can sustain everyday living support while preserving historical value is a central question in sustainable urban governance. Existing studies have focused mainly on heritage identification, conservation performance, or tourism development but have paid less attention to how relatively fixed heritage endowments are converted into contemporary living support. This study examines 143 national historical and cultural cities in China. It develops a fixed heritage endowment index (fixed HEI) and comparable living support indices (comparable LSI) for 2012 and 2022. Within a dual-scale framework of 5km grids and city/county units, Markov transition matrices, transition type profiling, and XGBoost-SHAP are combined to identify living support conversion in high-heritage resource units and its associated conditions. The share of nonzero LSI grids increased from 45.58% in 2012 to 61.27% in 2022, indicating an expansion of observable living support intensity under a harmonized POI framework rather than a precise increase in the absolute stock of facilities. In the high-resource sample, 70.22% of the HL grids remained HL, 29.78% shifted to HH, and 98.97% of the HH grids remained stable. The main model-based association structure focused on road network density, the impervious surface area ratio, and population density, and spatial lag robustness checks further revealed that neighborhood road network density provided additional explanatory information. These results show that high heritage endowments do not automatically translate into high levels of living support. This study moves research on historic cities from static resource evaluation to resource conversion diagnosis and suggests that heritage city governance should shift from resource identification to the governance of resource conversion capacity.

Airborne pathogens and antimicrobial resistance (AMR) pose growing health risks in cities, where enclosed spaces, inadequate ventilation, and high population density enhance their persistence and dissemination. However, the microbial burden and risk associated with high-occupancy public spaces remain poorly quantified. Here, we compared bioaerosol characteristics across university cafeterias and a subway station, dry- and mixed-waste collection facilities (WCFs), and an urban air monitoring site by using culture-based, molecular, source-tracking, and risk-assessment approaches. The results showed that Crowded Public Spaces (CPSs) harbored culturable bacterial and AMR burdens comparable to those in WCFs, both far exceeding levels at the urban air monitoring site. Human-associated sources contributed to ∼50% of airborne bacteria, and multidrug-resistant isolates (∼60%), high-risk β-lactam ARGs, and clinically relevant pathogens were further enriched in CPSs. We further applied a population-weighted infection burden (PWIB) metric that integrates infection risk with pedestrian volume and dwell time. Although contamination levels in CPSs were similar to those in conventional microbial hotspots, CPSs contributed more to the city-scale infection burden once population exposure was taken into account. These findings reveal that urban airborne microbial risk is shaped not only by contamination intensity but also by human occupancy and exposure patterns. This study highlights the value of incorporating human activity into microbial risk assessment in high-density urban environments.

Industrial land bias is a persistent outcome of China’s land allocation system, but why its efficiency penalty differs across cities remains insufficiently explained. This study examines this unevenness by linking land allocation, population density, and city type heterogeneity within a unified framework. Using panel data for 281 prefecture-level and above cities in China from 2010 to 2022, we combine two-way fixed effects estimation with robustness checks, dynamic panel analysis, transmission channel tests, subsample comparison, and interaction models. Results show that industrial land bias significantly reduces urban land economic efficiency, with the strongest penalty after a one-year lag. Population density is an important spatial transmission channel: industrial land bias lowers density mainly by expanding built-up land faster than population concentration. The penalty is the largest in border cities, smaller in coastal cities, and statistically insignificant in general cities. The negative effect weakens as the secondary industry share increases, suggesting that local production capacity helps absorb industrial land expansion. The contribution of this study is to explain why the same industrial land bias generates uneven efficiency penalties across coastal, general, and border cities, providing evidence for place-sensitive land supply policies.

ABSTRACT The territorial networks that humans create are examples of cellular networks, a broad class of polygonal structures that share a consistent set of geometrical and topological properties. Hexagonal polygons predominate in cellular networks, but previous studies of territorial networks have failed to determine whether measuring polygon contact number provides useful information as to how the network evolved. Here we argue that this was due to a failure to integrate contact number measurements with an understanding of a network’s historical development. We analyse the contact number distribution of the English parish boundary network, uncovering a region of hexagonality that corresponds well to an area already known to be culturally and demographically distinctive in the early Middle Ages. Spatial variation in contact number in the English parish boundary network is shown not to be a simple function of topographical homogeneity and population density; instead, such relationships are mediated by spatial variation in tenurial and social systems. We conclude that the actual cause of hexagonality is more complex than often assumed in models of cellular network evolution, and that a holistic approach to the study of contact number can yield novel insights for landscape historians, historical geographers and spatial analysts.

ABSTRACT Regional landslide risk assessment is essential for disaster prevention, yet traditional methods are limited by inadequate characterisation of conditioning factor heterogeneity in landslide susceptibility prediction (LSP) and spatial scale mismatch between administrative population data and slope units. This study develops an integrated framework combining automated slope unit division, heterogeneity quantification, and machine learning. Using Multi-Scale Segmentation in Anyuan County, 148,608 slope units were divided. Intra-unit heterogeneity for LSP was quantified through statistical features of conditioning factors. The Heterogeneous Slope-RF model incorporating heterogeneity achieved higher predictive accuracy (AUC = 0.941) than models ignoring it. For population density, an upper threshold of 120 persons/km² was applied, and a slope unit-scale RF model produced precise estimates (R² = 0.8157, MAE = 1.7 persons/km²). This refined distribution reduces overestimation of risk in urban centres and identifies dispersed settlements in high-susceptibility mountainous areas. By integrating this population distribution, landslide susceptibility, and AHP-based vulnerability, a spatial map of potential population loss risk was generated for Anyuan County. The study confirms that incorporating spatial heterogeneity and refined population modelling improves landslide risk assessment accuracy, supporting identification of genuine high-risk zones.

Complex group behavior can emerge from simple inter-individual interactions, yet how prior social experience shapes these interactions remains unclear. Using naturalistic and virtual-reality experiments, we varied prior social experience by exposing zebrafish larvae to high or low group densities, then measured its effects on future social interactions and group structure. We find that inter-individual distances decrease after exposure to high population densities and increase after exposure to low densities. These adaptations develop gradually over tens of minutes and remain stable for hours. Mechanistically, larvae estimate group density from the frequency of neighbor-evoked retinal looming events and couple interaction strength to that estimate. A time-varying computational model incorporating previous visual-social experiences accurately describes our observations. Our findings show that inter-individual interactions are not static, but continuously evolve with social experience and environmental demands, establishing larval zebrafish as a model for studying the neurobiological mechanisms of experience-dependent modulation of collective behavior.

ABSTRACT This study explores the relationship between green space availability which is measured as both proportional land cover and per capita values, and socio-economic indicators across 129 regions in eight Southeast Asian countries. Using a cross-sectional and quantitative approach, the research assesses correlations between green space availability and key metrics such as the Human Development Index (HDI), GDP per capita, health index, and life expectancy. Data were sourced from Global Data Lab and mined from Google Earth Engine. Descriptive statistics, Spearman correlation, multiple linear regression, and cluster analyses were applied to identify patterns and relationships. The findings highlight considerable differences in the amount of green space available per person across regions, with more developed areas generally having less green space per capita due to population pressures. Results show significant negative correlations between green space per capita and HDI (ρ = −0.6445, p < 0.05), GDP per capita (ρ = −5,492, p < 0.05), and health index (ρ = −0.6794, p < 0.05). Green space proportion also negatively correlated with HDI (ρ = −0.1345, p < 0.05). These results suggest that the observed negative relationships between green space and development are not necessarily indicative of a direct trade-off, highlighting the need to reinterpret green space indicators within rapidly urbanising Southeast Asian contexts. When population density is introduced as a control variable, these associations remain consistent, suggesting that density-related spatial pressures, rather than the absence of green space itself, largely drive these patterns.

Home and school urban food environments in relation to childhood overweight and obesity in Madrid.

ABSTRACTBackgroundAccurate forecasting of COVID‐19 cases is essential for effective public health planning and resource allocation. Traditional statistical and deep‐learning models often fail to jointly capture linear dynamics, nonlinear patterns, and exogenous drivers of disease transmission. This study proposes a hybrid ARIMA‐LSTM forecasting framework incorporating four exogenous variables—daily average temperature, rainfall, vaccination rate, and population density—at both the linear (ARIMAX) and nonlinear (LSTM residual) stages.MethodsDaily confirmed COVID‐19 cases in Malaysia from January 4 to September 18, 2021 were analyzed. A dual‐integration modeling strategy was implemented: an ARIMAX component modeled linear trends and exogenous effects (temperature, rainfall, vaccination rate, and population density), while a Long Short‐Term Memory (LSTM) network captured nonlinear residual structures. Four competing models were evaluated: standalone ARIMA, standalone LSTM, hybrid ARIMA‐LSTM without exogenous variables, and the proposed hybrid ARIMAX‐LSTM with exogenous variables. Performance was assessed using RMSE, MAE, MAPE, and R2, with statistical comparison via the Diebold–Mariano (DM) test.ResultsThe proposed hybrid ARIMAX‐LSTM model achieved superior predictive accuracy (RMSE = 948.62; MAE = 769.49; MAPE = 6.61%; R2 = 0.7883), representing approximately 49% lower prediction error than baseline models (RMSE = 1801.90–1857.94). The model explained 78.83% of variance compared with < 5% for models excluding exogenous variables. Improvements were statistically significant (p < 0.001). The hybrid model demonstrated robust performance during epidemic transitions, achieving 3.02% error during a sharp decline phase compared with 20%–23% for baseline approaches.ConclusionsIntegrating exogenous variables within both linear and nonlinear components substantially enhances COVID‐19 forecasting accuracy. The proposed hybrid ARIMAX‐LSTM framework provides a reliable tool for epidemic prediction and supports evidence‐based public health decision‐making. This approach is readily may be extensible to other infectious diseases and time‐series forecasting applications.