Search Results (3,350)

Accurately mapping urban residents’ exposure to green space at high spatiotemporal resolutions is essential for assessing disparities and equality across blocks and enhancing urban environment planning. In this study, we developed a framework to generate hourly green space exposure maps at 0.5 m resolution using multiple sources of remote sensing data and an Object-Based Image Classification with Graph Convolutional Network (OBIC-GCN) model. Taking the main urban area in Nanjing city of China as the study area, we proposed a Dynamic Residential Green Space Exposure (DRGE) metric to reveal disparities in green space access across four housing price blocks. The Palma ratio was employed to explain the inequity characteristics of DRGE, while XGBoost (eXtreme Gradient Boosting) and SHAP (SHapley Additive explanation) methods were utilized to explore the impacts of built environment factors on DRGE. We found that the difference in daytime and nighttime DRGE values was significant, with the DRGE value being higher after 6:00 compared to the night. Mean DRGE on weekends was about 1.5 times higher than on workdays, and the DRGE in high-priced blocks was about twice that in low-priced blocks. More than 68% of residents in high-priced blocks experienced over 8 h of green space exposure during weekend nighttime (especially around 19:00), which was much higher than low-price blocks. Moreover, spatial inequality in residents’ green space exposure was more pronounced on weekends than on workdays, with lower-priced blocks exhibiting greater inequality (Palma ratio: 0.445 vs. 0.385). Furthermore, green space morphology, quantity, and population density were identified as the critical factors affecting DRGE. The optimal threshold for Percent of Landscape (PLAND) was 25–70%, while building density, height, and Sky View Factor (SVF) were negatively correlated with DRGE. These findings address current research gaps by considering population mobility, capturing green space supply and demand inequities, and providing scientific decision-making support for future urban green space equality and planning. Full article

With rapid urbanization and growing population inflows into metropolitan areas, crowd gatherings have become increasingly frequent and dense, posing significant challenges to public safety management. Although existing crowd gathering detection methods have achieved notable progress, they still face major limitations: most rely heavily on local texture or density features and lack the capacity to model contextual information, making them ineffective under severe occlusions and complex backgrounds. Additionally, fixed-scale feature extraction strategies struggle to adapt to crowd regions with varying densities and scales, and insufficient attention to densely populated areas hinders the capture of critical local features. To overcome these challenges, we propose a point-supervised framework named Multi-Scale Convolutional Attention Network (MSCANet). MSCANet adopts a context-aware architecture and integrates multi-scale feature extraction modules and convolutional attention mechanisms, enabling it to dynamically adapt to varying crowd densities while focusing on key regions. This enhances feature representation in complex scenes and improves detection performance. Extensive experiments on public datasets demonstrate that MSCANet achieves high counting accuracy and robustness, particularly in dense and occluded environments, showing strong potential for real-world deployment. Full article

Behavior is not a mere sequence of responses to stimuli but the dynamic expression of internal processes such as planning, prediction, valuation, and inference. These functions arise from distributed and metabolically costly neural systems and are best understood by considering behavior and neural activity together. This article presents a narrative and conceptual review of the neuroscience of behavior, integrating biological, environmental, and computational perspectives. We synthesize evidence from motor control, neural population dynamics, predictive processing, and spontaneous behavior, showing that behavior cannot be explained without the neural systems that generate it, and that neural activity gains meaning only through detailed behavioral models. Neural dynamics correlate with latent variables, such as intention and prediction error, that structure adaptive action across timescales. Recent advances in behavioral analysis, dimensionality reduction, and computational modeling enable the analysis of neural and behavioral data with comparable complexity, revealing shared computational architectures that link population activity with the organization of action. Our methodology involved a targeted literature search in PubMed and Web of Science (1919–2025), supplemented by seminal earlier works. By combining mechanistic and functional analysis, we outline a unified framework that explains how brains, bodies, and environments together generate flexible, adaptive behavior. Full article

Under global warming, the frequency and intensity of extreme climate events have markedly increased. As one of the most climate-sensitive and ecologically fragile regions in the world, the Tibetan Plateau faces mounting environmental and demographic challenges. This study integrates multi-model ensemble simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) with population projection data from the Shared Socioeconomic Pathways (SSPs) under the high-emission scenario (SSP5-8.5). Three extreme climate indices—very wet days precipitation (R95p), warm days (TX90p), and consecutive dry days (CDDs)—were analyzed to assess future changes in climate extremes (2021–2100) and their relationships with demographic dynamics across Tibetan ethnic areas. The results indicate that, under high-emission conditions, both R95p and TX90p increase significantly, while CDDs slightly decreases, though drought risks remain pronounced in central regions. Over the same period, the total population is projected to decline by nearly 60%, with substantial differences in climate risk exposure across groups: working-age adults and less-educated individuals experience the highest exposure before mid-century, followed by a decline, whereas the elderly and highly educated populations will show continuously increasing exposure, stabilizing by the end of the century. The transformation of population patterns is reshaping socio-cultural structures, highlighting the need for culturally adaptive governance to ensure the sustainability of Tibetan ethnic communities. These findings enhance our understanding of the coupled interactions among climate change, population dynamics, and cultural transitions, providing a scientific basis for integrated adaptation strategies to promote sustainable development across the Tibetan Plateau. Full article

African swine fever (ASF) has reshaped wild boar (Sus scrofa) populations and management across Europe since its reintroduction in 2007. ASF reached Slovakia in August 2019, when wild boar population size and harvest were at six-decade maximums. We analyzed data from six years (2019–2024) of national surveillance and control to quantify spatio-temporal ASF patterns in free-ranging wild boar. Using monthly virological (PCR) and serological (antibody) data from active (hunted) and passive (found dead) surveillance, we (1) estimated temporal variation in the effective reproduction number (R_t); (2) modeled spatio-temporal prevalence in Slovakia and its eastern, central, and western regions; (3) linked these dynamics to management indicators such as wild boar density, harvest, and mortality; and (4) proposed measures to increase surveillance and control effectiveness. Passive surveillance showed greater diagnostic sensitivity than active surveillance for case detection (PCR: 46.5% vs. 0.48%; antibodies: 7.62% vs. 0.75%). R_t peaked at 3.83 in March 2021, then declined but periodically exceeded 1.0 through late 2024. Virological prevalence showed strong late-winter/early-spring seasonality and a persistent east-to-west gradient: peaks occurred first in the east (March 2021, March 2023), with the center surpassing the east in October 2023 and a subsequent rise in the west. Seroprevalence lagged and shifted westward later, peaking in March 2023 and increasing in western Slovakia from mid-2024. Wild boar density decreased by 36.3% from 2019 to 2024 and harvest-based density by 42.8%, returning to post-classical swine fever levels (2009–2013). We recommend prioritizing targeted carcass searches and rapid removal, maintaining low wild boar densities through sustained harvest of adult females, modernizing population monitoring methods, enhancing hunters’ compliance, and strengthening cross-border coordination to improve surveillance and control, thereby slowing ASF spread across Europe. Full article

Urban Heat Islands (UHIs) are intensifying under climate change, exacerbating thermal exposure risks for socially vulnerable populations. While the role of urban environmental features in shaping UHI patterns is well recognized, their differential impacts on diverse social groups remain underexplored—limiting the development of equitable, context-sensitive mitigation strategies. To address this challenge, we employ an interpretable ensemble machine learning framework to quantify how vegetation, water proximity, and built form influence UHI exposure across social strata and simulate the outcomes of alternative urban interventions. Drawing on data from 1660Dissemination Areas in Vancouver, we model UHI across seasonal and diurnal contexts, integrating environmental variables with socio-demographic indicators to evaluate both thermal and equity outcomes. Our ensemble AutoML framework demonstrates strong predictive accuracy across these contexts (R² up to 0.79), providing reliable estimates of UHI dynamics. Results reveal that increasing vegetation cover consistently delivers the strongest cooling benefits (up to 2.95 °C) while advancing social equity, though fairness improvements become consistent only when vegetation intensity exceeds 1.3 times the baseline level. Water-related features yield additional cooling of approximately 1.15–1.5 °C, whereas built-form interventions yield trade-offs between cooling efficacy and fairness. Notably, modest reductions in building coverage or road density can meaningfully enhance distributional justice with limited thermal compromise. These findings underscore the importance of tailoring mitigation strategies not only for climatic impact but also for social equity. Our study offers a scalable analytical approach for designing just and effective urban climate adaptations, advancing both environmental sustainability and inclusive urban resilience in the face of intensifying heat risks. Full article

Background: Non-smokers and individuals with minimal smoking history represent a significant proportion of lung cancer cases but are often overlooked in current risk assessment models. Pulmonary nodules are commonly detected incidentally—appearing in approximately 24–31% of all chest CT scans regardless of smoking status. However, most established risk models, such as the Brock model, were developed using cohorts heavily enriched with individuals who have substantial smoking histories. This limits their generalizability to non-smoking and light-smoking populations, highlighting the need for more inclusive and tailored risk prediction strategies. Purpose: We aimed to develop a longitudinal radiomics-based approach for lung cancer risk prediction, integrating time-varying radiomic modeling to enhance early detection in USPSTF-ineligible patients. Methods: Unlike conventional models that rely on a single scan, we conducted a longitudinal analysis of 122 patients who were later diagnosed with lung cancer, with a total of 622 CT scans analyzed. Of these patients, 69% were former smokers, while 30% had never smoked. Quantitative radiomic features were extracted from serial chest CT scans to capture temporal changes in nodule evolution. A time-varying survival model was implemented to dynamically assess lung cancer risk. Additionally, we evaluated the integration of handcrafted radiomic features and the deep learning-based Sybil model to determine the added value of combining local nodule characteristics with global lung assessments. Results: Our radiomic analysis identified specific CT patterns associated with malignant transformation, including increased nodule size, voxel intensity, textural entropy, as indicators of tumor heterogeneity and progression. Integrating radiomics, delta-radiomics, and longitudinal imaging features resulted in the optimal predictive performance during cross-validation (concordance index [C-index]: 0.69), surpassing that of models using demographics alone (C-index: 0.50) and Sybil alone (C-index: 0.54). Compared to the Brock model (67% accuracy, 100% sensitivity, 33% specificity), our composite risk model achieved 78% accuracy, 89% sensitivity, and 67% specificity, demonstrating improved early cancer risk stratification. Kaplan–Meier curves and individualized cancer development probability functions further validated the model’s ability to track dynamic risk progression for individual patients. Visual analysis of longitudinal CT scans confirmed alignment between predicted risk and evolving nodule characteristics. Conclusions: Our study demonstrates that integrating radiomics, sybil, and clinical factors enhances future lung cancer risk prediction in USPSTF-ineligible patients, outperforming existing models and supporting personalized screening and early intervention strategies. Full article

We investigate the phenomenon of quantum interference in spontaneous emission pathways for a quantum emitter embedded in a two-dimensional photonic crystal composed of a square lattice of dielectric cylindrical rods. Using a V-type three-level system as a model, we demonstrate that the anisotropic Purcell effect inherent in such photonic structures can amplify quantum interference to its theoretical maximum, where the degree of interference p reaches unity. This results in the complete suppression of spontaneous emission for one polarization (directional suppression) and the emergence of coherent population trapping without the need for external coherent fields. By employing density matrix formalism, we derive analytical expressions for the population dynamics and identify conditions for indefinite or long-lived excited-state population. Our findings can find application in quantum technologies, including high-precision atomic clocks, magnetometry, and quantum information processing. Full article

The control of infectious diseases plays a critical role in safeguarding the health of species and ecosystems. In this study, we investigate the combined effects of prey refuge and harvesting as mechanisms to limit the spread of disease within predator populations. A deterministic model is developed to examine the system dynamics through local stability analysis of equilibria, and the framework is further extended to an uncertain setting via a fuzzified model. The analysis shows that for small refuge values, the system reaches a stable state where infected predators move toward extinction, while prey and susceptible predators exhibit strong oscillations. As the refuge increases, the system undergoes a Hopf bifurcation, transitioning from periodic oscillations to a stable interior equilibrium. Beyond a critical threshold, oscillations disappear entirely. Harvesting of susceptible predators reveals that moderate harvesting induces oscillatory behavior in both prey and susceptible predator populations, whereas excessive harvesting can drive both predator classes to extinction. Harvesting of infected predators, by contrast, consistently drives their extinction regardless of harvesting intensity, with the other populations maintaining oscillatory patterns. These results indicate that an appropriate combination of prey refuge and harvesting can serve as an effective strategy for disease control in predator populations. Full article

We investigate a variation of the classical voter model where the set of influencing agents depends on an individual’s current opinion. The initial population is made up of a random sample of equally sized sub-populations for each state, and two types of interactions are considered: (i) direct neighbors and (ii) second neighbors (friends of direct neighbors, excluding the direct neighbors themselves). The neighborhood size, reflecting regular network connectivity, remains constant across all agents. Our findings show that varying the interaction range introduces asymmetries that affect the probability of consensus and convergence time. At low connectivity, direct neighbor interactions dominate, leading to consensus. As connectivity increases, the probability of either state reaching consensus becomes equal, reflecting symmetric dynamics. This asymmetric effect on the probability of consensus is shown to be independent of network topology in small-world and scale-free networks. Asymmetry also influences convergence time: while symmetric cases display decreasing times with increased connectivity, asymmetric cases show an almost linear increase. Unlike the probability of reaching consensus, the impact of asymmetry on convergence time depends on the network topology. The introduction of stubborn agents further magnifies these effects, especially when they favor the less dominant state, significantly lengthening the time to consensus. We conclude by discussing the implications of these findings for decision-making processes and political campaigns in human populations. Full article

It is well-known that EGF binding to EGFR stimulates signal transduction and endocytosis, with the latter leading to lysosomal degradation of EGFR. However, the majority of data on the regulation of endocytosis have been obtained in tumor-derived cells. Here, we perform a comprehensive analysis of the role of endolysosome acidification in the regulation of endocytic pathway in tumor cells and in endometrial MSCs as a model of proliferating, undifferentiated, non-immortalized cells. Using QD-labeled EGF, the dynamics of co-localization of EGF-receptor complexes with endocytic markers in the control and upon inhibition of V-ATPase by Bafilomycin A1 (BafA1) were studied using confocal microscopy. Image analysis showed that in HeLa and A549 cells, BafA1 significantly slowed down EGFR entry into and exit from EEA1-positive early endosomes without disrupting passage through Rab7, CD63, and Lamp1 compartments, but rather shifting it to later times. In enMSCs, only a portion of EGF-containing endosomes entered the degradation pathway, and lysosomal delivery was significantly delayed. Unlike HeLa, in enMSC early endosomes BafA1, increased the association of EGF-QDs with EEA1, suggesting a lower pH level, which is suboptimal for EEA1-dependent fusions. It is concluded that, unlike HeLa, enMSCs form a population of pH-independent endosomes containing activated EGFR for a long time. Full article

This study examined the spatial distribution of shamanic practice in contemporary South Korea, focusing on its territorial relationship with institutional religions. Contrary to portrayals of shamanism as a rural remnant or as absorbed by Pentecostal Christianity, population-adjust maps and spatial models reveal substantial concentrations in urban and peri-urban districts. Drawing on a geocoded dataset of 15,639 shamanic sites and 78,323 religious facilities across 229 districts, we estimated the ordinary least squares (OLS), spatial error models, and geographically weighted regression (GWR) models to evaluate how Protestant, Buddhist, and Catholic infrastructures were associated with shamanic site density. Protestant church density showed a consistent negative association with shamanic presence, strongest in regions with concentrated Protestant institutions. Buddhist temples had no uniform national effect but showed positive local associations in certain areas, suggesting localized symbiosis. Catholic sites displayed limited and inconsistent spatial relationships. These results demonstrate two contrasting dynamics: expulsion in Protestant strongholds and symbiosis, where Buddhist institutions allow more accommodation. Shamanism’s contemporary geography reflects adaptation to the territorial politics of institutional religion rather than a cultural revival. Full article

Population mobility links cities, propelling the spatiotemporal spread of urban pandemics and adding complexity to disease dynamics. It also closely shapes, and is shaped by, the selection and intensity of intervention measures. Revealing the multistage spatial-temporal dynamics of cross-regional epidemic continuity under this interaction is often overlooked but critically important. This study innovatively applies a self-organizing map (SOM) neural network to classify cities into six distinct types based on population mobility characteristics: high-inflow core (HIC), low-inflow core (LIC), low-inflow sub-core (LISC), high-outflow semi-peripheral (HOSP), equilibrious semi-peripheral (ESP), and low-outflow peripheral (LOP). Building on this, we propose a novel SEIR-AHQ theoretical framework and construct an epidemiological model using network-coupled ordinary differential equations (ODEs). This model captures the dynamic interplay between inter-city population mobility and intervention measures, and quantifies how heterogeneous city types shape the evolution of epidemic transmission across the coupled mobility network. The results show that: (1) Cities with stronger population mobility face significantly higher infection risks and longer epidemic durations, characterized by “higher peaks and longer tails” in infection curves. HIC cities experience the greatest challenges, and LOP cities experience the least. (2) Both higher transmission rates and delayed intervention timings lead to exponential growth in infections, with nonlinear effects amplifying small changes disproportionately. (3) Intervention efficacy follows a “diminishing marginal returns” pattern, where the incremental benefits of increasing intervention intensity gradually decrease. This study offers a novel perspective on managing interregional epidemics, providing actionable insights for crafting tailored and effective epidemic response strategies. Full article

In this paper, we develop a delayed malaria model that integrates a discrete time delay and a non-smooth threshold-based control strategy. Using the time delay τ as a bifurcation parameter, we investigate the local stability of the endemic equilibrium through analysis of the characteristic equation. We establish sufficient conditions for the occurrence of Hopf bifurcation, demonstrating how stability switches emerge as τ varies. Furthermore, when the infected population exceeds a critical threshold I_m, a sliding mode domain arises. We analyze the dynamics within this sliding region using the Utkin equivalent control method. Numerical simulations are provided to support the theoretical findings, illustrating the complex dynamical behaviors induced by both delay and threshold control. Full article

Precision application of fertiliser nutrients based on soil-available nutrients is a vital means of increasing castor (Ricinus communis L.) productivity. Fertiliser application based on the targeted yield model under inorganic fertilisers alone and Integrated Plant Nutrition System (IPNS) differ from the blanket recommendation practices. Field experiments were conducted in two locations to validate the Soil Test Crop Response (STCR) targeted yield model developed for hybrid castor on non-calcareous Alfisol. The main objective was to determine the effect of inorganic fertilisers and organic manures on microbial populations, enzyme dynamics in soil, and productivity of castor. Experimental field data revealed that combined application of inorganic fertilisers along with 12.5 t ha⁻¹ farmyard manure increased the soil microbial population and enzyme activity in the rhizosphere soils of castor. Castor responded positively with an increase in highest targeted yield level. The highest yield of 2726 and 2695 kg ha⁻¹ were attained in the treatment T₈ (STCR-IPNS −2.75 t ha⁻¹) in both locations, and Treatment T₅ (STCR-NPK alone −2.75 t ha⁻¹) was on par with T₈. The IPNS treatments showed higher percent achievement than the NPK treatments alone. Root length and dry matter production increased significantly with the application of a higher dose of fertiliser along with farmyard manure. Root dry matter production significantly contributed towards the castor seed yield. More soil-beneficial microorganisms and enzyme dynamics were observed in the IPNS treatment. Full article