Long-term spatiotemporal variations of ammonia in the Yangtze River Delta region of China and its driving factors.

This study focuses on the spatiotemporal distribution, urban-rural variations, and driving factors of ammonia Vertical Column Densities (VCDs) in China's Yangtze River Delta region (YRD) from 2008 to 2020. Utilizing data from the Infrared Atmospheric Sounding Interferometer (IASI), Generalized Additive Models (GAM), and the GEOS-Chem chemical transport model, we observed a significant increase of NH3 VCDs in the YRD between 2014 and 2020. The spatial distribution analysis revealed higher NH3 concentrations in the northern part of the YRD region, primarily due to lower precipitation, alkaline soil, and intensive agricultural activities. NH3 VCDs in the YRD region increased significantly (65.18%) from 2008 to 2020. The highest growth rate occurs in the summer, with an annual average growth rate of 7.2% during the period from 2014 to 2020. Agricultural emissions dominated NH3 VCDs during spring and summer, with high concentrations primarily located in the agricultural areas adjacent to densely populated urban zones. Regions within several large urban areas have been discovered to exhibit relatively stable variations in NH3 VCDs. The rise in NH3 VCDs within the YRD region was primarily driven by the reduction of acidic gases like SO2, as emphasized by GAM modeling and sensitivity tests using the GEOS-Chem model. The concentration changes of acidic gases contribute to over 80% of the interannual variations in NH3 VCDs. This emphasizes the crucial role of environmental policies targeting the reduction of these acidic gases. Effective emission control is urgent to mitigate environmental hazards and secondary particulate matter, especially in the northern YRD.

An assessment of atmospheric concentrations and spatiotemporal variation of BTEX and associated pollutants in India.

Benzene, toluene, ethylbenzene, and xylene (BTEX) pollution poses a serious threat to public health and the environment because of its respiratory and neurological effects, carcinogenic properties, and adverse effects on air quality. BTEX exposure is a matter of grave concern in India owing to the growing vehicular and development activities, necessitating the assessment of atmospheric concentrations and their spatial variation. This paper presents a comprehensive assessment of ambient concentrations and spatiotemporal variations of BTEX in India. The study investigates the correlation of BTEX with other criteria pollutants and meteorological parameters, aiming to identify interrelationships and diagnostic indicators for the source characterization of BTEX emissions. Additionally, the paper categorizes various regions in India according to the Air Quality Index (AQI) based on BTEX pollution levels. The results reveal that the northern zone of India exhibits the highest levels of BTEX pollution compared to central, eastern, and western regions. In contrast, the southern zone experiences the least pollution with BTEX. Seasonal analysis indicates that winter and post-monsoon periods, characterized by lower temperatures, are associated with higher BTEX levels due to the accumulation of localized emissions. When comparing the different zones in India, high traffic emissions and localized activities, such as solvent use and solvent evaporation, are found to be the primary sources of BTEX. The findings of the current study aid in source characterization and identification, and better understanding of the region's air quality problems, which helps in the development of focused BTEX pollution reduction and control strategies.

Ultrasound-generated bubbles enhance osteogenic differentiation of mesenchymal stromal cells in composite collagen hydrogels.

Hydrogels can improve the delivery of mesenchymal stromal cells (MSCs) by providing crucial biophysical cues that mimic the extracellular matrix. The differentiation of MSCs is dependent on biophysical cues like stiffness and viscoelasticity, yet conventional hydrogels cannot be dynamically altered after fabrication and implantation to actively direct differentiation. We developed a composite hydrogel, consisting of type I collagen and phase-shift emulsion, where osteogenic differentiation of MSCs can be non-invasively modulated using ultrasound. When exposed to ultrasound, the emulsion within the hydrogel was non-thermally vaporized into bubbles, which locally compacted and stiffened the collagen matrix surrounding each bubble. Bubble growth and matrix compaction were correlated, with collagen regions proximal (i.e., ≤ ∼60 µm) to the bubble displaying a 2.5-fold increase in Young's modulus compared to distal regions (i.e., > ∼60 µm). The viability and proliferation of MSCs, which were encapsulated within the composite hydrogel, were not impacted by bubble formation. In vitro and in vivo studies revealed encapsulated MSCs exhibited significantly elevated levels of RUNX2 and osteocalcin, markers of osteogenic differentiation, in collagen regions proximal to the bubble compared to distal regions. Additionally, alkaline phosphatase activity and calcium deposition were enhanced adjacent to the bubble. An opposite trend was observed for CD90, a marker of MSC stemness. Following subcutaneous implantation, bubbles persisted in the hydrogels for two weeks, which led to localized collagen alignment and increases in nuclear asymmetry. These results are a significant step toward controlling the 3D differentiation of MSCs in a non-invasive and on-demand manner.

Investigation of spatiotemporal distribution and formation mechanisms of ozone pollution in eastern Chinese cities applying convolutional neural network.

Severe ground-level ozone (O3) pollution over major Chinese cities has become one of the most challenging problems, which have deleterious effects on human health and the sustainability of society. This study explored the spatiotemporal distribution characteristics of ground-level O3 and its precursors based on conventional pollutant and meteorological monitoring data in Zhejiang Province from 2016 to 2021. Then, a high-performance convolutional neural network (CNN) model was established by expanding the moment and the concentration variations to general factors. Finally, the response mechanism of O3 to the variation with crucial influencing factors is explored by controlling variables and interpolating target variables. The results indicated that the annual average MDA8-90th concentrations in Zhejiang Province are higher in the northern and lower in the southern. When the wind direction (WD) ranges from east to southwest and the wind speed (WS) ranges between 2 and 3 m/sec, higher O3 concentration prone to occur. At different temperatures (T), the O3 concentration showed a trend of first increasing and subsequently decreasing with increasing NO2 concentration, peaks at the NO2 concentration around 0.02 mg/m3. The sensitivity of NO2 to O3 formation is not easily affected by temperature, barometric pressure and dew point temperature. Additionally, there is a minimum [Formula: see text] at each temperature when the NO2 concentration is 0.03 mg/m3, and this minimum [Formula: see text] decreases with increasing temperature. The study explores the response mechanism of O3 with the change of driving variables, which can provide a scientific foundation and methodological support for the targeted management of O3 pollution.

A new microscopy pipeline for studying the initial stages of nuclear and micronuclear rupture and repair.

Nuclear envelope repair is a fundamental cellular response to stress, especially for cells experiencing frequent nuclear ruptures, such as cancer cells. Moreover, for chromosomally unstable cancer cells, characterized by the presence of micronuclei, the irreversible rupture of these structures constitutes a fundamental step toward cancer progression and therapy resistance. For these reasons, the study of nuclear envelope rupture and repair is of paramount importance. Nonetheless, due to the constraint imposed by the stochastic nature of rupture events, a precise characterization of the initial stage of nuclear repair remains elusive. In this study, we overcame this limitation by developing a new imaging pipeline that deterministically induces rupture while simultaneously imaging fluorescently tagged repair proteins. We provide a detailed step-by-step protocol to implement this method on any confocal microscope and applied it to study the major nuclear repair protein, barrier-to-autointegration factor (BAF). As a proof of principle, we demonstrated two different downstream analysis methods and showed how BAF is differentially recruited at sites of primary and micronuclear rupture. Additionally, we applied this method to study the recruitment at primary nuclei of the inner nuclear membrane protein LEM-domain 2 (LEMD2) and Charged Multivesicular Protein 7 (CHMP7), the scaffolding protein of the endosomal sorting complex required for transport III (ESCRT-III) membrane remodeling complex. The CHMP7-LEMD2 binding is the fundamental step allowing the recruitment of ESCRT-III, which represents the other major nuclear repair mechanism. This demonstrates the method's applicability for investigating protein dynamics at sites of nuclear and micronuclear envelope rupture and paves the way to more time-resolved studies of nuclear envelope repair.

Spatio-temporal evolution of social-ecological system resilience in ethnic tourism destinations in mountainous areas and trend prediction: a case study in Wuling, China.

Mountainous ethnic tourism lands are important social-ecological system types. With tourism as the main disturbance factor, the theory of social-ecological system resilience provides a new way to realize the sustainable development of ethno-tourism in mountainous areas. This study divides the social-ecological system into social, economic, and ecological subsystems. It constructs an evaluation index system to assess the resilience of ethnic tourism destinations in mountainous areas, considering vulnerability and adaptability. We investigate 64 counties in the Wuling Mountain area and use set-pair analysis to assess the resilience index of the social-ecological system from 2000 to 2020 and reveal the temporal and spatial characteristics. Obstacle degree models and a genetic algorithm-back propagation neural network are utilized to determine the influencing factors and predict future development trends. The following results were obtained: (1) Temporally, the resilience index shows a steady upward trend, reaching a moderate level. The resilience of the social subsystem fluctuates and rises; the economic subsystem exhibits slow, fast, and slow growth rates with occasional abrupt changes; and the ecological subsystem demonstrates a stable, slightly increasing trend. (2) Spatially, the resilience index is high at the edges and low in the central area, exhibiting a concave distribution. Most counties have moderate or higher resilience. The social and ecological subsystems have low resilience in the south and high resilience in the north. The resilience of the economic subsystem is high at the edges and low in the central area. (3) On the distribution of major obstacle factors, the first two are similar at the county level, and the last three are significantly different. The similarity of the barrier factors is related to the degree of regional proximity of the county, and overall, the similarity is decreasing from north to south and from west to east in the distribution pattern within the area. and to a certain extent, it is affected by terrain and geomorphology. (4) The spatial distribution of the resilience index is similar in 2025 and 2030. The index decreases slightly and then increases annually, with a lower growth rate in the south than in the north. Lower values occur in the northern and southwestern parts, whereas higher values are observed around high-value areas. The region as a whole will develop in a coordinated and integrated manner in the future.

Development of land use regression, dispersion, and hybrid models for prediction of outdoor air pollution exposure in Barcelona.

BACKGROUND: Air pollution is the leading environmental risk factor for health. Assessing outdoor air pollution exposure with detailed spatial and temporal variability in urban areas is crucial for evaluating its health effects. AIM: We developed and compared Land Use Regression (LUR), dispersion (DM), and hybrid (HM) models to estimate outdoor concentrations for NO2, PM2.5, black carbon (BC), and PM2.5-constituents (Fe, Cu, Zn) in Barcelona. METHODS: Two monitoring campaigns were conducted. In the first, NO2 concentrations were measured twice at 984 home addresses and in the second, NO2, PM2.5, and BC were measured four times at 34 points across Barcelona. LUR and DM were constructed using conventional techniques, while HM was developed using Random Forest (RF). Model performance was evaluated using leave-one-out cross-validation (LOOCV) and 10-fold cross-validation (10-CV) for LUR and HM, and by comparing DM and LUR estimates with routine monitoring stations. NO2 levels estimated by all models were externally validated using the home monitoring campaign. Agreement between models was assessed using Spearman correlation (rs) and Bland-Altman (BA) plots. RESULTS: Models showed moderate to good performance. LUR exhibited R2LOOCV of 0.62 (NO2), 0.45 (PM2.5), 0.83 (BC), and 0.85 to 0.89 (PM2.5-constituents). DM model comparison showed R2 values of 0.39 (NO2), 0.26 (PM2.5), and 0.65 (BC). HM models had higher R210-CV 0.64 (NO2), 0.66 (PM2.5), 0.86 (BC), and 0.44 to 0.70 (PM2.5-constituents). Validation for NO2 showed R2 values of 0.56 (LUR), 0.44 (DM), and 0.64 (HM). Correlations between models varied from -0.38 to 0.92 for long-term exposure, and - 0.23 to 0.94 for short-term exposure. BA plots showed good agreement between models, especially for NO2 and BC. CONCLUSIONS: Our models varied substantially, with some models performing better in validation samples (NO2 and BC). Future health studies should use the most accurate methods to minimize bias from exposure measurement error.

Evaluating the spatiotemporal land ecological changes in the Yangtze-to-Huaihe Water Diversion Project area.

As a fundamental component of human existence, land is inextricably linked to human development, and its ecological functions are closely associated with multiple sustainable development goals. This paper presents a framework for constructing and optimizing ecological function space, with the Yangtze-to-Huaihe Water Diversion Project area serving as a case study. A comprehensive land ecological index system is established, encompassing natural foundation, land degradation, land production, ecological structure, and ecological protection. An identity-discrepancy-contrary connection method is employed to investigate changes in regional land ecological functions before (2013) and during (2017, 2020, and 2022) the project's construction based on remote sensing data. The results indicated that the mean values of the land ecological index for each period were 0.1883, 0.1981, 0.2253, and 0.1370, respectively. The study calculated the connection, differences, and contradictions in the land ecological impacts across the counties, revealing a gradual decrease in differences and a growing prominence of contradictions. The land ecology of the Yangtze-to-Huaihe Water Diversion Project area is affected by the project construction, particularly within the construction area, showing an overall improvement. Most counties exhibited a trend of ecological improvement compared to the land ecology before the project's construction. However, after the project implementation, most districts demonstrated a trend of ecological deterioration. As the distance from the construction canal increases, the characteristics of each section and stage vary, generally exhibiting an exponential decrease in the land ecological index. The study highlighted the significance of enhancing the land ecological pattern, improving water quality, increasing water supply along the project, and alleviating groundwater overexploitation. The study can serve as a reference for land ecological protection and restoration in water transfer areas and river basins worldwide.

Elevation-dependent dynamics of soil properties in a hilly watershed: a landform-based approach.

Understanding the variation of soil physical properties in relation to land use and elevation is essential for modeling soil-landscape relationships and sustainable land management. Hence, this study investigates the spatio-temporal variability of soil physical properties in a lower Himalayan watershed, where agriculture, forest, and grasslands are dominant. Samples from 104 sites in a 422 km2 watershed were collected using a gridded sampling scheme (2 km × 2 km resolution) over 57 weeks. Spatial patterns were analyzed using the Kriging technique, and Spearman rank correlation was employed to identify landform-dependent correlations between soil properties and elevation. The interdependence of the properties was detected using principal component analysis (PCA), while the random forest (RF) approach explored the factors influencing electrical conductivity (EC), organic content (OC), soil temperature (ST), and soil moisture (SM). The results revealed that forest landforms have higher coarser fractions (40%) compared to other landforms, while grasslands have higher soil fines (66%). A positive correlation was observed for elevation with sand content (0.15*), organic content (0.42*), and specific gravity (0.03), while a negative correlation was observed for silt (0.10), clay (0.21*), bulk density (0.52*), electrical conductivity (0.41*), soil moisture (0.28*), and temperature (0.31*). Elevation, soil texture, and specific gravity were identified as critical controls for EC, OC, ST, and SM, emphasizing the importance of soil properties, especially elevation and texture, in shaping spatial distributions. These findings contribute to creating a high-resolution regional inventory for effective land use management, adaptation to climate change, and improved livelihood, specifically for mountain people.

Evolution of COVID-19 dynamics in Guangdong Province, China: an endemic-epidemic modeling study.

BACKGROUND: From January 2020 to June 2022, strict interventions against COVID-19 were implemented in Guangdong Province, China. However, the evolution of COVID-19 dynamics remained unclear in this period. OBJECTIVES: This study aims to investigate the evolution of within- and between-city COVID-19 dynamics in Guangdong, specifically during the implementation of rigorous prevention and control measures. The intent is to glean valuable lessons that can be applied to refine and optimize targeted interventions for future crises. METHODS: Data of COVID-19 cases and synchronous interventions from January 2020 to June 2022 in Guangdong Province were collected. The epidemiological characteristics were described, and the effective reproduction number (Rt) was estimated using a sequential Bayesian method. Endemic-epidemic multivariate time-series model was employed to quantitatively analyze the spatiotemporal component values and variations, to identify the evolution of within- and between-city COVID-19 dynamics. RESULTS: The incidence of COVID-19 in Guangdong Province was 12.6/100,000 population (15,989 cases) from January 2020 to June 2022. The Rt predominantly remained below 1 and increased to a peak of 1.39 in Stage 5. As for the evolution of variations during the study period, there were more spatiotemporal components in stage 1 and 5. All components were fewer from Stage 2 to Stage 4. Results from the endemic-epidemic multivariate time-series model revealed a strong follow-up impact from previous infections in Dongguan, Guangzhou and Zhanjiang, with autoregressive components of 0.48, 0.45 and 0.36, respectively. Local risk was relatively high in Yunfu, Shanwei and Shenzhen, with endemic components of 1.17, 1.04 and 0.71, respectively. The impact of the epidemic on the neighboring regions was significant in Zhanjiang, Shenzhen and Zhuhai, with epidemic components of 2.14, 1.92, and 1.89, respectively. CONCLUSION: The findings indicate the presence of spatiotemporal variation of COVID-19 in Guangdong Province, even with the implementation of strict interventions. It's significant to prevent transmissions within cities with dense population. Preventing spatial transmissions between cities is necessary when the epidemic is severe. To better cope with future crises, interventions including vaccination, medical resource allocation and coordinated non-pharmaceutical interventions were suggested.

Spatio-temporal patterns of stomoxyine flies (Diptera: Muscidae) in a forested area of Thailand.

Understanding the distribution patterns of vector populations is crucial for comprehending the dynamics of vector-borne diseases. However, data on vector composition and abundance in areas of forest and wildlife-human interface in Thailand remain limited. This research aimed to investigate the spatio-temporal distribution and species diversity of stomoxyine flies (Diptera: Muscidae) in Salakpra Wildlife Sanctuary, Thailand's first wildlife sanctuary. A longitudinal entomological survey was conducted monthly from May 2022 to April 2023 in four habitats: core forest, grassland forest, a wildlife breeding center, and a local cattle farm. A total of 11,256 stomoxyine flies from four genera were captured. Based on morphological keys, nine species of stomoxyine flies were identified: Stomoxys pullus (29.63%), Stomoxys calcitrans (19.65%), Stomoxys indicus (16.09%), Haematostoma austeni (14.23%), Haematobia irritans exigua (8.22%), Haematobosca sanguinolenta (7.96%), Stomoxys uruma (1.98%), Stomoxys sitiens (1.75%), and Stomoxys bengalensis (0.49%). Heterogeneous variations in abundance across months and habitats were observed, in which abundance increased in the rainy season (June-October), exhibiting bimodal peaks at seasonal transitions. Human-disturbed areas, such as the cattle farm, exhibited the highest density and species diversity of stomoxyine flies. In contrast, areas with minimal human disturbance, like core forest, had low diversity and density but supported unique species, like the abundant Haematostoma austeni, which had minor populations in other types of habitats. The results of this study can be integrated into epidemiological models and lay the groundwork for more comprehensive research on vector-borne diseases at the wildlife-livestock interface to mitigate transmission risks and preserve biodiversity.

Title: Schémas spatio-temporels des stomoxes (Diptera : Muscidae) dans une zone forestière de Thaïlande. Abstract: Comprendre les schémas de distribution des populations de vecteurs est essentiel pour comprendre la dynamique des maladies à transmission vectorielle. Cependant, les données sur la composition et l'abondance des vecteurs dans les zones de forêt et d'interface faune-humain en Thaïlande restent limitées. Cette recherche visait à étudier la distribution spatio-temporelle et la diversité des espèces de stomoxes (Diptera : Muscidae) dans le sanctuaire faunique de Salakpra, le premier sanctuaire faunique de Thaïlande. Une enquête entomologique longitudinale a été menée mensuellement de mai 2022 à avril 2023 dans quatre habitats : forêt centrale, forêt de prairie, un centre d'élevage d'animaux sauvages et une ferme d'élevage locale. Au total, 11 256 stomoxes de quatre genres ont été capturés. Sur la base des clés morphologiques, neuf espèces de stomoxes ont été identifiées : Stomoxys pullus (29,63 %), Stomoxys calcitrans (19,65 %), Stomoxys indicus (16,09 %), Haematostoma austeni (14,23 %), Haematobia irritans exigua (8,22 %), Haematobosca sanguinolenta (7,96 %), Stomoxys uruma (1,98 %), Stomoxys sitiens (1,75 %) et Stomoxys bengalensis (0,49 %). Des variations hétérogènes de l'abondance selon les mois et les habitats ont été observées, l'abondance augmentant pendant la saison des pluies (juin à octobre), présentant des pics bimodaux aux transitions saisonnières. Les zones perturbées par l'homme, comme la ferme d'élevage, présentaient la plus forte densité et la plus grande diversité d'espèces de stomoxes. En revanche, les zones peu perturbées par l'homme, comme la forêt centrale, présentaient une faible diversité et une faible densité, mais abritaient des espèces uniques, comme l'abondant Haematostoma austeni, dont les populations étaient mineures dans d'autres types d'habitats. Les résultats de cette étude peuvent être intégrés dans des modèles épidémiologiques et posent les bases d'une recherche plus complète sur les maladies à transmission vectorielle à l'interface faune-bétail afin d'atténuer les risques de transmission et de préserver la biodiversité.

In vivo volumetric analysis of retinal vascular hemodynamics in mice with spatio-temporal optical coherence tomography.

Significance: Microcirculation and neurovascular coupling are important parameters to study in neurological and neuro-ophthalmic conditions. As the retina shares many similarities with the cerebral cortex and is optically accessible, a special focus is directed to assessing the chorioretinal structure, microvasculature, and hemodynamics of mice, a vital animal model for vision and neuroscience research. Aim: We aim to introduce an optical imaging tool enabling in vivo volumetric mouse retinal monitoring of vascular hemodynamics with high temporal resolution. Approach: We translated the spatio-temporal optical coherence tomography (STOC-T) technique into the field of small animal imaging by designing a new optical system that could compensate for the mouse eye refractive error. We also developed post-processing algorithms, notably for the assessment of (i) localized hemodynamics from the analysis of pulse wave-induced Doppler artifact modulation and (ii) retinal tissue displacement from phase-sensitive measurements. Results: We acquired high-quality, in vivo volumetric mouse retina images at a rate of 113 Hz over a lateral field of view of ∼ 500 µ m . We presented high-resolution en face images of the retinal and choroidal structure and microvasculature from various layers, after digital aberration correction. We were able to measure the pulse wave velocity in capillaries of the outer plexiform layer with a mean speed of 0.35 mm/s and identified venous and arterial pulsation frequency and phase delay. We quantified the modulation amplitudes of tissue displacement near major vessels (with peaks of 150 nm), potentially carrying information about the biomechanical properties of the retinal layers involved. Last, we identified the delays between retinal displacements due to the passing of venous and arterial pulse waves. Conclusions: The developed STOC-T system provides insights into the hemodynamics of the mouse retina and choroid that could be beneficial in the study of neurovascular coupling and vasculature and flow speed anomalies in neurological and neuro-ophthalmic conditions.

A generalized machine learning model for long-term coral reef monitoring in the Red Sea.

Coral reefs, despite covering less than 0.2 % of the ocean floor, harbor approximately 35 % of all known marine species, making their conservation critical. However, coral bleaching, exacerbated by climate change and phenomena such as El Niño, poses a significant threat to these ecosystems. This study focuses on the Red Sea, proposing a generalized machine learning approach to detect and monitor changes in coral reef cover over an 18-year period (2000-2018). Using Landsat 7 and 8 data, a Support Vector Machine (SVM) classifier was trained on depth-invariant indices (DII) derived from the Gulf of Aqaba and validated against ground truth data from Umluj. The classifier was then applied to Al Wajh, demonstrating its robustness across different sites and times. Results indicated a significant decline in coral cover: 11.4 % in the Gulf of Aqaba, 3.4 % in Umluj, and 13.6 % in Al Wajh. This study highlights the importance of continuous monitoring using generalized classifiers to mitigate the impacts of environmental changes on coral reefs.

The role of geographic distances in green industrial pilot policies evaluation: A sustainability transition perspective.

Green industrial policies (GIPs) aim to promote the adoption of green technology within a sustainability framework. While previous evaluations of GIP have focused more on the policy itself and the impacts within the policy boundaries, this paper further introduces the geographical factor to analyze the impact of different spatial geographic distances on implementing GIP. By integrating geographic distances into a spatial difference-in-differences analysis to assess the effects of municipal green industry policies (referred to as green industry pilot policies in this paper) enacted in 11 cities in China from 2006 to 2010, we find that these policies not only improve environmental and economic outcomes in targeted regions but also have spillover effects that may affect neighboring areas negatively, highlighting the importance of geographic considerations. The effectiveness of green industrial policies varies across cities, influenced by local socio-technical systems and regional characteristics such as infrastructure and information technology. These findings suggest that policy impacts are complex and multifaceted, requiring a comprehensive understanding of geographic interdependencies. By incorporating geographic factors, this research contributes to sustainability transition theory by offering insights into the spatial and temporal dynamics of socio-technical systems. The results underscore the need for policymakers to consider spatial-temporal aspects and potential secondary effects on adjacent regions when designing and implementing green industrial policies.

A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion.

Accurate monitoring of wheat phenological stages is essential for effective crop management and informed agricultural decision-making. Traditional methods often rely on labour-intensive field surveys, which are prone to subjective bias and limited temporal resolution. To address these challenges, this study explores the potential of near-surface cameras combined with an advanced deep-learning approach to derive wheat phenological stages from high-quality, real-time RGB image series. Three deep learning models based on three different spatiotemporal feature fusion methods, namely sequential fusion, synchronous fusion, and parallel fusion, were constructed and evaluated for deriving wheat phenological stages with these near-surface RGB image series. Moreover, the impact of different image resolutions, capture perspectives, and model training strategies on the performance of deep learning models was also investigated. The results indicate that the model using the sequential fusion method is optimal, with an overall accuracy (OA) of 0.935, a mean absolute error (MAE) of 0.069, F1-score (F1) of 0.936, and kappa coefficients (Kappa) of 0.924 in wheat phenological stages. Besides, the enhanced image resolution of 512 × 512 pixels and a suitable image capture perspective, specifically a sensor viewing angle of 40° to 60° vertically, introduce more effective features for phenological stage detection, thereby enhancing the model's accuracy. Furthermore, concerning the model training, applying a two-step fine-tuning strategy will also enhance the model's robustness to random variations in perspective. This research introduces an innovative approach for real-time phenological stage detection and provides a solid foundation for precision agriculture. By accurately deriving critical phenological stages, the methodology developed in this study supports the optimization of crop management practices, which may result in improved resource efficiency and sustainability across diverse agricultural settings. The implications of this work extend beyond wheat, offering a scalable solution that can be adapted to monitor other crops, thereby contributing to more efficient and sustainable agricultural systems.

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks.

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV's regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV's speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.

A Novel Bayesian Spatio-Temporal Surveillance Metric to Predict Emerging Infectious Disease Areas of High Disease Risk.

Identification of areas of high disease risk has been one of the top goals for infectious disease public health surveillance. Accurate prediction of these regions leads to effective resource allocation and faster intervention. This paper proposes a novel prediction surveillance metric based on a Bayesian spatio-temporal model for infectious disease outbreaks. Exceedance probability, which has been commonly used for cluster detection in statistical epidemiology, was extended to predict areas of high risk. The proposed metric consists of three components: the area's risk profile, temporal risk trend, and spatial neighborhood influence. We also introduce a weighting scheme to balance these three components, which accommodates the characteristics of the infectious disease outbreak, spatial properties, and disease trends. Thorough simulation studies were conducted to identify the optimal weighting scheme and evaluate the performance of the proposed prediction surveillance metric. Results indicate that the area's own risk and the neighborhood influence play an important role in making a highly sensitive metric, and the risk trend term is important for the specificity and accuracy of prediction. The proposed prediction metric was applied to the COVID-19 case data of South Carolina from March 12, 2020, and the subsequent 30 weeks of data.

Spatio-temporal differentiation characteristics of coal mine safety levels in China based on spatial spillover effects.

In order to solve the problem of "potpourri" of safety risk prevention and control measures, which is caused by the unclear mechanism of the spatial effect of coal mine safety level heterogeneity and its influencing factors. This paper discriminates the dominant factors of spatio-temporal heterogeneity of coal mine safety production level in China and their spatial effect types by means of GeoDetector and the Spatial Dubin Model (SDM), specifies the categories and degrees of acts on local and surrounding areas by changes in indicators, and provides further visualization of the detection outcomes with the assistance of the Neo4j graph database, and the findings indicate that:(1) All 15 indicators selected in the study have a certain influence on the generation of spatial heterogeneity in China's coal mine safety production level, and all of them show an enhancement relationship after the interaction of indicators. Especially, the combination of excavated environment and other indicators basically has a non-linear enhancement relationship. (2) In terms of spatial effects, the influence of the 5 effects on the spatio-temporal heterogeneity of coal mine safety level is, in descending order, industrial development effect > capital allocation effect > production environment effect > government supervision effect > enterprise management effect, which indicates that macroeconomic and market conditions have a much stronger influence on the generation of spatio-temporal heterogeneity in coal mine production safety status. (3) From the single indicator perspective, the average annual temperature, average annual wind speed, coal consumption and monitoring efficiency primarily affect the dependent variable through direct effects; GDP per capita, average labor compensation as well as railroad operating mileage have positive spatial spillover on the changes of coal mine safety production level in surrounding areas; the evaluation of the spatial effect for average labor compensation exhibits a positive indirect effect with low influence; for the two indicators of production efficiency and ex-factory price, not only do they have negative effects on the local coal mine safety level, but also have significant spillover effects on surrounding areas.

High-risk spatiotemporal patterns of leprosy in the southeastern region of Yunnan province from 2010 to 2022: an analysis at the township level.

BACKGROUND: Despite being preventable and curable, leprosy remains endemic in some undeveloped regions, including China. Wenshan Zhuang and Miao Autonomous Prefecture (Wenshan prefecture) currently bears the highest leprosy burden in China. In this ecological study, we aimed to analyze the epidemiological characteristics as well as identify and visualize the high-risk townships of Wenshan prefecture using the most updated leprosy data from 2010 to 2022. METHODS: Geographical information system combined with spatial scan statistics was used for newly detected leprosy cases abstracted from the Leprosy Management Information System in China. Global Moran's I index was used to uncover the spatial pattern of leprosy at the township level. Spatial scan statistics, encompassing purely temporal, purely spatial, spatial variation in temporal trends, and space-time analysis, were implemented for detecting the risk clusters. RESULTS: Between 2010 and 2022, Wenshan prefecture detected 532 new leprosy cases, comprising 352 (66.17%) males and 180 (33.83%) females. The aggregated time primarily occurred between October 2010 and March 2014. The distribution pattern of newly detected leprosy cases was spatially clustered. We identified four high-risk spatial clusters encompassing 54.51% of the new cases. Furthermore, spatial variation in temporal trends highlighted one cluster as a potential high-risk area. Finally, two space-time clusters were detected, and the most likely cluster was predominantly located in the central and northwest regions of Wenshan prefecture, spanning from January 2010 to September 2013. CONCLUSIONS: In this ecology study, we characterized the epidemiological features and temporal and spatial patterns of leprosy in Wenshan prefecture using the most recent leprosy data between 2010 and 2022. Our findings offer scientific insights into the epidemiological profiles and spatiotemporal dynamics of leprosy in Wenshan prefecture. Clinicians and policymakers should pay particular attention to the identified clusters for the prevention and control of leprosy.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis.

BACKGROUND: Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. METHODS: This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. RESULTS: A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. CONCLUSIONS: While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.