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Abstract Background China has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China. Methods 29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors. Results Tuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows. Conclusions Tuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

Abstract Background Dengue fever stands as one of the most extensively disseminated mosquito-borne infectious diseases worldwide. While numerous studies have investigated its influencing factors, a gap remains in long-term analysis, impeding the identification of temporal patterns, periodicity in transmission, and the development of effective prevention and control strategies. Thus, we aim to analyze the periodicity of dengue fever incidence and explore the association between various climate factors and the disease over an extended time series. Methods By utilizing monthly dengue fever cases and climate data spanning four decades (1978–2018) in Guangdong province, China, we employed wavelet analysis to detect dengue fever periodicity and analyze the time-lag relationship with climate factors. Additionally, Geodetector q statistic was employed to quantify the explanatory power of each climate factor and assess interaction effects. Results Our findings revealed a prolonged transmission period of dengue fever over the 40-year period, transitioning from August to November in the 1970s to nearly year-round in the 2010s. Moreover, we observed lags of 1.5, 3.5, and 3 months between dengue fever and temperature, relative humidity, and precipitation, respectively. The explanatory power of precipitation, temperature, relative humidity, and the Oceanic Niño Index (ONI) on dengue fever was determined to be 18.19%, 12.04%, 11.37%, and 5.17%, respectively. Dengue fever exhibited susceptibility to various climate factors, with notable nonlinear enhancement arising from the interaction of any two variables. Notably, the interaction between precipitation and humidity yielded the most significant effect, accounting for an explanatory power of 75.32%. Conclusions Consequently, future prevention and control strategies for dengue fever should take into account these climate changes and formulate corresponding measures accordingly. In regions experiencing the onset of high temperatures, humidity, and precipitation, it is imperative to initiate mosquito prevention and control measures within a specific window period of 1.5 months.

Soil is the basis of the existence of fruit tree and soil nutrients plays a crucial role in plant growth and berry quality. To investigate the characteristics and interrelationships between soil nutrients and berry quality in Cabernet Gernischet vineyards, this study focused on seven representative vineyards in the eastern foothills of the Helan Mountains. Fifteen soil physicochemical factors and 10 berry quality factors were measured, followed by variation analysis, correlation analysis, multiple linear regression (MLR), partial-least squares regression (PLSR), principal component analysis (PCA), and systematic cluster analysis. We identified the main soil nutrient indicators influencing berry quality and developed linear regression equations. Utilizing PCA, a comprehensive evaluation model for berry quality was constructed, which enabled the calculation and ranking of integrated berry quality scores. The results indicated that soil nutrients in the vineyards of the eastern foothills of the Helan Mountains are relatively deficient and alkaline. The coefficient of variation for soil nutrient factors ranged from 3.19 to 118.08% and for berry quality factors 2.41–26.37%. Correlation analysis revealed varying degrees of correlation between soil nutrient indicators and fruit quality indicators. PCA extracted four principal components with a cumulative contribution rate of 91.506%. Based on the scores of these components and their corresponding weights, a comprehensive model for evaluating the quality of Cabernet Gernischet berries was established. The vineyards were ranked from the highest to the lowest combined scores as Zhenbeibu (ZBB), Yuquanying (YQY), Dawukou (DWK), Beihaizi (BHZ), Shuxin (SX), Huangyangtan (HYT), and Hongde (HD). These findings provide insights into soil nutrient management and comprehensive quality assessment of vineyards in the eastern foothills of the Helan Mountains. In conclusion, this study offers a theoretical foundation for vineyard managers to enhance grape berries quality through soil nutrient management. This will aid in the diagnosis of vineyard soil nutrition and the efficient use of fertilizers, with critical practical and theoretical implications for the meticulous management of vineyards and the production of high-quality wines.

Abstract Background Overweight is a known risk factor for various chronic diseases and poses a significant threat to middle-aged and elderly adults. Previous studies have reported a strong association between overweight and air pollution. However, the spatial relationship between the two remains unclear due to the confounding effects of spatial heterogeneity. Methods We gathered height and weight data from the 2015 China Health and Retirement Long-term Survey (CHARLS), comprising 16,171 middle-aged and elderly individuals. We also collected regional air pollution data. We then analyzed the spatial pattern of overweight prevalence using Moran's I and Getis-Ord Gi* statistics. To quantify the explanatory power of distinct air pollutants for spatial differences in overweight prevalence across Southern and Northern China, as well as across different age groups, we utilized Geodetector's q-statistic. Results The average prevalence of overweight among middle-aged and elderly individuals in each city was 67.27% and 57.39%, respectively. In general, the q-statistic in southern China was higher than that in northern China. In the north, the prevalence was significantly higher at 54.86% compared to the prevalence of 38.75% in the south. SO2 exhibited a relatively higher q-statistic in middle-aged individuals in both the north and south, while for the elderly in the south, NO2 was the most crucial factor (q = 0.24, p

Abstract Backgrounds Tuberculosis (TB) remains a serious public health and human development problem, especially in developing countries. Despite the effectiveness of directly observed therapy, short course programs in reducing transmission and progression of TB, poverty reduction and socioeconomic development remain crucial factors in decreasing TB incidence. However, the geographical pathway on the planet is not yet clear. Objectives This study was to reconstruct the geographical evolutionary process of TB in 173 countries and territories from 2010 to 2019 to analyze the socioeconomic determinants that impact the global TB epidemic. In addition, the TB incidence in 2030 was predicted. Methods This study analyses TB incidence data from 173 countries and territories between 2010 and 2019. The Geotree model would be used to reconstruct the geographical evolutionary process of TB, which provides a simplified schema for geo-visualizing the trajectories of TB incidence and their socioeconomic drivers. Additionally, to estimate the future TB incidence in 2030, a multilevel model was utilized in conjunction with the hierarchical nature of the Geotree based on a stratified heterogeneity analysis. Results Global TB incidence was found to be associated with the country type and development stages. Between 2010 and 2019, the average TB incidence rate in 173 countries and territories was -27.48%, with marked spatially stratified heterogeneity by country type and development stage. Low-income and lower-middle-income countries were most vulnerable to TB. Upper-middle-income countries experienced a faster decline in TB incidence than high-income countries, and TB incidence generally decreased as the development stage increased, except for the lower-middle development stage in 2019.The highest average rate of decline in TB incidence was observed in the upper-middle development stage of high-income countries, with a reduction of 45.24%. Meanwhile, 37 high-income countries in the high development stage demonstrated an average rate of change of -13.93%. Socioeconomic determinants, including gross domestic product per capita, urbanization rate, and sociodemographic index, were found to inhibit TB incidence. Based on current trends, the predicted average global TB incidence in 2030 is 91.581 per 100,000 population. Conclusions The trajectories of the global TB incidence have been reconstructed to formulate targeted public health responses. To eliminate TB, countries at similar development stage can draw on the experiences of countries at higher development stages that are tailored to their unique characteristics. By learning from successful TB control strategies, countries can take strategic steps toward eradicating TB and improving public health outcomes.

Spatial stratified heterogeneity (SSH) refers to the geographical phenomena in which the geographical attributes within-strata are more similar than the between-strata, which is ubiquitous in the real world and offers information implying the causation of nature. Stratification, a primary approach to SSH, generates strata using a priori knowledge or thousands of supervised and unsupervised learning methods. Selecting reasonable stratification methods for spatial analysis in specific domains without prior knowledge is challenging because no method is optimal in a general sense. However, a systematic review of a large number of stratification methods is still lacking. In this article, we review the methods for stratification, categorize the existing typical stratification methods into four classes – univariate stratification, cluster-based stratification, multicriteria stratification, and supervised stratification – and construct their taxonomy. Finally, we present a summary of the software and tools used to compare and perform stratification methods. Given that different stratification methods reflect distinct human understandings of spatial distributions and associations, we suggest that further studies are needed to reveal the nature of geographical attributes by integrating SSH, advanced algorithms, and interdisciplinary methods.

Abstract Background Cardiovascular disease (CVD) is the leading cause of death in China. No previous study has reported CVD mortality at county-level, and little was known about the nonmedical ecological factors of CVD mortality at such small scale in mainland China. Understanding the spatiotemporal variations of CVD mortality and examining its nonmedical ecological factors would be of great importance to tailor local public health policies. Methods By using national mortality registration data in China, this study used hierarchical spatiotemporal Bayesian model to demonstrate spatiotemporal distribution of CVD mortality in 2844 counties during 2006 to 2020 and investigate how nonmedical ecological determinants have affected CVD mortality inequities from the spatial perspectives. Results During 2006–2020, the age-standardized mortality rate (ASMR) of CVD decreased from 284.77 per 100,000 in 2006 to 241.34 per 100,000 in 2020. Among 2844 counties, 1144 (40.22%) were hot spots counties with a higher CVD mortality risk compared to the national average and located mostly in northeast, north central, and westernmost regions; on the contrary, 1551 (54.53%) were cold spots counties and located mostly in south and southeast coastal counties. CVD mortality risk decreased from 2006 to 2020 was larger in counties where CVD mortality rate had been higher in 2006 in most of the counties, vice versa. Nationwide, nighttime light intensity (NTL) was the major influencing factor of CVD mortality, a higher NTL appeared to be negatively associated with a lower CVD mortality, with one unit increase in NTL, and the CVD mortality risk will decrease 11% (relative risk of NTL was estimated as 0.89 with 95% confidence interval of 0.83–0.94). Conclusions Substantial between-county discrepancies of CVD mortality distribution were observed during past 15 years in mainland China. Nonmedical ecological determinants were estimated to significantly explain the overall and local spatiotemporal patterns of this CVD mortality risk. Targeted considerations are needed to integrate primary care with clinical care through intensifying further strategies to narrow unequally distribution of CVD mortality at local scale. The approach to county-level analysis with small area models has the potential to provide novel insights into Chinese disease-specific mortality burden.

Abstract Background Utilizing population-based survey data in epidemiological research with a spatial perspective can integrate valuable context into the dynamics of HIV prevalence in West Africa. However, the situation in the Mano River Union (MRU) countries is largely unknown. This research aims to perform an ecological study to determine the HIV prevalence patterns in MRU. Methods We analyzed Demographic and Health Survey (DHS) and AIDS Indicator Survey (AIS) data on HIV prevalence in MRU from 2005 to 2020. We examined the country-specific, regional-specific and sex-specific ratios of respondents to profile the spatial–temporal heterogeneity of HIV prevalence and determine HIV hot spots. We employed Geodetector to measure the spatial stratified heterogeneity (SSH) of HIV prevalence for adult women and men. We assessed the comprehensive correct knowledge (CCK) about HIV/AIDS and HIV testing uptake by employing the Least Absolute Shrinkage and Selection Operator (LASSO) regression to predict which combinations of CCKs can scale up the ratio of HIV testing uptake with sex-specific needs. Results In our analysis, we leveraged data for 158,408 respondents from 11 surveys in the MRU. From 2005–2015, Cote d'Ivoire was the hot spot for HIV prevalence with a Gi_Bin score of 3, Z-Score 8.0–10.1 and P

Spatial heterogeneity is a fundamental research topic in the field of geography, and Geodetector is a widely used tool for studying this phenomenon. To understand the research advancements and knowledge diffusion trends surrounding Geodetector, we constructed an author evolutionary tree structure fusing its 847 core citations in the Web of Science database and Geotree model for the first time. The results of our literature statistics indicated that Geodetector has garnered the attention of 3123 authors from 48 countries since its publication in the Ecological Indicators journal in 2010, who have published core papers concerning ten important topics. The majority of these studies focused on spatial heterogeneity and its influencing factors. Our analysis of Geotree data revealed a significant correlation between the publication rate of scholars in large teams and their academic activities. Our analysis of the knowledge diffusion chain shown that only 2% of the total number of authors have contributed to over 20% of the scientific collaborations and knowledge diffusion, and they were recognized as experts in Geodetector research. To provide a comprehensive reference for future scholars, we have summarized the citing countries, five classical articles, main scientific domains, and core teams of Geodetector research.

Leaf-blight disease caused by the Fusarium oxysporum is an emerging problem in Dendrobium chrysotoxum production in China. Symptoms of leaf blight were observed on seedlings of D. chrysotoxum cultivated in a nursery in Ruili City, Yunnan Province, China. In this study, we isolated the Fusarium sp. associated with leaf-blight disease of D. chrysotoxum from the diseased seedlings. A pathogenicity test was performed to fulfill Koch’s postulates to confirm the pathogenicity of isolated strains and identified using morphological and molecular techniques. The results revealed that all four isolated Fusarium sp. isolates (DHRL-01~04) produced typical blight symptoms followed by marginal necrosis of leaves on the D. chrysotoxum plants. On the PDA medium, the fungal colony appeared as a white to purplish color with cottony mycelium growth. Microconidia are oval-shaped, whereas macroconidia are sickle-shaped, tapering at both ends with 2–4 septations. The phylogenetic trees were construed based on internal transcribed spacer (ITS), translation elongation factor (EF-1α), and RNA polymerase subunit genes RPB1 and RPB2 genes, respectively, and blasted against the NCBI database for species confirmation. Based on the NCBI database’s blast results, the isolates showed that more than 99% identify with Fusarium oxysporum. To our knowledge, this is the first comprehensive report on the identification of Fusarium oxysporum as the causal agent of Dendrobium chrysotoxum leaf blight in Yunnan Province, China, based on morphological and molecular characteristics.

Ethephon (ETH), a commonly employed growth regulator, poses potential health risks due to its residue in fruits and vegetables, leading to both acute and subchronic toxicity. However, the detection accuracy of ETH is compromised by the color effects of the samples during the detection process. In this work, a multienzyme reaction-mediated electrochemical biosensor (MRMEC) was developed for the sensitive, rapid, and color-interference-resistant determination of ETH. Nanozymes Fe3O4@Au–Pt and graphene nanocomplexes (GN–Au NPs) were prepared as catalysts and signal amplifiers for MRMEC. Acetylcholinesterase (AChE), acetylcholine (ACh), and choline oxidase (CHOx) form a cascade enzyme reaction to produce H2O2 in an electrolytic cell. Fe3O4@Au–Pt has excellent peroxidase-like activity and can catalyze the oxidation of 3,3′,5,5′-tetramethvlbenzidine (TMB) in the presence of H2O2, resulting in a decrease in the characteristic peak current of TMB. Based on the inhibitory effect of ETH on AChE, the differential pulse voltammetry (DPV) current signal of TMB was used to detect ETH, offering the limit of detection (LOD) of 2.01 nmol L−1. The MRMEC method effectively analyzed ETH levels in mangoes, showing satisfactory precision (coefficient of variations, 2.88–15.97%) and recovery rate (92.18–110.72%). This biosensor holds promise for detecting various organophosphorus pesticides in food samples.

The aim of this study is to explore the relationship between soil nutrients and berry quality for the wine grape Vitis vinifera cv. Cabernet Franc in the eastern foothills of the Helan Mountains, and subsequently to optimize soil nutrient conditions for optimal berry quality, thus providing guidance for vineyard soil management. Based on the basic data on soil nutrients and berry quality indicators, a partial least squares regression method was used to screen for major soil nutrient factors affecting the grape quality index. Then, the selected soil nutrient factors were taken as independent variables and the corresponding grape quality indicators were taken as dependent variables and a multilinear regression equation was formulated by the method of multivariate linear regression. Finally, the optimal solution for fruit quality and soil nutrients was solved using linear programming equations. The results showed that there was a lack of total nitrogen, organic matter, nitrate nitrogen, ammonium nitrogen, and available phosphorus in the soil nutrients, and an alkaline soil. There is a significant positive correlation between some soil nutrient indices, and there is also a multivariate linearity problem. Among all berry quality indices, titratable acid, tannin, and anthocyanin were negatively correlated with eleven and ten soil indices, respectively, while other berry quality indices were positively correlated with most soil nutrient indices. The optimal parameters for grape quality were determined using the method of linear programming equations, and the corresponding soil nutrient indicators content were defined.

Deepening the understanding of diurnal characteristics and driving mechanisms of surface urban heat islands (SUHIs) across different local climate zones (LCZs) and time scales is of great significance for guiding urban surface heat mitigation. However, a comprehensive investigation of SUHIs from the diurnal, local, multi-seasonal, and interactive perspectives remains a large gap. Here, we generalized major diurnal patterns of LCZ-based SUHI intensities (SUHIIs) throughout 2020 over the urban area of Beijing, China, based on diurnal temperature cycle modeling, block-level LCZ mapping, and hierarchical clustering. A geographical detector was then employed to explore the individual and interactive impacts of 10 morphological, socioeconomic, and meteorological factors on the multi-temporal spatial differentiations of SUHIIs. Results indicate six prevalent diurnal SUHII patterns with distinct features among built LCZ types. LCZs 4 and 5 (open high- and mid-rise buildings) predominantly display patterns one, two, and five, characterized by an afternoon increase and persistently higher values during the night. Conversely, LCZs 6, 8, and 9 (open, large, and sparsely built low-rise buildings) mainly exhibit patterns three, four, and six, with a decrease in SUHII during the afternoon and lower intensities at night. The maximum/minimum SUHIIs occur in the afternoon–evening/morning for patterns 1–3 but in the morning/afternoon for patterns 5–6. In all four seasons, the enhanced vegetation index (EVI) and gross domestic product (GDP) have the top two individual effects for daytime spatial differentiations of SUHIIs, while the air temperature (TEM) has the largest explanatory power for nighttime differentiations of SUHIIs. All factor interactions are categorized as two-factor or nonlinear enhancements, where nighttime interactions exhibit notably greater explanatory powers than daytime ones. The strongest interactions are EVI ∩ GDP (q = 0.80) during the day and TEM ∩ EVI (q = 0.86) at night. The findings of this study contribute to an improved interpretation of the diurnal continuous dynamics of local SUHIIs in response to various environmental conditions.

Abstract Background Previous research pointed to a close relationship between the incidence of tuberculosis (TB) in aging populations and socio-economic conditions, however there has been lack of studies focused on a region of unbalanced socio-economic development. The aim of this paper is to explore the spatio-temporal variation in TB incidence and examine risk determinants of the disease among aging populations in a typical region. Methods Data on TB-registered cases between 2009 and 2014, in addition to social-economic factors, were collected for each district/county in Beijing, Tianjin and Hebei, a region characterized by an aging population and disparities in social-economic development. A Bayesian space–time hierarchy model (BSTHM) was used to reveal spatio-temporal variation in the incidence of TB among the elderly in this region between 2009 to 2014. GeoDetector was applied to measure the determinant power (q statistic) of risk factors for TB among the elderly. Results The incidence of TB among the elderly exhibited geographical spatial heterogeneity, with a higher incidence in underdeveloped rural areas compared with that in urban areas. Hotspots of TB incidence risk among the elderly were mostly located in north-eastern and southern areas in the study region, far from metropolitan areas. Areas with low risk were distributed mainly in the Beijing-Tianjin metropolitan areas. Social-economic factors had a non-linear influence on elderly TB incidence, with the dominant factors among rural populations being income (q = 0.20) and medical conditions (q = 0.17). These factors had a non-linear interactive effect on the incidence of TB among the elderly, with medical conditions and the level of economic development having the strongest effect (q = 0.54). Conclusions The findings explain spatio-temporal variation in TB incidence and risk determinants of elderly TB in the presence of disparities in social-economic development. High-risk zones were located mainly in rural areas, far from metropolitan centres. Medical conditions and the economic development level were significantly associated with elderly TB incidence, and these factors had a non-linear interactive effect on elderly TB incidence. The findings can help to optimize the allocation of health resources and to control TB transmission in the aging population in this region.

The environmental Kuznets curve (EKC) is often used to analyze the relationship between environmental pollution health indicators and economic development level in different regions. In developed countries, the blood lead levels (BLLs) of children have been declining fitting the EKC since the 1970s. However, such figures in China have remained at relatively high levels, without any obvious decline, since 2010. We explored spatial variations and graded countermeasures using reported data on BLLs including the lead poisoning rates (LPRs) of children. We found that there were prefectures where either the mean BLLs of the children had reached 100.00 μg/L or the LPRs of more than 40% of the children had reached 100 μg/L. When we reduced the average BLLs to 50 μg/L or lowered the proportion of children with a lead poisoning rate (LPR) above 100 μg/L to 10.00%, the EKC trend decreased, and the linear slope after 2010 became -0.202. If the areas where children's BLLs exceeded 50.00 μg/L or the proportion of children with an LPR above 100 μg/L was more than 10.00% will be controlled, the linear slope of the EKC decrease trend after 2010 will became -0.989, and the national average of children's BLLs would decline by 22.17%. The study concluded that children's BLLs in different regions of China are situated at different EKC stages, and urgent prevention and control strategies must be put in place for undeveloped areas.

Studies in environmental fields often suffer from air quality datasets incomplete at certain places and times. Here, a Spatial-Temporal Point Interpolation based on Biased Sentinel Hospitals Areal Disease Estimation (STPI-BSHADE) interpolation method was introduced to address this issue. The method was based on the spatial statistic trinity theory, where the statistical error is determined by the population properties, the condition of the sample, and the method of estimation. In our study, the spatial association of the variables was quantified by the covariance and the ratio of air quality data between stations, resulting in linear unbiased estimates of the missing data. STPI-BSHADE was compared with two widely used statistical methods, inverse distance weighting (IDW) and Kriging. Theoretically, IDW and Kriging are short of the capacity of using the heterogeneous characteristics of the population and remedying the sample bias. Empirically, the accuracy of the STPI-BSHADE method was assessed using hourly particulate matter 2.5 data, collected from May 13 to December 31, 2014, in the Beijing-Tianjin-Hebei areas, where air quality presents spatial heterogeneity. The experimental results also demonstrated that STPI-BSHADE significantly outperformed the traditional methods.

Global greening and its relationship with climate change remain the hot topics in recent years, and are of critical importance for understanding the interactions between the terrestrial ecosystem carbon cycle and the climate system. China, especially north China, has contributed a lot to global greening during the past few decades. As a water-limited ecosystem, human activities, not precipitation amount, were thought as the main contributor to the greening of north China. Considering the importance of precipitation event characteristics (PEC) in the altered precipitation regimes, we integrated long-term normalized difference vegetation index (NDVI) and meteorological datasets to reveal the role of precipitation regimes, especially PECs, on vegetation growth across temperate grasslands in north China. Accompanied with a significantly decreased growing season precipitation (GSP), NDVI increased significantly in the largest area of the temperate grasslands during 1982–2015, i.e., greening. We found that 28.44% of the area was explained by PECs, including more heavy or extreme precipitation events, alleviated extreme drought, and fewer light events, while only 0.92% of the area was associated with GSP. NDVI did not always increase over the 30 years and there was a decrease during 1996–2005. Taking afforestation projects in desertified lands into account, we found that precipitation, mainly PECs, explained more the increase and decline of NDVI during 1982–1995 and 1996–2005, respectively, while an equivalent explanatory power of precipitation and afforestation projects to the increase in NDVI after 2005. Our study indicates a possible higher productivity under future precipitation regime scenario (e.g., fewer but larger precipitation events) or intensive afforestation activity, implying more carbon sequestration or livestock production of temperate steppe in the future.

BackgroundSince the introduction of HIV self-testing by UNAIDS in 2014, the practice has been extensively implemented around the world. HIV self-testing (HIVST) was developed in China around 2015, and the online purchase of HIVST kits through e-commerce platforms has since become the most important delivery method for self-testing, with advantages such as user-friendliness, speed, and better privacy protection. ObjectiveUnderstanding the spatiotemporal characteristics of online HIVST kit purchasing behavior and identifying potential impacting factors will help promote the HIV self-testing strategy. MethodsThe online retail data of HIVST kits from the 2 largest e-commerce platforms in China from 2015 to 2017 were collected for this study. The Bayesian spatiotemporal hierarchical model was used to investigate the spatiotemporal characteristics of online purchased HIVST kits. Ordinary least squares regression was used to identify potential factors associated with online purchase, including GDP per capita, population density, road density, HIV screening laboratory density, and newly diagnosed HIV/AIDS cases per 100,000 persons. The q statistics calculated by Geodetector were used to determine the interactive effect of every 2 factors on the online purchase. ResultsThe online purchase of HIVST kits increased rapidly in China from 2015 to 2017, with annual peak sales in May and December. Five economically superior regions in China, Pearl River Delta, Yangtze River Delta, Chengdu and surrounding areas, Beijing and Tianjin areas, and Shandong Peninsula, showed a comparatively higher spatial preference for online purchased HIVST kits. The GDP per capita (P

BackgroundChina has one of the highest tuberculosis (TB) burdens in the world. However, the unbalanced spatial and temporal trends of TB risk at a fine level remain unclear. ObjectiveWe aimed to investigate the unbalanced risks of pulmonary tuberculosis (PTB) at different levels and how they evolved from both temporal and spatial aspects using PTB notification data from 2851 counties over a decade in China. MethodsCounty-level notified PTB case data were collected from 2009 to 2018 in mainland China. A Bayesian hierarchical model was constructed to analyze the unbalanced spatiotemporal patterns of PTB notification rates during this period at subnational scales. The Gini coefficient was calculated to assess the inequality of the relative risk (RR) of PTB across counties. ResultsFrom 2009 to 2018, the number of notified PTB cases in mainland China decreased from 946,086 to 747,700. The average number of PTB cases in counties was 301 (SD 26) and the overall average notification rate was 60 (SD 6) per 100,000 people. There were obvious regional differences in the RRs for PTB (Gini coefficient 0.32, 95% CI 0.31-0.33). Xinjiang had the highest PTB notification rate, with a multiyear average of 155/100,000 (RR 2.3, 95% CI 1.6-2.8; P

Abstract Background Typhoons greatly threaten human life and property, especially in China. Therefore, it is important to make effective policy decisions to minimize losses associated with typhoons. Methods In this study, the GeoDetector method was used to quantify the determinant powers of natural and socioeconomic factors, and their interactions, on the population casualty rate of super typhoon Lekima. The local indicator of spatial association (LISA) method was followed to explore the spatial pattern of the population casualty rate under the influence of the identified dominant factors. Results Both natural and socioeconomic factors were found to have significantly impacted the population casualty rate due to super typhoon Lekima. Among the selected factors, maximum precipitation was dominant factor (q = 0.56), followed by maximum wind speed (q = 0.45). In addition, number of health technicians (q = 0.35) and number of health beds (q = 0.27) have a strong influence on the population casualty rate. Among the interactive effects of 12 influencing factors, the combined effects of maximum precipitation and ratio of brick-wood houses, the maximum precipitation and ratio of steel-concrete houses, maximum precipitation and number of health technicians were highest (q = 0.72). Furthermore, high-risk areas with very high casualty rates were concentrated in the southeastern part of Zhejiang and northern Shandong Provinces, while lower-risk areas were mainly distributed in northern Liaoning and eastern Jiangsu provinces. Conclusions These results contribute to the development of more specific policies aimed at safety and successful property protection according to the regional differences during typhoons.

Abstract Background Previous studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control. Methods A total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors. Results The risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8. Conclusions COVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission.

In this article, the problem of receiver autonomous integrity monitoring (RAIM) is transformed into a modeling problem using dynamic data and an artificial neural network. A new RAIM method based on a probabilistic neural network (P-RAIM) is presented to improve integrity monitoring performance. Compared with existing RAIM methods, P-RAIM has a greater ability to meet the monitoring requirements for localizer performance with vertical guidance down to altitudes of 250 feet (LPV-250) in a single global navigation satellite system. First, by projecting the pseudorange error model from the measurement domain into the positioning domain through multiconvolution, patterns including a satellite fault pattern and a fault-free pattern are obtained based on variance inflation theory. Second, the P-RAIM model is proposed as a modified dynamic-data-driven probabilistic neural network with five layers; moreover, unique methods for training sample collection and integrity support are presented. Then, particle swarm optimization is applied to optimize a fitness function based on the false alarm probability and missed detection probability thereby improving the ability of P-RAIM to meet the LPV-250 requirements, including the false alarm probability, missed detection probability, vertical alarm limit and alarm time. Finally, utilizing real satellite data from a receiver located in Beijing to verify the effectiveness and universality of P-RAIM, evaluation experiments show that both the false alarm probability and missed detection probability can be effectively reduced to meet the LPV-250 requirements when the positioning bias is no less than 40 m. Compared with least-squares-residuals RAIM, P-RAIM can more easily detect potential faulty satellites in a single constellation.

Typhoon disasters cause billions of dollars in losses each year, and many countries around the world are adversely affected by these events. Presently, the determinant powers of both natural and socioeconomic factors on disaster losses (such as the house collapse rate following a typhoon), as well as their interaction effects, remain unclear. In this study, the GeoDetector method was used to quantify the impacts of natural and socioeconomic factors and their interactions on the house collapse rate caused by Super Typhoon Lekima in 2019; and then detect the dominant factor, involving in the spatial pattern of house collapses was identified by the local indicators of spatial association (LISA) method. This study found that in addition to natural factors, socioeconomic factors also played a non-negligible role in the house collapse rate caused by Super Typhoon Lekima. The dominant factor was maximum precipitation, and the statistical value of q was 0.39. Next in importance were the elevation and maximum wind speed. Among the interactive effects of 14 influencing factors, the interaction between maximum precipitation and the ratio of four-six floor buildings was the largest (q = 0.74). In southeastern Zhejiang and northern Shandong, highly concentrated areas of ​collapsed houses were found. The results of the study can be used to develop more specific policies aimed at safety improvements and successful property protection.

Summary: Background: Social determinants of health (SDOH) produce a broad range of life expectancy (LE) disparities. In China, limited literatures were found to report association between SDOH and LE at ecological level during a consecutive period of time from the spatial perspectives. This study aimed to determine the existence, quantify the magnitude, and interpret the association between SDOH and LE in China. Methods: Provincial-level LE were estimated from mortality records during 2005–2020 from National Mortality Surveillance System in China. A spatial panel Durbin model was used to investigate LE associated SDOH proxies. Spatial spillover effects were introduced to interpret direct and indirect effects caused by SDOH during long-term and short-term period on LE disparities. Findings: Nationwide, LE increased from 73.1 (95% confidence interval (CI): 71.3, 74.4) years to 77.7 (95%CI: 76.5, 78.7) years from 2005 to 2020. Unequally spatial distribution of LE with High-High clustering in coastal areas and Low-Low clustering in western regions were observed. Locally, it was estimated that SDOH proxies statistically significant related to an increase of LE, including GDP (coefficient: 0.02, 95%CI: 0.00, 0.03), Gini index (coefficient: 2.35, 95%CI: 1.82, 2.88), number of beds in health care institutions (coefficient: 0.02, 95%CI: 0.00, 0.05) and natural growth rate of resident population (coefficient: 0.02, 95%CI: 0.01, 0.02). Direct and indirect effects decomposition during long-term and short-term of LE associated SDOH proxies demonstrated that GDP, urbanization rate, unemployment rate, education attainment, Gini index, number of beds in health care institutions, sex ratio, gross dependence ratio and natural growth rate of resident population not only affected local LE, but also exerted spatial spillover effects towards geographical neighbors. Interpretation: Spatial variations of LE existed at provincial-level in China. SDOH regarding socioeconomic development and equity, healthcare resources, as well as population characteristics not only affected LE disparities at local scale but also among nearby provinces. Externalities of policy of those SDOH proxies should be took into consideration to promote health equity nationally. Comprehensive approaches on the basis of population strategy should be consolidated to optimize supportive socioeconomic environment and narrow the regional gap to reduce health disparities and increase LE. Funding: National Key Research & Development Program of China (Grant No.2018YFC1315301); Ministry of Education of China Humanities and Social Science General Program (Grant No.18YJC790138).

Spatial heterogeneity represents a general characteristic of the inequitable distributions of spatial issues. The spatial stratified heterogeneity analysis investigates the heterogeneity among various strata of explanatory variables by comparing the spatial variance within strata and that between strata. The geographical detector model is a widely used technique for spatial stratified heterogeneity analysis. In the model, the spatial data discretization and spatial scale effects are fundamental issues, but they are generally determined by experience and lack accurate quantitative assessment in previous studies. To address this issue, an optimal parameters-based geographical detector (OPGD) model is developed for more accurate spatial analysis. The optimal parameters are explored as the best combination of spatial data discretization method, break number of spatial strata, and spatial scale parameter. In the study, the OPGD model is applied in three example cases with different types of spatial data, including spatial raster data, spatial point or areal statistical data, and spatial line segment data, and an R “GD” package is developed for computation. Results show that the parameter optimization process can further extract geographical characteristics and information contained in spatial explanatory variables in the geographical detector model. The improved model can be flexibly applied in both global and regional spatial analysis for various types of spatial data. Thus, the OPGD model can improve the overall capacity of spatial stratified heterogeneity analysis. The OPGD model and its diverse solutions can contribute to more accurate, flexible, and efficient spatial heterogeneity analysis, such as spatial patterns investigation and spatial factor explorations.

Abstract Background Hand, foot and mouth disease (HFMD) is a common infectious disease whose mechanism of transmission continues to remain a puzzle for researchers. The measurement and prediction of the HFMD incidence can be combined to improve the estimation accuracy, and provide a novel perspective to explore the spatiotemporal patterns and determinant factors of an HFMD epidemic. Methods In this study, we collected weekly HFMD incidence reports for a total of 138 districts in Shandong province, China, from May 2008 to March 2009. A Kalman filter was integrated with geographically weighted regression (GWR) to estimate the HFMD incidence. Spatiotemporal variation characteristics were explored and potential risk regions were identified, along with quantitatively evaluating the influence of meteorological and socioeconomic factors on the HFMD incidence. Results The results showed that the average error covariance of the estimated HFMD incidence by district was reduced from 0.3841 to 0.1846 compared to the measured incidence, indicating an overall improvement of over 50% in error reduction. Furthermore, three specific categories of potential risk regions of HFMD epidemics in Shandong were identified by the filter processing, with manifest filtering oscillations in the initial, local and long-term periods, respectively. Amongst meteorological and socioeconomic factors, the temperature and number of hospital beds per capita, respectively, were recognized as the dominant determinants that influence HFMD incidence variation. Conclusions The estimation accuracy of the HFMD incidence can be significantly improved by integrating a Kalman filter with GWR and the integration is effective for exploring spatiotemporal patterns and determinants of an HFMD epidemic. Our findings could help establish more accurate HFMD prevention and control strategies in Shandong. The present study demonstrates a novel approach to exploring spatiotemporal patterns and determinant factors of HFMD epidemics, and it can be easily extended to other regions and other infectious diseases similar to HFMD.

Background: Previous geographic studies of HIV infection have usually used prevalence data, which cannot indicate the hot-spot areas of current transmission. To develop quantitative analytic measures for accurately identifying hot-spot areas in growth of new HIV infection, we investigated the geographic distribution features of recent HIV infection and long-term HIV infection using data from a whole-population physical examination in four key counties in Liangshan prefecture, which are most severely affected by HIV in China.Methods: Through a whole-population physical examination during November 2017- June 2018 in the four key counties, a total of 5,555 HIV cases were diagnosed and 246 cases were classified as recently infected by laboratory HIV recency tests. The geospatial patterns of recent and long-term HIV infected cases were compared using ordinary least squares regression and Geodetector. Further, geospatial-heterogeneity was quantified and indicated using a residual map to visualize hot-spot areas where new infection is increasing.Results: The geographic location of HIV cases showed an uneven distribution along major roads and clustered at road intersections. The geographic mapping showed that several areas were clustered with more recently infected HIV cases than long-term infected cases. The quantitative analyses showed that the geospatial asymmetry between recent and long-term HIV infection was 0.30 and 0.31 in ordinary least squares regression and Geodetector analysis, respectively. The quantitative analyses found twenty-three townships showing an increase in the number of recent infections.Conclusions: Quantitative analysis of geospatial-heterogeneous areas by comparing between recent and long-term HIV infections allows accurate identification of hot-spot areas where new infections are expanding, which can be used as a potent methodological tool to guide targeted interventions and curb the spread of the epidemic.

Abstract Background The incidence of hand, foot and mouth disease (HFMD) varies over space and time and this variability is related to climate and social-economic factors. Majority of studies on HFMD were carried out in humid regions while few have focused on the disease in arid/semi-arid regions, more research in such climates would potentially make the mechanism of HFMD transmission clearer under different climate conditions. Methods In this paper, we explore spatial-temporal distribution of HFMD in Ningxia province, which has an arid/semi-arid climate in northwest China. We first employed a Bayesian space-time hierarchy model (BSTHM) to assess the spatial-temporal heterogeneity of the HFMD cases and its relationship with meteorological factors in Ningxia from 2009 to 2013, then used a novel spatial statistical software package GeoDetector to test the spatial-temporal heterogeneity of HFMD risk. Results The results showed that the spatial relative risks in northern part of Ningxia were higher than those in the south. The highest temporal risk of HFMD incidence was in fall season, with a secondary peak in spring. Meteorological factors, such as average temperature, relative humidity, and wind speed played significant roles in the spatial-temporal distribution of HFMD risk. Conclusions The study provide valuable information on HFMD distribution in arid/semi-arid areas in northwest China and facilitate understanding of the concentration of HFMD.

Utilizing the least squares residuals (LSR) algorithm to detect the faulty satellite, the faulty satellite with a large characteristic slope will bring a high miss detection risk (MDR) and that with a small characteristic slope will bring a high false alert risk (FAR). However, the magnitude of characteristic slopes whether large or small is currently indefinite. In this paper, analyzing the MDR whether exceeding its allowable value or not, we propose the critical value of characteristic slopes to define the magnitude of a characteristic slope. The slope with the value larger than the critical one can be defined as a large slope whereas the slope with a value smaller than the critical one can be defined as a small slope. To reduce the fault detection risk of the LSR algorithm, including the MDR caused by a large slope faulty satellite and the FAR caused by a small slope faulty satellite, a modified LSR algorithm based on the critical value of characteristic slopes is proposed. In the modified algorithm, the most potential faulty satellite is determined via correlation analysis. Then, a subset fault detection methodology will be used to reduce the MDR when the most potential faulty satellite owns a large slope, whereas a threshold amplification fault detection methodology will be used to reduce the FAR when the most potential faulty satellite owns a small slope. The performance evaluation simulations of the modified LSR algorithm show that both the MDR caused by a large slope faulty satellite and the FAR caused by a small slope faulty satellite could be effectively reduced.

The identification and accurate quantification of metabolites using electrospray ionization-mass spectrometry (ESI-MS) in hypersaline samples is a challenge due to matrix effects. Clean-up and desalting strategies that typically work well for samples with lower salt concentrations are often ineffective in hypersaline samples.

Ensuring the suitability of urban development land is essential for delineating spatial growth boundaries and urban spatial layouts. However, the significant impact of subjective uncertainty on the suitability evaluation process significantly reduces the reliability of the evaluation results. Thus, in this study, we developed a new method to address this issue and improve the accuracy of the evaluation results. Zhengzhou in China was considered as the research area and the data utilized were obtained from the following primary sources: Landsat TM/ETM/OLI image data, land use data, digital elevation model data, spatial primary geographical data, and digital map data. A new method for evaluating the suitability of urban development land was developed by combining logistic regression, principal component analysis, kriging interpolation, K-means, and the Geodetector method to evaluate and classify the suitability of urban development land in Zhengzhou City during 2013. By using logistic regression, we could accurately evaluate the effects of a single factor, thereby avoiding subjective assessments. The principal component can be used to reduce the dimensions of the evaluation results for a single factor where the weight of the principal component is determined by using the cumulative contribution rate in order to obtain the comprehensive evaluation result. Kriging interpolation can be used to predict the evaluation results for the grid surface by using the principal component to comprehensively evaluate the sample points. K-means can be used to automatically classify the evaluation results for the grid surface. Geodetector was used to detect the spatial differentiation of the results in order to confirm the validity of the spatial partition results. These methods can avoid interference due to human factors and yield more objective and accurate evaluation results. The results indicated that the proposed evaluation method can avoid the subjective influence of the evaluation index classification and the determination of the index weight to obtain extremely accurate evaluations and high effectiveness. The suitability grading and evaluation values were highly consistent with the spatial pattern, thereby demonstrating the applicability of the evaluation results. The method and evaluation results may provide a scientific reference to support decisions regarding land resource allocation during urban development.

A total of 55 volatiles including esters (29, 52.73%), alcohols (10, 18.18%), acids (3, 5.45%), alkanes (8, 14.55%), and other components (5, 9.09%) were evaluated in five regions. Total concentrations were 0.05–222.23 mg/L, which covered the highest esters (222.23 mg/L) and alcohols (120.65 mg/L) in Turpan, acid (0.53 mg/L) in Shihezi, and alkanes (1.43 mg/L) and others (3.10 mg/L) in the Ili River valley. It proved that numbers and concentrations of volatile compounds, including common ingredients of variety, were closely linked to ecological characteristics of a region. Esters and alcohols were the major ingredients in Xinjiang Cabernet Sauvignon wine. Additionally, appellation could affect performance of concentration, ODE, and OTH, especially for the same flavor substance by fermentation, aging, and even formation and transformation in wines. Therefore, three conditions for formation of flavors were successively appellations, metabolism and fermentation, and and appropriate altering according to technology and their decisive role in wine quality. Each volatile compound had its own flavor, the combination of which complicated the flavor. The unique materials in the region were grounded for the development of products with corresponding flavors by producing substrate for fermentation. When choosing a wine you enjoy, the right appellation should be considered first.

Abstract Background Hand, foot and mouth disease (HFMD) has become a substantial threat recently. However few studies have quantified spatiotemporal heterogeneity of HFMD and detected spatiotemporal interactive effect of potential driving factors on this disease. Methods Using GeoDetector and Bayesian space-time hierarchy model, we characterized the epidemiology of HFMD in Henan, one of the largest population provinces in China, from 2012 to 2013, and quantified the impacts of potential driving factors. Results Notably, 21.43 and 24.60% counties were identified as hot and cold spots, respectively. Spatially, the hotspots were mainly clustered in regions where the economic level was high. Temporally, the highest incidence period of HFMD was discovered to be in late spring and early summer. The impact of meteorological and socio-economic factors on the disease are significant, and this study found that a 1 °C rise in temperature was related to an increase of 4.09% in the HFMD incidence, a 1% increment in relative humidity was associated with a 1.77% increase of the disease, and a 1% increment in ratio of urban to rural population was associated with a 0.16% increase of the disease. Conclusion Meteorological and socio-economic factors presented significantly association with HFMD incidence, high-risk mainly appeared in large cities and their adjacent regions in hot and humid season. These findings will be helpful for HFMD risk control and disease-prevention policies implementation.

Abstract Background As a common infectious disease, hand, foot and mouth disease (HFMD) is affected by multiple environmental and socioeconomic factors, and its pathogenesis is complex. Furthermore, the transmission of HFMD is characterized by strong spatial clustering and autocorrelation, and the classical statistical approach may be biased without consideration of spatial autocorrelation. In this paper, we propose to embed spatial characteristics into a spatiotemporal additive model to improve HFMD incidence assessment. Methods Using incidence data (6439 samples from 137 monitoring district) for Shandong Province, China, along with meteorological, environmental and socioeconomic spatial and spatiotemporal covariate data, we proposed a spatiotemporal mixed model to estimate HFMD incidence. Geo-additive regression was used to model the non-linear effects of the covariates on the incidence risk of HFMD in univariate and multivariate models. Furthermore, the spatial effect was constructed to capture spatial autocorrelation at the sub-regional scale, and clusters (hotspots of high risk) were generated using spatiotemporal scanning statistics as a predictor. Linear and non-linear effects were compared to illustrate the usefulness of non-linear associations. Patterns of spatial effects and clusters were explored to illustrate the variation of the HFMD incidence across geographical sub-regions. To validate our approach, 10-fold cross-validation was conducted. Results The results showed that there were significant non-linear associations of the temporal index, spatiotemporal meteorological factors and spatial environmental and socioeconomic factors with HFMD incidence. Furthermore, there were strong spatial autocorrelation and clusters for the HFMD incidence. Spatiotemporal meteorological parameters, the normalized difference vegetation index (NDVI), the temporal index, spatiotemporal clustering and spatial effects played important roles as predictors in the multivariate models. Efron’s cross-validation R2 of 0.83 was acquired using our approach. The spatial effect accounted for 23% of the R2, and notable patterns of the posterior spatial effect were captured. Conclusions We developed a geo-additive mixed spatiotemporal model to assess the influence of meteorological, environmental and socioeconomic factors on HFMD incidence and explored spatiotemporal patterns of such incidence. Our approach achieved a competitive performance in cross-validation and revealed strong spatial patterns for the HFMD incidence rate, illustrating important implications for the epidemiology of HFMD.

Abstract Background Bacillary dysentery is the third leading notifiable disease and remains a major public health concern in China. The Beijing–Tianjin–Hebei urban region is the biggest urban agglomeration in northern China, and it is one of the areas in the country that is most heavily infected with bacillary dysentery. The objective of the study was to analyze the spatial-temporal pattern and to determine any contributory risk factors on the bacillary dysentery. Methods Bacillary dysentery case data from 1 January 2012 to 31 December 2012 in Beijing–Tianjin– Hebei were employed. GeoDetector method was used to determine the impact of potential risk factors, and to identify regions and seasons at high risk of the disease. Results There were 36,472 cases of bacillary dysentery in 2012 in the study region. The incidence of bacillary dysentery varies widely amongst different age groups; the higher incidence of bacillary dysentery mainly occurs in the population under the age of five. Bacillary dysentery presents apparent seasonal variance, with the highest incidence occurring from June to September. In terms of the potential meteorological risk factors, mean temperature, relative humidity, precipitation, mean wind speed and sunshine hours explain the time variant of bacillary dysentery at 83%, 31%, 25%, 17% and 13%, respectively. The interactive effect between temperature and humidity has an explanatory power of 87%, indicating that a hot and humid environment is more likely to lead to the occurrence of bacillary dysentery. Socio-economic factors affect the spatial distribution of bacillary dysentery. The top four factors are age group, per capita GDP, population density and rural population proportion, and their determinant powers are 61%, 27%, 25% and 21%, respectively. The interactive effect between age group and the other factors accounts for more than 60% of bacillary dysentery transmission. Conclusions Bacillary dysentery poses a higher risk in the population of children. It is affected by meteorological and socio-economic factors, and it is necessary to pay more attention to the meteorological period and situation, particularly in period with high temperature and humidity, as well as places in urban areas with high population density, and a low proportion of rural population.

Abstract Background Malaria remains an important public health concern in China and is particularly serious in Yunnan, a China’s provincial region of high malaria burden with an incidence of 1.79/105 in 2012. This study aims to examine the epidemiologic profile and spatiotemporal aspects of epidemics of malaria, and to examine risk factors which may influence malaria epidemics in Yunnan Province. Methods The data of malaria cases in 2012 in 125 counties of Yunnan Province was used in this research. The epidemical characteristics of cases were revealed, and time and space clusters of malaria were detected by applying scan statistics method. In addition, we applied the geographically weighted regression (GWR) model in identifying underlying risk factors. Results There was a total of 821 cases of malaria, and male patients accounted for 83.9% (689) of the total cases. The incidence in the group aged 20–30 years was the highest, at 3.00/105. The majority (84.1%) of malaria cases occurred in farmers and migrant workers, according to occupation statistics. On a space-time basis, epidemics of malaria of varying severity occurred in the summer and autumn months, and the high risk regions were mainly distributed in the southwest counties. Annual average temperature, annual cumulative rainfall, rice yield per square kilometer and proportion of rural employees mainly showed a positive association with the malaria incidence rate, according to the GWR model. Conclusions Malaria continues to be one of serious public health issues in Yunnan Province, especially in border counties in southwestern Yunnan. Temperature, precipitation, rice cultivation and proportion of rural employees were positively associated with malaria incidence. Individuals, and disease prevention and control departments, should implement more stringent preventative strategies in locations with hot and humid environmental conditions to control malaria.

In order to study effect of oak sawdust on the quality of Kyoho wine immersed by fermented Cabernet Sauvignon residual, Kyoho wine aged without oak sawdust (KWO), including KWO1 and KWO2 according to immersed orders by fermented Cabernet Sauvignon residual, was taken as control to compare the effect of oak sawdust on quality of Kyoho wine (KO), including KO1 and KO2 according to aged orders by oak sawdust. During the 15 days of aging, physical and chemical indicators, such as chroma, tonality, and total phenol in wine were determined simultaneously by using a spectrophotometer, including tannin content by KMnO4 titration, once every 3 days. The results showed that the chromaticities of Kyoho wine were 3.21, 3.02, 4.46, and 3.71 for KO1, KO2, KWO1, and KWO2, respectively. Similarly, the hues were in turn 0.73, 0.68, 0.97, and 0.72, respectively. Tannin contents were 1601.5 mg/L, 1517.3 mg/L, 337.2 mg/L, and 115.6 mg/L; total phenol contents were 277.67 mg/L, 222.1 mg/L, 64 mg/L, and 79.8 mg/L. Therefore, the contents of tannin and total phenol from KO1 wine were all the highest values. The chroma and tone of the four types of wine showed an upward trend of “S.” The chromaticity and tone were the lowest for the KO2 wine and the highest for the KWO1 wine with the larger difference between KO2 and KWO1.

Abstract Background Severe acute respiratory syndrome (SARS) originated in China in 2002, and it spread to 26 provinces in mainland China and 32 countries across five continents in a matter of months. This outbreak resulted in 774 deaths. However, the spatial features and potential determinants of SARS input-output flows remain unclear. Methods We used an adjusted spatial interaction model to examine the spatial effects and potential factors associated with SARS input-output flows. Results The presence of origin-based spatial dependence positively affected SARS input-output flows from the neighbours of the origin regions. Two components of the input-output flows, migrant and hospitalization flows, exhibited distinctive features. The origin-based and destination-based spatial dependence positively affected migrant flows (i.e., due to those seeking jobs) from the neighbours of origin and destination locations. Similarly, the destination-based spatial dependence also positively affected hospitalization flows (i.e., due to those seeking treatment) from the neighbours of destination regions. However, the origin-to-destination based spatial dependence negatively affected hospitalisation flows from the neighbours of origin-to-destination regions. The direct effects accounted for 78 % of the SARS input-output flows, which was 3.56-fold greater than the indirect effects. Differences in regional income drove the SARS input-output flows. Therefore, urban income had a positive effect, whereas rural income had a negative effect. Total interregional flows increased by 3.54 % with a 1 % increase in urban income, and intraregional flows increased by 8.35 %. In contrast, the total interregional flows decreased by 3.38 % with a 1 % increase in rural income, and intraregional flows declined by 2.29 %. Railway capacity, per person gross domestic product (PGDP), urban rate and the law of distance decay also affected the input-output flows. Conclusions Our results confirm that the SARS input-output flows presented significant geographic spatial heterogeneity and spatial effects. Income differences were the major cause of the flows between pairs of regions. Railway capacity, PGDP, and urban rate also played important roles. These findings provide valuable information for the Chinese government to control the future spread of nationwide epidemics.

The availability of advanced receiver autonomous integrity monitoring for vertical guidance down to altitudes of 200 ft (LPV-200) is discussed using real satellite orbit/ephemeris data collected at eight international global navigation satellite system service stations across China. Analyses were conducted for the availability of multi-constellation advanced receiver autonomous integrity monitoring and multi-fault advanced receiver autonomous integrity monitoring, and the sensitivity of availability in response to changes in error model parameters (i.e. user range accuracy, user range error, Bias-Nom and Bias-Max) was used to compute the vertical protection level. The results demonstrated that advanced receiver autonomous integrity monitoring availability based on multiple constellations met the requirements of LPV-200 despite multiple-fault detections that reduced the availability of the advanced receiver autonomous integrity monitoring algorithm; the advanced receiver autonomous integrity monitoring availability thresholds of the user range error and Bias-Nom used for accuracy were more relevant to geographic information than the user range accuracy and Bias-Max used for integrity at the eight international global navigation satellite system service stations. Finally, the possibility of using the advanced receiver autonomous integrity monitoring algorithm for a Category III navigation standard is discussed using two sets of predicted errors, revealing that the algorithm could be used in 79% of China.

To quantitatively assess the value of dual-energy CT (DECT) in differentiating malignancy and benignity of solitary pulmonary nodules.Sixty-three patients with solitary pulmonary nodules detected by CT plain scan underwent contrast enhanced CT scans in arterial phase (AP) and venous phase (VP) with spectral imaging mode for tumor type differentiation. The Gemstone Spectral Imaging (GSI) viewer was used for image display and data analysis. Region of interest was placed on the relatively homogeneous area of the nodule to measure iodine concentration (IC) on iodine-based material decomposition images and CT numbers on monochromatic image sets to generate spectral HU curve. Normalized IC (NIC), slope of the spectral HU curve (λHU) and net CT number enhancement on 70keV images were calculated. The two-sample t-test was used to compare quantitative parameters. Receiver operating characteristic curves were generated to calculate sensitivity and specificity.There were 63 nodules, with 37 malignant nodules (59%) and 26 benign nodules (41%). NIC, λHU and net CT number enhancement on 70keV images for malignant nodules were all greater than those of benign nodules. NIC and λHU had intermediate to high performances to differentiate malignant nodules from benign ones with the areas under curve of 0.89 and 0.86 respectively in AP, 0.96 and 0.89 respectively in VP. Using 0.30 as a threshold value for NIC in VP, one could obtain sensitivity of 93.8% and specificity of 85.7% for differentiating malignant from benign solitary pulmonary nodules. These values were statistically higher than the corresponding values of 74.2% and 53.8% obtained with the conventional CT number enhancement.DECT imaging with GSI mode provides more promising value in quantitative way for distinguishing malignant nodules from benign ones than CT enhancement numbers.

The spatiotemporal distribution pattern of the surface temperatures of urban forest canopies (STUFC) is influenced by many environmental factors, and the identification of interactions between these factors can improve simulations and predictions of spatial patterns of urban cool islands. This quantitative research uses an integrated method that combines remote sensing, ground surveys, and spatial statistical models to elucidate the mechanisms that influence the STUFC and considers the interaction of multiple environmental factors. This case study uses Jinjiang, China as a representative of a city experiencing rapid urbanization. We build up a multisource database (forest inventory, digital elevation models, population, and remote sensing imagery) on a uniform coordinate system to support research into the interactions that influence the STUFC. Landsat-5/8 Thermal Mapper images and meteorological data were used to retrieve the temporal and spatial distributions of land surface temperature. Ground observations, which included the forest management planning inventory and population density data, provided the factors that determine the STUFC spatial distribution on an urban scale. The use of a spatial statistical model (GeogDetector model) reveals the interaction mechanisms of STUFC. Although different environmental factors exert different influences on STUFC, in two periods with different hot spots and cold spots, the patch area and dominant tree species proved to be the main factors contributing to STUFC. The interaction between multiple environmental factors increased the STUFC, both linearly and nonlinearly. Strong interactions tended to occur between elevation and dominant species and were prevalent in either hot or cold spots in different years. In conclusion, the combining of multidisciplinary methods (e.g., remote sensing images, ground observations, and spatial statistical models) helps reveal the mechanism of STUFC on an urban scale.

As urban thermal environments are being caused by global climatic changes and urbanization is not uniform on diurnal, seasonal, or annual scales, the spatiotemporal patterns of surface urban heat islands (SUHI) similarly vary between cities across regions. This research assessed the spatiotemporal variations in SUHI intensities (SUHII), and then revealed their spatiotemporal patterns and relationships that existed within 32 major cities in China using spatialization technologies, such as the self-organizing map (SOM) method and statistical methods. Results showed that the spatial patterns of the SUHII patterns in China were significantly affected by the climatic types, whereas human heat discharge also disturbed the patterns to a certain extent. Specifically, the daytime SUHIIs in China had much higher seasonal variations in North China than in South China. The nighttime SUHIIs were much weaker and more stable than the daytime SUHIIs, and had far more obvious spatial patterns with much higher values in North China than in South China. As for the temporal regimes, the temporal variation in the SUHIIs in one city was more related to the development of the urbanization. To be specific, not all cities were experiencing increasingly worse urban thermal environments with urbanization as reported by previous studies. This research not only proposes a spatiotemporal framework to study the SUHIIs patterns and their relationships, but also provides an in-depth and comprehensive understanding of SUHIIs in China.

Integration of Landsat images and multisource data using spatial statistical analysis and geographical detector models can reveal the individual and interactive influences of anthropogenic activities and ecological factors on concentrations of atmospheric particulate matter less than 2.5 microns in diameter (PM2.5). This approach has been used in many studies to estimate biomass and forest disturbance patterns and to monitor carbon sinks. However, the approach has rarely been used to comprehensively analyze the individual and interactive influences of anthropogenic factors (e.g., population density, impervious surface percentage) and ecological factors (e.g., canopy density, stand age, and elevation) on PM2.5 concentrations. To do this, we used Landsat-8 images and meteorological data to retrieve quantitative data on the concentrations of particulates (PM2.5), then integrated a forest management planning inventory (FMPI), population density distribution data, meteorological data, and topographic data in a Geographic Information System database, and applied a spatial statistical analysis model to identify aggregated areas (hot spots and cold spots) of particulates in the urban area of Jinjiang city, China. A geographical detector model was used to analyze the individual and interactive influences of anthropogenic and ecological factors on PM2.5 concentrations. We found that particulate concentration hot spots are mainly distributed in urban centers and suburbs, while cold spots are mainly distributed in the suburbs and exurban region. Elevation was the dominant individual factor affecting PM2.5 concentrations, followed by dominant tree species and meteorological factors. A combination of human activities (e.g., population density, impervious surface percentage) and multiple ecological factors caused the dominant interactive effects, resulting in increased PM2.5 concentrations. Our study suggests that human activities and multiple ecological factors effect PM2.5 concentrations both individually and interactively. We conclude that in order to reveal the direct and indirect effects of human activities and multiple factors on PM2.5 concentrations in urban forests, quantification of fusion satellite data and spatial statistical methods should be conducted in urban areas.

Simulation technologies provide necessary validation tools for the conceptual design of complex products involving multiple disciplines. A variety of simulation models are developed in specific organizations or enterprises to verify the design plan, while they are hard to be shared and to be reused. Based on the analysis of several typical solutions to share and reuse different kinds of resources, a simulation model design method is proposed to provide a simple implementation of simulation model reuse for cloud-based simulation environment. This paper firstly creates the simulation models' metamodel and ontology for their universal description. Secondly, four rules are proposed to design/reprogram a simulation model into service-oriented form for its interoperability, and the ontology of service-oriented simulation model is established. Thirdly, the way to call one simulation model and the way to compose several simulation models into a simulation process are elaborated. Finally, a simple case of using this method to design an aircraft dynamic model is elaborated, and a prototype simulation system is constructed, and then a simple simulation process is composed to verify the practicability of the method. The result shows that the new design/reprogram method has big advantages on the compatibility, expansibility, and reusability despite the decreasing efficiency.