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Background The Guangxi government initiated two rounds of the Guangxi AIDS Conquering Project (GACP) in 2010 (Phase I) and 2015 (Phase II) to control human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemics. However, the effectiveness of GACP in HIV prevention and treatment has rarely been reported. This study aimed to assess the effectiveness of the GACP implemented in Guangxi, China and provide data for strategy and praxis improvements to achieve Joint United Nations Programme on HIV/AIDS (UNAIDS) 95-95 targets. Methods We used spatial approaches to trace the spatiotemporal distribution properties, epidemic trends, and correlation between macroscopic factors and HIV incidence using data from the Chinese HIV/AIDS case reporting system to explore the effects of the GACP. Results During the GACP era, the HIV epidemic stabilized in urban centers, showing a downward trend in the Hengzhou and Binyang Counties in the eastern region, whereas it continued to increase in rural areas of the northwest region, such as the Long’an, Mashan, Shanglin, and Wuming Districts. The linear directional mean (LDM) of HIV infection reported cases displayed a southeast–northwest direction, with an LDM value of 12.52°. Compared with that in Phase I, Hengzhou withdrew from the high-high clustering area, and the west–north suburban counties pulled out the low-low clustering area during Phase II. Significant HIV clusters were identified in the eastern region during Phase I, whereas these clusters emerged in the northwestern areas during Phase II. Regarding HIV, socioeconomic status, population mobility, and medical care levels were the key social drivers of heterogeneous spatial distribution. Conclusions The GACP assisted in effectively managing the HIV epidemic in urban and eastern areas of Nanning City. However, prevention and control efforts in rural regions, particularly those located in the northwest, may not have yielded comparable outcomes. To address this disparity, allocating additional resources and implementing tailored intervention measures for these rural areas are imperative.

Single‐atom catalysts, featuring some of the most unique activities, selectivity, and high metal utilization, have been extensively studied over the past decade. Given their high activity, selectivity, especially towards small molecules or key intermediate conversions, they can be synergized together with other active species (typically other single atoms, atomic clusters, or nanoparticles) in either tandem or parallel or both, leading to much better performance in complex catalytic processes. Although there have been reports on effectively combining the multiple components into one single catalytic entity, the combination and synergy between single atoms and other active species have not been reviewed and examined in a systematic manner. Herein, in this overview, the key synergistic interactions, binary complementary effects, and the bifunctional functions of single atoms with other active species are defined and discussed in detail. The integration functions of their marriages are investigated with particular emphasis on the homogeneous and heterogeneous combinations, spatial distribution, synthetic strategies, and the thus‐derived outstanding catalytic performance, together with new light shined on the catalytic mechanisms by zooming in several case studies. The dynamic nature of each of the active species and in particular their interactions in such new catalytic entities in the heterogeneous electrocatalytic processes are visited, on the basis of the in situ/operando evidence. Last, we feature the current challenges and future perspectives of these integrated catalytic entities that can offer guidance for advanced catalyst design by the rational combination and synergy of binary or multiple active species. Nurturing the marriages of single atoms with atomic clusters and nanoparticles: The three basic building blocks of single atoms, atomic clusters, and nanoparticles each feature rather different chemical properties. Nurturing their interactions in one purposely‐combined catalytic entity help catalyze complex electrochemical reactions, where there can be synergies among the intermediate steps driven by the combination of individual building blocks, leading to much higher activity and selectivity.

Species diversity has spatial heterogeneity in ecological systems. Although a large number of studies have demonstrated the influence of soil properties on species diversity, most of them have not considered their spatial variabilities. To remedy the knowledge gap, a 1 ha (100 m × 100 m) plots of arid desert riparian forest was set up in the Ebinur Wetland Nature Reserve (ELWNR) in the NW China. Then, the minimum data set of soil properties (soil MDS) was established using the Principal Component Analysis (PCA) and the Norm Value Determination to represent the total soil property data set (soil TDS). The Geo-statistics and two models ( i.e. , Random Forest/RF and Multiple Linear Regression/MLR) were used to measure the spatial variability of species diversity, and predict its spatial distribution by the soil MDS, respectively. The results showed that the soil MDS was composed of soil salt content (SSC), soil total phosphorus (STP), soil available phosphorus (SAP), soil organic carbon (SOC) and soil nitrate nitrogen (SNN); which represented the soil TDS perfectly ( R 2 = 0.62). Three species diversity indices ( i.e. , Shannon–Wiener, Simpson and Pielou indices) had a high spatial dependence (C 0 /(C 0 +C)< 25%; 0.72 m ≤ range≤ 0.77 m). Ordinary kriging distribution maps showed that the spatial distribution pattern of species diversity predicted by RF model was closer to its actual distribution compared with MLR model. RF model results suggested that the soil MDS had significant effect on spatial distribution of Shannon–Wiener, Simpson and Pielou indices ( Var ex = 56%, 49% and 36%, respectively). Among all constituents, SSC had the largest contribution on the spatial variability of species diversity (nearly 10%), while STP had least effect (< 5.3%). We concluded that the soil MDS affected spatial variability of species diversity in arid desert riparian forests. Using RF model can predict spatial variability of species diversity through soil properties. Our work provided a new case and insight for studying the spatial relationship between soil properties and plant species diversity.

CO 2 emissions have become increasingly prominent in China, and the primary emitters are economic belts that are spread throughout China. Two major economic belts, i.e., the Yangtze River Economic Belt (YTREB) and the Yellow River Economic Belt (YREB). Combined with stochastic impacts by regression on population, affluence and technology model, the spatial Durbin model under the space-and-time fixed effect and the Geographical and Time-Weighted Regression are employed to explore the spatio-temporal distribution characteristics and heterogeneous drivers of CO 2 emissions in the two economic belts. The results are as follows. First, CO 2 emissions exhibit obvious spatial correlation features in the YREB, but no such obvious spatial correlation is found in the YRETB. Second, in the YREB, the magnitude of the total influencing factors on CO 2 emissions follows an order where affluence ( A ) is the biggest driver, followed by energy intensity ( EI ), technology ( TEC ) and openness ( OP ), while the biggest driver in the YRETB is industrial structure supererogation ( ISS ), followed by population ( P ), energy intensity ( EI ), and affluence ( A ). Both direct and spatial spillover effects of the drivers are observed in the two economic belts. Third, the CO 2 emissions show a notable temporal lag effect in the YREB, but not in the YRETB. Fourth, the effects of the CO 2 emission drivers illustrate significant spatio-temporal heterogeneity in the two economic belts.

In this paper, we are concerned with two SIS epidemic reaction–diffusion models with mass action infection mechanism of the form SI , and study the spatial profile of population distribution as the movement rate of the infected individuals is restricted to be small. For the model with a constant total population number, our results show that the susceptible population always converges to a positive constant which is indeed the minimum of the associated risk function, and the infected population either concentrates at the isolated highest-risk points or aggregates only on the highest-risk intervals once the highest-risk locations contain at least one interval. In sharp contrast, for the model with a varying total population number which is caused by the recruitment of the susceptible individuals and death of the infected individuals, our results reveal that the susceptible population converges to a positive function which is non-constant unless the associated risk function is constant, and the infected population may concentrate only at some isolated highest-risk points, or aggregate at least in a neighborhood of the highest-risk locations or occupy the whole habitat, depending on the behavior of the associated risk function and even its smoothness at the highest-risk locations. Numerical simulations are performed to support and complement our theoretical findings.

In response to the growing demand for high-strength and high-toughness materials in industries such as aerospace and automotive, there is a need for metal matrix composites (MMCs) that can simultaneously increase strength and toughness. The mechanical properties of MMCs depend not only on the content of reinforcing elements, but also on the architecture of the composite (shape, size, and spatial distribution). This paper focuses on the design configurations of MMCs, which include both the configurations resulting from the reinforcements and the inherent heterogeneity of the matrix itself. Such high-performance MMCs exhibit excellent mechanical properties, such as high strength, plasticity, and fracture toughness. These properties, which are not present in conventional homogeneous materials, are mainly due to the synergistic effects resulting from the interactions between the internal components, including stress–strain gradients, geometrically necessary dislocations, and unique interfacial behavior. Among them, aluminum matrix composites (AMCs) are of particular importance due to their potential for weight reduction and performance enhancement in aerospace, electronics, and electric vehicles. However, the challenge lies in the inverse relationship between strength and toughness, which hinders the widespread use and large-scale development of MMCs. Composite material design plays a critical role in simultaneously improving strength and toughness. This review examines the advantages of toughness, toughness mechanisms, toughness distribution properties, and structural parameters in the development of composite structures. The development of synthetic composites with homogeneous structural designs inspired by biological composites such as bone offers insights into achieving exceptional strength and toughness in lightweight structures. In addition, understanding fracture behavior and toughness mechanisms in heterogeneous nanostructures is critical to advancing the field of metal matrix composites. The future development direction of architectural composites and the design of the reinforcement and toughness of metal matrix composites based on energy dissipation theory are also proposed. In conclusion, the design of composite architectures holds enormous potential for the development of composites with excellent strength and toughness to meet the requirements of lightweight structures in various industries.

Background Fibroblasts have important roles in the synthesis and remodeling of extracellular matrix (ECM) proteins during pulmonary fibrosis. However, the spatiotemporal distribution of heterogeneous fibroblasts during disease progression remains unknown. Results In the current study, silica was used to generate a mouse model of pathological changes in the lung, and single-cell sequencing, spatial transcriptome sequencing and an analysis of markers of cell subtypes were performed to identify fibroblast subtypes. A group of heterogeneous fibroblasts that play an important role at the early pathological stage were identified, characterized based on the expression of inflammatory and proliferation genes (termed inflammatory-proliferative fibroblasts) and found to be concentrated in the lesion area. The expression of GREM1/protein phosphatase 2 regulatory subunit B''alpha (PPP2R3A) in inflammatory-proliferative fibroblasts was found to initiate early pulmonary pathological changes by increasing the viability, proliferation and migration of cells. Conclusions Inflammatory-proliferative fibroblasts play a key role in the early pathological changes that occur in silicosis, and during this process, GREM1 is the driving factor that targets PPP2R3A and initiates the inflammatory response, which is followed by irreversible fibrosis induced by SiO 2 . The GREM1/PPP2R3A pathway may be a potential target in the early treatment of silicosis.

The interaction between the population and built environment is a constant topic in urban spaces and is the main driving force of urban evolution. Understanding urban population distribution and its relationship with the built environment could provide guidance for urban planning, traffic, and disaster management. Following this line of thought, this study conducted an empirical analysis in Xi’an, a rapidly developing western city in China. Well-permeated mobile phone location data were used to represent the spatiotemporal dynamics of the population, and the built environment was characterized from five perspectives—transportation, location, building, greenery, and land use—using multisource geospatial data. Finally, the dynamic heterogeneous influence of built environment factors on population distribution was examined using multiscale geographically weighted regression (MGWR). Overall, the influencing coefficients exhibited a significant dynamic changing process from a temporal perspective and simultaneously demonstrated spatial nonstationarity. Moreover, the specific findings about the influence of each built environment factor facilitate a deeper insight into dynamic population distribution and its determinants.

Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.

Remediating organic pollutants in heterogeneous aquifer presents a significant challenge due to the limited penetration of chemical reagents into these zones. Ozone micro‐nano bubbles, with their small diameters, can transport to low‐permeability zone (LPZ) and oxidize target contaminants; however, their mobility is often hindered by attachment to porous media surfaces. This study investigated the use of electrokinetic (EK) remediation to enhance the transport of rhamnolipid‐coated ozone micro‐nano bubbles (ROMNB). Column experiments showed that applying an electric field of 3 V/cm increased outlet ozone concentrations by 23.2∼24.7%, corresponding to a 25.9∼60.0% increase in nanobubble density at the outlet. A 2D heterogeneous sand tank experiment further demonstrated that, when the ROMNB injection point was positioned near the cathode, total toluene removal in the LPZ reached 89.3%, 21.8% higher than without EK. The analysis of oxidation byproducts showed that 43.2% and 0.075% of carbon in toluene from LPZ were converted to CO2 and benzoic acid, respectively, indicating the effective oxidation of toluene. Numerical modeling accurately reproduced the spatial distribution of residual toluene with the average error of 9.5%, confirming that electrophoresis significantly improved ROMNB penetration into LPZ. Furthermore, when the applied voltage gradient exceeded 0.5 V/cm, the simulated total toluene removal percentage in LPZ stabilized at 90.4∼94.1%, suggesting that the voltage could be optimized to reduce energy consumption. These findings highlight the potential of integrating ROMNB with EK for effective remediation of organic contaminants in low‐permeability subsurface environments.