Cold Spots Research Articles

Land Use Change and Ecosystem Service Value Measurement in the Peak Cluster Depression Basin in Southwest Guangxi Under Simulated Multiple Scenarios

Simulating the changes in ecosystem service value induced by land use changes in the peak cluster depression basin of southwestern Guangxi under multiple scenarios is of great importance for ensuring ecological security in the basin and enhancing regional ecosystem service value capabilities. Based on the CA-Logistic-Markov model, the land use of the study area was simulated under natural development, food security, and ecological protection scenarios in 2030. The ecosystem service value was calculated under each scenario using the equivalent factor correction model, and the spatiotemporal dynamic characteristics of the ecosystem service value in the peak cluster depression basin of southwestern Guangxi were quantitatively analyzed. The results showed as follows： ① Compared with the current situation of land use in 2020 and the predicted simulated land use in 2020, the standard Kappa index was 0.917, the location Kappa index was 0.952, and the numerical Kappa index was 0.958. In the simulation of the land use pattern of the peak cluster basin in southwest Guangxi in 2030, the CA-Markov model had high accuracy and reference-ability. ② Ecosystem service values in the study area for the periods 2000-2020 were 270.881 billion, 275.78 billion, and 278.332 billion yuan, indicating an overall upward trend. ③ The value of ecosystem services in the natural development, the food security, and the ecological protection scenarios was 282 138 million, 273 206 million, and 284 143 million, respectively. The study area exhibited a spatial distribution of ecosystem service values with higher values in the northwest and lower values in the southeast. In comparison to that in 2020, the ecosystem service value significantly decreased under the food security scenario, reflecting ecosystem degradation. Meanwhile, ecosystem service values increased under the natural development and ecological protection scenarios, signifying an improvement in ecological environmental quality. ④ Ecosystem service value hot spots and cold spots with high confidence had strong clustering and the hot spots were mainly concentrated in the northwestern mountainous area, whereas the cold spots with high confidence were mainly distributed in the southeastern plain area of the study area. The research results revealed the spatiotemporal pattern of land use change and ecosystem service value in the peak cluster depression basin of southwestern Guangxi under multiple scenarios, which can provide scientific basis for optimizing land use structure and spatial pattern and enhancing ecosystem services.

Mapping and assessing the future provision of lake ecosystem services in Lithuania

Lakes supply multiple ecosystem services (ES), key to supporting socio-ecologic systems and human well-being. In the context of future land use and climate changes, it is imperative to anticipate potential impacts on lake ES supply. Hence, studies that deal with future lake ES, such as mapping, are lacking. In this work, we mapped and assessed the future supply of three ES: (1) maintenance of nursery conditions (nursery ES), (2) maintenance of chemical conditions of freshwaters (nutrient regulation ES), and (3) direct and indirect cultural outputs (recreation ES) in Lithuania. Four future scenarios were utilised, integrating land use and climate changes: A0 - business as usual; A1-urbanisation; A2: land abandonment and afforestation; and A3 - agricultural intensification. The projected year was 2050, following the intermediate Representative Concentration Pathway (RCP 4.5). The future scenarios were simulated using the open-source software Dinamica EGO based on a 6-step modelling framework. Statistical differences among the scenarios and ES were analysed by applying a Kruskal-Wallis ANOVA test. Spatial analysis was done by performing a Moran's I and Getis-Ord (Gi∗) hotspot analysis. The results showed significant differences in nursery and nutrient regulation ES. The highest supply in nursery ES was observed for the A0 scenario. For nutrient regulation ES, the lowest ES supply was identified for the A1 scenario and for recreation ES, the highest was found in the A2 scenario. The eastern and northeastern regions of Lithuania showed a high ES supply. Hot spots were only identified in the eastern region. These regions are associated with a high area covered by forests and protected areas. The central region shows a low ES supply, identified as a cold spot where the agricultural landscape dominates. The results of the PCA analysis revealed an association between nursery and recreation ES. Nutrient regulation was not associated with the other two ES. Mapping and assessing the impact of future scenarios is vital to anticipating the potential dynamics of lake ES, especially in the context of climate change. This information is essential in the context of environmental management, helping decision-makers to ensure a sustainable ES supply and contributing to human wellbeing.

Spatial–Temporal Differentiation and Influencing Factors of Rural Education Development in China: A Systems Perspective

Education is the cornerstone of rural revitalization. This study aims to comprehensively evaluate the development of rural education in China from 2006 to 2020. From a systemic perspective, this study established a multidimensional evaluation index system for rural education and used the weight-TOPSIS method for measurement. Additionally, geographic information system and spatial econometric methods were employed to explore spatial–temporal differentiation and influencing factors. The results show that (1) rural education levels in China have generally improved in recent years, with higher development in northern, northeastern, and eastern regions and lower levels in central and southwestern regions. (2) In terms of spatial differentiation, rural education development among provinces has significant spatial agglomeration. The provinces around Beijing are hot spots, while remote southwestern provinces are cold spots. (3) Regarding dynamic evolution, the disparity in rural education development among provinces has widened, with a few provinces significantly ahead. There are club convergence features, and the hierarchy of rural education development between provinces is relatively stable, with less likelihood of lagging provinces catching up. (4) Economy, finance, industry, population, and urbanization are key factors influencing rural education, with spatial spillover effects on neighboring provinces. The study provides empirical support and policy insights for advancing balanced and high-quality rural education development.

Multi-Criteria Decision-Making Scenario Insights into Spatial Responses and Promotion Under Ecosystem Services

The Blue Economic Zone of the Shandong Peninsula is located in the transitional zone between land and sea, with a complex ecological environment. The determination of hot and cold spots in various ecosystem services is crucial for the coordinated development of ecosystem services and the optimization of the spatial pattern of the ecological environment. This study, based on natural and socio-economic data, utilizes various ecological models to simulate water yield (provisioning service), carbon sequestration (regulating service), biodiversity (supporting service), and aesthetic and scientific research values (cultural service). Using a multi-criteria decision-making approach, it identifies hot and cold spots of ecosystem services in different development–conservation scenarios. Combining the protection efficiency of different areas, it proposes a spatial pattern promotion scheme. The research indicates significant spatial differences in ecosystem services without clear trade-offs and synergies. Changes in the weights of ecosystem services in 11 scenarios result in significant differences in hot and cold spots. Compared to the neutral scenario (S6), the distribution of hot and cold spots in protection scenarios (S1–S5) is relatively scattered, while in development scenarios (S7–S11), hot spots show an increasing trend of concentration in the southeast, with cold spots scattered in the west and northwest. Four spatial pattern promotion schemes are proposed based on protection efficiency and policy preferences. Promotion areas should focus on ecological restoration and improvement to raise local ecosystem service levels. Protection areas should emphasize maintaining their existing high-level ecosystem services to achieve a synergistic enhancement of various ecosystem services.

Fine mapping and candidate gene mining of QSc/Sl.cib-7H for spike compactness and length and its pleiotropic effects on yield-related traits in barley (Hordeum vulgare L.).

A major locus for spike compactness and length was mapped on chromosome 7H and its pleiotropic effects, candidate genes and transcriptional regulatory network were analyzed. Spike compactness (SC) and length (SL) are important traits of barley (Hordeum vulgare L.) due to their close association with grain yield. In this study, a major SC and SL locus QSc/Sl.cib-7H was primarily identified on chromosome 7H by bulked segregant analysis, and further fine mapped to a recombination cold spot expanding 244.36-388.09Mb by developing a secondary population using residual heterozygous lines. This region is much more accurate than previously reported spike compactness loci on chromosome 7H.The strong effects of QSc/Sl.cib-7H on SL and SC were validated in two pair of near isogenic lines (NILs) and diverse genetic backgrounds. QSc/Sl.cib-7H exhibited pleiotropic effects on plant height (PH), thousand grain weight and grain length, and did not significantly influence the spikelet number of main spike (SMS) and grain width. Transcriptome analysis based on NILs showed that regulation of SC and SL might be related to the plant circadian rhythm pathway. The candidate genes were mined by analyzing variants and expression patterns of genes in the target region employing multiple genome and transcriptome data. This study takes a further step towards cloning of QSc/Sl.cib-7H, and the data obtained and the developed molecular markers will facilitate its utilization in barley breeding.

Time Series Clustering of Sea Surface Temperature in the Mediterranean and Black Sea Marine System

ABSTRACTSea surface temperature (SST) is a significant climatic variable that affects the climate of the Earth. Monitoring a location's SST pattern is useful for several research areas, including weather forecasting and climate change. In this study, the emerging hot spot and cold spot patterns of SST in the Mediterranean and Black Sea Marine System (MBMS) were examined, the spatial distribution characteristics and temporal changes of SST in the sub‐basins were analysed, and future predictions were made. A distinctive aspect of the research lies in the introduction of novel techniques, specifically the application of space time cube and evolving hot spot analysis, for visualising and evaluating SST in the MBMS. This approach sets the study apart by pioneering the utilisation of these methods in this particular context. In the examined region, SST demonstrates a decreasing trend from east to west and from south to north. The forecast suggests that this spatial distribution pattern will persist in 2033, further accentuated by the intensification of the warming effect. Nine different time series clusters are defined within this distribution pattern. Although it changes seasonally, the prevailing statistically significant hot spots in the study area are primarily characterised by new hot spots, intensifying hot spots, sporadic hot spots and oscillating hot spots. The trends of hot and cold spot clusters, along with SST values, were assessed for all sub‐basins in the MBMS. Conversely, the observed clustering category among statistically significant cold spots is identified as persistent cold spots, diminishing cold spots, sporadic cold spots, oscillating cold spots and historical cold spots. The spatiotemporal analysis in this research has provided notable insights, offering a spatial context to the previously explored temporal trends of SST in the MBMS.

Spatiotemporal Estimation of Black Carbon Concentration in Tehran Using Aerosol Optical Depth Remote Sensing Data and Meteorological Parameters: Health Risk Assessment and Relationship with Green Spaces

Black Carbon (BC) is an atmospheric pollutant with considerable adverse effects on human health, increasing the chance of cardiovascular disorders, respiratory issues, and cancers in exposed individuals. Accordingly, studying BC in urban areas is essential for understanding its associated health risks. In this study, the Multi-Angle Implementation of Atmospheric Correction (MAIAC) retrieved Aerosol Optical Depth (AOD) remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), along with in situ observations of BC concentration and meteorological parameters were utilized to estimate BC concentration in Tehran. In this regard, an ensemble machine learning algorithm, Gradient Boosting Machine (GBM), was employed to estimate BC concentration from 2010 to 2021, enabling a spatiotemporal analysis of BC levels in Tehran. Subsequently, the carcinogenic and non-carcinogenic effects of BC on children and adults were examined, as well as its relationship to urban green spaces. The Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Adjusted R-squared (R2adj), values for BC estimation ranged from 0.80 to 1.59 μg/m3, 0.59 to 1.10 μg/m3, and 0.70 to 0.94, respectively, indicating the promising performance of the GBM algorithm. The estimated annual average BC concentration over 11 years was 6.18± 2.46 μg/m3. Spatial variations in BC concentration and hotspot analysis at 99% and 95% confidence levels, showed that hotspots were primarily concentrated in the central and southern parts of Tehran. In contrast, cold spots were more scattered across the western and northeastern parts of the city. The cancer risk (CR) from BC exposure exceeded the recommended risk levels (1 × 10⁻⁶ to 1 × 10⁻⁴) established by the US Environmental Protection Agency (US EPA), demonstrating severe health risks for people in Tehran exposed to the current levels of BC concentrations. The average Hazard Quotient (HQ) value across all areas of Tehran was below the threshold value of 1, indicating that the non-carcinogenic health risk remains within acceptable limits. Results regarding green spaces indicated that greenery significantly influences BC concentration, revealing a negative correlation between green space coverage and BC concentration.

The spatiotemporal patterns and driving factors of cybercrime in the UK during the COVID-19 pandemic

Cybercrime is a complex human behavior and social phenomenon. The COVID-19 pandemic has significantly altered socioeconomic activities, potentially causing changes in crime patterns. However, there has been limited research on how the interaction between the pandemic and socioeconomic factors affects cybercrime. Here we explore the spatiotemporal patterns of police-recorded cybercrime, including fraud and cyber-dependent crime, and employ a machine learning approach to assess the correlation of various factors on cybercrimes at the level of internal regions and police areas within the United Kingdom. Our results show that fraud and cyber-dependent crime are mainly concentrated in London and the southeast region of England. Moreover, following the implementation of the third national lockdown, these areas experienced a noticeable increase, while changes in other regions were not as pronounced. The spatial autocorrelation analysis further suggests that there are significant spatial heterogeneities among regions, with spatiotemporal hotspots centered around London and cold spots mainly concentrated in the northeast of England. Additionally, we found that the identified patterns of fraud and cyber-dependent crime are primarily associated with socioeconomic factors, followed by government containment measures and mobility factors. These findings can help law enforcement and regulatory agencies better understand the social-environmental factors contributing to the prevalence of cybercrime within those areas.

Assessing the utility of low-emission pilot policies for facilitating pollution reduction and carbon mitigation: An empirical investigation using multi-temporal double difference analysis

Collaborative governance for pollution reduction and carbon mitigation can effectively resist the pressure brought about by ecological and environmental changes while reducing policy costs. To explore the “win-win” path of pollution reduction and carbon mitigation,” based on panel data from 286 cities in China from 2000 to 2021, this study regards the low-carbon city pilot policy (LCCP) as a quasi-natural experiment and explores its impact on urban carbon emissions and pm2.5 concentration. Multiple testing methods, including multi-temporal double difference, spatial autocorrelation, local cold and hot spot analysis, kernel density analysis, standard deviation ellipse, spatial Durbin model, and PSM-DID, were used to verify the credibility and scientificity of the research results. The results showed that (1) LCCP had a direct effect on reducing pollution and carbon emissions, reducing co2 emissions by 3% and pm2.5 by 2.9%, with a synergistic effect of 20.2%; (2) the synergistic effect of LCCP on pollution and carbon reduction was indirectly achieved through green finance, green technological innovation, and green transportation, with impacts of 3.6%, 5.5%, and 2.4% in eastern, central, and non-resource based cities, respectively; and (3) the spillover effect coefficient of LCCP on carbon emission reduction in surrounding cities was −0.040 and that on pm2.5 concentration was −1.232, which are significant at the 10% and 1% levels, respectively.

Monitoring of thermal conditions and snow dynamics at periglacial block accumulations in a low mountain range in central Germany

AbstractThe Rhoen Mountains, a relict periglacial landscape in central Germany, feature a wide range of openwork block accumulations. Although located in a temperate climate, those have characteristics comparable with cold regions of higher altitude or latitude such as arctic‐alpine species, longer lasting snow patches or the discussed existence of summer or even year‐round ice lenses in one of the largest of these landforms in the Central German Uplands. This study aims for a characterization of the microclimatic conditions of two neighbouring block accumulations. Therefore, temperatures were registered by data loggers along profiles, and snow dynamics were monitored using time‐lapse cameras and terrestrial laser scans. These observations are finally compared with geophysical measurements to address the question of potential isolated low‐altitude permafrost occurrences. Mean ground surface temperatures show an inverse thermal gradient along the Schafstein block accumulation. Furrows were identified as the cold spots in winter, whereas snow melt holes are signs of a chimney effect. In summer, cold air flows out at ventilation holes along the front causing temperatures of up to 25°C below air temperatures, although no clear signs of permafrost were detected. Temperature correlations reveal periods indicative of a recurring internal summer air circulation. Coarse blocky substrate also favours ground cooling of the smaller Mathesberg block accumulation compared with its surroundings. Winter temperatures are influenced by a persistent snowbank forming due to drifting and blowing snow at the leeward edge of a plateau as little amounts of snow are sufficient to be redistributed by westerlies. The prolonged melt of the snowbank might have had or still has a local hydrological and geomorphological impact. Uncertainties remain regarding the behaviour of the microclimate of block accumulations in a warming climate. Being ‘cold spots’ of high ecological value further investigations are suggested.

A novel micro ohmic heating cell for microorganism destruction kinetic studies: Performance validation

Ohmic heating cells used for microbial destruction are typically large compared with conventional capillary tubes. Limitations of commonly used large cells include large mass, high come-up-time (CUT), lack of uniform heating, and operation at atmospheric pressure. A new 3 mL Micro Ohmic Cell (MOC) was designed and fabricated for the kinetic study of microbial spore destruction. Two tiny titanium electrodes, mounted horizontally on a T-shaped cylindrical cell with a 3 cm gap, generate a rapid heating rate up to 140 °C. Fibre optic temperature sensors were used for direct temperature monitoring during CUT and holding time. Model solutions were heated under voltage gradients up to 90 V/cm. Uniform heating was achieved under stirring conditions with less than 1 °C variation across the cell volume, and the coldest spot was identified as the liquid-air interface. CUT was evaluated as influenced by both system and product parameters, resulting in less than 50 s comparable to those obtained using capillary tubes. The newly developed MOC was validated for microbial destruction kinetic study under ohmic heating conditions using Clostridium sporogenes spores in buffer and concentrated maple sap. With its small volume, short CUT, and uniform temperature similar to capillary tubes in conventional heating, the MOC has the potential to be considered as a standard device for kinetic studies under ohmic heating.

Urbanization and plant diversity in urban fringes: Differential responses across life forms

Urban fringe areas, as critical transition zones between urban expansion and natural environments, provide essential ecosystem services that enhance the overall health and sustainability of urban environments. However, previous studies have focused on urban built–up areas while neglecting the unique conditions of urban fringe zones. This study investigated the spatial distribution patterns of plant diversity across different life forms in Jiangdong New District and their responses to urbanization levels. The results indicate that hot and cold spots of diversity for trees and shrubs show similarity, while diversity in herbaceous plants differs significantly. Nighttime light intensity, POI (Point–of–Interest) density, and road density are primary influencing factors, with nighttime light intensity and POI density having the greatest combined impact, and herbs showing the highest sensitivity to these factors. Further analysis reveals that in the northwestern forest, grassland, and farmland mosaic areas, urbanization levels are positively correlated with tree and shrub diversity but negatively correlated with herb diversity. In the southeastern nursery planting area, urbanization levels consistently relate to the diversity of these three plant types. These findings underscore the necessity of adopting diverse conservation and management strategies in urban fringe areas to address the ecological needs and functional contributions of different plant types. This provides new perspectives on urban planning and ecological conservation that can help improve the overall health and sustainability of urban ecosystems.

Construction of Long-Term Grid-Scale Decoupling Model: A Case Study of Beijing-Tianjin-Hebei Region

Against the backdrop of rapid global economic development, the Beijing-Tianjin-Hebei (BTH) region, a pivotal economic hub and environmentally sensitive area in China, faces significant challenges in sustaining its landscape ecosystem. Given the region’s strategic importance and vulnerability to environmental pressures, this study investigated the intricate relationships between landscape ecological risk, urban expansion, and economic growth (EG) in the BTH region. Utilizing the landscape as the focal point, we constructed a decoupling model at the grid scale to explore the decoupling relationship between the landscape ecological risk index (ERI), construction area growth (CAG), and EG. The results showed that (1) distinct stages and regional disparities were observed in the trends of ERI, CAG, and EG within the BTH region. The hot and cold spot patterns for these factors did not align consistently. (2) From 1995 to 2019, the coupling relationship between ERI, CAG, and EG in the BTH region underwent a fluctuating transition, initially moving from an undesirable state to an ideal state, and subsequently reverting to an undesirable state. Although the overall trends in these relationships showed some convergence, there were notable spatial distribution differences. (3) The spatial heterogeneity of the two decoupling relationships in the BTH region was relatively poor. Further analysis revealed that the evolution of these decoupling relationships was closely intertwined with regional policy shifts and adjustments.

Geospatial Hot Spots and Cold Spots in US Cancer Disparities and Associated Risk Factors, 2004-2008 to 2014-2018.

Despite declining cancer death rates in the US, cancer remains the second deadliest disease and disparities persist. Although research has focused on identifying risk factors for cancer deaths and associated disparities, few studies have examined how these relationships vary over time and space. The primary objective of this study was to identify cancer mortality hot spots and cold spots - areas where cancer death rates decreased less than or more than neighboring areas over time. A secondary objective was to identify risk factors of cancer mortality hot spots and cold spots. We analyzed county-level cancer death rates from 2004 through 2008 and 2014 through 2018, exploring disparities in changes over time for socioeconomic and demographic variables. We used hot spot analysis to identify areas with larger decreases (cold spots) and smaller decreases (hot spots) in cancer death rates and random forest machine learning analysis to assess the relative importance of risk factors associated with hot spots and cold spots. We mapped spatial clustering areas. Geospatial analysis showed hot spots predominantly in the Plains states and Midwest and cold spots in the Southeast, Northeast, 2 Mountain West states (Utah and Idaho), and a portion of Texas. Factors with the strongest influence on hot spots and cold spots were unemployment, preventable hospital stays, mammography screening, and high school education. Geospatial disparities in changes in cancer death rates point out the critical role of access to care, socioeconomic position, and health behaviors in persistent cancer mortality disparities. Study results provide insights for interventions and policies that focus on addressing health care access and social determinants of health.

Identification of Key Determinants Influencing Spatiotemporal Heterogeneity of Urban Resilience

The identification of spatiotemporal heterogeneity, its key determinants, and the interaction effects between the driving factors of urban resilience (UR) within and between subregions is fundamental for understanding its underlying mechanisms. A resilience evaluation model was applied to analyze the temporal and spatial differences in UR in Hunan Province, China. A hot spot analysis identified clusters of cold and hot spots. The contributions of spatiotemporal differences in UR within and between subregions were assessed using the Theil index. A geodetector analysis determined the factors influencing UR and their interactions. There was an increasing trend in UR from 0.2692 in 2014 to 0.3422 in 2022. The number of cities with a high resilience gradually increased from 2014 to 2022, while there was a decreasing gradient in UR from northeast to southwest across the province. High-resilience cities had positive spillover effects on the surrounding area. Hot spots were predominantly located in the northeast, while cold spots were concentrated in the southwest. The barycenter of UR shifted from northeast to southwest by 2018, before moving southeast by 2022. The Theil index values declined over time both within and between subregions. Per capita GDP, the average wages of on-the-job employees, per capita social consumption, and doctor density were found to enhance UR. Two-factor interactions had a greater influence on the spatiotemporal heterogeneity of UR than single factors. Two-factor and nonlinear enhancements were identified as the primary mode of influence.

X-ray and optical observations of the millisecond pulsar binary PSR J1431–4715

We present the first X-ray observation of the energetic millisecond pulsar binary PSR J1431−4715, performed with XMM-Newton and complemented with fast optical multi-band photometry acquired with the ULTRACAM instrument at ESO-NTT. It is found as a faint X-ray source without a significant orbital modulation. This contrasts with the majority of systems that instead display substantial X-ray orbital variability. The X-ray spectrum is dominated by non-thermal emission and, due to the lack of orbital modulation, does not favour an origin in an intrabinary shock between the pulsar and companion star wind. While thermal emission from the neutron star polar cap cannot be excluded in the soft X-rays, the dominance of synchrotron emission favours an origin in the pulsar magnetosphere that we describe at both X-ray and gamma-ray energies with a synchro-curvature model. The optical multi-colour light curve folded at the 10.8 h orbital period is double-humped and dominated by ellipsoidal effects, but also affected by irradiation. The ULTRACAM light curves are fit with several models encompassing direct heating and a cold spot, or heat redistribution after irradiation either through convection or convection plus diffusion. Despite the inability to constrain the best irradiation models, the fits provide consistent system parameters, giving an orbital inclination of 59 ± 6° and a distance of 3.1 ± 0.3 kpc. The companion is found to be an F-type star, underfilling its Roche lobe (fRL = 73 ± 4%) with a mass of 0.20 ± 0.04 M⊙, confirming the redback status, but hotter than the majority of redbacks. The stellar dayside and nightside temperatures of 7500 K and 7400 K, respectively, indicate a weak irradiation effect on the companion, likely due to its high intrinsic luminosity. Although the pulsar mass cannot be precisely derived, a heavy (1.8−2.2 M⊙) neutron star is favoured.

Impact of land-use change on coupling coordination degree of regional water–food–carbon system

IntroductionThe objective of this study is to investigate the impact of land use changes on the coupling coordination of the regional water-food-carbon system in Hebei Province. Moreover, the findings aim to offer insights for achieving comprehensive and coordinated development of regional resources.MethodsBy constructing an evaluation index system of the coupled coordinated development of the water-food-carbon system, using the coupled coordination model to study the coupled coordination of the water-food-carbon (WFC) system in Hebei Province from 2010 to 2020, and applying the Pearson correlation coefficient and ArcGIS to analyze the impacts of land-use changes on the degree of coupled coordination.ResultsThe results show that: (1) The most notable characteristics of land type changes include a decrease in cropland and an increase in construction land, primarily driven by the conversion of cropland to construction sites. The total area converted amounts to 8207.20 km2. (2) The degree of coupled coordination of the water-food-carbon system in the study area as a whole shows an upward and then downward trend, and shows a spatial distribution pattern of “high in the north-east and low in the south-west”; (3) In Hebei Province, the degree of coupling coordination within the water-food-carbon system exhibits a stable positive correlation with forest land, grassland, and water area. Additionally, the transfer of forest land and grassland are significant factors influencing the delineation of cold and hot spots within the region.DiscussionTherefore, in addressing the coordinated development of the water-food-carbon system, it is essential to consider the influence of land. Resources should be allocated judiciously based on regional advantages to promote sustainable development effectively.

Dosimetric consequences of adapting the craniocaudal isocenter distance to daily patient position in craniospinal irradiation using volumetric modulated arc therapy.

In craniospinal irradiation, two or three isocenter groups along the craniocaudal axis are required to cover the long treatment target. Adapting the isocenter distance according to daily deviations in patient position is challenging because dosimetric hot or cold spots may occur in the field junction. The aim of this study was to quantify the effect of adapting the isocenter distance to patient position on the dose distribution of the field overlap region in craniospinal irradiation using partial-arc volumetric modulated arc therapy. The magnitude of isocenter distance deviations in craniocaudal direction was quantified by registering the setup images of 204 fractions of 12 patients to the planning images. The dosimetric effect of these deviations was determined by shifting the isocenters of the original treatment plan and calculating the resulting dose distribution. On fraction-level, deviations larger than 3mm caused more than 5 percentage point changes in the doses covering 2% (D2%) and 98% (D98%) of the junction volume in several patients. On treatment course-level, the changes in D2% and D98% of the junction volume were less than 5 percentage points in all cases except for one patient. Craniocaudal isocenter distance adaptation can be conducted provided that the mean isocenter distance deviation over the treatment course is within 3mm.

Study on the Driving Factors of the Spatiotemporal Pattern in Forest Lightning Fires and 3D Fire Simulation Based on Cellular Automata

Lightning-induced forest fires frequently inflict substantial damage on forest ecosystems, with the Daxing’anling region in northern China recognized as a high-incidence region for such phenomena. To elucidate the occurrence patterns of forest fires caused by lightning and to prevent such fires, this study employs a multifaceted approach, including statistical analysis, kernel density estimation, and spatial autocorrelation analysis, to conduct a comprehensive examination of the spatiotemporal distribution patterns of lightning-induced forest fires in the Greater Khingan Mountains region from 2016–2020. Additionally, the geographical detector method is utilized to assess the explanatory power of three main factors: climate, topography, and fuel characteristics associated with these fires, encompassing both univariate and interaction detections. Furthermore, a mixed-methods approach is adopted, integrating the Zhengfei Wang model with a three-dimensional cellular automaton to simulate the spread of lightning-induced forest fire events, which is further validated through rigorous quantitative verification. The principal findings are as follows: (1) Spatiotemporal Distribution of Lightning-Induced Forest Fires: Interannual variability reveals pronounced fluctuations in the incidence of lightning-induced forest fires. The monthly concentration of incidents is most significant in May, July, and August, demonstrating an upward trajectory. In terms of temporal distribution, fire occurrences are predominantly concentrated between 1:00 PM and 5:00 PM, conforming to a normal distribution pattern. Spatially, higher incidences of fires are observed in the western and northwestern regions, while the eastern and southeastern areas exhibit reduced rates. At the township level, significant spatial autocorrelation indicates that Xing’an Town represents a prominent hotspot (p = 0.001), whereas Oupu Town is identified as a significant cold spot (p = 0.05). (2) Determinants of the Spatiotemporal Distribution of Lightning-Induced Forest Fires: The spatiotemporal distribution of lightning-induced forest fires is influenced by a multitude of factors. Univariate analysis reveals that the explanatory power of these factors varies significantly, with climatic factors exerting the most substantial influence, followed by topographic and fuel characteristics. Interaction factor analysis indicates that the interactive effects of climatic variables are notably more pronounced than those of fuel and topographical factors. (3) Three-Dimensional Cellular Automaton Fire Simulation Based on the Zhengfei Wang Model: This investigation integrates the fire spread principles from the Zhengfei Wang model into a three-dimensional cellular automaton framework to simulate the dynamic behavior of lightning-induced forest fires. Through quantitative validation against empirical fire events, the model demonstrates an accuracy rate of 83.54% in forecasting the affected fire zones.

Revealing SARS-CoV-2 Mpro mutation cold and hot spots: Dynamic residue network analysis meets machine learning

Deciphering the effect of evolutionary mutations of viruses and predicting future mutations is crucial for designing long-lasting and effective drugs. While understanding the impact of current mutations on protein drug targets is feasible, predicting future mutations due to natural evolution of viruses and environmental pressures remains challenging. Here, we leveraged existing mutation data during the evolution of the SARS-CoV-2 protein drug target main protease (Mpro) to test the predictive power of dynamic residue network (DRN) analysis in identifying mutation cold and hot spots. We conducted molecular dynamics simulations on the Mpro of SARS-CoV-2 (Wuhan strain) and calculated eight DRN metrics (averaged BC, CC, DC, EC, ECC, KC, L, PR), each of which identifies a unique network feature within the protein. The sets of residues with the highest and lowest values for each metric, comprising potential cold and hot spots, were compared to published biochemical analyses and per residue mutation frequencies observed across five SARS-CoV-2 lineages, encompassing a total of 191,878 sequences. Individual DRN metrics displayed only modest power to predict the mutation frequency of individual residues. However, integrating the eight DRN metrics with additional structural and sequence-derived metrics allowed us to develop machine learning models which significantly improved the prediction of residue mutation frequency. While further refinements should enhance accuracy, we demonstrated a robust method to understand pathogen evolution. This approach can also guide the development of long-lasting drugs by targeting functional residues located in and near active site, and allosteric sites, that are less prone to mutations.