Anthropogenic shrub encroachment has accelerated the degradation of desert steppe soil over the past four decades.

Anthropogenic and natural shrub encroachment have similar ecological consequences on native grassland ecosystems. In fact, there is an accelerating trend toward anthropogenic shrub encroachment, as opposed to the century-long process of natural shrub encroachment. However, the soil quality during the transition of anthropogenic shrub encroachment into grasslands remains insufficiently understood. Here, we used a soil quality assessment method that utilized three datasets and two scoring methods to evaluate changes in soil quality during the anthropogenic transition from temperate desert grassland to shrubland. Our findings demonstrated that the soil quality index decreased with increasing shrub cover, from 0.49 in the desert grassland to 0.31 in the shrubland. Our final results revealed a gradual and significant decline of 36.73 % in soil quality during the transition from desert grassland to shrubland. Reduced soil moisture levels, nutrient availability, and microbial activity characterized this decline. Nearly four decades of anthropogenic shrub encroachment have exacerbated soil drought conditions while leading to a decrease in perennial herbaceous plants and an increase in bare ground cover; these factors can explain the observed decline in soil quality. These findings emphasize the importance of considering soil moisture availability and potential thresholds when implementing revegetation strategies in arid and semiarid regions.

Spatial-temporal distribution of global production-living-ecological space during the period 2000-2020.

Global production-living-ecology space closely corresponds with sustainable development's economic, social and ecological elements. The dataset of global production-living-ecological space in this paper was generated by combining the global land cover obtained using GlobeLand30 and the population density supplied by NASA's Socioeconomic Data and Applications Center in 2000, 2010, and 2020. The verification was carried out using the random sampling function of ArcGIS software on the basis of Google Earth sample images. The overall accuracy of the global production-living-ecological space data in 2020 was 83.94% and the Kappa coefficient was 0.81. The overall accuracy of the global production-living-ecological space data in 2010 was 87.00% and the Kappa coefficient was 0.84. The overall accuracy of the spatial data in 2000 was 86.06% and the Kappa coefficient was 0.83. The dataset fills a gap in the global production-living-ecology space database and will be an essential reference for assessing the coordinated development of sustainable development goals.

An integrated deep-learning and multi-level framework for understanding the behavior of terrorist groups.

Human security is threatened by terrorism in the 21st century. A rapidly growing field of study aims to understand terrorist attack patterns for counter-terrorism policies. Existing research aimed at predicting terrorism from a single perspective, typically employing only background contextual information or past attacks of terrorist groups, has reached its limits. Here, we propose an integrated deep-learning framework that incorporates the background context of past attacked locations, social networks, and past actions of individual terrorist groups to discover the behavior patterns of terrorist groups. The results show that our framework outperforms the conventional base model at different spatio-temporal resolutions. Further, our model can project future targets of active terrorist groups to identify high-risk areas and offer other attack-related information in sequence for a specific terrorist group. Our findings highlight that the combination of a deep-learning approach and multi-scalar data can provide groundbreaking insights into terrorism and other organized violent crimes.

Benefit analysis of multi-approach biomass energy utilization toward carbon neutrality.

To reduce greenhouse gas (GHG) emissions, biomass has been increasingly developed as a renewable and clean alternative to fossil fuels because of its carbon-neutral characteristics. China has been investigating the rational development and use of bioenergy for developing its clean energy and achieving carbon neutrality. Substituting fossil fuels with multi-source and multi-approach utilized bioenergy and corresponding carbon reduction in China remain largely unexplored. Here, a comprehensive bioenergy accounting model with a multi-dimensional analysis was developed by combining spatial, life cycle, and multi-path analyses. Accordingly, the bioenergy production potential and GHG emission reduction for each distinct type of biomass feedstock through different conversion pathways were estimated. The sum of all available organic waste (21.55 EJ yr-1) and energy plants on marginal land (11.77 EJ yr-1) in China produced 23.30 EJ of bioenergy and reduced 2,535.32 Mt CO2-eq emissions, accounting for 19.48% and 25.61% of China's total energy production and carbon emissions in 2020, respectively. When focusing on the carbon emission mitigation potential of substituting bioenergy for conventional counterparts, bioelectricity was the most effective, and its potential was 4.45 and 8.58 times higher than that of gaseous and liquid fuel alternatives, respectively. In this study, life cycle emission reductions were maximized by a mix of bioenergy end uses based on biomass properties, with an optimal 78.56% bioenergy allocation from biodiesel, densified solid biofuel, biohydrogen, and biochar. The main regional bioenergy GHG mitigation focused on the Jiangsu, Sichuan, Guangxi, Henan, and Guangdong provinces, contributing to 31.32% of the total GHG mitigation potential. This study provides valuable guidance on exploiting untapped biomass resources in China to secure carbon neutrality by 2060.

Alternative Pathway to Phase Down Coal Power and Achieve Negative Emission in China.

Although widely recognized as the key to climate goals, coal "phase down" has long been argued for its side effects on energy security and social development. Retrofitting coal power units with biomass and coal co-firing with a carbon capture and storage approach provides an alternative way to avoid these side effects and make deep carbon dioxide emission cuts or even achieve negative emission. However, there is a lack of clear answers to how much the maximum emission reduction potential this approach can unlock, which is the key information to promote this technology on a large scale. Here, we focus on helping China's 4536 coal power units make differentiated retrofit choices based on unit-level heterogeneity information and resource spatial matching results. We found that China's coal power units have the potential to achieve 0.4 Gt of negative CO2 emission in 2025, and the cumulative negative CO2 emission would reach 10.32 Gt by 2060. To achieve negative CO2 emission, the biomass resource amount should be 1.65 times the existing agricultural and forestry residues, and the biomass and coal co-firing ratio should exceed 70%. Coal power units should grasp their time window; otherwise, the maximum negative potential would decrease at a rate of 0.35 Gt per year.

[Mechanism of Astragali Radix-Coptis Rhizoma pair in treating type 2 diabetes mellitus based on network pharmacology].

This study aims to explore the main mechanism of Astragali Radix-Coptis Rhizoma pair(hereinafter referred to as the pair) in the treatment of type 2 diabetes mellitus(T2 DM) based on network pharmacology and animal experiment. The main Chinese medicine compound prescriptions for T2 DM were retrieved from CNKI database and the medicinals with high frequency among these prescriptions were screened. The active components in the above medicinals were searched from TCMSP, TCMID, and previous research, targets of the components from SwissTargetPrediction and SEA, and targets for the treatment of T2 DM from DISGENET, TTD, and DrugBank. Thereby, the medicinal-component-disease-target network was constructed with Cytoscape. The targets were input in String database to yield the related proteins and the protein-protein interaction(PPI) network was constructed by Cytoscape. The biological functions of proteins in the PPI network were analyzed by Cluego. Then, high-fat high-sugar diet and 30 mg·kg~(-1) streptozotocin(STZ, intraperitoneal injection, once) were employed to induce T2 DM in rats and the T2 DM rats were classified into the control group, model group, positive drug(metformin) group, and pair group. After one month of administration, the changes of blood glucose and blood lipids [triglyceride(TG), cholesterol(CHO), low density lipoprotein(LDL), high density lipoprotein(HDL)] were detected with biochemical methods and pathological changes of islet and collagen deposition in pancreatic tissue by HE staining and Masson staining, respectively. The result showed that pair can be used for T2 DM treatment. ras-related C3 botulinum toxin substrate 1(RAC1), paraoxonase 1(PON1), beta-galactoside alpha 2,6-sialyltransferase 1(ST6 GAL1), insulin receptor(INSR), sex hormone-binding globulin(SHBG), ileal sodium/bile acid cotransporter(SLC10 A2), endothelin-1 receptor A(EDNRA), peroxisome proliferator-activated receptor A(PPARA), endothelin receptor B(EDNRB), and 5-hydroxytryptamine receptor 2 A(HTR2 A) were the targets of the pair for the treatment of T2 DM. The main biological functions of the pair were regulating the metabolism of blood glucose and li-pids and protecting the cardiovascular system. The fasting blood glucose, and serum TG, CHO, and LDL were higher(P<0.01) and the HDL was lower(P<0.05) in the model group than in the control group on the 7 th, 14 th, and 28 th days. The fas-ting blood glucose and the serum TG, CHO, and LDL decreased(P<0.05) and the serum HDL increased(P<0.05) in the metformin group and the pair group as compared with those in the model group on the 14 th and 28 th days. There were no significant differences in blood glucose, TG, CHO, LDL, and HDL between the metformin group and the pair group. Rats in the model group demonstrated damaged structures of islets and pancreas, obviously increased deposition of collagen in islets and pancreas, and blurred cell boundaries. Metformin and the pair significantly alleviated the damaged structures and collagen deposition. The pair can effectively regulate the disorders of blood glucose and lipid metabolism in T2 DM and protect the structure and functions of pancreas and islets by controlling cardiovascular system, which is worthy of clinical application and can be used for drug development.

Assessing the sweet sorghum-based ethanol potential on saline-alkali land with DSSAT model and LCA approach.

BACKGROUND: The key problem of non-grain energy plants' scale development is how to estimate the potential of GHG emission reduction accurately and scientifically. This study presents a method coupled DSSAT (the Decision Support System for Agrotechnology Transfer) and the life cycle assessment (LCA) method to simulate the spatial distribution of sweet sorghum-based ethanol production potential on saline-alkali land. The GHG (greenhouse gas) emission mitigation and net energy gains of the whole life of sweet sorghum-based ethanol production were then analyzed. RESULTS: The results of the case study in Dongying, Shandong Province, China showed that developing sweet sorghum-based ethanol on saline-alkali land had GHG emission mitigation and energy potentials. The LC-GHG emission mitigation potential of saline-alkali land in Dongying was estimated at 63.9 thousand t CO2 eq, equivalent to the carbon emission of 43.4 Kt gasoline. The LC-NEG potential was predicted at 5.02 PJ, equivalent to the caloric value of 109 Kt gasoline. On average, LC-GHG emission mitigation and LC-NEG were predicted at 55.09 kg CO2 eq/t ethanol and 4.33 MJ/kg ethanol, respectively. CONCLUSIONS: The question of how to evaluate the potential of sweet sorghum-based ethanol development scientifically was solved primarily in this paper. The results will provide an important theoretical support for planning the bioenergy crops on saline-alkali land and develop the fuel ethanol industry.

Simulating the Linkages Between Economy and Armed Conflict in India With a Long Short-Term Memory Algorithm.

This article analyzes the linkages between the economy and armed conflict in India using annual frequency data for the period 1989-2016, the maximum time period for which consistent data are available for the country. An adequate set of economic indicators was established to fully reflect the economic condition. Long short-term memory (LSTM), which is a machine-learning algorithm for time series, was employed to simulate the relationship between the economy and armed conflict events. In addition, LSTM was applied to predict the trend of armed conflict with two strategies: multiyear predictions and yearly predictions. The results show that both strategies can adequately simulate the relationship between the economy and armed conflict, with all simulation accuracies above 90%. The accuracy of the yearly prediction is higher than that of the multiyear prediction. Theoretically, the future state and trend of armed conflict can be predicted with LSTM and future economic data if future economic data can be predicted.

PHYTOCHROME-INTERACTING FACTORS Interact with the ABA Receptors PYL8 and PYL9 to Orchestrate ABA Signaling in Darkness.

PHYTOCHROME-INTERACTING FACTORS (PIFs) are a group of basic helix-loop-helix transcription factors that can physically interact with photoreceptors, including phytochromes and cryptochromes. It was previously demonstrated that PIFs accumulated in darkness and repressed seedling photomorphogenesis, and that PIFs linked different photosensory and hormonal pathways to control plant growth and development. In this study, we show that PIFs positively regulate the ABA signaling pathway during the seedling stage specifically in darkness. We found that PIFs positively regulate ABI5 transcript and protein levels in darkness in response to exogenous ABA treatment by binding directly to the G-box motifs in the ABI5 promoter. Consistently, PIFs and the G-box motifs in the ABI5 promoter determine ABI5 expression in darkness, and overexpression of ABI5 could rescue the ABA-insensitive phenotypes of pifq mutants in the dark. Moreover, we discovered that PIFs can physically interact with the ABA receptors PYL8 and PYL9, and that this interaction is not regulated by ABA. Further analyses showed that PYL8 and PYL9 promote PIF4 protein accumulation in the dark and enhance PIF4 binding to the ABI5 promoter, but negatively regulate PIF4-mediated ABI5 activation. Taken together, our data demonstrate that PIFs interact with ABA receptors to orchestrate ABA signaling in darkness by controlling ABI5 expression, providing new insights into the pivotal roles of PIFs as signal integrators in regulating plant growth and development.

Risk factors and predicted distribution of visceral leishmaniasis in the Xinjiang Uygur Autonomous Region, China, 2005-2015.

BACKGROUND: Visceral leishmaniasis (VL) is a neglected disease that is spread to humans by the bites of infected female phlebotomine sand flies. Although this vector-borne disease has been eliminated in most parts of China, it still poses a significant public health burden in the Xinjiang Uygur Autonomous Region. Understanding of the spatial epidemiology of the disease remains vague in the local community. In the present study, we investigated the spatiotemporal distribution of VL in the region in order to assess the potential threat of the disease. METHODS: Based on comprehensive infection records, the spatiotemporal patterns of new cases of VL in the region between 2005 and 2015 were analysed. By combining maps of environmental and socioeconomic correlates, the boosted regression tree (BRT) model was adopted to identify the environmental niche of VL. RESULTS: The fitted BRT models were used to map potential infection risk zones of VL in the Xinjiang Uygur Autonomous Region, revealing that the predicted high infection risk zones were mainly concentrated in central and northern Kashgar Prefecture, south of Atushi City bordering Kashgar Prefecture and regions of the northern Bayingolin Mongol Autonomous Prefecture. The final result revealed that approximately 16.64 million people inhabited the predicted potential infection risk areas in the region. CONCLUSIONS: Our results provide a better understanding of the potential endemic foci of VL in the Xinjiang Uygur Autonomous Region with a 1 km spatial resolution, thereby enhancing our capacity to target the potential risk areas, to develop disease control strategies and to allocate medical supplies.

Spatiotemporal Variation and Hotspot Detection of the Avian Influenza A(H7N9) Virus in China, 2013⁻2017.

This study aims to describe the spatial and temporal characteristics of human infections with H7N9 virus in China using data from 19 February 2013 to 30 September 2017 extracted from Centre for Health Protection of the Department of Health (CHP) and electronic databases managed by China's Center for Disease Control (CDC) and provincial CDCs synthetically using the Geographic Information System (GIS) software ArcMap™ 10.2 and SaTScan. Based on the multiple analyses of the A(H7N9) epidemics, there was a strong seasonal pattern in A(H7N9) virus infection, with high activity in the first quarter of the year, especially in January, February, and April, and a gradual dying out in the third quarter. Spatial distribution analysis indicated that Eastern China contained the most severely affected areas, such as Zhejiang Province, and the distribution shifted from coastline areas to more inland areas over time. In addition, the cases exhibited local spatial aggregation, with high-risk areas most found in the southeast coastal regions of China. Shanghai, Jiangsu, Zhejiang, and Guangdong were the high-risk epidemic areas, which should arouse the attention of local governments. A strong cluster from 9 April 2017 to 24 June 2017 was also identified in Northern China, and there were many secondary clusters in Eastern and Southern China, especially in Zhejiang, Fujian, Jiangsu, and Guangdong Provinces. Our results suggested that the spatial-temporal clustering of H7N9 in China is fundamentally different, and is expected to contribute to accumulating knowledge on the changing temporal patterns and spatial dissemination during the fifth epidemic and provide data to enable adequate preparation against the next epidemic.

Estimating the potential of energy saving and carbon emission mitigation of cassava-based fuel ethanol using life cycle assessment coupled with a biogeochemical process model.

Global warming and increasing concentration of atmospheric greenhouse gas (GHG) have prompted considerable interest in the potential role of energy plant biomass. Cassava-based fuel ethanol is one of the most important bioenergy and has attracted much attention in both developed and developing countries. However, the development of cassava-based fuel ethanol is still faced with many uncertainties, including raw material supply, net energy potential, and carbon emission mitigation potential. Thus, an accurate estimation of these issues is urgently needed. This study provides an approach to estimate energy saving and carbon emission mitigation potentials of cassava-based fuel ethanol through LCA (life cycle assessment) coupled with a biogeochemical process model-GEPIC (GIS-based environmental policy integrated climate) model. The results indicate that the total potential of cassava yield on marginal land in China is 52.51 million t; the energy ratio value varies from 0.07 to 1.44, and the net energy surplus of cassava-based fuel ethanol in China is 92,920.58 million MJ. The total carbon emission mitigation from cassava-based fuel ethanol in China is 4593.89 million kgC. Guangxi, Guangdong, and Fujian are identified as target regions for large-scale development of cassava-based fuel ethanol industry. These results can provide an operational approach and fundamental data for scientific research and energy planning.

Spatiotemporal Patterns and Risk Factors for Scrub Typhus From 2007 to 2017 in Southern China.

BACKGROUND: Substantial outbreaks of scrub typhus, coupled with the discovery of this vector-borne disease in new areas, suggest that the disease remains remarkably neglected. The objectives of this study were to map the contemporary and potential transmission risk zones of the disease and to provide novel insights into the health burden imposed by scrub typhus in southern China. METHODS: Based on the assembled data sets of annual scrub typhus cases and maps of environmental and socioeconomic correlates, a boosted regression tree modeling procedure was used to identify the environmental niche of scrub typhus and to predict the potential infection zones of the disease. Additionally, we estimated the population living in the potential scrub typhus infection areas in southern China. RESULTS: Spatiotemporal patterns of the annual scrub typhus cases in southern China between 2007 and 2017 reveal a tremendous, wide spread of scrub typhus. Temperature, relative humidity, elevation, and the normalized difference vegetation index are the main factors that influence the spread of scrub typhus. In southern China, the predicted highest transmission risk areas of scrub typhus are mainly concentrated in several regions, such as Yunnan, Guangxi, Guangdong, Hainan, and Fujian. We estimated that 162 684 million people inhabit the potential infection risk zones in southern China. CONCLUSIONS: Our results provide a better understanding of the environmental and socioeconomic factors driving scrub typhus spread, and estimate the potential infection risk zones beyond the disease's current, limited geographical extent, which enhances our capacity to target biosurveillance and help public health authorities develop disease control strategies.

Spatiotemporal Variation and Hot Spot Detection of Visceral Leishmaniasis Disease in Kashi Prefecture, China.

Visceral leishmaniasis (VL) remains a serious public health problem in China. To explore the temporal, spatial, and spatiotemporal characteristics of visceral leishmaniasis (VL), the spatial and spatiotemporal clustering distribution and their relationships with the surrounding geographic environmental factors were analyzed. In this study, the average nearest-neighbor distance (ANN), Ripley's K-function and Moran's I statistics were used to evaluate spatial autocorrelation in the VL distribution of the existing case patterns. Getis⁻Ord Gi* was used to identify the hot-spot and cold-spot areas based on Geographic Information System (GIS), and spatiotemporal retrospective permutation scan statistics was used to detect the spatiotemporal clusters. The results indicated that VL continues to be a serious public health problem in Kashi Prefecture, China, particularly in the north-central region of Jiashi County, which is a relatively high-risk area in which hot spots are distributed. Autumn and winter months were the outbreak season for VL cases. The detection of spatial and spatiotemporal patterns can provide epidemiologists and local governments with significant information for prevention measures and control strategies.

Mapping the Potential Global Codling Moth (Cydia pomonella L.) Distribution Based on a Machine Learning Method.

The spread of invasive species may pose great threats to the economy and ecology of a region. The codling moth (Cydia pomonella L.) is one of the 100 worst invasive alien species in the world and is the most destructive apple pest. The economic losses caused by codling moths are immeasurable. It is essential to understand the potential distribution of codling moths to reduce the risks of codling moth establishment. In this study, we adopted the Maxent (Maximum Entropy Model), a machine learning method to predict the potential global distribution of codling moths with global accessibility data, apple yield data, elevation data and 19 bioclimatic variables, considering the ecological characteristics and the spread channels that cover the processes from growth and survival to the dispersion of the codling moth. The results show that the areas that are suitable for codling moth are mainly distributed in Europe, Asia and North America, and these results strongly conformed with the currently known occurrence regions. In addition, global accessibility, mean temperature of the coldest quarter, precipitation of the driest month, annual mean temperature and apple yield were the most important environmental predictors associated with the global distribution of codling moths.

Mapping the transmission risk of Zika virus using machine learning models.

Zika virus, which has been linked to severe congenital abnormalities, is exacerbating global public health problems with its rapid transnational expansion fueled by increased global travel and trade. Suitability mapping of the transmission risk of Zika virus is essential for drafting public health plans and disease control strategies, which are especially important in areas where medical resources are relatively scarce. Predicting the risk of Zika virus outbreak has been studied in recent years, but the published literature rarely includes multiple model comparisons or predictive uncertainty analysis. Here, three relatively popular machine learning models including backward propagation neural network (BPNN), gradient boosting machine (GBM) and random forest (RF) were adopted to map the probability of Zika epidemic outbreak at the global level, pairing high-dimensional multidisciplinary covariate layers with comprehensive location data on recorded Zika virus infection in humans. The results show that the predicted high-risk areas for Zika transmission are concentrated in four regions: Southeastern North America, Eastern South America, Central Africa and Eastern Asia. To evaluate the performance of machine learning models, the 50 modeling processes were conducted based on a training dataset. The BPNN model obtained the highest predictive accuracy with a 10-fold cross-validation area under the curve (AUC) of 0.966 [95% confidence interval (CI) 0.965-0.967], followed by the GBM model (10-fold cross-validation AUC = 0.964[0.963-0.965]) and the RF model (10-fold cross-validation AUC = 0.963[0.962-0.964]). Based on training samples, compared with the BPNN-based model, we find that significant differences (p = 0.0258* and p = 0.0001***, respectively) are observed for prediction accuracies achieved by the GBM and RF models. Importantly, the prediction uncertainty introduced by the selection of absence data was quantified and could provide more accurate fundamental and scientific information for further study on disease transmission prediction and risk assessment.

Mapping the spatial distribution of Aedes aegypti and Aedes albopictus.

Mosquito-borne infectious diseases, such as Rift Valley fever, Dengue, Chikungunya and Zika, have caused mass human death with the transnational expansion fueled by economic globalization. Simulating the distribution of the disease vectors is of great importance in formulating public health planning and disease control strategies. In the present study, we simulated the global distribution of Aedes aegypti and Aedes albopictus at a 5×5km spatial resolution with high-dimensional multidisciplinary datasets and machine learning methods Three relatively popular and robust machine learning models, including support vector machine (SVM), gradient boosting machine (GBM) and random forest (RF), were used. During the fine-tuning process based on training datasets of A. aegypti and A. albopictus, RF models achieved the highest performance with an area under the curve (AUC) of 0.973 and 0.974, respectively, followed by GBM (AUC of 0.971 and 0.972, respectively) and SVM (AUC of 0.963 and 0.964, respectively) models. The simulation difference between RF and GBM models was not statistically significant (p>0.05) based on the validation datasets, whereas statistically significant differences (p<0.05) were observed for RF and GBM simulations compared with SVM simulations. From the simulated maps derived from RF models, we observed that the distribution of A. albopictus was wider than that of A. aegypti along a latitudinal gradient. The discriminatory power of each factor in simulating the global distribution of the two species was also analyzed. Our results provided fundamental information for further study on disease transmission simulation and risk assessment.

Spatiotemporal Distribution of U5MR and Their Relationship with Geographic and Socioeconomic Factors in China.

Epidemiological studies conducted around the world have reported that the under-five mortality rate (U5MR) is closely associated with income and educational attainment. However, geographic elements should also remain a major concern in further improving child health issues, since they often play an important role in the survival environment. This study was undertaken to investigate the relationship between the U5MR, geographic, and socioeconomic factors, and to explore the associated spatial variance of the relationship in China using the geographically weighted regression (GWR) model. The results indicate that the space pattern of a high U5MR had been narrowed notably during the period from 2001 to 2010. Nighttime lights (NL) and the digital elevation model (DEM) both have obvious influences on the U5MR, with the NL having a negative impact and DEM having a positive impact. Additionally, the relationship between the NL and DEM varied over space in China. Moreover, the relevance between U5MR and DEM was narrowed in 2010 compared to 2001, which indicates that the development of economic and medical standards can overcome geographical limits.

Understanding the dynamics of terrorism events with multiple-discipline datasets and machine learning approach.

Terror events can cause profound consequences for the whole society. Finding out the regularity of terrorist attacks has important meaning for the global counter-terrorism strategy. In the present study, we demonstrate a novel method using relatively popular and robust machine learning methods to simulate the risk of terrorist attacks at a global scale based on multiple resources, long time series and globally distributed datasets. Historical data from 1970 to 2015 was adopted to train and evaluate machine learning models. The model performed fairly well in predicting the places where terror events might occur in 2015, with a success rate of 96.6%. Moreover, it is noteworthy that the model with optimized tuning parameter values successfully predicted 2,037 terrorism event locations where a terrorist attack had never happened before.