Land-use classification based on high-resolution remote sensing imagery and deep learning models.

High-resolution imagery and deep learning models have gained increasing importance in land-use mapping. In recent years, several new deep learning network modeling methods have surfaced. However, there has been a lack of a clear understanding of the performance of these models. In this study, we applied four well-established and robust deep learning models (FCN-8s, SegNet, U-Net, and Swin-UNet) to an open benchmark high-resolution remote sensing dataset to compare their performance in land-use mapping. The results indicate that FCN-8s, SegNet, U-Net, and Swin-UNet achieved overall accuracies of 80.73%, 89.86%, 91.90%, and 96.01%, respectively, on the test set. Furthermore, we assessed the generalization ability of these models using two measures: intersection of union and F1 score, which highlight Swin-UNet's superior robustness compared to the other three models. In summary, our study provides a systematic analysis of the classification differences among these four deep learning models through experiments. It serves as a valuable reference for selecting models in future research, particularly in scenarios such as land-use mapping, urban functional area recognition, and natural resource management.

Identification of nonvolatile organic compounds (NVOCs) in biopharmaceuticals through non-target analysis and quantification using complexation-precipitation extraction.

Single-use systems in biopharmaceutical manufacturing can potentially release chemical constituents (leachables) into drug products. Prior to conducting toxicological risk assessments, it is crucial to establish the qualitative and quantitative methods for these leachables. In this study, we conducted a comprehensive screening and structure elucidation of 23 leachables (nonvolatile organic compounds, NVOCs) in two antibody drugs using multiple (self-built and public) databases and mass spectral simulation. We identified 7 compounds that have not been previously reported in medical or medicinal extractables and leachables. The confidence levels for identified compounds were classified based on analytical standards, literature references, and fragment assignments. Most of the identified leachables were found to be plasticizers, antioxidants, slip agents or polymer degradants. Polysorbate (namely Tween) is commonly used as an excipient for protein stabilization in biopharmaceutical formulations, but its ionization in liquid chromatography-electrospray ionization mass spectrometry can interfere with compound quantification. To address this, we employed a complexation-precipitation extraction method to reduce polysorbate content and quantify the analytes. The developed quantitative method for target NVOCs demonstrated high sensitivity (limit of quantification: 20 or 50 µg/L), accuracy (recoveries: 77.2 to 109.5 %) and precision (RSD ≤ 8.2 %). Overall, this established method will facilitate the evaluation of NVOC safety in drug products.

Accelerated Redox Reactions Enable Stable Tin-Lead Mixed Perovskite Solar Cells.

The facile oxidation of Sn2+ to Sn4+ poses an inherent challenge that limits the efficiency and stability of tin-lead mixed (Sn-Pb) perovskite solar cells (PSCs) and all-perovskite tandem devices. In this work, we discover the sustainable redox reactions enabling self-healing Sn-Pb perovskites, where their intractable oxidation degradation can be recovered to their original state under light soaking. Quantitative and operando spectroscopies are used to investigate the redox chemistry, revealing that metallic Pb0 from the photolysis of perovskite reacts with Sn4+ to regenerate Pb2+ and Sn2+ spontaneously. Given the sluggish redox reaction kinetics, V3+ /V2+ ionic pair is designed as an effective redox shuttle to accelerate the recovery of Sn-Pb perovskites from oxidation. The target Sn-Pb PSCs enabled by V3+ /V2+ ionic pair deliver an improved power conversion efficiency (PCE) of 21.22 % and excellent device lifespan, retaining nearly 90 % of its initial PCE after maximum power point tracking under light for 1,000 hours.

The role of ion migration, octahedral tilt, and the A-site cation on the instability of Cs1-xFAxPbI3.

Organic-inorganic hybrid perovskites are promising materials for the next generation photovoltaics and optoelectronics; however, their practical application has been hindered by poor structural stability mainly caused by ion migration and external stimuli. Understanding the mechanism(s) of ion migration and structure decomposition is thus critical. Here we observe the sequence of structural changes at the atomic level that precede structural decomposition in the technologically important Cs1-xFAxPbI3 using ultralow dose transmission electron microscopy. We find that these changes differ, depending upon the A-site composition. Initially, there is a random loss of FA+, complemented by the loss of I-. The remaining FA+ and I- ions then migrate, unit cell by unit cell, into an ordered and more stable phase with a √2 x √2 superstructure. Further ion loss is accompanied by A-site dependent octahedral tilt modes and associated tetragonal phases with different stabilities. These observations of the loss of FA+/I- ion pairs, ion migration, octahedral tilt modes, and the role of the A-cation, provide insights into the atomic-scale structural mechanisms that drive and block ion loss and ion migration, opening pathways to inhibit ion loss, migration and improve structural stability.

Projecting spatiotemporal dynamics of severe fever with thrombocytopenia syndrome in the mainland of China.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease with increasing incidence and geographic extent. The extent to which global climate change affects the incidence of SFTS disease remains obscure. We use an integrated multi-model, multi-scenario framework to assess the impact of global climate change on SFTS disease in China. The spatial distribution of habitat suitability for the tick Haemaphysalis longicornis was predicted by applying a boosted regression tree model under four alternative climate change scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5) for the periods 2030-2039, 2050-2059, and 2080-2089. We incorporate the SFTS cases in the mainland of China from 2010 to 2019 with environmental variables and the projected distribution of H. longicornis into a generalized additive model to explore the current and future spatiotemporal dynamics of SFTS. Our results demonstrate an expanded geographic distribution of H. longicornis toward Northern and Northwestern China, showing a more pronounced change under the RCP8.5 scenario. In contrast, the environmental suitability of H. longicornis is predicted to be reduced in Central and Eastern China. The SFTS incidence in three time periods (2030-2039, 2050-2059, and 2080-2089) is predicted to be increased as compared to the 2010s in the context of various RCPs. A heterogeneous trend across provinces, however, was observed, when an increased incidence in Liaoning and Shandong provinces, while decreased incidence in Henan province is predicted. Notably, we predict possible outbreaks in Xinjiang and Yunnan in the future, where only sporadic cases have been reported previously. These findings highlight the need for tick control and population awareness of SFTS in endemic regions, and enhanced monitoring in potential risk areas.

Modeling analysis of armed conflict risk in sub-Saharan Africa, 2000-2019.

Sub-Saharan Africa has suffered frequent outbreaks of armed conflict since the end of the Cold War. Although several efforts have been made to understand the underlying causes of armed conflict and establish an early warning mechanism, there is still a lack of a comprehensive assessment approach to model the incidence risk of armed conflict well. Based on a large database of armed conflict events and related spatial datasets covering the period 2000-2019, this study uses a boosted regression tree (BRT) approach to model the spatiotemporal distribution of armed conflict risk in sub-Saharan Africa. Evaluation of accuracy indicates that the simulated models obtain high performance with an area under the receiver operator characteristic curve (ROC-AUC) mean value of 0.937 and an area under the precision recall curves (PR-AUC) mean value of 0.891. The result of the relative contribution indicates that the background context factors (i.e., social welfare and the political system) are the main driving factors of armed conflict risk, with a mean relative contribution of 92.599%. By comparison, the climate change-related variables have relatively little effect on armed conflict risk, accounting for only 7.401% of the total. These results provide novel insight into modelling the incidence risk of armed conflict, which may help implement interventions to prevent and minimize the harm of armed conflict.

The implications for potential marginal land resources of cassava across worldwide under climate change challenges.

The demand for energy plants is foreseen to grow as worldwide energy and climate policies promote the use of bioenergy for climate change mitigation. To avoid competing with food production, it's critical to assess future changes in marginal land availability for energy plant development. Using a machine learning method, boosted regression tree, this study modeled potential marginal land resources suitable for cassava under current and different climate change scenarios, based on cassava occurrence records and environmental covariates. The findings revealed that, currently, over 80% of the 1357.24 Mha of available marginal land for cassava cultivation is distributed in Africa and South America. Under three climate change scenarios, by 2030, worldwide suitable marginal land resources were predicted to grow by 39.71Mha, 66.21 Mha, and 39.31Mha for the RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively; by 2050, the potential marginal land suitable for cassava will increase by 38.98Mha, 83.02 Mha, and 55.43Mha, respectively; by 2080, the global marginal land resources were estimated to rise by 40.82 Mha, 99.74 Mha, and 21.87 Mha from now, respectively. Our results highlight the impacts of climate change on potential marginal land resources of cassava across worldwide, which provide the basis for assessing bioenergy potential in the future.

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.

Epidemiological characteristics and risk factors for cystic and alveolar echinococcosis in China: an analysis of a national population-based field survey.

BACKGROUND: Human cystic and alveolar echinococcosis are neglected tropical diseases that WHO has prioritized for control in recent years. Both diseases impose substantial burdens on public health and the socio-economy in China. In this study, which is based on the national echinococcosis survey from 2012 to 2016, we aim to describe the spatial prevalence and demographic characteristics of cystic and alveolar echinococcosis infections in humans and assess the impact of environmental, biological and social factors on both types of the disease. METHODS: We computed the sex-, age group-, occupation- and education level-specific prevalences of cystic and alveolar echinococcosis at national and sub-national levels. We mapped the geographical distribution of echinococcosis prevalence at the province, city and county levels. Finally, by analyzing the county-level echinococcosis cases combined with a range of associated environmental, biological and social factors, we identified and quantified the potential risk factors for echinococcosis using a generalized linear model. RESULTS: A total of 1,150,723 residents were selected and included in the national echinococcosis survey between 2012 and 2016, of whom 4161 and 1055 tested positive for cystic and alveolar echinococcosis, respectively. Female gender, older age, occupation at herdsman, occupation as religious worker and illiteracy were identified as risk factors for both types of echinococcosis. The prevalence of echinococcosis was found to vary geographically, with areas of high endemicity observed in the Tibetan Plateau region. Cystic echinococcosis prevalence was positively correlated with cattle density, cattle prevalence, dog density, dog prevalence, number of livestock slaughtered, elevation and grass area, and negatively associated with temperature and gross domestic product (GDP). Alveolar echinococcosis prevalence was positively correlated with precipitation, level of awareness, elevation, rodent density and rodent prevalence, and negatively correlated with forest area, temperature and GDP. Our results also implied that drinking water sources are significantly associated with both diseases. CONCLUSIONS: The results of this study provide a comprehensive understanding of geographical patterns, demographic characteristics and risk factors of cystic and alveolar echinococcosis in China. This important information will contribute towards developing targeted prevention measures and controlling diseases from the public health perspective.

Exploring the worldwide impact of COVID-19 on conflict risk under climate change.

Objectives: Understand whether and how the COVID-19 pandemic affects the risk of different types of conflict worldwide in the context of climate change. Methodology: Based on the database of armed conflict, COVID-19, detailed climate, and non-climate data covering the period 2020-2021, we applied Structural Equation Modeling specifically to reorganize the links between climate, COVID-19, and conflict risk. Moreover, we used the Boosted Regression Tree method to simulate conflict risk under the influence of multiple factors. Findings: The transmission risk of COVID-19 seems to decrease as the temperature rises. Additionally, COVID-19 has a substantial worldwide impact on conflict risk, albeit regional and conflict risk variations exist. Moreover, when testing a one-month lagged effect, we find consistency across regions, indicating a positive influence of COVID-19 on demonstrations (protests and riots) and a negative relationship with non-state and violent conflict risk. Conclusion: COVID-19 has a complex effect on conflict risk worldwide under climate change. Implications: Laying the theoretical foundation of how COVID-19 affects conflict risk and providing some inspiration for the implementation of relevant policies.

Exploring the global geography of cybercrime and its driving forces.

Cybercrime is wreaking havoc on the global economy, national security, social stability, and individual interests. The current efforts to mitigate cybercrime threats are primarily focused on technical measures. This study considers cybercrime as a social phenomenon and constructs a theoretical framework that integrates the social, economic, political, technological, and cybersecurity factors that influence cybercrime. The FireHOL IP blocklist, a novel cybersecurity data set, is used to map worldwide subnational cybercrimes. Generalised linear models (GLMs) are used to identify the primary factors influencing cybercrime, whereas structural equation modelling (SEM) is used to estimate the direct and indirect effects of various factors on cybercrime. The GLM results suggest that the inclusion of a broad set of socioeconomic factors can significantly improve the model's explanatory power, and cybercrime is closely associated with socioeconomic development, while their effects on cybercrime differ by income level. Additionally, results from SEM further reveals the causal relationships between cybercrime and numerous contextual factors, demonstrating that technological factors serve as a mediator between socioeconomic conditions and cybercrime.

Exploring the direct and indirect impacts of climate variability on armed conflict in South Asia.

Although numerous studies have examined the effects of climate variability on armed conflict, the complexity of these linkages requires deeper understanding to assess the causes and effects. Here, we assembled an extensive database of armed conflict, climate, and non-climate data for South Asia. We used structural equation modeling to quantify both the direct and indirect impacts of climate variability on armed conflict. We found that precipitation impacts armed conflict via direct and indirect effects which are contradictory in sign. Temperature affects armed conflict only through a direct path, while indirect effects were insignificant. Yet, an in-depth analysis of indirect effects showed that the net impact is weak due to two strong contradictory effects offsetting each other. Our findings illustrate the complex link between climate variability and armed conflict, highlighting the importance of a detailed analysis of South Asia's underlying mechanisms at the regional scale.

Determining the potential distribution of Oryctes monoceros and Oryctes rhinoceros by combining machine-learning with high-dimensional multidisciplinary environmental variables.

The African coconut beetle Oryctes monoceros and Asiatic rhinoceros beetle O. rhinoceros have been associated with economic losses to plantations worldwide. Despite the amount of effort put in determining the potential geographic extent of these pests, their environmental suitability maps have not yet been well established. Using MaxEnt model, the potential distribution of the pests has been defined on a global scale. The results show that large areas of the globe, important for production of palms, are suitable for and potentially susceptible to these pests. The main determinants for O. monoceros distribution were; temperature annual range, followed by land cover, and precipitation seasonality. The major determinants for O. rhinoceros were; temperature annual range, followed by precipitation of wettest month, and elevation. The area under the curve values of 0.976 and 0.975, and True skill statistic values of 0.90 and 0.88, were obtained for O. monoceros and O. rhinoceros, respectively. The global simulated areas for O. rhinoceros (1279.00 × 104 km2) were more than that of O. monoceros (610.72 × 104 km2). Our findings inform decision-making and the development of quarantine measures against the two most important pests of palms.

Association of ABCC2 polymorphism with clopidogrel response in Chinese patients undergoing percutaneous coronary intervention.

Aim: In this study, we investigated the association between ABCC2 polymorphism and clopidogrel response as well as the associated hypothetical mechanism. Methods: Chinese patients (213) with coronary artery disease (CAD) who underwent percutaneous coronary intervention (PCI) and received clopidogrel were recruited. Thereafter, their ADP-induced platelet inhibition rates (PAIR%) were determined via thromboelastometry. Further, the single-nucleotide polymorphisms (SNPs) of ABCC2 were genotyped using high-resolution melting curve (HRM)-PCR, while CYP2C19*2 and *3 polymorphisms were genotyped via real-time PCR. Results: The allele frequencies of ABCC2 rs717620 were 74.88 and 25.12% for the C and T alleles, respectively. Further, ABCC2 rs717620 TT carriers exhibited significantly higher PAIR% values (72.60 ± 27.69) than both CT (61.44 ± 23.65) and CC carriers (52.72 ± 21.99) (p = 0.047 and p = 0.001, respectively), and ABCC2 rs717620 CT carriers showed significantly higher mean PAIR% values than ABCC2 rs717620 CC carriers (p = 0.011). However, the PAIR% values corresponding to ABCC2 rs2273697 and ABCC2 rs3740066 carriers were not different. Additionally, CYP2C19*2 AA carriers presented significantly lower PAIR% values than CYP2C19*2 GA (p = 0.015) and GG (p = 0.003) carriers, and CYP2C19*3 GA carriers also presented significantly lower PAIR% values than CYP2C19*3 GG carriers (p = 0.041). In patients with CYP2C19 extensive metabolizers (EM), ABCC2 rs717620 TT carriers showed significantly higher PAIR% values (89.77 ± 9.73) than CT (76.76 ± 26.00) and CC carriers (74.09 ± 25.29) (p = 0.040 and p = 0.009, respectively). In patients with CYP2C19 poor metabolizers (PM), ABCC2 rs717620 CC carriers showed significantly lower PAIR% values (51.72 ± 25.78) than CT carriers (75.37 ± 23.57) (p = 0.043). Furthermore, after adjusting for confounding factors, ABCC2 rs717620 was identified as a strong predictor of clopidogrel hyperreactivity. Conclusion: We proposed a new target, ABCC2 rs717620, in the efflux pathway that affects individual responses to clopidogrel. The TT allele of ABCC2 rs717620 was also identified as an independent risk factor for clopidogrel hyperreactivity, and CYP2C19*2 and *3 showed association with an increased risk for clopidogrel resistance. Additionally, ABCC2 rs717620 may affect individual responses to clopidogrel via post-transcriptional regulation and interaction with CYP2C19. These findings provide new insights that may guide the accurate use of clopidogrel.

In Situ Bonding Regulation of Surface Ligands for Efficient and Stable FAPbI3 Quantum Dot Solar Cells.

Quantum dots (QDs) of formamidinium lead triiodide (FAPbI3 ) perovskite hold great potential, outperforming their inorganic counterparts in terms of phase stability and carrier lifetime, for high-performance solar cells. However, the highly dynamic nature of FAPbI3 QDs, which mainly originates from the proton exchange between oleic acid and oleylamine (OAm) surface ligands, is a key hurdle that impedes the fabrication of high-efficiency solar cells. To tackle such an issue, here, protonated-OAm in situ to strengthen the ligand binding at the surface of FAPbI3 QDs, which can effectively suppress the defect formation during QD synthesis and purification processes is selectively introduced. In addition, by forming a halide-rich surface environment, the ligand density in a broader range for FAPbI3 QDs without compromising their structural integrity, which significantly improves their optoelectronic properties can be modulated. As a result, the power conversion efficiency of FAPbI3 QD solar cells (QDSCs) is enhanced from 7.4% to 13.8%, a record for FAPbI3 QDSCs. Furthermore, the suppressed proton exchange and reduced surface defects in FAPbI3 QDs also enhance the stability of QDSCs, which retain 80% of the initial efficiency upon exposure to ambient air for 3000 hours.

Climate drives the spatiotemporal dynamics of scrub typhus in China.

Scrub typhus is a climate-sensitive and life-threatening vector-borne disease that poses a growing public health threat. Although the climate-epidemic associations of many vector-borne diseases have been studied for decades, the impacts of climate on scrub typhus remain poorly understood, especially in the context of global warming. Here we incorporate Chinese national surveillance data on scrub typhus from 2010 to 2019 into a climate-driven generalized additive mixed model to explain the spatiotemporal dynamics of this disease and predict how it may be affected by climate change under various representative concentration pathways (RCPs) for three future time periods (the 2030s, 2050s, and 2080s). Our results demonstrate that temperature, precipitation, and relative humidity play key roles in driving the seasonal epidemic of scrub typhus in mainland China with a 2-month lag. Our findings show that the change of projected spatiotemporal dynamics of scrub typhus will be heterogeneous and will depend on specific combinations of regional climate conditions in future climate scenarios. Our results contribute to a better understanding of spatiotemporal dynamics of scrub typhus, which can help public health authorities refine their prevention and control measures to reduce the risks resulting from climate change.

The current and future risk of spread of Leptotrombidium deliense and Leptotrombidium scutellare in mainland China.

BACKGROUND: The chigger mites Leptotrombidium deliense (L. deliense) and Leptotrombidium scutellare (L. scutellare) are two main vectors of mite-borne diseases in China. However, the associated environmental risk factors are poorly understood, and the potential geographic ranges of the two mite species are unknown. METHODS: We combined an ensemble boosted regression tree modelling framework with contemporary records of mites and multiple environmental factors to explore the effects of environmental variables on both mites, as well as to predict the current and future environmental suitability distributions of both species. Additionally, the human population living in the potential spread risk zones of each species was also estimated across mainland China. RESULTS: Our results indicated that climate, land cover, and elevation are significantly associated with the spatial distributions of the two mite species. The current environmental suitability distribution of L. deliense is mainly concentrated in southern China, and that of L. scutellare is mainly distributed in southern and eastern coastal areas. With climate warming, the geographical distribution of the two mites generally tends to expand to the north and northwest. In addition, we estimated that 305.1-447.6 and 398.3-430.7 million people will inhabit the future spread risk zones of L. deliense and L. scutellare, respectively, in mainland China. CONCLUSIONS: Our findings provide novel insights into understanding the current and future risks of spread of these two mite species and highlight the target zones for helping public health authorities better prepare for and respond to future changes in mite-borne disease risk.

Modelling armed conflict risk under climate change with machine learning and time-series data.

Understanding the risk of armed conflict is essential for promoting peace. Although the relationship between climate variability and armed conflict has been studied by the research community for decades with quantitative and qualitative methods at different spatial and temporal scales, causal linkages at a global scale remain poorly understood. Here we adopt a quantitative modelling framework based on machine learning to infer potential causal linkages from high-frequency time-series data and simulate the risk of armed conflict worldwide from 2000-2015. Our results reveal that the risk of armed conflict is primarily influenced by stable background contexts with complex patterns, followed by climate deviations related covariates. The inferred patterns show that positive temperature deviations or precipitation extremes are associated with increased risk of armed conflict worldwide. Our findings indicate that a better understanding of climate-conflict linkages at the global scale enhances the spatiotemporal modelling capacity for the risk of armed conflict.

Prediction of global marginal land resources for Pistacia chinensis Bunge by a machine learning method.

Biofuel has attracted worldwide attention due to its potential to combat climate change and meet emission reduction targets. Pistacia chinensis Bunge (P. chinensis) is a prospective plant for producing biodiesel. Estimating the global potential marginal land resources for cultivating this species would be conducive to exploiting bioenergy yielded from it. In this study, we applied a machine learning method, boosted regression tree, to estimate the suitable marginal land for growing P. chinensis worldwide. The result indicated that most of the qualified marginal land is found in Southern Africa, the southern part of North America, the western part of South America, Southeast Asia, Southern Europe, and eastern and southwest coasts of Oceania, for a grand total of 1311.85 million hectares. Besides, we evaluated the relative importance of the environmental variables, revealing the major environmental factors that determine the suitability for growing P. chinensis, which include mean annual water vapor pressure, mean annual temperature, mean solar radiation, and annual cumulative precipitation. The potential global distribution of P. chinensis could provide a valuable basis to guide the formulation of P. chinensis-based biodiesel policies.