Assessing the impact of armed conflict on the progress of achieving 17 sustainable development goals.

With global armed conflicts reaching a 30-year high in 2023, understanding their impact on the progress of Sustainable Development Goals (SDGs) is crucial. Here, we used the propensity score matching method to assess the specific impacts of armed conflict on achieving the 17 SDGs in affected countries from 2000 to 2021. The results indicate that, compared to the hypothetical scenarios without conflict, progress on more than half of the SDGs has slowed by over 5% in countries experiencing armed conflict.

The impacts of climate change on violent conflict risk: a review of causal pathways.

The potential impacts of climate change on violent conflict are high on the agenda of scholars and policy makers. This article reviews existing literature to clarify the relationship between climate change and conflict risk, focusing on the roles of temperature and precipitation. While some debate remains, substantial evidence shows that climate change increases conflict risk under specific conditions.

Organometallic Compound Stabilizes All-Inorganic Tin-Based Perovskite Nanocrystals Against Antisolvent Post-Treatment.

The isolation and purification of all-inorganic Sn-based perovskite nanocrystals (PNCs) remain troublesome, as common antisolvents accelerate the collapse of the optically active perovskite structure. Here, we mitigate such instabilities and endow strong resistance to antisolvent by incorporating the organometallic compound zinc diethyldithiocarbamate, Zn(DDTC), during the solution-based synthesis of all-inorganic CsSnI nanocrystals. Thiourea is shown to form through the thermal-driven conversion of Zn(DDTC) during synthesis, which binds to un-passivated Sn sites on the crystal surface and shields it from irreversible oxidation reactions.

Global risk dynamics of Borrelia miyamotoi in the context of climate change.

The impact of Borrelia miyamotoi on human health, facilitated by the expanding geographical distribution and increasing population of Ixodes ticks, remains obscure in the context of global climate change. We employed multiple models to evaluate the effect of global climate change on the risk of B. miyamotoi worldwide across various scenarios.

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.

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

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 CsFAPbI using ultralow dose transmission electron microscopy.

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.

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

The facile oxidation of Sn to Sn 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 Pb from the photolysis of perovskite reacts with Sn to regenerate Pb and Sn spontaneously.

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.

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.

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.

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.

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.

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.

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.

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

Quantum dots (QDs) of formamidinium lead triiodide (FAPbI ) 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 FAPbI 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 FAPbI QDs, which can effectively suppress the defect formation during QD synthesis and purification processes is selectively introduced.

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

In this study, we investigated the association between polymorphism and clopidogrel response as well as the associated hypothetical mechanism. 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 thromboelastometry.

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.

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).

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.

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.

Quantifying risk factors and potential geographic extent of African swine fever across the world.

African swine fever (ASF) has spread to many countries in Africa, Europe and Asia in the past decades. However, the potential geographic extent of ASF infection is unknown. Here we combined a modeling framework with the assembled contemporary records of ASF cases and multiple covariates to predict the risk distribution of ASF at a global scale.