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Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and water cycle algorithm (WCA), have become predominant approaches for landslide displacement prediction. However, these algorithms suffer from poor reproducibility across replicate cases. In this study, a hybrid approach integrating k-fold cross validation (CV), metaheuristic support vector regression (SVR), and the nonparametric Friedman test is proposed to enhance reproducibility. The five previously mentioned metaheuristics were compared in terms of accuracy, computational time, robustness, and convergence. The results obtained for the Shuping and Baishuihe landslides demonstrate that the hybrid approach can be utilized to determine the optimum hyperparameters and present statistical significance, thus enhancing accuracy and reliability in ML-based prediction. Significant differences were observed among the five metaheuristics. Based on the Friedman test, which was performed on the root mean square error (RMSE), Kling-Gupta efficiency (KGE), and computational time, PSO is recommended for hyperparameter tuning for SVR-based displacement prediction due to its ability to maintain a balance between precision, computational time, and robustness. The nonparametric Friedman test is promising for presenting statistical significance, thus enhancing reproducibility.

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields.

Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose significant threats to people's lives and property [...].Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose significant threats to people's lives and property [...].

Understanding the relationship between urban form and structure and spatial inequality of property flood risk has been a longstanding challenge in urban planning and emergency management. Here we explore eight urban form and structure features to explain variability in spatial inequality of property flood risk among 2567 US counties. Using datasets related to human mobility and facility distribution, we identify notable variation in spatial inequality of property flood risk, particularly in coastline and metropolitan counties. The results reveal variations in spatial inequality of property flood risk can be explained based on principal components of development density, economic activity, and centrality and segregation. The classification and regression tree model further demonstrates how these principal components interact and form pathways that explain spatial inequality of property flood risk. The findings underscore the critical role of urban planning in mitigating flood risk inequality, offering valuable insights for crafting integrated strategies as urbanization progresses.

Disseminated intravascular coagulation (DIC) is a complication of sepsis currently lacking effective therapeutic options. Excessive inflammatory responses are emerging triggers of coagulopathy during sepsis, but the interplay between the immune system and coagulation are not fully understood. Here we utilize a murine model of intraperitoneal lipopolysaccharide stimulation and show neutrophils in the circulation mitigate the occurrence of DIC, preventing subsequent septic death. We show circulating neutrophils release extracellular vesicles containing mitochondria, which contain superoxide dismutase 2 upon exposure to lipopolysaccharide. Extracellular superoxide dismutase 2 is necessary to induce neutrophils’ antithrombotic function by preventing endothelial reactive oxygen species accumulation and alleviating endothelial dysfunction. Intervening endothelial reactive oxygen species accumulation by antioxidants significantly ameliorates disseminated intravascular coagulation improving survival in this murine model of lipopolysaccharide challenge. These findings reveal an interaction between neutrophils and vascular endothelium which critically regulate coagulation in a model of sepsis and may have potential implications for the management of disseminated intravascular coagulation. Disseminated intravascular coagulation is associated with sepsis and a number of inflammatory components have been linked to sepsis associated coagulopathy. Here the authors show neutrophils can prevent lethal coagulopathy via the production of extracellular vesicles that carry superoxide dismutase 2 in a murine model of lipopolysaccharide induced sepsis.

Overuse of nitrogen (N) fertilizer has led to low N use efficiency (NUE) and high N loss in single rice cropping systems in southeast China. Application of controlled-release urea (CRU) is considered as an effective N fertilizer practice for improving crop yields and NUE. Here, field experiments were conducted during 2015–2017 to assess the effects of two CRUs (resin-coated urea (RCU) and polyurethane-coated urea (PCU)) on rice yields, NUE and soil fertility at two sites (Lincheng town (LC) and Xintang town (XT)). Four treatments were established at each site: (1) control with no N application (CK), (2) split application of conventional urea (U, 270 kg N ha −1 ), (3) single basal application of RCU (RCU, 216 kg N ha −1 ), and (4) single basal application of PCU (PCU, 216 kg N ha −1 ). The N application rate in the CRU treatment compared to the U treatment was reduced by 20%. However, the results showed that, compared to split application of urea, single basal application of CRU led to similar rice grain yields and aboveground biomass at both sites. No significant difference in the N uptake by rice plant was observed between the U and CRU treatments at either site. There were no significant differences in the N apparent recovery efficiency (NARE) among the U, RCU and PCU treatments, with the exception of that in XT in 2015. Compared to application of U, application of CRU increased the N agronomic efficiency (NAE) and N partial factor productivity (NPFP) by 17.4–52.6% and 23.4–29.8% at the LC site, and 15.0–84.1% and 23.2–33.4% at the XT site, respectively, during 2015–2017. Yield component analysis revealed that greater rice grain yield in response to N fertilizer was attributed mainly to the number of panicles per m 2 , which increased in the fertilized treatments compared to the CK treatment. The application of CRU did not affect the soil fertility after rice harvest in 2016. Overall, these results suggest that single basal application of CRU constitutes a promising alternative N management practice for reducing N application rates, time- and labor-consuming in rice production in southeast China.

Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals.

The community and home-based elderly care service system has been proved an effective pattern to mitigate the elderly care dilemma under the background of accelerating aging in China. In particular, the participation of social organizations in community and home-based elderly care service has powerfully fueled the multi-supply of elderly care. As the industry of the elderly care service is in the ascendant, the management lags behind, resulting in the waste of significant social resources. Therefore, performance evaluation is proposed to resolve this problem. However, a systematic framework for evaluating performance of community and home-based elderly care service centers (CECSCs) is absent. To overcome this limitation, the SBM-DEA model is introduced in this paper to evaluate the performance of CECSCs. 186 social organizations in Nanjing were employed as an empirical study to develop the systematic framework for performance evaluation. Through holistic analysis of previous studies and interviews with experts, a systematic framework with 33 indicators of six dimensions (i.e., financial management, hardware facilities, team building, service management, service object and organization construction) was developed. Then, Sensitivity Analysis is used to screen the direction of performance optimization and specific suggestions were put forward for government, industrial associations and CECSCs to implement. The empirical study shows the proposed framework using SBM-DEA and sensitivity analysis is viable for conducting performance evaluation and improvement of CECSCs, which is conducive to the sustainable development of CECSCs.

CD47 is ubiquitously expressed on the surface of cells and plays a critical role in self-recognition. By interacting with SIRPα, TSP-1 and integrins, CD47 modulates cellular phagocytosis by macrophages, determines life span of individual erythrocytes, regulates activation of immune cells, and manipulates synaptic pruning during neuronal development. As such, CD47 has recently be regarded as one of novel innate checkpoint receptor targets for cancer immunotherapy. In this review, we will discuss increasing awareness about the diverse functions of CD47 and its role in immune system homeostasis. Then, we will discuss its potential therapeutic roles against cancer and outlines, the possible future research directions of CD47- based therapeutics against cancer.

This study presents a hybrid approach based on two-step cluster and decision tree C5.0 algorithms to establish a deformation forecasting model for a step-like landslide. The Zhujiadian landslide, a typical step-like landslide in the Three Gorges Reservoir area, was selected as a case study. Approximately , 6  years of historical records of landslide displacement, precipitation , and reservoir level were used to build the forecasting model. The forecasting model consisted of seven comprehensive rules governing hydrologic parameters and their magnitudes and was developed to predict landslide deformation. This model was applied to rapidly forecast the likelihood of step-like landslide deformation resulting from rainfall and water level fluctuations in the Three Gorges Reservoir area. Given the satisfactory accuracy of the trained model, the presented approach can be used to establish forecasting models for step-like landslides and to facilitate rapid decision making.