Background The frequent occurrence of global disasters poses unprecedented challenges to nursing practice. The frontline nurses in disaster relief are exposed to these events and bear significant levels of stress and psychological distress. Resilience and posttraumatic growth (PTG) are essential protective factors that contribute to sustaining their mental health. The purpose of this study was to determine the directional relationship between resilience and PTG using a cross-lagged design. Furthermore, employing longitudinal mediation to test whether the T1 resilience of frontline nurses would promote the development of T3 resilience through the mediating role of T2 PTG. Methods A total of 258 frontline nurses were selected as subjects. They completed self-reported measurements in three periods. The present study was conducted using a cross-lagged panel model and a longitudinal mediation model. Results The results of cross-lagged path analysis from T2 to T3 showed that PTG could positively predict the development of resilience (β = 0.235, p < 0.001). Resilience did not positively predict the development of PTG (p > 0.05). The analysis of mediating effect results showed that the development of PTG at T2 mediated the relationship between resilience from T1 to T3. Limitations Findings may be limited by self-report, recall bias of resilience before the epidemic and short tracking frequency. Conclusions These results can identify individuals with an increased risk of low resilience under disaster and the mediating role of posttraumatic growth in promoting the development of nurses' resilience, which provides a theoretical basis for psychological crisis intervention and the resilience promotion plan for posttraumatic growth under disaster events.

BackgroundDepression is one of the most common mental illnesses among middle-aged and older adults in China. It is of great importance to find the crucial factors that lead to depression and to effectively control and reduce the risk of depression. Currently, there are limited methods available to accurately predict the risk of depression and identify the crucial factors that influence it.MethodsWe collected data from 25,586 samples from the harmonized China Health and Retirement Longitudinal Study (CHARLS), and the latest records from 2018 were included in the current cross-sectional analysis. Ninety-three input variables in the survey were considered as potential influential features. Five machine learning (ML) models were utilized, including CatBoost and eXtreme Gradient Boosting (XGBoost), Gradient Boosting decision tree (GBDT), Random Forest (RF), Light Gradient Boosting Machine (LightGBM). The models were compared to the traditional multivariable Linear Regression (LR) model. Simultaneously, SHapley Additive exPlanations (SHAP) were used to identify key influencing factors at the global level and explain individual heterogeneity through instance-level analysis. To explore how different factors are non-linearly associated with the risk of depression, we employed the Accumulated Local Effects (ALE) approach to analyze the identified critical variables while controlling other covariates.ResultsCatBoost outperformed other machine learning models in terms of MAE, MSE, MedAE, and R2metrics. The top three crucial factors identified by the SHAP were r4satlife, r4slfmem, and r4shlta, representing life satisfaction, self-reported memory, and health status levels, respectively.ConclusionThis study demonstrates that the CatBoost model is an appropriate choice for predicting depression among middle-aged and older adults in Harmonized CHARLS. The SHAP and ALE interpretable methods have identified crucial factors and the nonlinear relationship with depression, which require the attention of domain experts.

OBJECTIVE:To explore the stability of resilience among frontline nurses and to analyse the predictive role of internal and external factors on the patterns of resilience transformation in China during public health emergencies. METHODS:The study used a longitudinal design and surveyed 258 frontline nurses at three different time points: February-March 2020 (T1), May-June 2020 (T2) and May-June 2022 (T3). The survey included the 10-item Connor-Davidson resilience scale, the Emotion Regulation Questionnaire and the Simple Coping Style Questionnaire. Latent profile analysis and latent transition analysis were used to examine the potential classes and changes. Multivariate logistic regression analysis was applied to evaluate the predictors of resilience transitions. RESULTS:The resilience of frontline nurses was divided into three categories: fragile group, general group and high resilience group. From T1 to T2, the general group exhibited the least stability, with a probability of maintaining its original latent state at 72.9%. Marriage and positive coping styles significantly impacted the transition between resilience categories. From T2 to T3, the fragile group showed the lowest stability, with a 74.9% likelihood of retaining its initial latent state. In this context, supportive hospital management (including psychological counselling, restful environments, and both spiritual and material incentives) and individuals' emotional regulation and sleep quality significantly affected the transition between resilience categories. CONCLUSIONS:These findings emphasise the necessity for early intervention. For frontline nurses, conducting initial assessments of resilience coupled with sustained hospital support is crucial for maintaining mental health and improving the quality of nursing care in public health emergencies. IMPACT:This study offers a fresh perspective for understanding the resilience of frontline nurses during public health emergencies. At the same time, it reveals the factors that promote or hinder the change in resilience among frontline nurses at both individual and organisational levels. This provides a theoretical basis for future resilience interventions and helps us formulate effective crisis management strategies to respond to future public health emergencies. For frontline nurses with diverse resilience characteristics and relevant transformation factors, a personalised multi-mode resilience improvement plan can be developed to mitigate public health emergencies' potential adverse psychological impact on frontline nurses, especially those in the fragile group. PATIENT OR PUBLIC CONTRIBUTION:No patient or public contribution.

Predicting terrorism risk is crucial for formulating detailed counter-strategies. However, this task is challenging mainly because the risk of the concerned potential victim is not isolated. Terrorism risk has a spatiotemporal interprovincial contagious characteristic. The risk diffusion mechanism comes from three possibilities: cross-provincial terrorist attacks, internal and external echoes, and internal self-excitation. This study proposed a novel spatiotemporal graph convolutional network (STGCN)-based extension method to capture the complex and multidimensional non-Euclidean relationships between different provinces and forecast the daily risks. Specifically, three graph structures were constructed to represent the contagious process between provinces: the distance graph, the province-level root cause similarity graph, and the self-excited graph. The long short-term memory and self-attention layers were extended to STGCN for capturing context-dependent temporal characters. At the same time, the one-dimensional convolutional neural network kernel with the gated linear unit inside the classical STGCN can model single-node-dependent temporal features, and the spectral graph convolution modules can capture spatial features. The experimental results on Afghanistan terrorist attack data from 2005 to 2020 demonstrate the effectiveness of the proposed extended STGCN method compared to other machine learning prediction models. Furthermore, the results illustrate the crucial of capturing comprehensive spatiotemporal correlation characters among provinces. Based on this, this article provides counter-terrorism management insights on addressing the long-term root causes of terrorism risk and performing short-term situational prevention.

Ensuring the rational and orderly circulation of medical supplies during a public health emergency is crucial to quickly containing the further spread of the epidemic and restoring the order of rescue and treatment. However, due to the shortage of medical supplies, there are challenges to rationalizing the allocation of critical medical supplies among multiple parties with conflicting interests. In this paper, a tripartite evolutionary game model is constructed to study the allocation of medical supplies in the rescue environment of public health emergencies under conditions of incomplete information. The game's players include Government-owned Nonprofit Organizations (GNPOs), hospitals, and the government. By analyzing the equilibrium of the tripartite evolutionary game, this paper makes an in-depth study on the optimal allocation strategy of medical supplies. The findings indicate that: (1) the hospital should reasonably increase its willingness to accept the allocation plan of medical supplies, which can help medical supplies allocate more scientifically. (2) The government should design a reasonable reward and punishment mechanism to ensure the rational and orderly circulation of medical supplies, which can reduce the interference of GNPOs and hospitals in the allocation process of medical supplies. (3) Higher authorities should strengthen the supervision of the government and the accountability for loose supervision. The findings of this research can guide the government in promoting better circulation of medical supplies during public health emergencies by formulating more reasonable allocation schemes of emergency medical supplies, as well as incentives and penalties. At the same time, for GNPOs with limited emergency medical supplies, the equal allocation of emergency supplies is not the optimal solution to improve the efficiency of emergency relief, and it is simpler to achieve the goal of maximizing social benefits by allocating limited emergency resources to the demand points that match the degree of urgency. For example, in Corona Virus Disease 2019, emergency medical supplies should be prioritized for allocation to government-designated fever hospitals that are have a greater need for medical supplies and greater treatment capacity.

Epidemic spatial-temporal risk analysis, e.g., infectious number forecasting, is a mainstream task in the multivariate time series research field, which plays a crucial role in the public health management process. With the rise of deep learning methods, many studies have focused on the epidemic prediction problem. However, recent primary prediction techniques face two challenges: the overcomplicated model and unsatisfactory interpretability. Therefore, this paper proposes an Interpretable Spatial IDentity (ISID) neural network to predict infectious numbers at the regional weekly level, which employs a light model structure and provides post-hoc explanations. First, this paper streamlines the classical spatio-temporal identity model (STID) and retains the optional spatial identity matrix for learning the contagion relationship between regions. Second, the well-known SHapley Additive explanations (SHAP) method was adopted to interpret how the ISID model predicts with multivariate sliding-window time series input data. The prediction accuracy of ISID is compared with several models in the experimental study, and the results show that the proposed ISID model achieves satisfactory epidemic prediction performance. Furthermore, the SHAP result demonstrates that the ISID pays particular attention to the most proximate and remote data in the input sequence (typically 20 steps long) while paying little attention to the intermediate steps. This study contributes to reliable and interpretable epidemic prediction through a more coherent approach for public health experts.

The construction industry has long been criticized for recurring accidents, wherein opportunistic behaviors are the primary cause of losing faith and increasing risk, infringing upon the interests of the state, society and people. While government regulation can be crucial in curbing opportunistic behaviors, the existing mixed strategy game model fails to accurately capture the strategic interactions between the government, owner, supervisor, and contractor. To bridge this gap, we propose a multi-stage dynamic game model with asymmetric information in the context of a typical construction project, wherein two urgent opportunistic behaviors may arise: moral hazard and covert collusion. According to project characteristics, the regulatory issues are further classified as hidden information for general projects and hidden effort for dominant projects. On this basis, the government's optimal regulation strategies are derived, i.e., the optimal fines for poor quality and the optimal fine coefficient for quality effort reduction. Subsequently, several significant managerial implications are presented to summarize and analyze impacts of government regulation on construction projects. The findings show that government regulation can achieve systemic optimality but may hurt the owner's interests in some cases. This could potentially hinder the healthy development of the construction industry as the owner may forgo purchasing the construction project. Furthermore, general projects are more vulnerable to opportunistic behaviors as opposed to dominant projects. The developed model and derived regulatory strategy can assist the government in more effectively governing and controlling opportunistic behaviors. This research also contributes several valuable managerial insights into the domain of government regulation on construction projects.

The success of terrorist attacks reflects the capability of terrorists and the vulnerability of the security defense, explainable prediction of the average attack success rate at the country-annual level is crucial for governments. In this study, terrorist attack data from 146 countries between 2002 to 2020 was obtained from the global terrorism database (GTD), and a two-stage prediction task was conducted. First, multiple machine learning models, including XGBoost and Random Forest (RF), are used to predict the average success rate of terrorist attacks in the next year, considering terrorism root factors and statistical results from the previous year. The results show that the RF model performs the best. Second, the prediction outputs of the RF model are explained using interpretable methods, including Accumulate Local Effect (ALE) and SHapley Additive exPlanation (SHAP), to provide counterterrorism insights applicable to countries around the world.

为揭示现行事故调查与问责中存在的问题,探索加强问责威慑效应的途径,对国内三起危化品爆炸事故进行纵贯研究.首先采用定性研究对事故进行比较分析;其次使用Python对典型事故前后的法规与信息动态进行文本挖掘;最后运用问责威慑理论与事故学习理论探索构建有效的化工事故调查问责机制.研究表明:尽管行政问责与法律问责强度不断提升,但问责的威慑效应存在递减趋势,从而未能强化企业在日常生产中的安全要求.重大危险源监控薄弱、企业安全文化建设严重不足,成为后续安全事故频发的潜在根源.因此,需要构建融于事故学习的新型事故调查问责机制,形成关口前移、重视安全风险早期管控、持续提升组织学习力的事故调查问责新路径.

Focusing on the tendency of terrorist organizations to explosive attack, this article applied the institutional theory as the basis to explain the inherent logic of attack type similarity from the perspective of mimetic, coercive, and normative isomorphism. Subsequently, the study conducted an empirical analysis of the data onto 1825 terrorist organizations recorded in the Global Terrorism Database with the logistic regression method. The results show that: (1) Terrorist organizations will learn from pre-existing terrorist organizations' experiences, and mimetic isomorphism will promote explosive tendency; (2) Due to the normative isomorphism effect, terrorist groups' tendency to explosive attacks is weakened by their increased duration; (3) If terrorist organizations are hostile to a strong government, coercive isomorphism positively moderates the negative effects of increasing duration. The study suggests that counter-terrorism approaches such as destroying the learnable experience of attacks, addressing the root causes of terrorism, and maintaining a strong government may be helpful in stopping increasing terrorist activities, which is essential for reducing terrorist organizations' vivosphere, blocking the inter-flow and imitation between terrorist organizations, and ultimately interrupting the terrorist propagation chain.

The factors that may impact the risk of terrorist attacks are numerous and interrelated in a complex manner. This complexity makes the prediction of terrorist attacks challenging and leads to information redundancy and the obscuring of critical points. This paper aims at identifying crucial indicators from the perspective of predicting the risk of terrorist attacks. Both root cause and incident level factors are taken into account, which are qualified using 28 indicators. A random forest (RF) model is established to predict terrorist attack risk, and the prediction performance is recorded as the baseline result in terms of MAE, MSE, and R-2. A recursive feature elimination method utilizing random forest kernels (RF-RFE) is proposed to identify crucial ones from the 28 initial indicators. The RF-RFE process gradually eliminates the least important indicators and compares the corresponding prediction performance with the baseline result. The prediction performance is relatively stable until the number of input indicators is reduced from 28 to less than 8. The indicators that make up the input set at the hedging point with eight indicators are considered as the most important ones, including Human loss, GDP Growth, Military Expenditure, Population Growth, Population(lg.), Unemployment, Urban Population Growth, Internal Conflict, etc. The identified crucial indicators indicate that foresight and preemptive measures should be taken not only for specific intelligence and response operations, but also to improve the underlying government stability, economic quality, and other basic elements of citizens' lives.

针对部分地区恐怖袭击短期内频发的问题,提出了基于长短时记忆(LSTM)神经网络模型的恐怖袭击事件发生时间预测方法.首先,建立了恐怖袭击事件演化模型,对局部地区存在的恐怖袭击事件短期内数量剧增现象进行了分析.其次,以演化模型为基础从全球恐怖主义数据库(GTD)中提取出17项代表恐怖袭击事件特性的指标,并构建了用于预测的LSTM模型.采用伊拉克2001年9月至2016年底的恐怖袭击事件数据进行实验分析.结果表明,基于LSTM的预测方法能够较准确的预测短期内恐怖袭击事件的发生时间.

Aiming at the large-scale flood disaster happened in Hubei province from late June to early July in 2016, this paper describes the case by extracting the event chain consisting of precipitation state chain, the disaster evolution chain, and the response disposition chain.Partial process of disaster evolution and response actions are analyzed based on generalized stochastic Petri net.A Markov chain is modeled to conduct the key factor analysis.Finally, confirmed that the rapid development of emergency disposal work can effectively curb the spread of disaster and reduce the extent of the damage.