Post-traumatic growth promotes resilience development: A Longitudinal Mediation Model.

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 (ß = 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.

Identifying the most crucial factors associated with depression based on interpretable machine learning: a case study from CHARLS.

Background: Depression 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. Methods: We 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. Results: CatBoost 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. Conclusion: This 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.

Photoperiod-Dependent Nutrient Accumulation in Rice Cultivated in Plant Factories: A Comparative Metabolomic Analysis.

Plant factories offer a promising solution to some of the challenges facing traditional agriculture, allowing for year-round rapid production of plant-derived foods. However, the effects of conditions in plant factories on metabolic nutrients remain to be explored. In this study, we used three rice accessions (KongYu131, HuangHuaZhan, and Kam Sweet Rice) as objectives, which were planted in a plant factory with strict photoperiods that are long-day (12 h light/12 h dark) or short-day (8 h light/16 h dark). A total of 438 metabolites were detected in the harvested rice grains. The difference in photoperiod leads to a different accumulation of metabolites in rice grains. Most metabolites accumulated significantly higher levels under the short-day condition than the long-day condition. Differentially accumulated metabolites were enriched in the amino acids and vitamin B6 pathway. Asparagine, pyridoxamine, and pyridoxine are key metabolites that accumulate at higher levels in rice grains harvested from the short-day photoperiod. This study reveals the photoperiod-dependent metabolomic differences in rice cultivated in plant factories, especially the metabolic profiling of taste- and nutrition-related compounds.

Spatiotemporal multi-graph convolutional network-based provincial-day-level terrorism risk prediction.

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.

Interpretable spatial identity neural network-based epidemic prediction.

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.

Government regulation strategies for inhibiting opportunistic behaviors in construction projects.

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.

Allocation strategy of medical supplies during a public health emergency: a tripartite evolutionary game perspective.

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.

Applying Lightweight Deep Learning-Based Virtual Vision Sensing Technology to Realize and Develop New Media Interactive Art Installation.

The work intends to optimize the situation that interactive art devices and remote control based on traditional technology cannot meet people's actual needs to a certain extent. With the assistance of Lightweight Deep Learning (LDL) models, Interactive Artistic Installation (IAI) shows excellent creative potential in terms of dimension, space, and sense. Virtual Vision Sensing Technology (VST) explores the emotional semantics in the human-machine environment with the help of interactive art, finds the emotional interaction elements between human and machine, and promotes Human-Computer Interaction (HCI). From the perspective of the media elements of interactive art, this paper reviews the virtual VST that subverts the expression of interactive art. Then, from the perspective of artistic creation, the impact of virtual VST on IAI thinking, methods, and artistic experience is analyzed. Thereupon, a scene construction method is designed where the physical equipment is premodeled. The model is loaded in real time with visual information. The proposed method does not require complex vision and laser scanning equipment or high-configured computer systems. The proposed new media IAI model realizes the real-time loading of the scene model. According to the physical equipment dynamic information obtained by the visual data acquisition system, the proposed method can keep the virtual scene and physical models in motion synchronization. Finally, experiment results corroborate that the environment will significantly interfere with the experimental results. The training data set with boundary occlusion will be more suitable for model training and better test results (about 97% accuracy). Hence, the research content can make the Virtual Reality works have better performance, especially the sense of experience from the perspective of aesthetics. Meanwhile, it also enriches the research theory in the field of new media art installation technology.

The Tendency of Terrorist Organizations to Explosive Attacks: An Institutional Theory Perspective.

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.