AHP Based Grassroots Emergency Response Capability Evaluation Index System Construction

The primary and secondary catastrophes of emergencies, as well as their comprehensive disasters, add a significant deal of complexity and risk to the grassroots emergency response system’s creation. The completeness of the emergency response system development is significantly related to improving the basic capacity of grassroots emergency security. The weight comparison calculation and analysis of the links at all levels in the emergency response system is carried out using the hierarchical analysis method based on the basic experience of current domestic related fields for the construction of the emergency response process system, and the results of the relative importance ranking of indicators are obtained, and this is used to provide emergency response and emergency management decision makers with emergent information.

Demand Management for Resilience Enhancement of Integrated Energy Distribution System against Natural Disasters

For energy sustainability, the integrated energy distribution system (IEDS) is an efficient and clean energy system, which is based on the coordinated operation of a power distribution network, a gas distribution network and a district heating system. In this paper, considering the damage of natural disasters to IEDS, a demand management strategy is proposed to improve resilience of IEDS and ensure stable operation, which is divided into three stages. In the first stage, the electricity, natural gas and thermal energy are co-optimized in the simulating fault state to develop the importance ranking of transmission lines and gas pipelines. In the second stage, the natural disasters are classified as surface natural disasters and geological natural disasters. According to the types of natural disasters, the demand management strategy includes semi-emergency demand management scheme and full-emergency demand management scheme in the electrical resilience mode and the integrated resilience mode, respectively. In the third stage, the non-sequential Monte-Carlo simulation and scenario reduction algorithm are applied to describe potential natural disaster scenarios. According to the importance ranking of transmission lines and gas pipelines, a demand management strategy is formulated. Finally, the proposed strategy is applied on an IEEE 33-bus power system and a 19-node natural gas system. Its effectiveness is verified by numerical case studies.

Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases

The discretionary damage of mental suffering in fatal car accident cases in Taiwan is subjective, uncertain, and unpredictable; thus, plaintiffs, defendants, and their lawyers find it difficult to judge whether spending much of their money and time on the lawsuit is worthwhile and which legal factors judges will consider important and dominant when they are assessing the mental suffering damages. To address these problems, we propose k-nearest neighbor, classification and regression trees, and random forests as learning algorithms for regression to build optimal predictive models. In addition, we reveal the importance ranking of legal factors by permutation feature importance. The experimental results show that the random forest model outperformed the other models and achieved good performance, and “the mental suffering damages that plaintiff claims” and “the age of the victim” play important roles in assessments of mental suffering damages in fatal car accident cases in Taiwan. Therefore, litigants and their lawyers can predict the discretionary damages of mental suffering in advance and wisely decide whether they should litigate or not, and then they can focus on the crucial legal factors and develop the best litigation strategy.

Identifying important nodes from content-associated heterogeneous graph by LeaderRank

Most real-world systems consist of a large number of interacting entities of many types. However, most of the current researches on systems are based on the assumption that the type of node or link in the network is unique. In other words, the network is homogeneous, containing the same type of nodes and links. Based on this assumption, differential information between nodes and edges is ignored. This paper firstly introduces the research background, challenges and significance of this research. Secondly, the basic concepts of the model are introduced. Thirdly, a novel type-sensitive LeaderRank algorithm is proposed and combined with distance rule to solve the importance ranking problem of content-associated heterogeneous graph nodes. Finally, the writer influence data set is used for experimental analysis to further prove the validity of the model.

CRank: Reusable Word Importance Ranking for Text Adversarial Attack

Deep learning models have been widely used in natural language processing tasks, yet researchers have recently proposed several methods to fool the state-of-the-art neural network models. Among these methods, word importance ranking is an essential part that generates text adversarial examples, but suffers from low efficiency for practical attacks. To address this issue, we aim to improve the efficiency of word importance ranking, making steps towards realistic text adversarial attacks. In this paper, we propose CRank, a black box method utilized by our innovated masking and ranking strategy. CRank improves efficiency by 75% at the ’cost’ of only a 1% drop of the success rate when compared to the classic method. Moreover, we explore a new greedy search strategy and Unicode perturbation methods.

Collective conceptualization of parental support of dual career athletes: The EMPATIA framework

Background This study aimed to use a concept mapping methodology to develop a European framework of the needs of parents/guardians (P/G) for supporting athletes combining sport and education (dual career, DC). Methods By means of a concept mapping methodology, 337 French, Irish, Italian, Portuguese, and Slovenian parents sorted and rated 80 potential statements associated to parenting DC athletes. Results Five distinct clusters emerged: 1. P/G’ roles, needs and awareness to support athletes, including 22 statements (mean:3.7; range: 3.2–4.2 pt); 2. Requirements for effective planning of DC pathway, including 19 statements (mean:3.7; range: 3.2–4.5 pt); 3. Educational opportunity, including 13 statements (mean:3.5; range: 3.1–4.0 pt); 4. Policy and provision for DC, including 19 statements (mean:3.7; range: 3.1–4.2 pt); and 5. Athletes’ lifestyle & self-management, including 7 statements (mean:4.0; range: 3.5–4.5 pt). Estimates of effect size (Partial eta-squared) were calculated for ANOVAs to assess the degree of variability on the statement importance ranking as the dependent variable accounted for by the demographic data. The concept mapping showed good validity (stress value: 0.11) and high reliability (rSHT: 0.99, rSHM: 0.98; rRR:0.98). One-third of the statements indicated differences (p<0.05) in relation to the P/Gs’ gender and the athletes’ education level, competition level and sport typology. Conclusion In synthesizing the opinions, experience and needs of P/Gs of DC athletes the present framework provided sound theoretical underpinnings to inform the development of an online educational programme for empowering parenting DC athletes (https://edu.empatiasport.eu/eng/), as well as be a foundation for future Pan-European DC research on how these statements interact with each other, in different European contexts.

Inflammatory Biomarkers and Intracranial Hemorrhage after Endovascular Thrombectomy

ABSTRACT: Background: Intracranial hemorrhage after endovascular thrombectomy is associated with poorer prognosis compared with those who do not develop the complication. Our study aims to determine predictors of post-EVT hemorrhage – more specifically, inflammatory biomarkers present in baseline serology. Methods: We performed a retrospective review of consecutive patients treated with EVT for acute large vessel ischemic stroke. The primary outcome of the study is the presence of ICH on the post-EVT scan. We used four definitions: the SITS-MOST criteria, the NINDS criteria, asymptomatic hemorrhage, and overall hemorrhage. We identified nonredundant predictors of outcome using backward elimination based on Akaike Information Criteria. We then assessed prediction accuracy using area under the receiver operating curve. Then we implemented variable importance ranking from logistic regression models using the drop in Naegelkerke R2 with the exclusion of each predictor. Results: Our study demonstrates a 6.3% SITS (16/252) and 10.0% NINDS (25/252) sICH rate, as well as a 19.4% asymptomatic (49/252) and 29.4% (74/252) overall hemorrhage rate. Serologic markers that demonstrated association with post-EVT hemorrhage were: low lymphocyte count (SITS), high neutrophil count (NINDS, overall hemorrhage), low platelet to lymphocyte ratio (NINDS), and low total WBC (NINDS, asymptomatic hemorrhage). Conclusion: Higher neutrophil counts, low WBC counts, low lymphocyte counts, and low platelet to lymphoycyte ratio were baseline serology biomarkers that were associated with post-EVT hemorrhage. Our findings, particularly the association of diabetes mellitus and high neutrophil, support experimental data on the role of thromboinflammation in hemorrhagic transformation of large vessel occlusions.