scholarly journals Needs and opportunities for seismic early warning prior to aftershocks for search and rescue teams: An in-depth analysis of practitioners’ perceptions

Author(s):  
Samuel Auclair ◽  
Pierre Gehl ◽  
Mickael Delatre
2009 ◽  
Vol 80 (5) ◽  
pp. 755-771 ◽  
Author(s):  
K. Fleming ◽  
M. Picozzi ◽  
C. Milkereit ◽  
F. Kuhnlenz ◽  
B. Lichtblau ◽  
...  

2020 ◽  
Vol 10 (13) ◽  
pp. 4427 ◽  
Author(s):  
David Bañeres ◽  
M. Elena Rodríguez ◽  
Ana Elena Guerrero-Roldán ◽  
Abdulkadir Karadeniz

Artificial intelligence has impacted education in recent years. Datafication of education has allowed developing automated methods to detect patterns in extensive collections of educational data to estimate unknown information and behavior about the students. This research has focused on finding accurate predictive models to identify at-risk students. This challenge may reduce the students’ risk of failure or disengage by decreasing the time lag between identification and the real at-risk state. The contribution of this paper is threefold. First, an in-depth analysis of a predictive model to detect at-risk students is performed. This model has been tested using data available in an institutional data mart where curated data from six semesters are available, and a method to obtain the best classifier and training set is proposed. Second, a method to determine a threshold for evaluating the quality of the predictive model is established. Third, an early warning system has been developed and tested in a real educational setting being accurate and useful for its purpose to detect at-risk students in online higher education. The stakeholders (i.e., students and teachers) can analyze the information through different dashboards, and teachers can also send early feedback as an intervention mechanism to mitigate at-risk situations. The system has been evaluated on two undergraduate courses where results shown a high accuracy to correctly detect at-risk students.


2012 ◽  
Vol 17 (2) ◽  
pp. 485-505 ◽  
Author(s):  
M. Picozzi ◽  
D. Bindi ◽  
M. Pittore ◽  
K. Kieling ◽  
S. Parolai

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Jianping Zhao ◽  
Haitao Song

This paper presents an in-depth analysis and research on the identification of psychological crisis signals of college students using the optimized Dufferin equation. The early warning index system of college students’ psychological crisis was established and tested on 300 junior college students, and the early warning system of college students’ psychological crisis was established by using structural equation model, focusing on the mediating effect of coping mode between stress source and stress response and the mediating effect of stress source between social support and stress response. At the same time, the characteristics of psychological crises among college students of different genders and grades were compared and analyzed. To address the shortcomings of the classical Dufferin equation with limited noise immunity, the use of a higher-order double-coupled Dufferin system was further improved. A detection model based on the higher-order double-coupled system was established, and its feasibility was verified by the psychological crisis signal. The geometric features of the phase trajectory are adopted as the basis for judging the system state, which greatly reduces the computational effort. Based on defining the conceptual connotation of college students’ psychological crisis behavior system, the vulnerability of college students’ psychological crisis behavior system is interpreted from the perspective of system self-organization theory, and the vulnerability of college students’ psychological crisis behavior is mainly expressed in latent and manifest states, and its vulnerability transformation is a self-organization process. A questionnaire survey was conducted for ordinary college students to examine the performance of college students’ vulnerability state of the subject who endured college students’ psychological crisis behavior, and it was concluded that most college students appear to be normal and healthy on the surface, but college students’ vulnerability is in an uncertain state of intermediate transition.


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