Spatio-temporal Joint Modelling on Moderate and Extreme Air Pollution in Spain

Very unhealthy air quality is consistently connected with numerous diseases. Appropriate extreme analysis and accurate predictions are in rising demand for exploring potential linked causes and for providing suggestions for the environmental agency in public policy strategy. This paper aims to model the spatial and temporal pattern of both moderate and extremely poor PM10 concentrations (of daily mean) collected from 342 representative monitors distributed throughout mainland Spain from 2017 to 2021. We firstly propose and compare a series of Bayesian hierarchical generalized extreme models of annual maxima PM10 concentrations, including both the fixed effect of altitude, temperature, precipitation, vapour pressure and population density, as well as the spatio-temporal random effect with the Stochastic Partial Differential Equation (SPDE) approach and a lag-one dynamic auto-regressive component (AR(1)). Under WAIC, DIC and other criteria, the best model is selected with good predictive ability based on the first four-year data (2017--2020) for training and the last-year data (2021) for testing. We bring the structure of the best model to establish the joint Bayesian model of annual mean and annual maxima PM10 concentrations and provide evidence that certain predictors (precipitation, vapour pressure and population density) influence comparably while the other predictors (altitude and temperature) impact reversely in the different scaled PM10 concentrations. The findings are applied to identify the hot-spot regions with poor air quality using excursion functions specified at the grid level. It suggests that the community of Madrid and some sites in northwestern and southern Spain are likely to be exposed to severe air pollution, simultaneously exceeding the warning risk threshold

Security of Deputy Signature

E-system, a new commerce model, is a new era for business direction. When a principal is absent (goes on an errand or on leave), a well-designed deputy system keeps the business operations working. In the network world, identity verification and any substitute for traditional signature can be done by digital signature [1]. Deputy signature guarantees the existence of deputy system in e-system. Current deputy mechanism addresses the verification of deputy signature. No research has been done on the prevention of the illegal use of deputy system when the principal returns and the deputy system is not in use. We propose a mechanism to solve the problem of illegal use of deputy system when the power of deputy system is not legally “ON.

An overview of out-of-step protection in power systems

Power system is subjected to an extensive variety of little or bigger disturbance to the system during the operation. The power system that designed as one of the main requirement is to survive from the larger type of disturbances like faults. The power swing in certain system is the variation in three phase power flow in the power system. This paper mainly discussed the power swing and distance relay and the effect of the power swing on the distance relay and demonstrate about the basic power system stability and power swing phenomena. Moreover, out of step protection and detection applications are revised as well. At the end, the paper also demonstrated the past study of out of step application of TNB 275 KV network

SHA-SCP: A UI Element Spatial Hierarchy Aware Smartphone User Click Behavior Prediction Method

Predicting user click behavior and making relevant recommendations based on the user's historical click behavior are critical to simplifying operations and improving user experience. Modeling UI elements is essential to user click behavior prediction, while the complexity and variety of the UI make it difficult to adequately capture the information of different scales. In addition, the lack of relevant datasets also presents difficulties for such studies. In response to these challenges, we construct a fine-grained smartphone usage behavior dataset containing 3,664,325 clicks of 100 users and propose a UI element spatial hierarchy aware smartphone user click behavior prediction method (SHA-SCP). SHA-SCP builds element groups by clustering the elements according to their spatial positions and uses attention mechanisms to perceive the UI at the element level and the element group level to fully capture the information of different scales. Experiments are conducted on the fine-grained smartphone usage behavior dataset, and the results show that our method outperforms the best baseline by an average of 10.52%, 11.34%, and 10.42% in Top-1 Accuracy, Top-3 Accuracy, and Top-5 Accuracy, respectively

The Research on the Detection of Noteworthy Symptom Descriptions

The advance of mobile devices and communication technologies enable patients to communicate with their doctors in a more convenient way. We have developed an App that allows patients to record their symptoms and submit them to their doctors. Physicians can keep track of patients’ conditions by looking at the self-report messages. Nevertheless, physicians are usually busy and may be overwhelmed by the large amount of incoming messages. As a result, critical messages may not receive immediate attentions, and patient care is compromised. It is imperative to identify the messages that require physicians’ attention, called noteworthy messages. In this research, we propose an approach that applies text-mining technologies to identify medical symptoms conveyed in the messages and their associated sentiment orientation, as well as other factors. Noteworthy messages are subsequently characterized by symptom sentiment and symptom change features. We then construct a prediction model to identify messages that are noteworthy to the physicians. We show from our experiments using data collected from a teaching hospital in Taiwan that the different features have different degrees of impact on the performance of the prediction model, and our proposed approach can effectively identify noteworthy messages

A Study of Developing a System Dynamics Model for the Learning Effectiveness Evaluation

[[abstract]]This study used the research method of system dynamics and applied the Vensim software to develop a learning effectiveness evaluation model. This study developed four cause-and-effect chains affecting learning effectiveness, including teachers’ teaching enthusiasm, family involvement, school’s implementation of scientific activities, and creative teaching method, as well as the system dynamics model based on the four cause-and-effect chains. Based on the developed system dynamic model, this study performed simulation to investigate the relationship among family involvement, learning effectiveness, teaching achievement, creative teaching method, and students’ learning interest. The results of this study verified that there are positive correlations between family involvement and students’ learning effectiveness, as well as students’ learning effectiveness and teachers’ teaching achievements. The results also indicated that the use of creative teaching method is able to increase students’ learning interest and learning achievement.[[journaltype]]國外[[incitationindex]]SCI[[ispeerreviewed]]Y[[booktype]]電子版[[countrycodes]]US