Importance Ranking of Accident Factors of Construction Tower Crane by AHP Technique

In Korea construction industry, the increase of tower crane’s usage continuously and the accidents of tower crane are increasing simultaneously. But research on tower crane is insufficient for reducing the tower crane accident. This study aims to derive the importance ranking of accident factors of cab-control tower crane by AHP analysis. AHP survey was conducted on experts such as construction engineer, construction manager, safety engineer, and tower crane operator, who have more than 10-year career. The results of AHP analysis reveal that top ranking factor of cab-control tower crane’s accident is erection work. Therefore, the derived factors should be managed, and the priority measures taken for reducing the tower crane accidents according to the ranking of accident factors.

Ranking important nodes in complex networks by simulated annealing

In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented.First, the concept of an importance sequence（IS） to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.

An Efficient Character-Level Adversarial Attack Inspired by Textual Variations in Online Social Media Platforms

In recent years,the growing popularity of social media platforms has led to several interesting natural language processing(NLP)applications.However,these social media-based NLP applications are subject to different types of adversarial attacks due to the vulnerabilities of machine learning(ML)and NLP techniques.This work presents a new low-level adversarial attack recipe inspired by textual variations in online social media communication.These variations are generated to convey the message using out-of-vocabulary words based on visual and phonetic similarities of characters and words in the shortest possible form.The intuition of the proposed scheme is to generate adversarial examples influenced by human cognition in text generation on social media platforms while preserving human robustness in text understanding with the fewest possible perturbations.The intentional textual variations introduced by users in online communication motivate us to replicate such trends in attacking text to see the effects of such widely used textual variations on the deep learning classifiers.In this work,the four most commonly used textual variations are chosen to generate adversarial examples.Moreover,this article introduced a word importance ranking-based beam search algorithm as a searching method for the best possible perturbation selection.The effectiveness of the proposed adversarial attacks has been demonstrated on four benchmark datasets in an extensive experimental setup.

Elements’important ranking of China drug safety management system:applying the non-structural fuzzy group decision method

Drug safety management is an important issue in China drug management system and attracts great attentions from the whole society.In order to reduce drug incident,this study discusses some important elements associated with China drug safety management system and analyzes the data collected by questionnaires.Besides,a methodology for rating the important elements is described and applied.The non-structural fuzzy group decision method not only considers the insufficient precise information but also combines the opinions of different kinds of respondents in China’s four municipalities.The results indicate that the sample systems are the most important in these important elements,and the order of importance is sampling systems,licensing systems,traceability systems,transaction models,pharmacovigilance and emergence management.This study not only points out the important ranking of the pivotal elements in China drug safety management but also gives some specific proposals about how to enhance drug safety management in China.

Wildfire Susceptibility Assessment in Southern China:A Comparison of Multiple Methods

Wildfire is a primary forest disturbance.A better understanding of wildfire susceptibility and its dominant influencing factors is crucial for regional wildfire risk management.This study performed a wildfire susceptibility assessment using multiple methods,including logistic regression,probit regression,an artificial neural network,and a random forest(RF) algorithm.Yunnan Province,China was used as a case study area.We investigated the sample ratio of ignition and nonignition data to avoid misleading results due to the overwhelming number of nonignition samples in the models.To compare model performance and the importance of variables among the models,the area under the curve of the receiver operating characteristic plot was used as an indicator.The results show that a cost-sensitive RF had the highest accuracy(88.47%) for all samples,and 94.23% accuracy for ignition prediction.The identified main factors that influence Yunnan wildfire occurrence were forest coverage ratio,month,season,surface roughness,10 days minimum of the 6 h maximum humidity,and 10 days maxima of the 6 h average and maximum temperatures.These seven variables made the greatest contributions to regional wildfire susceptibility.Susceptibility maps developed from the models provide information regarding the spatial variation of ignition susceptibility,which can be used in regional wildfire risk management.