|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Emergency logistics scheduling with multiple supply-demand points based on grey interval显示文摘This study aimed to address the problem of post-disaster emergency material dispatching from multiple supply points to multiple demand points.In large-scale natural disasters,it is very important for multiple emergency material supply points to serve as sources of materials for multiple disaster sites and to determine emergency material scheduling solutions accurately.Furthermore,the quantity of emergency materials required at each disaster site is uncertain.To address this issue,in this study,we developed an emergency material scheduling model with multiple logistics supply points for multiple demand points based on the grey interval numbers.To optimize the proposed multi-supply-point and multi-demand-point emergency material scheduling mode,a multi-objective optimization algorithm based on a genetic algorithm was used.Experimental results demonstrate that the multi-objective optimization method can solve the emergency logistics scheduling problem better than the particle swarm optimization multi-objective solution algorithm.Additionally,the multi-supply point and multi-demand point emergency material dispatch model and optimization algorithm provides robust support for emergency management system decision-makers when they need to respond quickly to disaster relief activities. | Zhiming Ding Xinrun Xu Shan Jiang Jin Yan Yanbo Han | 2022 | Journal of Safety Science and Resilience2022，3，2： | 5 |
| 2 | ARIMA and NAR based prediction model for time series analysis of COVID-19 cases in India显示文摘In this paper,we have applied the univariate time series model to predict the number of COVID-19 infected cases that can be expected in upcoming days in India.We adopted an Auto-Regressive Integrated Moving Average(ARIMA)model on the data collected from 31st January 2020 to 25th March 2020 and verified it using the data collected from 26th March 2020 to 04th April 2020.A nonlinear autoregressive(NAR)neural network was developed to compare the accuracy of predicted models.The model has been used for daily prediction of COVID-19 cases for next 50 days without any additional intervention.Statistics from various sources,including the Ministry of Health and Family Welfare(MoHFW)and http://gffzz7f4dde9d11784f45hbcbqf0wfn6cn65ko.ffgz.tsg.suse.edu.cn/are used for the study.The results showed an increasing trend in the actual and forecasted numbers of COVID-19 cases with approximately 1500 cases per day,based on available data as on 04th April 2020.The appropriate ARIMA(1,1,0)model was selected based on the Bayesian Information Criteria(BIC)values and the overall highest R 2 values of 0.95.The NAR model architecture constitutes ten neurons,which was optimized using the Levenberg-Marquardt optimization training algorithm(LM)with the overall highest R 2 values of 0.97. | Farhan Mohammad Khan Rajiv Gupta | 2020 | Journal of Safety Science and Resilience2020，1，1： | 5 |
| 3 | Virtual and augmented reality technologies for emergency management in the built environments: A state-of-the-art review显示文摘With the rapid technological advancements in recent decades,virtual reality(VR)and augmented reality(AR)technologies have been increasingly adopted to address various challenges in emergency management in the built environments.This paper presents a review of state-of-the-art applications in this rapidly evolving area.A total of 84 relevant articles are identified based on searching in the Web of Science Core Collection and snowballing.These papers are then organized based on a taxonomy developed in this study.Next,a range of VR/AR appli-cations presented in these papers that are aimed to enhance various processes associated with pre-emergency preparedness,responses during emergency and post-emergency recovery are reviewed in detail.The existing VR/AR applications are also described from a human-computer interaction perspective.Finally,current research trends,knowledge gaps and directions for future research are discussed.The findings presented in this paper are expected to provide a synthetic and critical review of state-of-the-art VR/AR applications for emergency management in the built environment and facilitate further advancements in both research and practice in this area. | Yiqing Zhu Nan Li | 2021 | Journal of Safety Science and Resilience2021，2，1： | 4 |
| 4 | Multi-hazard disaster scenario method and emergency management for urban resilience by integrating experiment-simulation-field data显示文摘Due to the frequent occurrence of multi-hazard disasters worldwide in recent years,effective multi-hazard sce-nario analysis is imperative for disaster rescue and emergency management.The response procedure for different single hazards were investigated and formulated before.However,the investigations of disaster scenario rarely systematically address the entire development and response process of multi-hazards,including the coupling mechanisms,evolution dynamics,scenario assessment and emergency response.To this end,this paper presents our methodology of multi-hazard disaster scenario that integrates experiment-simulation-field data,focusing on three dimensions consisting of multi-hazard coupling,structures and systems,and emergency management.The newly proposed scenario method mainly comprises three aspects:experiments and simulations,multi-hazard field investigation,scenario analysis and response.Specifically,in order to study the large-scale,high-intensity and multi-hazard coupling effects,we carried out reduced-scale experiments and field measurement experiments to develop experimental similarity theory and prototype simulations of multi-hazards.In addition,a variety of field rescue and survey equipment,such as robots,Unmanned Aerial Vehicle(UAV),and Virtual Reality/Augmented Reality(VR/AR)technologies were utilized to acquire real-time data of multi-hazard field.Furthermore,we also examine the mechanism and framework of multi-hazard scenarios to formulate the detailed procedures of man-agement and response.They are incorporated with the experiments,simulations,field data and models to con-struct a new scenario platform.The proposed scenario method was applied in a case study of the coupled wind and snow multi-hazard to verify its effectiveness.The new method contributes to the disaster relief,decision-making and emergency management for multi-hazard disaster to improve the urban resilience. | Rui Ba Qing Deng Yi Liu Rui Yang Hui Zhang | 2021 | Journal of Safety Science and Resilience2021，2，2： | 3 |
| 5 | Multi-objective scheduling of relief logistics based on swarm intelligence algorithms and spatio-temporal traffic flow显示文摘Emergency supplies scheduling needs to consider the state of the demanders,and reasonably scheduling and resource allocation are the heart of efficient rescue.Taking rescue time,scheduling cost and demanders’satisfac-tion as goals,in this paper,an emergency supplies scheduling model based on multi-objective optimization was proposed to provide a wealth of decision-making information.Then four multi-objective optimization algorithms are employed to obtain the optimal set of scheduling models.In addition,we design the minimum time cost model and the shortest route cost model by considering the change of the road network status.The extensive simulation experiments are conducted on a real urban traffic dataset.The experimental results show that the two cost models can serve different scheduling needs and provide efficient scheduling for emergency supplies. | Zhiming Ding Zilin Zhao Detian Liu Yang Cao | 2021 | Journal of Safety Science and Resilience2021，2，4： | 3 |
| 6 | Using knowledge inference to suppress the lamp disturbance for fire detection显示文摘Fire detection in buildings is crucial for people’s lives and property.Conventional temperature and smoke sen-sors have many disadvantages:the limited cover range;detection delays;the difficulty in distinguishing smoke and fire.Recently,research on convolutional neural networks(CNN)for fire image detection has become a hot topic.However,existing fire classification and object detection methods are often interfered with by flash-lights,red objects and the high-brightness background,resulting in a high false alarm rate.Besides,light and lamps often exist in buildings.To address this issue,this paper focuses on introducing scene prior knowledge and causal inference mechanisms to suppress the lamp disturbance.Firstly,we train the YoloV3 network to detect and recognize lamps.Secondly,to reduce the dataset bias,we mask the lamp regions with the pro-posed Local Grabcut segmentation method.Last,compared with direct fire classification methods,our proposed methods reduce about 34.6%false alarm rate based on InceptionV4 networks.The experimental results verify the effectiveness among different CNN architectures(Resnet101,Firenet,Densenet121).The code is online at http://gffzz188fe103f8f1460asbcbqf0wfn6cn65ko.ffgz.tsg.suse.edu.cn/kailaisun/fire-detection-without-lamp. | Kailai Sun Qianchuan Zhao Xinwei Wang | 2021 | Journal of Safety Science and Resilience2021，2，3： | 3 |
| 7 | Control measures during the COVID-19 outbreak reduced the transmission of hand,foot,and mouth disease显示文摘Control measures during the coronavirus disease 2019(COVID-19)outbreak may have limited the spread of infectious diseases.This study aimed to analyze the impact of COVID-19 on the spread of hand,foot,and mouth disease(HFMD)in China.A mathematical model was established to fit the reported data of HFMD in six selected cities in China's Mainland from 2015 to 2020.The absolute difference(AD)and relative difference(RD)between the reported incidence in 2020,and simulated maximum,minimum,or median incidence of HFMD in 2015-2019 were calculated.The incidence and R effof HFMD have decreased in six selected cities since the outbreak of COVID-19,and in the second half of 2020,the incidence and R effof HFMD have rebounded.The results show that the total attack rate(TAR)in 2020 was lower than the maximum,minimum,and median TAR fitted in previous years in six selected cities(except Changsha City).For the maximum,median,minimum fitted TAR,the range of RD(%)is 42·20-99·20%,36·35-98·41%48·35-96·23%(except Changsha City)respectively.The preventive and control measures of COVID-19 have significantly contributed to the containment of HFMD transmission. | Yan Niu Li Luo Jia Rui Shiting Yang Bin Deng Zeyu Zhao Shengnan Lin Jingwen Xu Yuanzhao Zhu Yao Wang Meng Yang Xingchun Liu Tianlong Yang Weikang Liu Peihua Li Zhuoyang Li Chan Liu Jiefeng Huang Tianmu Chen | 2021 | Journal of Safety Science and Resilience2021，2，2： | 3 |
| 8 | Experiment and simulation study of emergency evacuation during violent attack in classrooms显示文摘In recent years,as a disaster,terrorist attacks have occurred throughout the world.However,emergency evac-uation behaviors during these incidents were not clear,and the traditional emergency plans were not suitable for such incidents.In this paper,evacuation behaviors under armed assault attack in a classroom were studied based on evacuation experiments.A total of 103 participants took part in three experiments.In each experiment,the attacker’s attacking route was set differently to study the impact of the attacking route on evacuation be-haviors.Pre-evacuation delay,panic of the evacuees,exit choices,evacuation time,and evacuees’trajectories in the experiments were all analyzed.The results of the experiments showed that when a terrorist attack occurs,there is a long delay before evacuation,and most of the evacuees were in the state of“observation”before they moved.When one of the participants started to evacuate or shout,other participants would begin to recognize the danger and escape quickly.These three experiments showed that the route of the attacker had a significant impact on the routes and exit choices of the evacuees.Rather than searching for the nearest exit,the primary purpose of evacuees was to keep a safe distance from the attacker.The average speed of the evacuees in these three experiments was 1.07 m/s,0.81 m/s,and 0.84 m/s,respectively.The density distribution during the crowd evacuation process was uneven,with the highest density occurring at the area from the seats to the aisles.The research can provide data support for the design of emergency plans and the computer simulation of the armed assault attack. | Ning Ding Yihang Ma Dapeng Dong Yadi Wang | 2021 | Journal of Safety Science and Resilience2021，2，4： | 2 |
| 9 | Forecasting of COVID-19: spread with dynamic transmission rate显示文摘The COVID-19 was firstly reported in Wuhan,Hubei province,and it was brought to all over China by people travelling for Chinese New Year.The pandemic coronavirus with its catastrophic effects is now a global concern.Forecasting of COVID-19 spread has attracted a great attention for public health emergency.However,few re-searchers look into the relationship between dynamic transmission rate and preventable measures by authorities.In this paper,the SEIR(Susceptible Exposed Infectious Recovered)model is employed to investigate the spread of COVID-19.The epidemic spread is divided into two stages:before and after intervention.Before intervention,the transmission rate is assumed to be a constant since individual,community and government response has not taken into place.After intervention,the transmission rate is reduced dramatically due to the societal actions or measures to reduce and prevent the spread of disease.The transmission rate is assumed to follow an exponential function,and the removal rate is assumed to follow a power exponent function.The removal rate is increased with the evolution of the time.Using the real data,the model and parameters are optimized.The transmission rate without measure is calculated to be 0.033 and 0.030 for Hubei and outside Hubei province,respectively.After the model is established,the spread of COVID-19 in Hubei province,France and USA is predicted.From results,USA performs the worst according to the dynamic ratio.The model has provided a mathematical method to evaluate the effectiveness of the government response and can be used to forecast the spread of COVID-19 with better performance. | Yiping Zeng Xiaojing Guo Qing Deng Shengfeng Luo Hui Zhang | 2020 | Journal of Safety Science and Resilience2020，1，2： | 2 |
| 10 | Public opinion analysis of novel coronavirus from online data显示文摘Novel coronavirus,now named COVID-19,has swept the world,which is regarded as‘public enemy number one’by WHO.In these months,the coronavirus has become a hot topic and led various public opinion.The traditional strategies for public opinion analyzing seldom take the entities and behaviors into consideration.Focusing on the high fluctuation of public opinion of novel coronavirus event,we propose a Key-Information-oriented Convolutional Neural Network(KIN-CNN)to analyze both relevant entities and behaviors in addition to public opinion trend on Chinese corpus.Firstly,we establish a knowledge set according to the characteristic of distribution in corpus of emotions,behaviors and entities.Secondly,we integrate the other prior knowledge to initialize the convolution kernel for better model performance.Thirdly,as COVID-19 event develops,the dominant public opinion trend is obtained by our approach.Furthermore,the relationship of dominant public opinion with entities and behaviors is established as well in this research. | Lu Chen Yang Liu Yudong Chang Xinzhi Wang Xiangfeng Luo | 2020 | Journal of Safety Science and Resilience2020，1，2： | 2 |
| 11 | Projecting the criticality of COVID-19 transmission in India using GIS and machine learning methods显示文摘There is a new public health catastrophe forbidding the world.With the advent and spread of 2019 novel coro-navirus(2019-nCoV).Learning from the experiences of various countries and the World Health Organization(WHO)guidelines,social distancing,use of sanitizers,thermal screening,quarantining,and provision of lock-down in the cities being the effective measure that can contain the spread of the pandemic.Though complete lockdown helps in containing the spread,it generates complexity by breaking the economic activity chain.Besides,laborers,farmers,and workers may lose their daily earnings.Owing to these detrimental effects,the government has to open the lockdown strategically.Prediction of the COVID-19 spread and analyzing when the cases would stop increasing helps in developing a strategy.An attempt is made in this paper to predict the time after which the number of new cases stops rising,considering the strong implementation of lockdown conditions using three different techniques such as Decision Tree,Support Vector Machine,and Gaussian Process Regression algorithm are used to project the number of cases.Thus,the projections are used in identifying inflection points,which would help in planning the easing of lockdown in a few of the areas strategically.The criticality in a region is evaluated using the criticality index(CI),which is proposed by authors in one of the past of research works.This research work is made available in a dashboard to enable the decision-makers to combat the pandemic. | Farhan Mohammad Khan Akshay Kumar Harish Puppala Gaurav Kumar Rajiv Gupta | 2021 | Journal of Safety Science and Resilience2021，2，2： | 2 |
| 12 | Large-scale experimental investigation of the effects of gas explosions in underdrains显示文摘This study involved the construction and explosion of a large-scale(80-meter-long)underdrain and detailed investigations of the damaging impacts of a gas explosion to provide an experimental foundation for similarity modeling and infrastructural designs.The experiment vividly recreated the scene and explosion damage of the'11.22″explosion accident in Qingdao,China,thus allowing for evaluations of the movements and destruction of the cover plates.The damage mechanism was determined by analyzing the overpressure curves inside and outside the underground canal.It was determined that the cover plates were first lifted by the precursor wave,which induced a maximum overpressure of 0.06 MPa and resulted in explosion venting.The pressure entered the deflagration stage at the end of the explosion.The combustion wave overpressure reached 3.115 MPa close to the initiation point,and had a significant influence on the projectile energy of the cover plates there.Overall,64%of the cover plates were only affected by the precursor wave,while 36%of the cover plates were subjected to both the precursor wave and the combustion wave;these cover plates were severely damaged.The results of this study provide fundamental insights relevant to the prevention and control of underdrain gas explosions. | Longfei Hou Yuanzhi Li Xinming Qian Chi-Min Shu Mengqi Yuan Weike Duanmu | 2021 | Journal of Safety Science and Resilience2021，2，2： | 2 |
| 13 | Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study显示文摘The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health. | Yunrong Yang Zhidong Cao Pengfei Zhao Dajun Daniel Zeng Qingpeng Zhang Yin Luo | 2021 | Journal of Safety Science and Resilience2021，2，3： | 2 |
| 14 | Federated transfer learning for disaster classification in social computing networks显示文摘Social media analytics have played an important role in disaster identification.Recent advances in deep learning(DL)technologies have been applied to design disaster classification models.However,the DL-based models are hindered by insufficient training samples,because data collection and labeling are very expensive and time-consuming.To solve this issue,a privacy-preserving federated transfer learning approach for disaster classification(FedTL)is proposed,which can allow distributed social computing nodes to collaboratively train a comprehensive model.In the FedTL,Paillier homomorphic encryption method is used to protect the social computing nodes’data privacy.In particular,the transfer learning technology is adopted as a novel application to reduce the computation and communication costs in the federated learning system.The FedTL is verified by a real disaster image dataset collected from social networks.Theoretical analyses and experiment results show that the FedTL is effective,secure,efficient.In addition,the FedTL is highly extensible and can be easily applied in other transfer learning models. | Zehui Zhang Ningxin He Dongyu Li Hang Gao Tiegang Gao Chuan Zhou | 2022 | Journal of Safety Science and Resilience2022，3，1： | 1 |
| 15 | Data-driven decision-making model for determining the number of volunteers required in typhoon disasters显示文摘Volunteer teams provide valuable support after large-scale disasters.However,excessive volunteer participation poses challenges for formal operations.Therefore,an appropriate decision-making method is required to quickly determine the number of volunteers required after a disaster.This study proposes a data-driven decision-making(D^(3)M)method for typhoon disaster volunteerism that can effectively predict the number of volunteers required.Disaster data from actual cases were gathered,analyzed,and preprocessed to prepare the model.Feature selection,D^(3)M model training and optimization,and model validation were performed to fine-tune the volunteer participant predictions.Using data from an actual typhoon in the Philippines,the rationality and efficacy of the method were verified through a comparative analysis of the experimental results.The proposed method learns from disaster-event data to quickly predict the number of volunteers needed,such that it not only reasonably allocates volunteers to assist professional teams in rescue but also avoids secondary problems caused by an overwhelming response. | Sheng-Qun Chen Jie Bai | 2023 | Journal of Safety Science and Resilience2023，4，3： | 1 |
| 16 | Sharing the post-earthquake situation for emergency response management in transborder areas: The e-Atlas tool显示文摘Usually,the response after a disastrous event requires decision-makers in emergency operative rooms to quickly outline and understand the current post-event situation,for planning and defining strategies for a contextualized response.Moreover,in many cases,disastrous events affect cross-border territories and/or involve multiple orga-nizations in responding to the demand generated by the event.These cases require a harmonized and coordinated response by all the institutions/subjects involved in emergency management.To address this challenge,this pa-per proposes the e-Atlas(emergency atlas)tool for showing and sharing information that embraces a response management perspective to explicitly support decision-makers in managing the criticalities of a post-earthquake situation.The e-Atlas has been designed and tested to:i)acquire sensible information for allowing decision-makers to understand in almost real-time the post-earthquake situational picture,and ii)share the representation of the situational picture among different users,allowing each of them to use it according to its specific require-ments.Applications of the tool are illustrated,to show how it is used in a transborder context in the North-East area of Italy. | Stefano Grimaz Petra Malisan Andrea Pividori | 2022 | Journal of Safety Science and Resilience2022，3，1： | 1 |
| 17 | Modeling of an Organizational Environment by System Dynamics and Fuzzy Logic 显示文摘 | BOULOIZ H GARBOLINO E TKIOUAT M | 2013 | Open Journal of Safety Science and Technology2013，3，4： | 1 |
| 18 | An investiga- tion on the dynamic stability of scissor lift 显示文摘 | DONG R G PAN C S HARTSELL J J | 2012 | Open Journal of Safety Science and Technology2012，2，1： | 1 |
| 19 | Development of a hazard and operability-based method for identifying safety management vulnera- bilities in high risk systems显示文摘 | R Kennedy B Kirwana | 1998 | The Journal of Safety Science1998，30，3： | 1 |
| 20 | Blast wave parameters for spherical explosives detonation in free air显示文摘 | I Sochet D Gardebas S Calderara | 2011 | Open Journal of Safety Science and Technology2011，1，2： | 1 |
您所在的机构暂未开通此部分的功能，请联系管理员