An innovative multi-layer composite explosion containment vessel(CECV)utilizing a sliding steel platealuminum honeycomb-fiber cloth sandwich is put forward to improve the anti-explosion capacity of a conventional sing...An innovative multi-layer composite explosion containment vessel(CECV)utilizing a sliding steel platealuminum honeycomb-fiber cloth sandwich is put forward to improve the anti-explosion capacity of a conventional single-layer explosion containment vessel(SECV).Firstly,a series of experiments and finite element(FE)simulations of internal explosions are implemented to understand the basic anti-explosion characteristics of a SECV and the rationality of the computational models and methods is verified by the comparison between the experimental results and simulation results.Based on this,the CECV is designed in detail and a variety of FE simulations are carried out to investigate effects of the sandwich structure,the explosive quantity and the laying mode of the fiber cloth on anti-explosion performance and dynamic response of the CECV under internal explosions.Simulation results indicate that the end cover is the critical position for both the SECV and CECV.The maximum pressure of the explosion shock wave and the maximum strain of the CECV can be extremely declined compared to those of the SECV.As a result,the explosive quantity the CECV can sustain is up to 20 times of that the SECV can sustain.Besides,as the explosive quantity increases,the internal pressure of the CECV keeps growing and the plastic deformation and failure of the sandwich structure become more and more severe,yielding plastic strain of the CECV in addition to elastic strain.The results also reveal that the laying angles of the fiber cloth's five layers have an impact on the anti-explosion performance of the CECV.For example,the CECV with fiber cloth layered in 0°/45°/90°/45°/0°mode has the optimal anti-capacity,compared to 0°/0°/0°/0°/0°and 0°/30°/60°/30°/0°modes.Overall,owing to remarkable anti-explosion capacity,this CECV can be regarded as a promising candidate for explosion resistance.展开更多
One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelli...One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.展开更多
In order to improve the anti-explosion performance ofρ-Al_(2)O_(3) bonded corundum castables,H_(2)O_(2) was added(0,0.025%,0.050%,0.075%,0.100%and 0.125%,by mass)as the anti-explosion agent.After mixing and casting,s...In order to improve the anti-explosion performance ofρ-Al_(2)O_(3) bonded corundum castables,H_(2)O_(2) was added(0,0.025%,0.050%,0.075%,0.100%and 0.125%,by mass)as the anti-explosion agent.After mixing and casting,specimens were prepared.Some specimens were cured at room temperature for 12 h and demoulded for the anti-explosion performance test at different temperatures(450,500,550,600,650,700,750 and 800℃);the other specimens were cured,dried and fired,and tested in terms of the apparent porosity,the density,the cold mechanical properties,the air permeability and the pore size distribution.The results show that:(1)with the increase of the H_(2)O_(2) addition,the anti-explosion performance of castables increases gradually,the average pore size increases gradually,and the density and the strength decrease gradually;(2)by comprehensive consideration,the appropriate addition of H_(2)O_(2) shall be within 0.075%.展开更多
The explosivity experiment of anti-explosive ammonium nitrate (AEAN) shows that the explosive characteristic of AEAN is eliminated. The adiabatic decompositions of ammonium nitrate and AEAN were investigated with an a...The explosivity experiment of anti-explosive ammonium nitrate (AEAN) shows that the explosive characteristic of AEAN is eliminated. The adiabatic decompositions of ammonium nitrate and AEAN were investigated with an accelerating rate calorimeter (ARC). The curves of thermal decomposition temperature and pressure versus time, self-heating rate and pressure versus temperature for two systems were obtained. The kinetic parameters such as apparent activation energy and pre-exponential factor were calculated. The safety of AEAN was analyzed. It was indicated that AEAN has a higher thermal stability than AN. At the same time, it can be shown that the elimination of its explosive characteristic is due to the improvement on the thermal stability of AEAN.展开更多
The increasing utilization of digital technologies presents risks to critical systems due to exploitation by terrorists. Cybersecurity entails proactive and reactive measures designed to protect software and electroni...The increasing utilization of digital technologies presents risks to critical systems due to exploitation by terrorists. Cybersecurity entails proactive and reactive measures designed to protect software and electronic devices from any threats. However, the rising cases of cyber threats are carried out by domestic terrorists who share particular ideologies or grievances. This paper analyzes the increasing cyber-attack instances and mechanisms to counter these threats. Additionally, it addresses the growing concern of domestic terrorism and its impact on national security. Finally, it provides an overview of gaps and possible areas of future research to promote cybersecurity.展开更多
针对恐怖袭击事件文本语料库匮乏的问题,文章制定了恐怖袭击事件的实体标注规范,通过对全球恐怖主义数据库(GTD)的数据进行实体标注,构建了恐怖袭击事件的实体语料库。同时,针对数据标注工作的高人力和高时间成本问题,由于百度通用信息...针对恐怖袭击事件文本语料库匮乏的问题,文章制定了恐怖袭击事件的实体标注规范,通过对全球恐怖主义数据库(GTD)的数据进行实体标注,构建了恐怖袭击事件的实体语料库。同时,针对数据标注工作的高人力和高时间成本问题,由于百度通用信息抽取(Universal Information Extraction,UIE)模型在极小样本上具有较强的泛化能力,采用UIE模型进行辅助标注。实验结果证明了标注方案的有效性,并在一定程度上减少了标注时间。展开更多
基金supported by the National Natural Science Foundation of China (Grant No.11902157)Natural Science Foundation of Jiangsu Province (Grant No.BK20180417)the Scientific and Technological Innovation Project of Army Engineering Univeristy of PLA (Grant No.KYGYZXJK150025)。
文摘An innovative multi-layer composite explosion containment vessel(CECV)utilizing a sliding steel platealuminum honeycomb-fiber cloth sandwich is put forward to improve the anti-explosion capacity of a conventional single-layer explosion containment vessel(SECV).Firstly,a series of experiments and finite element(FE)simulations of internal explosions are implemented to understand the basic anti-explosion characteristics of a SECV and the rationality of the computational models and methods is verified by the comparison between the experimental results and simulation results.Based on this,the CECV is designed in detail and a variety of FE simulations are carried out to investigate effects of the sandwich structure,the explosive quantity and the laying mode of the fiber cloth on anti-explosion performance and dynamic response of the CECV under internal explosions.Simulation results indicate that the end cover is the critical position for both the SECV and CECV.The maximum pressure of the explosion shock wave and the maximum strain of the CECV can be extremely declined compared to those of the SECV.As a result,the explosive quantity the CECV can sustain is up to 20 times of that the SECV can sustain.Besides,as the explosive quantity increases,the internal pressure of the CECV keeps growing and the plastic deformation and failure of the sandwich structure become more and more severe,yielding plastic strain of the CECV in addition to elastic strain.The results also reveal that the laying angles of the fiber cloth's five layers have an impact on the anti-explosion performance of the CECV.For example,the CECV with fiber cloth layered in 0°/45°/90°/45°/0°mode has the optimal anti-capacity,compared to 0°/0°/0°/0°/0°and 0°/30°/60°/30°/0°modes.Overall,owing to remarkable anti-explosion capacity,this CECV can be regarded as a promising candidate for explosion resistance.
文摘One of the biggest dangers to society today is terrorism, where attacks have become one of the most significantrisks to international peace and national security. Big data, information analysis, and artificial intelligence (AI) havebecome the basis for making strategic decisions in many sensitive areas, such as fraud detection, risk management,medical diagnosis, and counter-terrorism. However, there is still a need to assess how terrorist attacks are related,initiated, and detected. For this purpose, we propose a novel framework for classifying and predicting terroristattacks. The proposed framework posits that neglected text attributes included in the Global Terrorism Database(GTD) can influence the accuracy of the model’s classification of terrorist attacks, where each part of the datacan provide vital information to enrich the ability of classifier learning. Each data point in a multiclass taxonomyhas one or more tags attached to it, referred as “related tags.” We applied machine learning classifiers to classifyterrorist attack incidents obtained from the GTD. A transformer-based technique called DistilBERT extracts andlearns contextual features from text attributes to acquiremore information from text data. The extracted contextualfeatures are combined with the “key features” of the dataset and used to perform the final classification. Thestudy explored different experimental setups with various classifiers to evaluate the model’s performance. Theexperimental results show that the proposed framework outperforms the latest techniques for classifying terroristattacks with an accuracy of 98.7% using a combined feature set and extreme gradient boosting classifier.
文摘In order to improve the anti-explosion performance ofρ-Al_(2)O_(3) bonded corundum castables,H_(2)O_(2) was added(0,0.025%,0.050%,0.075%,0.100%and 0.125%,by mass)as the anti-explosion agent.After mixing and casting,specimens were prepared.Some specimens were cured at room temperature for 12 h and demoulded for the anti-explosion performance test at different temperatures(450,500,550,600,650,700,750 and 800℃);the other specimens were cured,dried and fired,and tested in terms of the apparent porosity,the density,the cold mechanical properties,the air permeability and the pore size distribution.The results show that:(1)with the increase of the H_(2)O_(2) addition,the anti-explosion performance of castables increases gradually,the average pore size increases gradually,and the density and the strength decrease gradually;(2)by comprehensive consideration,the appropriate addition of H_(2)O_(2) shall be within 0.075%.
文摘The explosivity experiment of anti-explosive ammonium nitrate (AEAN) shows that the explosive characteristic of AEAN is eliminated. The adiabatic decompositions of ammonium nitrate and AEAN were investigated with an accelerating rate calorimeter (ARC). The curves of thermal decomposition temperature and pressure versus time, self-heating rate and pressure versus temperature for two systems were obtained. The kinetic parameters such as apparent activation energy and pre-exponential factor were calculated. The safety of AEAN was analyzed. It was indicated that AEAN has a higher thermal stability than AN. At the same time, it can be shown that the elimination of its explosive characteristic is due to the improvement on the thermal stability of AEAN.
文摘The increasing utilization of digital technologies presents risks to critical systems due to exploitation by terrorists. Cybersecurity entails proactive and reactive measures designed to protect software and electronic devices from any threats. However, the rising cases of cyber threats are carried out by domestic terrorists who share particular ideologies or grievances. This paper analyzes the increasing cyber-attack instances and mechanisms to counter these threats. Additionally, it addresses the growing concern of domestic terrorism and its impact on national security. Finally, it provides an overview of gaps and possible areas of future research to promote cybersecurity.
文摘针对恐怖袭击事件文本语料库匮乏的问题,文章制定了恐怖袭击事件的实体标注规范,通过对全球恐怖主义数据库(GTD)的数据进行实体标注,构建了恐怖袭击事件的实体语料库。同时,针对数据标注工作的高人力和高时间成本问题,由于百度通用信息抽取(Universal Information Extraction,UIE)模型在极小样本上具有较强的泛化能力,采用UIE模型进行辅助标注。实验结果证明了标注方案的有效性,并在一定程度上减少了标注时间。