Non-flow aqueous zinc-bromine batteries without auxiliary components(e.g.,pumps,pipes,storage tanks)and ion-selective membranes represent a cost-effective and promising technology for large-scale energy storage.Unfort...Non-flow aqueous zinc-bromine batteries without auxiliary components(e.g.,pumps,pipes,storage tanks)and ion-selective membranes represent a cost-effective and promising technology for large-scale energy storage.Unfortunately,they generally suffer from serious diffusion and shuttle of polybromide(Br^(-),Br^(3-))due to the weak physical adsorption between soluble polybromide and host carbon materials,which results in low energy efficiency and poor cycling stability.Here,we develop a novel self-capture organic bromine material(1,10-bis[3-(trimethylammonio)propyl]-4,4'-bipyridinium bromine,NVBr4)to successfully realize reversible solid complexation of bromide components for stable non-flow zinc-bromine battery applications.The quaternary ammonium groups(NV^(4+)ions)can effectively capture the soluble polybromide species based on strong chemical interaction and realize reversible solid complexation confined within the porous electrodes,which transforms the conventional“liquid-liquid”conversion of soluble bromide components into“liquid-solid”model and effectively suppresses the shuttle effect.Thereby,the developed non-flow zinc-bromide battery provides an outstanding voltage platform at 1.7 V with a notable specific capacity of 325 mAh g^(-1)NVBr4(1 A g^(-1)),excellent rate capability(200 mAh g^(-1)NVBr4 at 20 A g^(-1)),outstanding energy density of 469.6 Wh kg^(-1)and super-stable cycle life(20,000 cycles with 100%Coulombic efficiency),which outperforms most of reported zinc-halogen batteries.Further mechanism analysis and DFT calculations demonstrate that the chemical interaction of quaternary ammonium groups and bromide species is the main reason for suppressing the shuttle effect.The developed strategy can be extended to other halogen batteries to obtain stable charge storage.展开更多
Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on ma...Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.展开更多
Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep...Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.展开更多
This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate ...This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.展开更多
Aiming at prevalent violations of non-motorists at urban intersections in China, this paper intends to clarify the characteristics and risks of non-motorist violations at signalized intersections through questionnaire...Aiming at prevalent violations of non-motorists at urban intersections in China, this paper intends to clarify the characteristics and risks of non-motorist violations at signalized intersections through questionnaires and video recordings, which may serve as a basis for non-motorized vehicle management. It can help improve the traffic order and enhance the degree of safety at signalized intersections. To obtain the perception information, a questionaire survey on the Internet was conducted and 972 valid questionnaires were returned. It is found that academic degree contributes little to non-motorist violations, while electrical bicyclists have a relatively higher frequency of violations compared with bicyclists. The video data of 18 228 non-motorist behaviors indicate that the violation rate of all non-motorists is 26.5%; the number of conflicts reaches 1 938, among which violation conflicts account for 66.8%. The study shows that the violation rates and the violation behavior at three types of surveyed intersections are markedly different. It is also concluded that the conflict rates and the violation rates are positively correlated. Furthermore, signal violation, traveling in the wrong direction, and overspeeding to cross the intersection are the most dangerous among traffic violation behaviors.展开更多
The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation...The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.展开更多
In order to improve the wettability and biocompatibility of the poly （butylene terephthalate） non-woven （PBTNW）, the method of surface modification is used to graft copolymerization of chitosan （CS） onto the PBT...In order to improve the wettability and biocompatibility of the poly （butylene terephthalate） non-woven （PBTNW）, the method of surface modification is used to graft copolymerization of chitosan （CS） onto the PBTNW under alkylpolyglycoside （APG） inducing. The product is thoroughly characterized with the Fourier transform infrared spectroscopy （FrIR）, the electron spectroscopy for chemical analysis （ESCA）, the thermogravimetric （TG） and the scanning electron microscopy （SEM）. It is found that chitosan is successfully grafted onto PBTNW. In addition, the water contact angles, hemolysis tests and cytotoxicity evaluation tests show an improvement in wettability and biocompatihility as a result of graft copolymerization of chitosan. So the CS-grafted PBTNW exhibits greater superiority than the original PBTNW. The CS-grafted PBTNW can be a candidate for blood filter materials and other medical applications.展开更多
基金the Guangdong Basic and Applied Basic Research Foundation(grant number:2019A1515011819,2021B1515120004)National Natural Science Foundation of China(22005207)Open Research Fund of Songshan Lake Materials Laboratory(2021SLABFN04).
文摘Non-flow aqueous zinc-bromine batteries without auxiliary components(e.g.,pumps,pipes,storage tanks)and ion-selective membranes represent a cost-effective and promising technology for large-scale energy storage.Unfortunately,they generally suffer from serious diffusion and shuttle of polybromide(Br^(-),Br^(3-))due to the weak physical adsorption between soluble polybromide and host carbon materials,which results in low energy efficiency and poor cycling stability.Here,we develop a novel self-capture organic bromine material(1,10-bis[3-(trimethylammonio)propyl]-4,4'-bipyridinium bromine,NVBr4)to successfully realize reversible solid complexation of bromide components for stable non-flow zinc-bromine battery applications.The quaternary ammonium groups(NV^(4+)ions)can effectively capture the soluble polybromide species based on strong chemical interaction and realize reversible solid complexation confined within the porous electrodes,which transforms the conventional“liquid-liquid”conversion of soluble bromide components into“liquid-solid”model and effectively suppresses the shuttle effect.Thereby,the developed non-flow zinc-bromide battery provides an outstanding voltage platform at 1.7 V with a notable specific capacity of 325 mAh g^(-1)NVBr4(1 A g^(-1)),excellent rate capability(200 mAh g^(-1)NVBr4 at 20 A g^(-1)),outstanding energy density of 469.6 Wh kg^(-1)and super-stable cycle life(20,000 cycles with 100%Coulombic efficiency),which outperforms most of reported zinc-halogen batteries.Further mechanism analysis and DFT calculations demonstrate that the chemical interaction of quaternary ammonium groups and bromide species is the main reason for suppressing the shuttle effect.The developed strategy can be extended to other halogen batteries to obtain stable charge storage.
基金appreciation to King Saud University for funding this work through Researchers Supporting Project number(RSPD2025R685),King Saud University,Riyadh,Saudi Arabia.
文摘Crime hotspot detection is essential for law enforcement agencies to allocate resources effectively,predict potential criminal activities,and ensure public safety.Traditional methods of crime analysis often rely on manual,time-consuming processes that may overlook intricate patterns and correlations within the data.While some existing machine learning models have improved the efficiency and accuracy of crime prediction,they often face limitations such as overfitting,imbalanced datasets,and inadequate handling of spatiotemporal dynamics.This research proposes an advanced machine learning framework,CHART(Crime Hotspot Analysis and Real-time Tracking),designed to overcome these challenges.The proposed methodology begins with comprehensive data collection from the police database.The dataset includes detailed attributes such as crime type,location,time and demographic information.The key steps in the proposed framework include:Data Preprocessing,Feature Engineering that leveraging domain-specific knowledge to extract and transform relevant features.Heat Map Generation that employs Kernel Density Estimation(KDE)to create visual representations of crime density,highlighting hotspots through smooth data point distributions and Hotspot Detection based on Random Forest-based to predict crime likelihood in various areas.The Experimental evaluation demonstrated that CHART shows superior performance over benchmark methods,significantly improving crime detection accuracy by getting 95.24%for crime detection-I(CD-I),96.12%for crime detection-II(CD-II)and 94.68%for crime detection-III(CD-III),respectively.By designing the application with integrating sophisticated preprocessing techniques,balanced data representation,and advanced feature engineering,the proposed model provides a reliable and practical tool for real-world crime analysis.Visualization of crime hotspots enables law enforcement agencies to strategize effectively,focusing resources on high-risk areas and thereby enhancing overall crime prevention and response efforts.
文摘Crime scene investigation(CSI)image is key evidence carrier during criminal investiga-tion,in which CSI image retrieval can assist the public police to obtain criminal clues.Moreover,with the rapid development of deep learning,data-driven paradigm has become the mainstreammethod of CSI image feature extraction and representation,and in this process,datasets provideeffective support for CSI retrieval performance.However,there is a lack of systematic research onCSI image retrieval methods and datasets.Therefore,we present an overview of the existing worksabout one-class and multi-class CSI image retrieval based on deep learning.According to theresearch,based on their technical functionalities and implementation methods,CSI image retrievalis roughly classified into five categories:feature representation,metric learning,generative adversar-ial networks,autoencoder networks and attention networks.Furthermore,We analyzed the remain-ing challenges and discussed future work directions in this field.
文摘This paper presents a detailed statistical exploration of crime trends in Chicago from 2001 to 2023, employing data from the Chicago Police Department’s publicly available crime database. The study aims to elucidate the patterns, distribution, and variations in crime across different types and locations, providing a comprehensive picture of the city’s crime landscape through advanced data analytics and visualization techniques. Using exploratory data analysis (EDA), we identified significant insights into crime trends, including the prevalence of theft and battery, the impact of seasonal changes on crime rates, and spatial concentrations of criminal activities. The research leveraged a Power BI dashboard to visually represent crime data, facilitating an intuitive understanding of complex patterns and enabling dynamic interaction with the dataset. Key findings highlight notable disparities in crime occurrences by type, location, and time, offering a granular view of crime hotspots and temporal trends. Additionally, the study examines clearance rates, revealing variations in the resolution of cases across different crime categories. This analysis not only sheds light on the current state of urban safety but also serves as a critical tool for policymakers and law enforcement agencies to develop targeted interventions. The paper concludes with recommendations for enhancing public safety strategies and suggests directions for future research, emphasizing the need for continuous data-driven approaches to effectively address and mitigate urban crime. This study contributes to the broader discourse on urban safety, crime prevention, and the role of data analytics in public policy and community well-being.
基金The National Key Technology R&D Program during the 11th Five-Year Plan Period(No.2009BAG13A05)the National Natural Science Foundation of China(No.51078086)
文摘Aiming at prevalent violations of non-motorists at urban intersections in China, this paper intends to clarify the characteristics and risks of non-motorist violations at signalized intersections through questionnaires and video recordings, which may serve as a basis for non-motorized vehicle management. It can help improve the traffic order and enhance the degree of safety at signalized intersections. To obtain the perception information, a questionaire survey on the Internet was conducted and 972 valid questionnaires were returned. It is found that academic degree contributes little to non-motorist violations, while electrical bicyclists have a relatively higher frequency of violations compared with bicyclists. The video data of 18 228 non-motorist behaviors indicate that the violation rate of all non-motorists is 26.5%; the number of conflicts reaches 1 938, among which violation conflicts account for 66.8%. The study shows that the violation rates and the violation behavior at three types of surveyed intersections are markedly different. It is also concluded that the conflict rates and the violation rates are positively correlated. Furthermore, signal violation, traveling in the wrong direction, and overspeeding to cross the intersection are the most dangerous among traffic violation behaviors.
基金supported in part by the Basic Public Welfare Research Program of Zhejiang Province under Grant LGF20G030001.
文摘The network is a major platform for implementing new cyber-telecom crimes.Therefore,it is important to carry out monitoring and early warning research on new cyber-telecom crime platforms,which will lay the foundation for the establishment of prevention and control systems to protect citizens’property.However,the deep-learning methods applied in the monitoring and early warning of new cyber-telecom crime platforms have some apparent drawbacks.For instance,the methods suffer from data-distribution differences and tremendous manual efforts spent on data labeling.Therefore,a monitoring and early warning method for new cyber-telecom crime platforms based on the BERT migration learning model is proposed.This method first identifies the text data and their tags,and then performs migration training based on a pre-training model.Finally,the method uses the fine-tuned model to predict and classify new cyber-telecom crimes.Experimental analysis on the crime data collected by public security organizations shows that higher classification accuracy can be achieved using the proposed method,compared with the deep-learning method.
文摘In order to improve the wettability and biocompatibility of the poly （butylene terephthalate） non-woven （PBTNW）, the method of surface modification is used to graft copolymerization of chitosan （CS） onto the PBTNW under alkylpolyglycoside （APG） inducing. The product is thoroughly characterized with the Fourier transform infrared spectroscopy （FrIR）, the electron spectroscopy for chemical analysis （ESCA）, the thermogravimetric （TG） and the scanning electron microscopy （SEM）. It is found that chitosan is successfully grafted onto PBTNW. In addition, the water contact angles, hemolysis tests and cytotoxicity evaluation tests show an improvement in wettability and biocompatihility as a result of graft copolymerization of chitosan. So the CS-grafted PBTNW exhibits greater superiority than the original PBTNW. The CS-grafted PBTNW can be a candidate for blood filter materials and other medical applications.
