Applicability and Behavior Patterns of Road Space from the Perspective of Young Women:A Case Study of Wangjing South Station

In order to understand the travel characteristics and behavior patterns of women in Wangjing area and explore whether the existing situation can meet women's needs for the use of street space,the area around Wangjing South Station of Metro Line 14 was taken as an example for analysis and research.Wangjing area was classified to the following six use attributes:company enterprise,transportation hub,education and culture,residential area,municipal facilities,leisure and entertainment.The proportion of each use attribute was evaluated according to four levels:A 25%and above(including 25%),B 15%-25%,C 15%-5%,D 5%and below(including 5%).Finally,whether the plot had composite functions was judged,and the spatio-temporal laws and behavior patterns of surrounding women were analyzed from the perspectives of time and space.

Associations of sleeping patterns and isotemporal substitution of other behavior with the prevalence of CKD in Chinese adults

Studies have found a U-shaped relationship between sleep duration and chronic kidney disease(CKD)risk,but limited research evaluated the association of reallocating excessive sleep to other behavior with CKD.We included 104538 participants from the nationwide cohort of the Risk Evaluation of Cancers in Chinese Diabetic Individuals:A Longitudinal Study,with self-reported time of daily-life behavior.Using isotemporal substitution models,we found that substituting 1 h of sleeping with sitting,walking,or moderate-to-vigorous physical activity was associated with a lower CKD prevalence.Leisure-time physical activity displacement was associated with a greater prevalence reduction than occupational physical activity in working population.In stratified analysis,a lower CKD prevalence related to substitution toward physical activity was found in long sleepers.More pronounced correlations were observed in long sleepers with diabetes than in those with prediabetes,and they benefited from other behavior substitutions toward a more active way.The U-shaped association between sleep duration and CKD prevalence implied the potential effects of insufficient and excessive sleep on the kidneys,in which the pernicious link with oversleep could be reversed by time reallocation to physical activity.The divergence in the predicted effect on CKD following time reallocation to behavior of different domains and intensities and in subpopulations with diverse metabolic statuses underlined the importance of optimizing sleeping patterns and adjusting integral behavioral composition.

A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior

With technology constantly becoming present in people’s lives, smart homes are increasing in popularity. A smart home system controls lighting, temperature, security camera systems, and appliances. These devices and sensors are connected to the internet, and these devices can easily become the target of attacks. To mitigate the risk of using smart home devices, the security and privacy thereof must be artificially smart so they can adapt based on user behavior and environments. The security and privacy systems must accurately analyze all actions and predict future actions to protect the smart home system. We propose a Hybrid Intrusion Detection (HID) system using machine learning algorithms, including random forest, X gboost, decision tree, K -nearest neighbors, and misuse detection technique.

Mobile Robot Combination Autonomous Behavior Strategy to Inspect Hazardous Gases in Relatively Narrow Man–Machine Environment

Selecting the optimal speed for dynamic obstacle avoidance in complex man–machine environments is a challenging problem for mobile robots inspecting hazardous gases.Consideration of personal space is important,especially in a relatively narrow man–machine dynamic environments such as warehouses and laboratories.In this study,human and robot behaviors in man–machine environments are analyzed,and a man–machine social force model is established to study the robot obstacle avoidance speed.Four typical man–machine behavior patterns are investigated to design the robot behavior strategy.Based on the social force model and man–machine behavior patterns,the fuzzy-PID trajectory tracking control method and the autonomous obstacle avoidance behavior strategy of the mobile robot in inspecting hazardous gases in a relatively narrow man–machine dynamic environment are proposed to determine the optimal robot speed for obstacle avoidance.The simulation analysis results show that compared with the traditional PID control method,the proposed controller has a position error of less than 0.098 m,an angle error of less than 0.088 rad,a smaller steady-state error,and a shorter convergence time.The crossing and encountering pattern experiment results show that the proposed behavior strategy ensures that the robot maintains a safe distance from humans while performing trajectory tracking.This research proposes a combination autonomous behavior strategy for mobile robots inspecting hazardous gases,ensuring that the robot maintains the optimal speed to achieve dynamic obstacle avoidance,reducing human anxiety and increasing comfort in a relatively narrow man–machine environment.

Investigating the Effect of Sidewalk Flooring on the Behavioral Pattern (Walking) of Citizens in Urban Spaces (Case Study: Vali asr St., Tehran)

The present study has evaluated the effect of architectural forms on the walking activity of citizens as a behavioral model in urban physical spaces.The research hypothesis claims that by designing purposeful and appropriate architectural forms,the behavior and actions of users in urban physical spaces can be to some extent,it designed or controlled,and that the pattern and domains of human behavior in urban streets are the result of the components of environmental quality that are included in the design of that street.The present theoretical proposition has been tested in two sequences from Valiasr Street in Tehran.At the theoretical level,the research method is descriptive-analytical and at the experimental level,it is a survey that has been done using the behavioral research method.The results show that the floor form and street form are the most influential architectural forms in urban physical spaces on the activity of users walking from space in the study sample.Also,some environmental factors have a direct effect on human reactions;The research findings show that people’s speed is directly related to the dimensions of sidewalk carpets and a person tries to take a step according to the senses he receives from the sidewalk flooring form and as a result his speed changes according to those forms.

A FEATURE SELECTION ALGORITHM DESIGN AND ITS IMPLEMENTATION IN INTRUSION DETECTION SYSTEM

Objective Present a new features selection algorithm. Methods based on rule induction and field knowledge. Results This algorithm can be applied in catching dataflow when detecting network intrusions, only the sub dataset including discriminating features is catched. Then the time spend in following behavior patterns mining is reduced and the patterns mined are more precise. Conclusion The experiment results show that the feature subset catched by this algorithm is more informative and the dataset’s quantity is reduced significantly.

Privacy at home： Analysis of behavioral patterns in the spatial confisuration of traditional and modern houses in the city of Hamedan based on the notion of space syntax

The Iranian concept of home goes far beyond physicat aspects, and its essence is interwoven with the spiritual nature of humankind. This concept has gained new meanings with the modernization and industrialization of societies. In Iranian architecture, every need is realized in socio-physical systems as wetl as in design issues. Therefore, spatial retationships are centrat to architecture, especially residential architecture that addresses a great proportion of an individuat＇s dairy rife. Space syntax seeks to exptain how spatial configurations express social or cuttural meanings. One such meaning is confidentiatity, which was mainly introduced into Iranian architecture as a resutt of retigious betiefs. In Iranian architecture, confidentiality is viewed from the aspect of privacy. This study is a case study that makes use of description, anatysis, and logica[ reasoning. The objective is to analyze behavioral patterns in the spatiat configurations of traditional and modern houses in Hamedan. In so doing, library research, software simutation with the UCL Depthmap package, and comparison techniques are utilized. The findings indicate that the spatial configurations of houses have changed in the course of time. In terms of the indices of spatial configurations, however, the striking difference between traditionat and modern houses in Hamedan revolves around the integration and equivalence of aU spaces in a house. In other words, the hierarchy of access to spaces and the recognition of territories are limited in modern houses. Hence, privacy in modern houses fades.

Breeding ecology of the Yellow-bellied Warbler (Abroscopus superciliaris)

The life history of birds dictates their activities and is crucial to population success.However,the life history traits of only one-third of the world’s bird species have been described;the rest are poorly understood.We studied the breeding ecology of the Yellow-bellied Warbler(Abroscopus superciliaris)and documented reproductive information throughout the egg and nestling periods.The data included natural nest sites,nest components,nest size,egg laying dates and time,egg morph,egg size,clutch size,egg incubation,nestling brooding and feeding,nestling morph and growth,and reproductive outcome.This study provided particular information of breeding ecology that has not been reported before in the Yellow-bellied Warbler.

Behavior pattern mining based on spatiotemporal trajectory multidimensional information fusion

Trajectory data mining is widely used in military and civil applications,such as early warning and surveillance system,intelligent traffic system and so on.Through trajectory similarity measurement and clustering,target behavior patterns can be found from massive spatiotemporal trajectory data.In order to mine frequent behaviors of targets from complex historical trajectory data,a behavior pattern mining algorithm based on spatiotemporal trajectory multidimensional information fusion is proposed in this paper.Firstly,spatial–temporal Hausdorff distance is pro-posed to measure multidimensional information differences of spatiotemporal trajectories,which can distinguish the behaviors with similar location but different course and velocity.On this basis,by combining the idea of k-nearest neighbor and density peak clustering,a new trajectory clustering algorithm is proposed to mine behavior patterns from trajectory data with uneven density distribu-tion.Finally,we implement the proposed algorithm in simulated and radar measured trajectory data respectively.The experimental results show that the proposed algorithm can mine target behavior patterns from different complex application scenarios more quickly and accurately com-pared to the existing methods,which has a good application prospect in intelligent monitoring tasks.

Behavior pattern extraction by trajectory analysis

Trajectory clustering and behavior pattern extraction are the foundations of research into activity perception of objects in motion. In this paper, a new framework is proposed to extract behavior patterns through trajectory analysis. Firstly, we introduce directional trimmed mean distance （DTMD）, a novel method used to measure similarity between trajectories. DTMD has the attributes of anti-noise, self-adaptation and the capability to determine the direction for each trajectory. Secondly, we use a hierarchical clustering algorithm to cluster trajectories. We design a length-weighted linkage rule to enhance the accuracy of trajectory clustering and reduce problems associated with incomplete trajectories. Thirdly, the motion model parameters are estimated for each trajectory＇s classification, and behavior patterns for trajectories are extracted. Finally, the difference between normal and abnormal behaviors can be distinguished.

Cluster and characteristic analysis of Shanghai metro stations based on metro card and land-use data

Metro is an important form of public transport in Shanghai.Based on the metro card data,we conduct the cluster analysis of Shanghai metro stations according to the pattern of passenger flow changing with time.Then the characteristics of travel time and surrounding land use are investigated for different types of stations to explore the relationship between urban land-use characteristics and travel activities reflected by passenger flow at metro stations.It is found that the passenger flow pattern of metro stations is closely related to the location conditions of stations and its surrounding land-use patterns.Based on various characteristics,285 metro stations are classified into four types,including residential-oriented stations,employmentoriented stations,employment-residence-oriented stations,and integrated functionaloriented stations,reflecting the interaction between spontaneous travel behavior and urban land-use characteristics and providing a reference for optimizing the urban functional structure and the spatial allocation of facilities.

A framework for cloned vehicle detection

Rampant cloned vehicle offenses have caused great damage to transportation management as well as public safety and even the world economy.It necessitates an efficient detection mechanism to identify the vehicles with fake license plates accurately,and further explore the motives through discerning the behaviors of cloned vehicles.The ubiquitous inspection spots that deployed in the city have been collecting moving information of passing vehicles,which opens up a new opportunity for cloned vehicle detection.Existing detection methods cannot detect the cloned vehicle effectively due to that they use the fixed speed threshold.In this paper,we propose a two-phase framework,called CVDF,to detect cloned vehicles and discriminate behavior patterns of vehicles that use the same plate number.In the detection phase,cloned vehicles are identified based on speed thresholds extracted from historical trajectory and behavior abnormality analysis within the local neighborhood.In the behavior analysis phase,consider the traces of vehicles that uses the same license plate will be mixed together,we aim to differentiate the trajectories through matching degree-based clustering and then extract frequent temporal behavior patterns.The experimental results on the real-world data show that CVDF framework has high detection precision and could reveal cloned vehicles’behavior effectively.Our proposal provides a scientific basis for traffic management authority to solve the crime of cloned vehicle.