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.

Tourist behavior patterns in the ecotourism destination in the west of China: A case study of Gannan Tibetan Autonomous Prefecture

This study takes Gannan Tibetan Autonomous Prefecture as the place of case study and tourists as research objects. From the perspectives of geographical distribution of source tourist markets, Tourist activity behavioral and spatial patterns of Tourists, this study looks into the geographical structure of the source tourists and spatial patterns by geography. The analysis of 341 questionnaires on tourists shows that:(1) The tourism cycle of Gannan is in the development phase, competing with adjacent Aba, and greatly impacted by the substitution effect and shadow effect of Aba.(2) The spatial distribution of tourist sources is concentrated, indicating that Gannan is a regional tourism destination. The temporal distance of tourists is mainly concentrated within the 6-hour traffi c circle.(3) Gannan Tibetan Autonomous Prefecture has already become the composite tourist destination dominated by leisure vacation. The minority folkcustom and special landscape are the most attractive tourism resources. Due to the impact of man-land harmonious lifestyle in the tourist areas, the environmental attitude of tourists is improved, and the transportation and shopping are the most vulnerable links in tourism service in Gannan Tibetan Autonomous Prefecture.(4) The spatial behavior of tourists in Gannan is mainly of single-destination style(52%), Transit leg and circle tour style(7%) as well as circle tour style(41%). The spatial distribution of tourist fl ow in Gannan shows a signifi cant feature "more in the north, less in the south and dependent on National Road". Tourism resources, transport facilities, regional competition and lack of route connecting different ecological units are important causes of the spatial distribution of self-help tourists.

Visitors’consistent stay behavior patterns within free-roaming scenic architectural complexes:Considering impacts of temporal,spatial,and environmental factors

GPS positioning data are increasingly utilized in environmental behavior studies to explore the spatial-temporal behavioral patterns of individuals.However,individuals’stay behavioral pattern and its influencing factors,which are particularly significant for the design and management of scenic architectural complexes,have not been thoroughly examined.Using GPS trajectory data collected from the Palace Museum in Beijing(China),this paper investigated the visitors’stay behavior patterns associated with temporal,spatial,and environmental influencing factors.Types of stay behavior and characteristics of stay in main stay areas were automatically recognized using Python algorithms for further and quantitative analysis.Results showed that visitors’stay time exhibited a consistent pattern regarding psychological time allocation,a relatively unsignificant pattern regarding lunch hour,and no clear pattern regarding fatigue feature.Grouped regression analysis showed positive linear relationships with similar slopes between the average stay length and the number of stay occurrences in each type of stay area.Partial correlation analysis revealed the underlying connection between the impact of seats and greenery on stay behavior.Individually,each of the two environmental elements showed limited effect on stay frequency and stay length,while incorporating greenery into seating areas would notably increase both stay frequency and stay length.

The Role of Beliefs, Perception, and Behavioral Patterns in the Evolution of Psychophysical Disorders

In accounts of the development and progression of psychophysical disorders such as Hereditary Spastic Paraplegia (HSP) and Facioscapulohumeral Muscular Dystrophy (FSHD), the role of beliefs, perceptions, and behavioral patterns has often been overlooked in favor of a genetically determinist paradigm. This paper explores the impact of NeuroPhysics Treatment (NPT) on patients with HSP and FSHD. Through a series of clinical case reports, I demonstrate how intensive four-day NPT sessions can lead to rapid restoration of lost functions, challenging the conventional view of these disorders. I hypothesize that, by modulating the patient’s perceptual and behavioral frameworks, NPT facilitates the emergence of healthier patterns, suggesting that environmental and psychological factors significantly influence the manifestation and management of these conditions. These findings indicate that the role of genetic inheritance may be overstated and that beliefs and perceptions could play a crucial role in the evolution of psychophysical disorders. The implications of this research extend beyond the traditional treatment paradigms, advocating for a more holistic approach that integrates the psychophysical dimensions of health and challenges the deterministic perspective of genetic inheritance.

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.

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.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

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.

Identification of Socio-Demographic, Behavioral Patterns and Their Relationship with HIV Status among Female Sex Workers

The prevalence of HIV in high risk population is influenced significantly the behavioral and sociodemographic characteristics. However, considering the complexity of behavior among female sex workers, the relationship between a particular behavioral pattern and the HIV status of this “at risk” population assumes significance. Data generated in a community-based cross-sectional study earlier carried out to assess the prevalence estimates, at district level, of HIV status in eight districts of State of Andhra Pradesh, India was used to carry out factor analysis to explore the role of demographic and behavioral pattern and their relationship with the HIV status among female sex workers. Data on 3083 female sex workers in the study revealed that there existed nine patterns among demographic and behavioral characteristics, which explained 62% of the total variation through factor analysis. Further, cluster analysis was performed to identify the groups of individuals having similar characteristics. Two of those clusters had sizeable numbers having similar characteristics. FSWs belonging to cluster 2 had significantly high risk factors compared with Cluster 1. The overall prevalence of HIV was 11.4% (10.6% in cluster 1 and 15.9% in cluster 2) among high risk population. There exists a strong relationship between behavioral patterns and HIV positive.

油气钻井现场工人不安全行为模式研究——基于文本挖掘视角

为加强油气钻井现场安全管理,理解钻井现场工人不安全行为规律,提出基于文本挖掘视角的钻井现场工人不安全行为模式分析框架,以钻井现场工人违章数据为实例进行验证。首先采用频率分析和网络分析方法发现钻井工人高频不安全行为热点,其次采用文本社群聚类分析钻井工人不安全行为聚类,最后采用文本关联规则发现和检验典型不安全行为发生模式。研究结果表明:钻井现场工人不安全行为模式包括7个不安全行为社群和66条不安全行为发生模式,其中油气钻井现场系统与外部系统耦合处、班前会议、钻井下钻和钻进作业、登高作业是现场不安全行为监管的脆弱点。研究结果可为钻井现场安全管理优化提供支持,有利于为钻井现场智慧安全治理提供参考。

图书馆老年用户数字技能提升的影响机理和对策路径研究

数字技术的迅猛发展对老年群体的数字技能提出了更高要求。公共图书馆具有提升老年群体数字技能水平的社会职责。文章通过对30位受访者的半结构化访谈,运用扎根理论,借助Nvivo12软件完成三级编码工作,揭示公共图书馆服务老年用户提升数字技能的影响机理,构建行为机制概念模型。分析结果显示,老年用户利用图书馆提升数字技能的意向受到行为态度、知觉行为控制、主观规范等因素的影响。文章就图书馆提供老年用户数字技能服务提出建议,以期为进一步完善图书馆服务提供参考。

春小麦产量对不同气候年型的响应及其与品种干旱敏感性的关系

[目的]本研究旨在分析旱地春小麦产量增加的品种特性及其与气候条件的关系,为区域小麦品种选育和作物布局提供参考依据。[方法]在半干旱雨养农业区布设春小麦长期定位试验(雨养处理),开展不同播期以及播前和生育期间不同灌水处理试验,采用K-means聚类分析方法划分春小麦气候年型,并利用参数化后的春小麦生长发育模型设置不同气候情景,模拟研究半干旱区雨养春小麦产量对气候年型的响应及其与品种干旱敏感性之间的关系。[结果]春小麦生长发育的气候条件可以分为5个不同的年型,其主要由播前土壤含水量和生育期大气干湿程度(潜在蒸散与降水量之差)决定;所构建的春小麦模型对发育期、地上部生物量、收获指数以及产量的模拟值与实测值相比,一致性指数均大于0.8,相对均方根误差均小于30%。通过模型模拟发现,在播期与播前土壤含水量固定的情形下,当生育期降水量小于140 mm时,对干旱敏感的春小麦品种产量高于对干旱呈中性的品种,而在生育期降水量大于200 mm时,对干旱不敏感的春小麦品种产量最高。[结论]本研究所构建的春小麦模型能够较为准确地模拟春小麦发育期、地上部生物量、收获指数及产量形成。对干旱敏感的春小麦品种在极端干旱气候年型下更容易获得较高的产量,而对干旱不敏感的春小麦品种则在水分条件相对适宜的气候年型下容易高产。

基于使用特性的两阶段多因素作业运行时间预测算法

为了解决用户所提供的作业预计时间不准确对高性能计算平台调度系统的连锁影响,以鹤思调度系统为研究模板,提出了一种通用的两阶段多因素作业运行时间预测算法(TSMF)。TSMF融合了复杂的用户行为模式和作业上下文特征,以确保预测准确可靠,并能够无缝嵌入到大多数高性能计算平台的调度系统中,从而改善其性能。在北京大学高性能计算集群的数据集及真实调度系统上进行的多角度模拟实验显示,TSMF在预测准确性方面表现出色,能够在绝大部分作业上实现精准预测。例如,在多达60.8%的作业中,其预测误差在1min以内。此外,TSMF显著改进了实际情境中的调度算法,提高了资源利用率并大幅缩短了用户等待时间。

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.

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.

Driving Style Recognition Based on Driver Behavior Questionnaire

The ability to classify driver behavior lays the foundation for more advanced driver assistance systems. The present study aims to research driver pattern and classification feature. Driver behavior self-reported investigation was conducted with standardized driver behavior questionnaire (DBQ) by 225 nonprofessional drivers on the internet in Beijing. Questionnaire’s reliability was verified by statistics analysis. Confirmatory factor analysis (CFA) was used to analyze the underlying factor structure. Speed advantage, space occupation, the contend right of way and the contend space advantage were extracted from the ques-tionnaire results to quantify driver characteristics. Based on fuzzy C-means (FCM) algorithm and taking the four factors as pattern features, the number of driver classification distribution was discussed. Then the number of driver classification was determined by statistical indices. The comparison of classification results with the survey finding on whether the driver occurred in traffic accidents within five years shows that the classification result is the same as the actual driving conditions. Finally, correlation between the demographic and types of driving behavior has been analyzed. Female were more likely than male to careful driving, and the older the driver and the less driving experience, the more careful and moderate driving behavior is.

Analysis of deformation and internal flow patterns for rising single bubbles in different liquids

Gas–liquid multiphase flow is a significant phenomenon in chemical processes. The rising behaviors of single bubbles in the quiescent liquids have been investigated but the internal flow patterns and deformation rules of bubbles, which influence the mass transfer efficiency to a large extent, have received much less attention. In this paper, the volume of fluid method was used to calculate the bubble shapes, pressure, velocity distributions,and the flow patterns inside the bubbles. The rising behavior of the bubbles with four different initial diameters,i.e., 3 mm, 5 mm, 7 mm and 9 mm was investigated in four various liquids including water, 61.23% glycerol,86.73% glycerol and 100% glycerol. The results show that the liquid properties and bubble initial diameters have great impacts on bubble shapes. Moreover, flow patterns inside the bubbles with different initial diameters were analyzed and classified into three types under the condition of different bubble shapes. Three correlations for predicting the maximum internal circulation inside the bubbles in 86.73% glycerol were presented and the R-square values were all bigger than 0.98. Through analyzing the pressure and velocity distributions around the bubbles, four rules of bubble deformation were also obtained to explain and predict the shapes.

Multifactorial study of mobile phone dependence in medical students: Relationship to health-related lifestyle, Type A behavior, and depressive state

We investigated factors contributing to mobile phone dependence. To 139 medical students, we administered a self-reporting questionnaire designed to evaluate mobile phone dependence, health-related lifestyle, patterns of behavior, and depressive state. Multivariate logistic regression analysis revealed that scores for poor health-related lifestyle, Type A behavior pattern, and presence of depression are independently associated with degree of mobile phone dependency. These findings suggest that persons with an unhealthy lifestyle, Type A behavior traits, or depression might benefit from mobile phone use guidance.

“潜水”还是“学习”:在线协作学习中“在线倾听”行为再审视

学习者“在线倾听”行为是一种复杂的社会化活动,是在线协作学习对话的前提和基础,但并未引起正视和积极理解。基于对话教学理论视角,研究采用滞后序列分析、内容分析、社会网络分析等研究方法,分析学习者“在线倾听”行为模式特征,及其对学习者认知过程、批判性思维和学习绩效的潜在影响。研究发现,学习者“在线倾听”行为是在线协作学习的常态行为,存在显著性差异,在时间结构上呈现周期性和阵发性;专注的“在线倾听”,高频次的回顾/反思倾听,有益于学习者高水平知识建构和批判性思维发展;高、低绩效学习者群体的“在线倾听”行为模式存在显著差异,高绩效组“在线倾听”行为更专注、行为路径更完整。这些发现为理解和解释数字教育场景下在线学习发生的内隐规律和内在机制提供理论支撑和实践参考。

A Fast Multi-Pattern Matching Algorithm for Mining Big Network Data

The rapid development of mobile network brings opportunities for researchers to analyze user behaviors based on largescale network traffic data. It is important for Internet Service Providers(ISP) to optimize resource allocation and provide customized services to users. The first step of analyzing user behaviors is to extract information of user actions from HTTP traffic data by multi-pattern URL matching. However, the efficiency is a huge problem when performing this work on massive network traffic data. To solve this problem, we propose a novel and accurate algorithm named Multi-Pattern Parallel Matching(MPPM) that takes advantage of HashMap in data searching for extracting user behaviors from big network data more effectively. Extensive experiments based on real-world traffic data prove the ability of MPPM algorithm to deal with massive HTTP traffic with better performance on accuracy, concurrency and efficiency. We expect the proposed algorithm and it parallelized implementation would be a solid base to build a high-performance analysis engine of user behavior based on massive HTTP traffic data processing.