PATTERNS, OR NO PATTERNS, THAT IS THE QUESTION

Policymakers regularly intensify efforts to increase electric vehicle (EV) adoption to reduce greenhouse gas (GHG) emissions caused by the transportation sector. Despite various incentives, EV adoption has certain drawbacks, including limited ranges, long charging times, and sparse electric vehicle supply equipment (EVSE) infrastructure. Access to EVs is often a privilege for specific communities, causing inequity in adoption and access to public charging infrastructure. To enable a user-centered and equitable deployment of EVSE infrastructure, a detailed understanding of EV charging behavior and its different facets is crucial. This research establishes a holistic approach to understanding user behavior using real-world charging data collected at various public and residential EVSE networks. Throughout this work, multivariate charging session data is analyzed for EV user charging behavior using various statistical methods, advanced algorithms, and time series analysis. This novel and holistic approach to understanding charging station utilization offers new insights into EVSE network service level requirements and explains the relationship between charging behavior, urbanicity, and locale. The findings of this work indicate that significant differences in charging behavior between charging networks exist. An urban-rural divide in charging station utilization is identified, which is inconsistent throughout different areas and regions. Users from rural areas charge further away from their homes than urban users, indicating a lack of EVSE in rural areas and supporting the existence of an urban-rural divide in EVSE access. Charging behavior is further found to differ significantly between provinces, especially regarding intensity and etiquette. The results show that longer charging times usually concur with lower charging frequencies, reducing the service level and profitability of EVSE. Residential and public charging demand follows an inverse daily pattern, represented by a bi-daily switch in peak station utilization. While public charging demand times are concentrated during the day, residential EVSE is mainly used in the evening and throughout the night. The switch in charging demand proposed the “EV Duck Curve” and suggests that residential charging behavior can increase the renewable energy “Duck Curve” issue, which may be further amplified by a future increase in public direct current fast charger deployment. This work aims to demonstrate the differences in human charging behavior in various contexts by applying robust and multifaceted methods. Current EV charging behavior research lacks a holistic approach that incorporates various types and locations of charging networks when exploring user behavior. This research suggests that findings on charging behavioral patterns in one network are not transferable

Prediction of humans’ behaviors during a disaster: The Behavioral Pattern during Disaster Indicator (BPDI)

Work-related stress and burnout among physicians are of increasing relevance. The aim of this study was to investigate work-related behavior and experience patterns and predictors of mental health of physicians working in medical practice in Germany. We surveyed a stratified, random sample of 900 physicians from different specialties. The questionnaire included the standardized instruments Work-related Behavior and Experience Pattern (AVEM) and the Short Form-12 Health Survey (SF-12). Only one third of physicians reported high or very high general satisfaction with their job, but 64% would choose to study medicine again. Only 18% of physicians presented a healthy behavior and experience pattern. Almost 40% presented a pattern of reduced motivation to work, 21% were at risk of overexertion, and 22% at risk for burnout. Willingness to study medicine again, fulfilled job expectations, professional years, marital status, and behavior patterns were significant predictors of mental health and accounted for 35.6% of the variance in mental health scores. Job-related perceptions also had a significant effect on burnout. The strong influence of work-related perceptions suggests a need for realistic expectation management in medical education, as well as support in stress management and coping strategies during medical training.

Lithium chloride, cholecystokinin and meal patterns: Evidence that cholecystokinin suppresses meal size in rats without causing malaise

Twenty-four hour activity budgets and patterns of behavior in captive ocelots ( Leopardus pardalis)

As Internet use has proliferated, Web-based learning systems have become more and more popular. Numerous researchers have spent a great deal of effort to facilitate the promotion of high quality Web-based learning environments, such as intelligent Web-based learning systems and adaptive learning. To facilitate such researches, students' behavioral patterns must be observed and experimentally analyzed. However, building a Web-based learning system and the requisite collecting of behavioral patterns usually takes a great deal of time and effort. To solve this problem, this paper proposes a learning behavioral model based on Colored Petri Nets (CPN) to model and generate students' behavioral patterns. To verify the viability of the proposed model, this paper compares actual data collected from elementary school students with the behavioral pattern generated by the proposed model. The results prove: (1) The generated behavioral pattern approaches actual student behavior; (2) The generated behavioral pattern serves as adequate test data to test whether the predicted learning content of an intelligent e-learning system is appropriate; and (3) The proposed model is capable of recommending the appropriate learning content for students utilizing e-learning systems.

Metopic synostosis is a condition in which the metopic suture of the human cranium fuses prematurely and may be related to poor behavioral inhibition leading to behaviors commonly associated with Attention-Deficit Hyperactivity Disorder (ADHD). The purpose of this project was to examine the behavior patterns among children with a history of metopic synostosis. It was hypothesized that children with a history of metopic synostosis would exhibit many of the same behavioral patterns associated with ADHD. It was also hypothesized that children with a history of simple synostosis not involving the metopic suture would not evidence this type of behavioral pattern. In order to test these hypotheses, the behavior of three groups of children was compared including (1) children who had a history of metopic synostosis (M= 7.63 years, SD = 1.92 years), (2) children who had a history of simple craniosynostosis not involving the metopic suture (M= 7.54 years, SD = 1.88 years), and (3) a group of children diagnosed with ADHD (M=7.78 year, SD = 1.87 years). It was found using the Home and School versions of the Attention Deficit Disorders Evaluation Scale (ADDES) that children with a history of metopic synostosis demonstrate significantly more behavioral disturbances than children with a history of simple craniosynostosis not involving the metopic suture. Using the BASC Teacher Rating Form it was found that children with a history of metopic synostosis have a behavior pattern similar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture. Using the BASC Parent Rating Form it was found that children with a history of metopic synostosis have a behavior pattern dissimilar to children diagnosed with ADHD and a dissimilar behavior pattern compared to children who have a history of craniosynostosis not involving the metopic suture.

Most users of online services have unique behavioral or usage patterns. These behavioral patterns can be exploited to identify and track users by using only the observed patterns in the behavior. We study the task of identifying users from statistics of their behavioral patterns. Specifically, we focus on the setting in which we are given histograms of users' data collected during two different experiments. We assume that, in the first dataset, the users' identities are anonymized or hidden and that, in the second dataset, their identities are known. We study the task of identifying the users by matching the histograms of their data in the first dataset with the histograms from the second dataset. In recent works, the optimal algorithm for this user identification task is introduced. In this paper, we evaluate the effectiveness of this method on three different types of datasets and in multiple scenarios. Using datasets such as call data records, web browsing histories, and GPS trajectories, we show that a large fraction of users can be easily identified given only histograms of their data; hence these histograms can act as users' fingerprints. We also verify that simultaneous identification of users achieves better performance compared to one-by-one user identification. We show that using the optimal method for identification gives higher identification accuracy than heuristics-based approaches in practical scenarios. The accuracy obtained under this optimal method can thus be used to quantify the maximum level of user identification that is possible in such settings. We show that the key factors affecting the accuracy of the optimal identification algorithm are the duration of the data collection, the number of users in the anonymized dataset, and the resolution of the dataset. We analyze the effectiveness of k-anonymization in resisting user identification attacks on these datasets.

The possible effect of a specific overt behavior pattern upon serum cholesterol levels, blood coagulation, and incidence of arcus senilis and clinical coronary heart disease was studied in two groups of women randomly chosen for study by lay selectors after careful orientation. Women of group A were selected on the sole basis of their habitual exhibition of a behavior pattern (type A) characterized by excessive competitive "drive," ambitiousness, and immersion in "deadlines." Women of group B were selected by lay selectors on the sole basis of their exhibition of entirely opposite behavioral traits (type B). Each group of subjects included a series of nuns respectively exhibiting the type A and B behavior patterns. The women exhibiting pattern A also exhibited markedly higher serum cholesterol levels, often more rapid blood coagulation, three to five times as much arcus senilis, three to seven times more diastolic hypertension, and a four-fold higher incidence of clinical coronary disease. A history of myocardial infarction or angina pectoris only was found in group A. These striking differences were independent of differences in parental incidence of coronary disease, caloric or fat intake, obesity, physical activity, smoking, or "femininity." The strikingly higher incidence of clinical disease in pattern A women also was not found ascribable per se to differences in heredity, obesity, hypertension, caloric or fat intake, smoking, physical activity, blood coagulation, or serum cholesterol levels. The results closely resemble those previously found in men with similar behavior patterns, and strongly suggest that the type A behavior pattern is itself in some way largely responsible for the observed findings.

Knowing the behavioral patterns of city residents is of great value in formulating and adjusting urban planning strategies, such as urban road planning, urban commercial development, and urban pedestrian flow control. Based on the high penetration rate of cell phones, it is possible to indirectly understand the behavior of city residents based on the call records of users. However, the behavioral patterns of large‐scale users over a long period of time can present characteristics such as large dispersion, difficult to discover patterns, and difficult to explain behavioral patterns. In this paper, we design and implement a human behavior pattern analysis system based on massive mobile communication data based on serial data modeling method and visual analysis technology. For the problem that it is difficult to capture the behavioral patterns of residents in cities in call records, this paper constructs base station trajectories based on users’ cell phone call records and uses users’ long‐time base station trajectories to mine users’ potential behavioral patterns. Since users with similar activity characteristics will exhibit similar base station trajectories, this paper focuses on the similarity between text sequences and base station trajectory sequences and combines the word embedding method in natural language processing to build a Cell2vec model to identify the semantics of base stations in cities. In order to obtain the group behavior patterns of users from the base station trajectories of group users, a user clustering method based on users’ regional mobile preferences is proposed, and the results are projected using the Stochastic Neighbor Embedding (t‐SNE) algorithm to expose the clustering features of large‐scale cell phone users in the low‐dimensional space. To address the problem that user behavior patterns are difficult to interpret, a visual analysis model with group as well as regional semantics is designed for the spatial and temporal characteristics of user behavior. Among them, the clustering model uses the distance between scatter points to map the similarity between users, which helps analysts to explore the behavioral characteristics of group users.

For optimal health promotion in adolescents, it is important to know their typical behavior patterns. The aims of this study were to identify and compare sex-specific health-related behavior patterns of adolescents in Germany. The KiGGS Survey and the MoMo Study assessed the health behavior of 832 boys and 811 girls by means of a questionnaire. The indices physical activity, diet quality and media use were included in cluster analyses for boys and girls separately. For each sex, 4 clusters with similar characteristics on the three included indices were identified. However, cluster size, level of the characteristics and correlates were different in boys and girls: fewer girls showed high activity levels. Overall, physical activity and media use were less pronounced in the behavior pattern of girls. There was a stronger association of behavior patterns with socio-economic status in girls. In boys, behavior patterns were significantly associated with prevalence of overweight. The similar cluster solutions for boys and girls support the assumption that there are typical health-related behavior patterns among German adolescents. This finding might facilitate the precise definition of homogeneous target groups for health promotion programs. Furthermore, it seems that sex-specific foci should be set.

Background: Changing trends in dietary habits, behavioral patterns, and prevalence of overweight/obesity among adolescents is of great concern.Methods: 6469 school children from five districts of Kerala, India were enrolled. Dietary habits and behavioral patterns were collected using a pretested questionnaire. Anthropometric measurements were recorded and compared with regional Indian Academy Pediatrics (IAP) reference charts. Data was computed and analyzed. Results: Mean age: Boys 14.51±0.93, Girls 14.42±1.04 years. Male: Female ratio 0.92:1. Practices like missing breakfast, non-compliance to weekly Iron Folic Acid supplementation and not taking exercise >2 hours/week were more in girls. Increased consumption of junk food, screen time >2 hours/week and not sharing life events with parents were more in boys. Mean anthropometric parameters were <50th percentile of reference charts. 6.1% had low BMI (boys, 4.2% vs. girls,7.6%,), 12.3% were overweight (boys, 14.9% vs. girls,10.2%) and 4.4% had obesity (boys, 4.7% vs. girls, 4.2%). Mean anthropometric parameters were higher in more urbanized districts. Daily consumption of junk food showed significant association with obesity and exercise >2 hours/week with normal BMI.Conclusions: Dietary and behavioral patterns and prevalence of overweight/obesity showed variability with respect to gender and region of residence. 12% were overweight and 4% had obesity. Daily consumption of junk food showed significant association with overweight/obesity and daily exercise >2 hours/week with normal BMI. Health education addressing behavioral change is recommended for better health.

Recent years have witnessed significant growth in the adoption of portable wireless communication and computing devices (e.g., laptops, PDAs, smart phones) and large-scale deployment of wireless networks (e.g., cellular, WLANs). We envision that future usage of mobile devices and services will be highly personalized. Users will incorporate these new technologies into their daily lives, and the way they use new devices and services will reflect their personality and lifestyle. Therefore it is imperative to study and characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks and services. In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses. We represent the data using location-preference vectors, and utilize unsupervised learning (clustering) to classify trends in user behavior using novel similarity metrics. Matrix decomposition techniques are used to identify (and differentiate between) major patterns. We discover multi-modal user behavior and hundreds of distinct groups with unique behavioral patterns in both campuses, and their sizes follow a power-law distribution. Our methods and findings might provide new directions in network management and behavior-aware network protocols and applications, to name a few.

Wireless networks and personalized mobile devices are deeply integrated and embedded in our lives. Such wide adoptions of new technologies will impact user behavior and in turn will affect network performance. It is imperative to characterize the fundamental structure of wireless user behavior in order to model, manage, leverage and design efficient mobile networks. One major challenge in characterizing user behavior stems from the significant size and complexity of user behavioral data. Without summarization and dimension reduction, the sheer amount of data does not provide much useful information. The key contribution of the paper is a novel similarity metric based on a matrix representation of mobility preferences and its decomposition. This method provides an efficient way to reduce important spatiotemporal dynamics in user mobility into a few eigen-behavior vectors. This also facilitates nodes to exchange their mobility summaries and determine their mutual similarity locally. Without any assumption on the properties of user population, we use unsupervised learning (clustering) techniques to classify WLAN users. Such a user grouping scheme based on learned user behavior is crucial for applications relying on the usage context of each mobile device (e.g., participatory sensing, social-relationship-aware message forwarding). In this study, using our systematic TRACE approach, we analyze wireless users' behavioral patterns by extensively mining wireless network logs from two major university campuses to showcase its efficacy. While our findings partly validate intuitive repetitive behavioral trends and user grouping, it is surprising to find the qualitative commonalities and striking consistency of user behavior from the two universities. We discover multimodal user behavior for more than 60 percent of the users, and there are hundreds of distinct groups with unique behavioral patterns in both campuses. The sizes of the major groups follow a power-law distribution.

It has been highlighted and spread that the cutting edge system and intelligence home system raised from dramatic development of IT technology. Through the progressed Application of Home Networking Tech. and diverse multi-media, there are plenty of efforts to pursue the realization of human convenience. As a home networking services is on the increase, so is the demand for context-aware services, utilizing a profile of users as well as various context information, a sensor, and other environmental information in order to recognize a pattern of users' activities. And more enhanced services that can analyze users' pattern of behaviors and reflect individual tendency in the service should be offered so that users can obtain the information they want much faster. Thus, an intellectual control model will be discussed herein, which can offer active service based on a pattern of users' behaviors, in order to suggest a device that predicts users' activities and operates in a more intelligent way.