Behavioral Characteristic Research Articles

Proactivity or passivity? An investigation of the effect of service robots’ proactive behaviour on customer co-creation intention

The implementation of robots in tourism services is transforming how companies interact with their customers and create value. Although corporate initiatives are beginning to focus on proactive robot services, little is known about the impact of this behavioural characteristic of service robots on customer value co-creation. Through two experiments and one field survey, this research demonstrates that customers’ co-creation intention is higher when a robot’s service proactivity is high (vs. low), and that this relationship is mediated by perceived robotic empathy. Furthermore, customer task orientation negatively moderates the mediating effect of empathy perception that underlies the relationship between service proactivity and customers’ co-creation intention. These findings provide new insights into the effect of service robots’ behavioural characteristics on customer–robot interactions.

Experimental Study on Influences of Self-Preservation Effects and Memory Effects on the Hydrate Decomposition Process

Abstract In order to explore the characteristics of the hydrate decomposition behavior at the pore scale, this study carries out a pore-scale experimental study of methane hydrate decomposition based on the high-pressure visual model under the etched glass. A mathematical model is also constructed to analyze the behavioral characteristic of the self-preservation effect and memory effect during the hydrate decomposition period. This study draws the following conclusions: (1) The self-preservation effect and memory effect exist during the methane hydrate decomposition lead by depressurization, which generally inhibits the hydrate decomposition process. (2) The hydrate self-preservation effect is a transition of the surface water film’s phase state to inhibit the methane hydrate decomposition, in which the liquid phase transforms into a metastable “quasiliquid film”. (3) The multiple syntheses induced by the hydrate memory effect are a periodic attenuation process. The times of synthesis are a critical factor affecting the hydrate gas production and decomposition rate. (4) The self-preservation and memory effects during the hydrate decomposition period are associated with each other. The two are correlated at some degree with playing a dominant role alternately at different stages. The self-preservation effect is an abnormal behavior of the hydrate, which refers to the hydrate’s transition from the solid phase to the gas-liquid mixed phase. The memory effect is another abnormal behavior, which refers to the transition from the gas-liquid mixed phase to the solid phase. The sustaining pressure drop is the key reason of the disappearance of the two effects. This research was aimed at providing a theoretical basis for the exploitation and optimization of marine natural gas hydrates.

Intelligent Vehicle Moving Trajectory Prediction Based on Residual Attention Network

Skilled drivers have the driving behavioral characteristic of pre-sighted following, and similarly intelligent vehicles need accurate prediction of future trajectories. The LSTM (Long Short-Term Memory) is a common model of trajectory prediction. The existing LSTM models pay less attention to the interactions between the target and the surrounding vehicles. Furthermore, the impacts on future trajectories of the target vehicle have also barely been a focus of the current models. On these bases, a Residual Attention-based Long Short-Term Memory (RA-LSTM) model was proposed, an interaction tensor based on the surroundings of the target vehicle at the predictive moments was constructed and the weight coefficients of the interaction tensor for the surrounding vehicles relative to the target vehicle were calculated and re-programmed in this study. The proposed RA-LSTM model can implicitly represent the different degrees of influence of the surrounding vehicles on the target vehicle; the probability distributions of the future trajectory coordinates of the target vehicle is predicted based on the extracted interaction features. The RA-LSTM model was tested and verified in multiple scenarios by using the NGSIM (next generation simulation) public dataset, and the results showed that the prediction accuracy of the proposed model is significantly improved compared with the current LSTM models.

THE PROBLEM OF AN ATHLETE’S ASSERTIVENESS AND DEGREE OF THEIR SELF-CONFIDENCE

In one form or another, the problem of an athlete’s assertiveness and confidence in one’s abilities and self is covered in almost any scientific theory, which directly or indirectly relates to personality. It is well-known that athletes differ by degree of self- confidence. The very concept of “confidence” exists in many languages all over the world. In some languages, there are even several words to denote it. At the same time, the concept of assertiveness concerning athlete training has been introduced only recently. Importantly, pedagogical correction of insecurities has created the basis for theoretical generalizations and concepts of self-confidence. In the 1960s, there originated the idea of confidence as a multi-faceted characteristic consisting of emotional, behavioural and cognitive components. Thus, assertiveness is a complex characteristic of an athlete’s personality which includes emotional (positive feelings when winning, self-affirmation, satisfaction with sports results), behavioural (a set of behavioural patterns that are effective in various socio-psychological situations), cognitive (self-presentation, self-concept components), as well as axiological (sociality, maturity) components. Self-confidence as a behavioural characteristic of an assertive personality is seen as one’s ability to demand and request more of oneself and others.

Research and Development: Model of Clinical Collaborative Supervision for Scientific Based English Instruction

This research aimed to establish an instructional supervision model for scientific-based English instruction at Islamic Junior Boarding Schools. Research questions were stated as to how was the formulation of the model. This research employed Research and Development with three phases of (1) foundational study seeking the potencies and problems using policy analysis, field study, and theoretical review; (2) design and development study by designing the hypothetical model and developing the model formulation to be validated; and (3) Try out the study by doing one group pre-post experimental and perceptional studies. Finally, the model was established informs of model book and tutorial usage. The model combined principles of clinical Supervision and collaborative orientation to control, which were compatible with the behavioural characteristic of Islamic Boarding Junior High Schools in a pesantren environment. This model obtained score validation of 89 %, categorized as valid, can be used without revision. While the pre-assessment score was 2.60 and the post-assessment score was 3.39with, the t-test was -7.0886on significant 0.05 and t table 2.447. This model was concluded to be practical and applicable to use as a Scientific Based English Instruction Supervision for Islamic Boarding Junior High Schools.

Uniqueness of gait kinematics in a cohort study

Gait, the style of human walking, has been studied as a behavioral characteristic of an individual. Several studies have utilized gait to identify individuals with the aid of machine learning and computer vision techniques. However, there is a lack of studies on the nature of gait, such as the identification power or the uniqueness. This study aims to quantify the uniqueness of gait in a cohort. Three-dimensional full-body joint kinematics were obtained during normal walking trials from 488 subjects using a motion capture system. The joint angles of the gait cycle were converted into gait vectors. Four gait vectors were obtained from each subject, and all the gait vectors were pooled together. Two gait vectors were randomly selected from the pool and tested if they could be accurately classified if they were from the same person or not. The gait from the cohort was classified with an accuracy of 99.71% using the support vector machine with a radial basis function kernel as a classifier. Gait of a person is as unique as his/her facial motion and finger impedance, but not as unique as fingerprints.

Individual differences in the engagement of habitual control over alcohol seeking predict the development of compulsive alcohol seeking and drinking.

Excessive drinking is an important behavioural characteristic of alcohol addiction, but not the only one. Individuals addicted to alcohol crave alcoholic beverages, spend time seeking alcohol despite negative consequences and eventually drink to intoxication. With prolonged use, control over alcohol seeking devolves to anterior dorsolateral striatum, dopamine-dependent mechanisms implicated in habit learning and individuals in whom alcohol seeking relies more on these mechanisms are more likely to persist in seeking alcohol despite the risk of punishment. Here, we tested the hypothesis that the development of habitual alcohol seeking predicts the development of compulsive seeking and that, once developed, it is associated with compulsive alcohol drinking. Male alcohol-preferring rats were pre-exposed intermittently to a two-bottle choice procedure and trained on a seeking-taking chained schedule of alcohol reinforcement until some individuals developed punishment-resistant seeking behaviour. The associative basis of their seeking responses was probed with an outcome-devaluation procedure, early or late in training. After seeking behaviour was well established, subjects that had developed greater resistance to outcome devaluation (were more habitual) were more likely to show punishment-resistant (compulsive) alcohol seeking. These individuals also drank more alcohol, despite quinine adulteration, even though having similar alcohol preference and intake before and during instrumental training. They were also less sensitive to changes in the contingency between seeking responses and alcohol outcome, providing further evidence of recruitment of the habit system. We therefore provide direct behavioural evidence that compulsive alcohol seeking emerges alongside compulsive drinking in individuals who have preferentially engaged the habit system.

The connection between leader behaviour and employee sickness absence in public administration

Purpose The purpose of this study is to find the connection between leader behaviour and employee sickness absence in public administration. Design/methodology/approach The research data was collected with the help of an online questionnaire. The SPSS statistical programme and structural equation modelling in AMOS were used to analyse the data. Findings The research was conducted in public administration institutions, and 3,220 employees from public administration were included in the research sample. The author found a negative one-way relationship between certain types of behaviour and sickness absence. The author defines leader behaviour as a multidimensional construct in which each dimension represents a separate cluster of leader behavioural characteristics. Leaders’ “progressiveness” is the most important dimension, and a one-point increase in “progressiveness” (five-point scale) leads to a reduction of 2.8 days in sickness absence for one employee. Research limitations/implications The author focused only on one segment of factors (the behaviour of leaders) that affects sickness absence. To explain the maximum possible measure of the variability in sickness absence, it would be best to include several different influencing factors. Practical implications The study represents a structured model of the link between sickness absence and leader behaviour. With the model, it is possible to determine which behavioural forms of leaders influence sickness absence, where leader behaviour is treated as a complex whole, and not as an individual behavioural characteristic. Originality/value The study addresses calls for research on the relationship between leader behaviour and employee sickness absence within countries.

Intraindividual variability in driving simulator parameters of healthy drivers of different ages

Intraindividual variability is a fundamental behavioural characteristic of aging but has been examined to a very limited extent in driving. This study investigated intraindividual variability in driving simulator measures in healthy drivers of different ages using the coefficient of variation (COV) as a variability measure. Participants were healthy volunteers who were regular drivers, who were divided into a “young” group, a “middle-aged” group, and an “old” group. They drove in two environments (rural, 72 drivers; urban, 60 drivers), under conditions of moderate and high traffic load, without and with distraction (conversation). Significant differences in COV were observed in the rural condition for headway distance and lateral position as a function of traffic load, with high traffic (without and with distraction) resulting in increased COV of headway and decreased COV of lateral position. Significant differences in COV were observed in the urban condition for headway distance only, with high traffic (without and with distraction) resulting in increased COV of headway. No age effects were found for any of the driving conditions. The results indicate that traffic load affected headway distance and lateral position in opposite directions in all three age groups: high traffic resulted in increased variability of headway in both rural and urban conditions but in decreased variability of lateral position in the rural conditions compared to moderate traffic irrespective of distraction. The study indicates that driving conditions affect the intraindividual variability of driving measures in selective ways, which may be linked to the extent of automatization of the driving variables and to adaptive changes to traffic condition challenges.

Impulsivity in heroin-dependent individuals: structural and functional abnormalities within frontostriatal circuits.

High levels of impulsivity are a risk factor for the initiation of heroin use and a core behavioral characteristic of heroin dependence. Impulsivity also contributes to the maintenance of drug use and hinders effective therapy. Here we sought to identify neuroimaging markers of impulsivity in heroin-dependent individuals (HDI), with a focus on the nucleus accumbens (NAc), a key region implicated in impulsivity and drug addiction generally. Volume and resting-state functional connectivity (RSFC) differences of the bilateral NAc were investigated between 21 HDI and 21 age-, gender-, nicotine-, alcohol-matched healthy controls (HC). The neuroimaging results were then correlated with the Barratt Impulsivity Scales (BIS-11). Higher motor impulsivity (t = 2.347, p = 0.0253) and larger right NAc volume (F (1,38) = 4.719, p = 0.036) was observed in HDI. The right NAc volume was positively correlated with BIS total (r = 0.6196, p = 0.0239) /motor (r = 0.5921, p = 0.0330) scores in HC and BIS motor (r = 0.5145, p = 0.0170) score in HDI. A negative correlation was found between RSFC of the right NAc-bilateral superior frontal gyrus (SFG) and motor impulsivity in HDI (left: r=-0.6537, p = 0.0013; right: r=-0.6167, p = 0.0029) and HC (left: r=-0.6490,p = 0.0164; right: r=-0.6993, p = 0.0078). We aimed to reveal novel multimodality neuroimaging biomarkers of the higher impulsivity in HDI by focusing on the NAc and corresponding functional circuits. Higher motor impulsivity was observed in HDI. Furthermore, the volume of the right NAc and the RSFC strength of right NAc-SFG could be neuroimaging biomarkers for the severity of impulsivity in HDI. These potential biomarkers could be a target for novel treatments in HDI.

Two new life types and assessment of web-associated behavioral characteristics of some Oligonychus species on various host plants.

The webbing life type of three spider mite species of the genus Oligonychus was studied based on web-associated behavioral characteristics. All tested Oligonychus species-viz., avocado brown mite, Oligonychus punicae (Hirst), date palm mite, Oligonychus afrasiaticus (McGregor), and banks grass mite, Oligonychus pratensis (Banks)-exhibited a complicated-web (CW) life type on various host plants. A new life type, 'CW-c,' was documented for O. punicae on the adaxial leaf side of Conocarpus erectus L. (Combretaceae). The weaving pattern with guy ropes is spun by the O. punicae female and is associated with stalked eggs; it appeared as a unique character in the CW-life type. Oligonychus afrasiaticus, also showed a new life type, 'CW-d,' when reared on the abaxial side of leaves of four host plants, viz., desert fan palm, maize, sorghum, and sugarcane. Oligonychus afrasiaticus showed a site for quiescence and a site for oviposition as variables, whereas the site for defecation (SD) was a persistent characteristic on all four tested host plants. Oligonychus pratensis showed the characteristics of the sub-type 'CW-u' on the abaxial leaf side of Washingtonia filifera (Lindl.) H. Wendl. (Arecaceae). The SD was a distinguishing behavioral characteristic used to separate O. afrasiaticus from O. pratensis when inhabiting the same host plant, W. filifera.

Systematic Review on Human Gait Analysis using Deep Learning Models

Human Gait is known as a behavioral characteristic of humans, compared with the other biometrics gait is found to be a difficult process to conceal. Human gait analysis is usually done by extracting the features from the body. Analysis of gait involves evaluating the individual by means of kinematic analysis while walking along a surface. The main objective and the purpose of gait recognition is to give the best method where risks are recognized in places where there is a need for high security in any public place and to detect diseases like Parkinson’s. In order to acquire a normal person’s identification and validation performance, various Deep Learning techniques are totally studied and modeled the biometrics of gait which is based on walking data. It is reviewed that among various essential metrics that are used, deep learning convolution neural networks are typically better Machine Learning models. The main objective of the present study was to examine in detail individual gait patterns. Finally, this paper recommends deep learning methods and suggests the directions for future gait analysis and also for its applications.

An agent-based model for teaching–learning processes

This paper presents an agent-based model for describing the increase the knowledge by accumulating the information needed to complete a learning task or objectives, based on phenomena studied by behavioral and learning scientists. From the simulations, the average increase rate in knowledge, the skewness and kurtosis of knowledge distributions, and grade distributions are determined. These tools make it possible to evaluate the efficiency of teaching strategies and the performance of learning in the classroom. The present model significantly reproduces the phenomenology obtained in Bordogna and Albano (2001), showing first and second-order phase transitions and the temporal dynamics of knowledge. Furthermore, the results of our model allow us to built a gas model analogy. Some of the study cases show characteristics of systems far from the state of thermodynamic equilibrium. This allows us to use the known techniques from gas models to interpret the dynamics of the simulated learning process. The presented model does not describe the teaching–learning process in all its complexity. We use a simple behavioral characteristic of the persons, namely, the inattentional private experience. However, it allows us to prove learning strategies to optimize the learning process. Also, this model is a starting point to propose new models with more elements from neuroscience and sociophysics to study in greater depth the dynamics of the classroom.

Debris Flow Behavioral Characteristic Based on Rheological Properties: A Case Study on Mt. Hwangnyeong and Mt. Umyeon

Debris flow, one of the major natural disasters in Korea, usually occurs because of heavy rainfall during the rainy season. Recently in Korea, rainfall, as well as the frequency of debris flow, has continually increased as a result of climate change. Therefore, it is necessary to study the characteristics of the debris flow behavior for hazard mitigation and damage assessment. In this study, vane-type rheometer tests were conducted to estimate the rheological properties (viscosity, yield stress) of two soil samples collected from Mt. Hwangnyeong and Mt. Umyeon Several series of small-scale flume experiments were also performed to evaluate the characteristics of the debris flow behaviors (flow velocity, runout distance, and deposition volume) of these two sites. The results of the experiments show that front velocity, runout distance, and deposition volume of the debris flow gradually decrease with increase in viscosity and yield stress. Especially in the case of the Mt. Hwangnyeong sample, which has a high fine content, experimental results showed that the rate of increase in front velocity, runout distance, and deposition volume tended to high as viscosity and yield stress decreased compared to Mt. Umyeon.

An improved model for predicting trip mode distribution using convolution deep learning

Trip mode selection is a behavioral characteristic of passengers with immense importance for travel demand analysis, transportation planning, and traffic management. Identification of trip mode distribution will allow transportation authorities to adopt appropriate strategies to reduce travel time, traffic, and air pollution. The majority of existing trip mode inference models operate based on human-selected features and traditional machine learning algorithms. However, human-selected features are sensitive to changes in traffic and environmental conditions and susceptible to personal biases, which can make them inefficient. One way to overcome these problems is to use neural networks capable of extracting high-level features from raw input. In this study, the convolutional neural network (CNN) architecture is used to predict the trip mode distribution based on raw GPS trajectory data. The key innovation of this paper is the design of the layout of the input layer of CNN as well as normalization operation, in a way that is not only compatible with the CNN architecture but can also represent the fundamental features of motion including speed, acceleration, jerk, and bearing rate. The highest prediction accuracy achieved with the proposed configuration for the convolutional neural network with batch normalization is 85.26%.

An Informative and Comprehensive Behavioral Characteristics Analysis Methodology of Android Application for Data Security in Brain-Machine Interfacing.

Recently, brain-machine interfacing is very popular that link humans and artificial devices through brain signals which lead to corresponding mobile application as supplementary. The Android platform has developed rapidly because of its good user experience and openness. Meanwhile, these characteristics of this platform, which cause the amazing pace of Android malware, pose a great threat to this platform and data correction during signal transmission of brain-machine interfacing. Many previous works employ various behavioral characteristics to analyze Android application (or app) and detect Android malware to protect signal data secure. However, with the development of Android app, category of Android app tends to be diverse, and the Android malware behavior tends to be complex. This situation makes existing Android malware detections complicated and inefficient. In this paper, we propose a broad analysis, gathering as many behavior characteristics of an app as possible and compare these behavior characteristics in several metrics. First, we extract static and dynamic behavioral characteristic from Android app in an automatic manner. Second, we explain the decision we made in each kind of behavioral characteristic we choose for Android app analysis and Android malware detection. Third, we design a detailed experiment, which compare the efficiency of each kind of behavior characteristic in different aspects. The results of experiment also show Android malware detection performance of these behavior characteristics combine with well-known machine learning algorithms.

A Survey on Writer Identification System for Indic Scripts

Handwriting is a skill, expression of someone’s personality that cannot be imitated. It is a physical way of communicating thoughts and ideas and a method for speaking with others. Handwriting is considered as the behavioural characteristic of a person. It is concerned with the sentiments, attitude and brain of a person. Thus no two persons can have the same handwriting. Indeed, even indistinguishable twins who share appearance and hereditary qualities do not have the same handwriting. Handwriting based writer identification has attracted a great deal of attention by researchers to prove someone’s authenticity. It is the process of identifying a person through his/her handwriting. This paper introduces a survey on the work accomplished for writer identification for Indic scripts. The work done on Indic scripts is confined as compared to the non-Indic scripts due to non-availability of the dataset. The anatomy of the paper comprises introduction, stages of writer identification system, applications, reported work and conclusion. The primary focus of this paper is to present the review on writer identification system for Indic scripts, mainly, Devanagari, Gurumukhi, Tamil, Telugu, Bengali, Kannada, Gujarati, Oriya, and Malayalam. This study provides guidance to the research analysts in this area by giving the studies of work done on Indic scripts.

Social Correspondence as a Manifestation Form of Civic Consciousness (the Case of Pionerskaya Pravda Newspaper, 1925–1929)

The paper analyzes the Soviet experience of forming a cultural model of civic consciousness in mind and behavior of young people through the media in the case of Pionerskaya Pravda newspaper in 1925–1929. Pionerskaya Pravda is treated as a subject of constructing a Soviet model of civic consciousness and as a specialized ideology­oriented communication channel with children’s audience. The paper examines the texts of the questions from the newspaper readers basing on the typology proposed by the paper authors and reveals the primary approved manifestation forms of civic consciousness among children. It concludes that civic consciousness in the newspaper materials is mainly interpreted as a behavioral characteristic of personality.

Developers’ Behavioral Characteristic and Its Impact On Performance: A Case Study Of Iot Development Product Design

The IoT industry is facing a significant demand to provide more efficient paths for developer communities. At the same time, products are often developed without consideration of usability or are limited to general usability heuristics. Moreover, there is not much research on developers’ behavioral characteristics and its impact on human performance. This paper attempts to show how understanding their skill-, rule-, and knowledge-based behaviors can benefit developer experience and performance efficiency. This study references recent developer products to highlight 1) characteristics of developer behavior and 2) key system design considerations that help improve developer performance. We also discuss gaps identified in industry practices and the opportunities towards more consideration of the cognitive aspects in developer product design processes.

The Effect of Injectable Medication Procedure on the Level of Pain among Early Childhood patients

Abstract Children are often exposed to painful procedures during hospitalization. Validated age appropriate scales are available to assess pain among pediatric patients. The aim of this study is to find out the effect of injectable medication procedure on the level of pain among early childhood patients. This descriptive cross -sectional study was designed to assess the physiological and behavioral characteristic of pain using CRIES pain rating scale for both medical and surgical conditions, which contains (cry, spo2, heart rate, facial expression and sleep pattern). The study has been conducted for twelve weeks duration in the pediatric wards at medical and surgical hospitals in the Sulaimani city – Kurdistan region of Iraq between 1st March to 1st Jun 2019. Fifty early childhood aged between (1 month- 3years) were participated and assessed pre injectable medication procedure and after. CRIES pain scales were applied to the children and show that there are highly significant differences between pain and the physiological, behavior characteristics between pre and after the procedure in the p-value < .00001 such as crying was developed from high pitch crying to inconsolable in post-procedure. Consumption of O2 also increased. In terms of heart rate was increased up to 20% than baseline, while, in the post-procedure, the change was more than 20% of the baseline. Facial expression was also changed to contracted face in the post procedure. Sleeping from short interval to absent sleeping was shown. Significant variation between pre and post-procedure were assessed. Further variation between medical and surgical condition for mean score of every scale of CRIES were assessed. The implications of this study will impact on health professional’s education; practice and future research are also recommending.