Characteristics Of Human Behavior Research Articles

Hierarchical MPC-based authority allocation strategy for human–machine shared vehicles considering human–machine conflict

The uncertainty of driver behavior is an important factor affecting the safety of human–machine co-driving vehicles. Traditional rule-based models often fail to capture the nonlinear and complex characteristics of human steering behavior. To overcome this, we propose a data network-driven approach utilizing gated recurrent unit (GRU) neural networks to accurately predict driver steering behavior. The GRU-based driver model is integrated with vehicle dynamics to construct a control-oriented driver–vehicle model. Considering that human–machine conflict may cause vehicle instability, an exponential function combining proportional–integral–derivative is proposed to quantify the human–machine conflict level based on steering difference. To reasonably allocate human–computer permissions based on human–machine interaction, a hierarchical authority allocation framework is proposed. The upper layer provides a reference authority allocation via an exponential function, while the lower layer employs a real-time model predictive control (MPC) optimizer to track this reference, ensuring optimal vehicle path tracking and stability. The proposed system’s effectiveness is validated through driver-in-the-loop testing, demonstrating significant improvements in safety and performance. The results show that in the human–machine conflict scenario, the proposed authority allocation strategy can still ensure the path tracking and safety of the vehicle.

Analysis of an Infectious Disease Vaccination Prediction System Based on the MF-Conv LSTM Model

Infectious diseases can seriously threaten people's life safety and have a serious impact on social stability. Therefore, it should improve society’s stability under infectious diseases and ensure the safety of people's lives. A personnel flow feature extraction model based on Multi-Feature Convolutional Long Short-Term Memory (MF-Conv LSTM) is designed based on the characteristics of human daily activity behavior. This can optimize the accuracy of transmission simulation prediction for infectious disease vaccination. When using multi-feature ensemble analysis to extract human daily activity features as input for infectious disease simulation and prediction models, the learner's prediction score for the recurrent infectious diseases reached 0.8705. When using multi-feature ensemble analysis, the predicted scores did not exceed 0.85. The designed infectious disease vaccine transmission prediction model can accurately simulate the infectious behavior of infectious diseases. This provides direction for developing strategies to disrupt the infectious diseases’ spread. This reduces the infectious diseases’ harm to people's personal safety and improves social stability during the spread of large-scale infectious diseases.

Genetics and economics: Studies and perspectives

This paper analyzes the relationship between genetics and economics and the effects of both genetics and the environment on the phenotype of human beings. Among other issues, it describes how data from the human genome can be used to explain certain economic characteristics, such as the existence of entrepreneurship, attitude towards risk, income level, propensity to invest, and ease of receiving education. A series of works related to this issue are analyzed, and the various ways of drawing conclusions from existing data are described. The paper concludes by stating that this type of study is in its infancy, but that it helps to better understand, along with the evaluation of the environment, certain characteristics of human behavior and opens an interesting field of study for economics.

A comparative evaluation of measures to assess randomness in human-generated sequences

Whether and how well people can behave randomly is of interest in many areas of psychological research. The ability to generate randomness is often investigated using random number generation (RNG) tasks, in which participants are asked to generate a sequence of numbers that is as random as possible. However, there is no consensus on how best to quantify the randomness of responses in human-generated sequences. Traditionally, psychologists have used measures of randomness that directly assess specific features of human behavior in RNG tasks, such as the tendency to avoid repetition or to systematically generate numbers that have not been generated in the recent choice history, a behavior known as cycling. Other disciplines have proposed measures of randomness that are based on a more rigorous mathematical foundation and are less restricted to specific features of randomness, such as algorithmic complexity. More recently, variants of these measures have been proposed to assess systematic patterns in short sequences. We report the first large-scale integrative study to compare measures of specific aspects of randomness with entropy-derived measures based on information theory and measures based on algorithmic complexity. We compare the ability of the different measures to discriminate between human-generated sequences and truly random sequences based on atmospheric noise, and provide a systematic analysis of how the usefulness of randomness measures is affected by sequence length. We conclude with recommendations that can guide the selection of appropriate measures of randomness in psychological research.

Research on entertainment robots based on artificial intelligence interaction for human posture recognition and sports activity monitoring

With the application of artificial intelligence technology in sports activity monitoring, new technologies such as interactive robots and the Internet of Things are being more widely used, which can provide entertainment and interactive guidance for sports training. This article analyzes the research on human pose recognition and sports activity monitoring of entertainment robots based on artificial intelligence interaction. This paper designs and develops an entertainment robot system that can recognize human posture and monitor physical activities. Users can interact with the robot and get real-time feedback about their posture and activities, thereby improving motor skills and physical fitness. After in-depth analysis of the theory of dynamic Bayesian network, we used EM algorithm to build a dynamic Bayesian network model, carried out detailed research on the random variables represented by the nodes of the dynamic model, and used the Internet of Things human posture recognition technology to extract the features of human behavior according to the characteristics of human behavior. The results show that the attitude recognition accuracy can be improved by adding the dynamic Bayesian network based on the random forest algorithm. The entertainment robot can accurately identify human posture and monitor sports activities, and has good real-time and stability. Through interaction with the robot, users can get personalized suggestions for individual postures and activities, improving the training effect and enjoyment of physical activities. Therefore, through the in-depth research and analysis of the dynamic Bayesian network and the human posture recognition of the Internet of Things, it is found that the sports activity monitoring system proposed in this paper can comprehensively monitor students’ sports activities, so as to achieve the goal of comprehensively improving students’ physical fitness.

PHRASEOLOGICAL UNITS WITH A ZOONYMIC COMPONENT THAT CHARACTERIZE THE CHARACTER OF A PERSON IN THE KAZAKH AND ENGLISH LANGUAGES

The article deals with phraseological units with zoonymic components that characterize the character of a person in the Kazakh and English languages, explores linguistic expressive means using animal images to describe human qualities and behavioral characteristics. In the work, we will review phraseological units with zoonymic components in both languages and analyze them using a comparative method.The purpose of the scientific article is to identify similarities and differences in the description of the peculiarities of the use of phraseological units, analysis of phraseological units with zoonimic components in Kazakh and English languages in the discourse of representatives of the two cultures.The main section of the article proves that phraseological units with zoonymic components are a reflection of cultural characteristics and views on human character in the Kazakh and English languages. The analysis showed that phraseological units with animals to describe human qualities are used in the two languages. Phraseological units in the Kazakh language are closely related to animals, that is zoonyms reffered to livestock, while English language mainly relies on zoonyms related to birds, insects and sea creatures. The results of the study may be useful to linguists, translators and cultural scientists who study the interaction of language and culture.The relevance of the research topic was the comparative analysis of phraseological phrases with zoonymic components in the Kazakh and English languages, revealing similarities and differences in the way, how human character is perceived and described by the different representatives of cultures. The authors presented their own vision of the problems based on the analysis of the works of the scientists on this topic. Comprehensive analysis of phraseological units with zoonymic components, which have become the main object of the article's research were carried out.

TRANSLATION OF PSYCHOLOGICAL TESTS: A CASE STUDY ON THE TRANSLATION OF MBTI TESTS

As a field of study, psychology has been a significant part of human life for decades. Several types of psychological tests have been used in order to understand and assess people’s behavior. Because of their significant roles in identifying the characteristics of human behavior, the need for their translation has also increased to a great extent in recent years. For this reason, in order to provide a functional translation of a psychological test in a different language, it is important to consider the intricacies of translating psychological texts, including linguistic and cultural differences between languages. For this purpose, this study aims to analyze and describe French and Turkish and translations of a certain type of MBTI test written in English. With the aim of identifying and explaining the differences in the source and target texts, the study benefits from J.C. Catford’s model of translation shifts. As a result of a detailed analysis of the source and target texts, it is found out that French and Turkish translations of the specific psychological test cause both semantic and stylistic shifts. Apart from these translation shifts, it is seen that the translators have omitted some parts both in French and Turkish translations.

Human emergency behaviour and psychological stress characteristic mining based on large-scale emergencies

AbstractHuman emergency behaviour and psychological stress response in emergencies are important scientific issues in basic emergency management research. The analysis of the dynamic characteristics of large-scale human behaviour based on electronic footprint data provides a new method for quantitative research on this problem. Previous studies usually assumed that human behaviors were randomly distributed in time, but few studies have studied the psychological stress response of human groups under the influence of emergencies and carried out prediction methods through social media data. Based on the data from five emergencies and daily events in the Qzone, this paper explores the statistical characteristics of human communication behaviors such as time, space and social interaction. The research results reveal the psychological evolution of human groups when they encounter public security emergencies by analysing the causes of individual psychological stress responses in the group. We find that the time interval between people’s posting behaviour and interactive comment behaviour in mobile QQ space before and after an emergency can be approximately described by a power-law distribution. The time interval distribution of Posting and reply is an obvious heavy-tailed distribution. These behavioural characteristics are consistent with people’s psychological stress characteristics. Individual psychological stress responses gradually evolve into social-psychological responses with changes in behavioural characteristics. The greater the social-psychological stress response is, the more panic the public will be, which will cause the outbreak of group irrational behaviour. The research results are theoretically helpful in understanding the impact of emergencies on human communication behaviour patterns and reveal the psychological stress process of mass panic in large-scale emergencies.

Music as aposematic signal: predator defense strategies in early human evolution.

The article draws attention to a neglected key element of human evolutionary history-the defense strategies of hominins and early humans against predators. Possible reasons for this neglect are discussed, and the historical development of this field is outlined. Many human morphological and behavioral characteristics-musicality, sense of rhythm, use of dissonances, entrainment, bipedalism, long head hair, long legs, strong body odor, armpit hair, traditions of body painting and cannibalism-are explained as predator avoidance tactics of an aposematic (warning display) defense strategy. The article argues that the origins of human musical faculties should be studied in the wider context of an early, multimodal human defense strategy from predators.

СОВРЕМЕННЫЕ ГЛОБАЛЬНЫЕ ТРАНСФОРМАЦИИ ОБЩЕСТВЕННОПОЛИТИЧЕСКОГО ПРОСТРАНСТВА ПОД ВЛИЯНИЕМ ЦИФРОВЫХ ИНФОРМАЦИОННО-КОММУНИКАЦИОННЫХ ТЕХНОЛОГИЙ

This article is devoted to the newest challenges posed to society by the rapid development of digital communication technologies, especially global social media. The relevance of the topic lies in the fact that social media are constantly strengthening their monopoly as channels of information dissemination and communication platforms. At the same time, the commercial nature of social networks and the focus of IT giants on maximizing profits leads to unforeseen consequences that essentially transform socio-political life around the world. The text examines the main problems associated with the specifics of the functioning of social media and significantly influencing the socio-political sphere starting with the so-called “human factors”, such as the deliberate use of the capabilities of social media by certain interest groups and the psychological characteristics of human behavior that influence formatting of the online space, and ending with the built-in mechanisms of social media, such as recommendation algorithms and the collection and analysis of gigantic volumes of personal data for commercial use, which is increasingly turning into use for political purposes.

Bifurcation analysis on the reduced dopamine neuronal model

Bursting is a crucial form of firing in neurons, laden with substantial information. Studying it can aid in understanding the neural coding to identify human behavioral characteristics conducted by these neurons. However, the high-dimensionality of many neuron models imposes a difficult challenge in studying the generative mechanisms of bursting. On account of the high complexity and nonlinearity characteristic of these models, it becomes nearly impossible to theoretically study and analyze them. Thus, this paper proposed to address these issues by focusing on the midbrain dopamine neurons, serving as the central neuron model for the investigation of the bursting mechanisms and bifurcation behaviors exhibited by the neuron. In this study, we considered the dimensionality reduction of a high-dimensional neuronal model and analyzed the dynamical properties of the reduced system. To begin, for the original thirteen-dimensional model, using the correlation between variables, we reduced its dimensionality and obtained a simplified three-dimensional system. Then, we discussed the changing characteristics of the number of spikes within a burst by simultaneously varying two parameters. Finally, we studied the co-dimension-2 bifurcation in the reduced system and presented the bifurcation behavior near the Bogdanov-Takens bifurcation.

Path to modeling dynamic performance shaping factors in nuclear power plants operation – A review

AbstractThe evolution of the mechanism of human behavior formation analysis has significantly influenced the development of human reliability analysis (HRA), which aims to calculate human error probability (HEP) with performance shaping factors (PSFs). This paper reviews the typical HRA methods in different generations, the role of PSFs, and their interrelation‐ships in human risk modeling, with the background of nuclear power plants (NPPs). In a retrospective of typical HRA methods, PSF plays a fundamental role in assessing human performance during task operation. However, the subjectivity in defining and evaluating PSFs often leads to a partial representation of human behavior characteristics and human risk evolution, resulting in the neglect of PSF inter‐relationships and conservative HEP estimation. Recent studies have emphasized employing simulation platforms to simulate the task process and obtain data relevant to PSFs that can enable the exploration of the mutual effects to support the calculation of HEP more accurately. Compared to certain previous methods involving over‐simplification and inappropriate assumptions resulting in inaccurate results, current HRA methods are prone to the construction of HEP models based on objective data acquisition and dynamic calculations with process models. This shift enables a better illustration of the intricate relationships among PSFs. Reflecting on the current trend of HRA methodology, this paper proposes a possible PSF quantification based on physiological measurement providing accessible and objective data. It improves the shortcomings in data scarcity and time‐invariance of HEP calculation, thus more accurately and realistically responds to the accumulation and fluctuation of human risks throughout a task.

Think twice: Re-assessing confidence improves visual metacognition

Metacognition is a fundamental feature of human behavior that has adaptive functional value. Current understanding of the factors that influence metacognition remains incomplete, and we lack protocols to improve metacognition. Here, we introduce a two-step confidence choice paradigm to test whether metacognitive performance may improve by asking subjects to reassess their initial confidence. Previous work on perceptual and mnemonic decision-making has shown that (type 1) perceptual sensitivity benefits from reassessing the primary choice, however, it is not clear whether such an effect occurs for type 2 confidence choices. To test this hypothesis, we ran two separate online experiments, in which participants completed a type 1 task followed by two consecutive confidence choices. The results of the two experiments indicated that metacognitive sensitivity improved after re-evaluation. Since post-decisional evidence accumulation following the first confidence choice is likely to be minimal, this metacognitive improvement is better accounted for by an attenuation of metacognitive noise during the process of confidence generation. Thus, here we argue that metacognitive noise may be filtered out by additional post-decisional processing, thereby improving metacognitive sensitivity. We discuss the ramifications of these findings for models of metacognition and for developing protocols to train and manipulate metacognitive processes.

Tracking the emergence of an organized use of space: A direct comparison of the spatial patterning within Middle and Upper Paleolithic open-air sites

Although the ‘organization of space’ is said to be one of the defining characteristics of modern human behavior, the identification and documentation of such organization has proven to be elusive, especially as rendered in artifact patterning. Without directly comparing artifact patterns within multiple sites, there is no benchmark with which to conclude one site to be more or less ‘organized’ than another. We can objectively identify patterns within the distribution of archaeological materials, but the decision of whether that patterning constitutes as ‘organized’ is entirely subjective without a comparative model. In this paper, I present the results of a study in which the spatial distribution of artifacts within nine Middle and Upper Paleolithic sites in France are directly compared to one another, and discernible changes in patterning can be identified. The differences in spatial patterning between the Middle and Upper Paleolithic sites suggest that the organization of space likely became increasingly formalized into and throughout the Upper Paleolithic alongside other cultural norms of behavior. Though more sites are needed to thoroughly document this phenomenon, this study suggests that direct comparisons of spatial patterning have the potential to yield more objective results on the question of spatial organization.

Perspectives in fraud theories – A systematic review approach

Background: Increasing progress is being made in the field of accounting fraud, and extensive theoretical research is needed to develop future research topics using trend analysis. Our research consists of a literature review that examines the most common fraud theories and attempts to interpret the characteristics of human behaviour that lead to fraud as well as current methods of detecting corporate fraud. Methods: We searched the Scopus database for articles on a fraud theory. We analyse articles published between 2004 and 2022, using a keyword search for ‘Fraud Triangle’, ‘Fraud Diamond’, ‘Fraud Pentagon’, and ‘Fraud Hexagon’. Furthermore, we include all document types like articles, conference papers, reviews, book chapters, conference reviews, notes, and data papers. The investigation was limited to papers published in English from 2004 to 2022, not including the current year 2023, as documents are still being published. The last research was done at the end of January 2023. The results from the above criteria are to collect 302 papers. We used VOS program viewer in our bibliometric analysis. Results: According to our network analysis, the Fraud Diamond theory seems to be the most functional fraud theory. According to our findings of the published articles, the main human behavioural characteristics that can lead a manager to commit fraud are the components of the fraud diamond theory: capability - opportunity - pressure - rationalization. Thus, the fraud diamond theory analyses more than the fraud triangle, pentagon, and hexagon theories. So human behavioural characteristics have a positive effect and can lead to fraud in companies. Conclusions: Future research needs to analyse more Pentagon and Hexagon fraud theories, which are more recent and have not yet been analysed in detail. Also, future research needs to analyse more of the human behaviour characteristics related to the Pentagon and Hexagon fraud theories.

A survey on the application of process discovery techniques to smart spaces data

During the last years, a number of studies have experimented with applying process discovery techniques with the goal of automatically modelling human routines as if they were business processes. However, while promising results have already been achieved, applying process-oriented techniques to smart spaces data comes with its own set of challenges, due to the nature of smart spaces data and characteristics of human behaviour. This paper surveys existing approaches that apply process discovery to smart spaces data and analyses how they deal with the following challenges identified in the literature: choosing a suitable modelling formalism for human behaviour; bridging the abstraction gap between sensor and event logs; segmenting logs in traces; handling multi-user environments; and addressing human behaviour evolution. The main contribution of this article is two-fold: (i) providing the research community with an analysis of the existing applications of process discovery to smart spaces and how they address the above challenges, and (ii) assisting further research efforts by outlining opportunities for future work.

Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system

For residential buildings with radiator heating system, the supply temperature is conventionally adjusted by weather compensator, which results in large fluctuations in the indoor air temperature and high energy consumption of heating. The solutions on current researches mainly focus on heat prediction of demand side, so that the supply temperature can be adjusted according to the predicted heat demand and achieve a heat balance. However, this requires a significant amount of historical data, which limits their applicability. And the lack of pre-simulation platform of the new strategies makes their practical application more difficult. In this paper, a digital twinning of secondary district heating system (DHS) is created, which considers both human behavior and radiator heat dissipation characteristics. A new supply temperature regulate strategy based on indoor temperature feedback is proposed. It contains an indoor temperature prediction model, which require a small amount of measured data and can keep the prediction error within ±0.6 °C. The control effects are analyzed by comparing the proposed regulation strategy with the weather compensator. The result shows that the new temperature regulate strategy is superior to the weather compensator in maintaining indoor temperature and can save energy by 5.4%.

Minimum taxi fleet algorithm considering human spatiotemporal behaviors.

With the development of information technology, more and more travel data have provided great convenience for scholars to study the travel behavior of users. Planning user travel has increasingly attracted researchers' attention due to its great theoretical significance and practical value. In this study, we not only consider the minimum fleet size required to meet the urban travel needs but also consider the travel time and distance of the fleet. Based on the above reasons, we propose a travel scheduling solution that comprehensively considers time and space costs, namely, the Spatial-Temporal Hopcroft-Karp (STHK) algorithm. The analysis results show that the STHK algorithm not only significantly reduces the off-load time and off-load distance of the fleet travel by as much as 81% and 58% and retains the heterogeneous characteristics of human travel behavior. Our study indicates that the new planning algorithm provides the size of the fleet to meet the needs of urban travel and reduces the extra travel time and distance, thereby reducing energy consumption and reducing carbon dioxide emissions. Concurrently, the travel planning results also conform to the basic characteristics of human travel and have important theoretical significance and practical application value.

Research on Human Behavior Feature Recognition and Intelligent Early Warning Methods in Safety Supervision Scene Video based on Yolov7

With the continuous improvement of infrastructure construction in China, the construction of various supporting power facilities is constantly followed up. A large number of ultra-high voltage or high-voltage power facilities, such as high-voltage transmission towers, transformers, or substations, are gradually emerging in populated areas, farmland, or fishponds where people still pass by sometimes. In such circumstances, people who do not have the qualification to contact power facilities at times approach, contact, or even climb these dangerous high-voltage transmission and distribution facilities illegally for various reasons, which not only poses a huge hidden peril or even threat to the safety of people’s lives and property but also cause a huge negative impact on the smooth operation of power infrastructure. To solve this intractable problem, this topic proposes a pedestrian detection and recognition algorithm, which can accurately distinguish between ordinary pedestrians and power grid maintenance inspectors based on YOLOv7, providing a reliable method for timely detection and early warning of pedestrians who may contact power facilities.

대안적 문화체험 모델로서의 지방구술사 기반 관광에 대한 탐색적 연구

As in the industrialization era, capitalism dominates the world, people become interested in experiencing spatio-temporal entities that satisfy all human needs between marketability and publicity, visibility and invisibility, past and present, and authenticity and pseudo-events. This macroscopic shift in the social order has a great influence on people’s cultural experience in the region where they live. The ideological value that enables the convergent production and consumption system can be said to be the convergent self-regulation mechanism of modernism and postmodernism. Oral history research that leads the transboundary cultural experience has recently attracted attention throughout the social sciences that study convergent social order. In particular, local oral history helps us understand the phenomenon of reorganization of time and space that is the basis of human lifestyle in the terms of microscopic life in the community.The oral history of the local community, a combination of objective records and subjective memories, can be an objective and subjective new meta-knowledge that features self-construction of transboundary sign experience in the structure of human-society intimacy. Local history research through personal oral history is a so-called bottom-up historical approach and discusses how human behavioral characteristics change the appearance of social culture itself and how closely related they are to social systems. The development of local tourism based on local oral history as an alternative cultural experience model enables in-depth research on the interaction of the region, society, and culture. It also can serve as a major basis for the development of sustainable and systematic eco-friendly local tourism development.