Analysis Of Behavior Research Articles

Current Analysis and Future Perspectives of a Smart Sensor Monitoring System for Multi-Scenario Recreation

With the accelerated aging of the population and the improvement of health awareness, the recreation industry is experiencing a booming development. Smart sensors, as the core component of recreation monitoring systems, play a key role in multi-scenario applications. This paper reviews the current research status of a multi-scenario recreation smart sensor monitoring system and analyzes its application in different scenarios such as family, community, and hospital. Firstly, the technological progress of smart sensors in physiological parameter monitoring, behavior analysis, and environment sensing is described. Second, the overall architecture design and data fusion method of a multi-scenario recreation monitoring system are discussed. Secondly, the characteristics and application requirements of smart sensors in different scenarios are compared and analyzed. Finally, the current technical challenges, including data security and privacy, system reliability, and intelligence level, are pointed out, and the future development trend is also prospected. The study shows that the introduction of emerging technologies such as multimodal sensing fusion, edge computing, and artificial intelligence will further enhance the performance of the recreation monitoring system. In the future, the construction of an intelligent and personalized recreation monitoring system covering the whole life cycle is of great significance in promoting the high-quality development of the recreation industry.

Unveiling engagement patterns of Yellowdig users: analysis of learning behaviors in an online undergraduate course

Unveiling engagement patterns of Yellowdig users: analysis of learning behaviors in an online undergraduate course

Multi-agent behavioral evolutionary game analysis of digital twin technology adoption in manufacturing enterprises

ABSTRACT Digital twin technology is crucial in breaking through the bottleneck of integrating the physical and information worlds in the manufacturing industry. However, manufacturing enterprises lack sufficient economic motivation and subjective willingness to adopt due to concerns about technological challenges and cost inputs. To elucidate the mechanisms that influence the adoption of digital twin technology, this study establishes an evolutionary game model from a stakeholder perspective, dynamically analyzes the strategy selection and system stability conditions of each participant and conducts numerical simulations. The findings reveal that the adoption process of digital twin technology in manufacturing enterprises occurs in phases. The coefficient of product attribute enhancement emerges as a key factor in promoting adoption. Government financial and consumption subsidies further encourage the adoption of digital twin technology and enhance customer preference for twin products and derivative services. The customer digital collaboration coefficient and demand matching coefficient significantly affect customers’ perceived utility benefits, which in turn influence manufacturing enterprises’ decisions to adopt digital twin technology by converting them into spillover benefits. Finally, this study provides targeted recommendations to various stakeholders to further promote the adoption of digital twin technology.

Comparison of the Thermal Behavior and Chemical Composition of Milk Powders of Animal and Plant Origin

The present study aims to perform a comparative analysis of the chemical composition and thermal behavior of two distinct milk types, namely animal and plant-based. The thermal analysis revealed the presence of the following classes of compounds: hydrocarbons, heterocycles, aldehydes, ketones, amines and alcohols. All types of milk contain saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), though the relative proportions of these vary depending on the specific milk type. Animal milk powders contain SFAs, including palmitic, stearic, and myristic acids, as well as moderate amounts of MUFAs, such as oleic and palmitoleic acids. They also contain lower PUFAs, including linoleic and alpha-linolenic acids. In contrast, plant-based milk powders, particularly soy milk powder, are rich in both linoleic and alpha-linolenic acids. Plant-based milk typically exhibits lower levels of SFAs and higher levels of MUFAs and PUFAs when compared to milk of animal origin. In conclusion, the fatty acid profiles of animal and plant-based milk powders reflect the different nutritional attributes and health implications associated with each. Thermal behavior analysis offers insights into the stability and potential flavor changes that may occur during processing and storage. The comparative analysis highlights significant differences in the chemical composition and thermal behavior of animal and plant-based milk powders.

Understanding Non-Wage Workers Behavior Towards BPJS Employment Programs: A Boundedly Rational Planned Behavior Analysis

Understanding Non-Wage Workers Behavior Towards BPJS Employment Programs: A Boundedly Rational Planned Behavior Analysis

Prevention and rehabilitation training of aerobics sports injuries based on intelligent wearable sensing devices

Aerobics is a kind of sports activity, which can not only exercise the body but also cultivate the sentiment and reduce the psychological burden. With the improvement of people’s living standards, aerobics is becoming more and more popular, but in fitness activities, there are often some sports injury accidents, which cause certain harm to human health. Therefore, people must take effective precautions against aerobics. Intelligent wearable sensor device is a new high-tech product developed based on Internet technology. It can not only realize real-time monitoring and diagnosis analysis of human body status information, but also combine with mobile terminals such as mobile phones to apply in the field of health management, and also provide personalized services according to user needs. The data collected by the sensor can be used to judge the human health status and exercise situation and make corresponding decisions, to help patients reduce or eliminate the disease burden. It collects the patient’s body data stores it in the database, and then generates corresponding action commands and corresponding motion tracks or speed control modules according to the results fed back by the sensors. At last, it sends these signals to the cloud server to complete the operation process required by the entire system, to achieve the purpose of real-time measurement, processing user health, and assisting athletes in learning training methods. According to human neuroscience, based on intelligent wearable sensing devices, this paper analyzed the prevention and rehabilitation of aerobics injuries and discussed the factors that lead to injuries in aerobics, injury treatment effects, rehabilitation time, and injury treatment satisfaction. The experimental results show that the patient’s satisfaction with the application of intelligent wearable sensing devices in the prevention of aerobics injury and rehabilitation training has increased by 6.84%. The intelligent wearable sensor device realizes the collection and processing of human health data. Applying big data analysis to user behavior analysis, can provide scientific and effective guidance and suggestions for athletes and improve their physical fitness and competitive level.

Acoustic tags versus camera—a case study on feeding behaviour of European seabass in sea cages

IntoductionWith the expansion of the aquaculture industry, the need arises for scalable, reliable, and robust methods to assess fish behaviour in sea cages to guide operational management, which includes feeding optimisation and welfare assessments. Fish cage monitoring utilising either acoustic transmitters or underwater cameras is well-studied. However, the relationship between those two different measurement types seems to have not been explored, nor have they been evaluated together in one experimental site.MethodsIn our 1-month study, we compared the activity of 14 sentinel fish and the artificial intelligence (AI)-inferred speed of individuals from the European seabass (Dicentrarchus labrax) sea cage population in three feeding trials. Comparisons include a maximum activity comparison using persistent peaks, fish behavioural pattern establishment and retention, and periodical behavioural patterns.ResultsOur results demonstrate that under certain circumstances, both technologies are interchangeable from the perspective of persistent peaks and periodicity, but complementary when it comes to behaviour analysis such as food anticipatory behaviour (FAB).DiscussionWe anticipate that our findings will stimulate advances where multiple sensor types are in use to achieve a more holistic understanding of fish behaviour in the aquaculture sector using underwater technologies.

Toxicological Assessment of Ketamine in Juvenile Zebrafish (Danio rerio)

This study investigates the toxic effects of ketamine on juvenile zebrafish, driven by increasing concerns over ketamine’s prevalence and its potential neurotoxic effects that may disrupt behavior and metabolism. Employing a high-throughput behavior tracking system, the research analyzed the locomotor activity of 6-day post-fertilization (6 dpf) zebrafish exposed to various concentrations of ketamine. The integration of behavioral analysis with metabolic profiling was a notable innovation, as it establishes a comprehensive understanding of ketamine’s effects on both acute behavioral inhibition and metabolic responses. The findings reveal that ketamine exposure significantly inhibits locomotor activity in juvenile zebrafish, with these effects becoming more pronounced at higher concentrations. Additionally, the detection of normethketamine, the primary metabolite of ketamine, using UPLC-LTQ/Orbitrap HRMS, confirms the zebrafish’s ability to metabolize the drug. This underscores the utility of zebrafish as a model organism for studying the impact of ketamine on behavior and metabolism, providing valuable insights that may extend to other vertebrates.

Mapping the field of workplace deviant behavior: a comprehensive bibliometric analysis

Purpose This study aims to conduct a rigorous bibliometric analysis of workplace deviant behavior (WDB) to identify prevailing trends, patterns, and themes in the field. The analysis seeks to outline the domain’s intellectual and social structure and provide a research roadmap. Design/methodology/approach The study employed a systematic procedure to scrutinize research scenario. A search in the Scopus database produced 1,677 papers from reputable publications spanning 42 years (1976–2023). Science mapping techniques, including co-citation, co-authorship, and co-occurrence analysis, were used to obtain insights into the research landscape. By implementing these techniques, the authors comprehensively understood the interconnectedness and patterns within the research field. Findings This study demonstrates an apparent surge in the overall number and impact of studies conducted on WDB, most of which are published in prestigious publications. Furthermore, the findings support Bradford’s law, defying Lotka’s law in this field. Through co-citation analysis, four clusters were identified in this area. Subsequently, co-occurrence was used to pinpoint several strong themes. Finally, the authors of the USA and China demonstrated the most significant collaboration. Originality/value This study is a pioneering approach to applying bibliometric analysis to explore WDB research; it addresses a gap in the comprehensive review of earlier research and contributes to enriching the body of knowledge in this area. The results of this research are helpful for practitioners, scholars and future researchers in this subject, even if it has its roots in scientometrics.

Methods for determining and preventing cheating in Esports

The article discusses methods for identifying and preventing cheating in Esports. Given that the gaming industry has become an integral part of human culture and entertainment in the digital world, the problem of cheating has also grown with its development. Cheating in Esports is the use of unauthorized software or hardware to gain an advantage over other players or violate the rules of the game. That is why it is necessary to ensure long-term effectiveness in combating cheating in Esports and create conditions for the development of a healthy and competitive environment. In particular, 78,8 % of professional players reported regular encounters with cheating (more than once a week), while this figure was 45,3 % among anti-cheat system developers. Experts noted the highest effectiveness of behavioral pattern analysis methods (4,5 out of 5,0) and automated anti-cheat systems (4,2 out of 5,0) for detecting violations during the game. An analysis of the prevalence of various types of cheating in Esports disciplines revealed that the most problematic are aimbots in FPS games (45,2 % of cases) and macros in «fighting games» (65,4 % of cases). At the same time, experts note the growing role of technical measures (35 %) and organizational measures (28 %) in combating cheating.

Low-impact, near real-time risk assessment for legacy IT infrastructures

In an era where cybersecurity threats are evolving at an unprecedented pace, this paper introduces a methodology for near real-time risk assessment of high-profile, high security infrastructures, where data security and operational continuity inherently limits observability. Our approach addresses the challenges of this limited observability and minimized disruption, offering a new perspective on processing and evaluating cybersecurity knowledge. We present an innovative method that leverages attack graphs and attacker behavior analysis to assess risks and vulnerabilities. Our research includes the development of an automated risk assessment mechanism, graphical security modeling, and a Markov chain-based model for attacker behavior. Our methodology utilizes a blend of direct and indirect event sources, incorporating an attacker behavioral model based on a random walk method akin to Google’s PageRank. The proof-of-concept solution calculates potential risk according to the actual threat landscape, providing a more accurate and timely assessment.

An Event Frequency‐Inverse Session Frequency Based Weighting Mechanism for Data Embedding Methodologies

ABSTRACTContextWith the rapid increase in data complexity and volume, analyzing and understanding large‐scale datasets has become a critical challenge, especially in domains like e‐commerce. This paper addresses this challenge by introducing a novel business process that utilizes an event frequency‐inverse session frequency (EF‐ISF) based weighting mechanism for embedding user navigational behavior in e‐commerce websites.ObjectiveThe primary research problem tackled in this study is the detection of patterns and distributions within user behavior data, which is often complicated by dynamic page content and session sparsity. We aim to enhance the interpretability and accuracy of user behavior analysis by integrating EF‐ISF with various embedding techniques, including Word2Vec, Node2Vec, DeepWalk, Struc2Vec, and LSTM Autoencoder.MethodologyOur approach is particularly effective in datasets where page refreshes lead to event repetitions, where sequential events with temporal dependencies are prevalent, and where event distributions across sessions are sparse. By applying this methodology across multiple e‐commerce datasets, we observe consistent performance in clustering quality, successfully detecting underlying patterns and groups within the data.ResultsA prototype implementation demonstrates the practical utility of the proposed method, with evaluation metrics confirming that EF‐ISF weighting not only enhances the embeddings but also facilitates a more nuanced and significant segmentation of users' browsing behaviors.ConclusionThese findings provide a valuable contribution to the field of e‐commerce data analytics, offering businesses a refined approach to optimizing user experience and engagement through a deeper understanding of user behavior patterns. This methodology can significantly improve the quality of insights derived from user behavior data, ultimately leading to better decision‐making and enhanced user satisfaction.

Phagocytic Function Analyses of GABBR-Related Microglia in Immature Developing Epileptic Brain Based on 10× Single-Nucleus RNA Sequencing Technology

Background: Epilepsy is a neurological disorder defined by the occurrence of epileptic seizures, which can significantly affect children, often leading to learning and cognitive impairments. Microglia, the resident immune cells of the central nervous system, are essential in clearing damaged neurons through phagocytosis. Notably, GABBR-associated microglia have been implicated in regulating phagocytic activity. Since the phagocytic function of microglia is critical in the pathogenesis of epilepsy, this study aims to investigate the role of GABBR-associated microglia in the development of the immature brain following epileptic seizures. Methods: Epilepsy was induced in a mouse model by the intraperitoneal injection of KA. Changes in the expression of the GABBR-related gene, GABBR2, in hippocampal microglia were analyzed using single-nucleus RNA sequencing (snRNA-seq). Cognitive and emotional changes in the mice were assessed through behavioral analyses. The expression of GABBR2 was semi-quantitatively measured using Western blotting, quantitative reverse transcription PCR, and immunofluorescence. Additionally, the spatial relationship between GABBR2 and hippocampal neurons was evaluated using Imaris software. Results: The snRNA-seq analysis revealed that GABBR2 expression was elevated in activated microglia in the hippocampus during chronic epilepsy compared to the early phase of seizures. Behavioral assessments demonstrated heightened anxiety levels and learning and memory impairments in the chronic epilepsy group compared to the control group. GABBR2 expression was upregulated in chronic epilepsy. Three-dimensional reconstruction analyses revealed a significantly increased contact volume between GABBR-associated microglia and neurons in the chronic epilepsy group compared to the control group. Conclusions: GABBR-associated microglia significantly contribute to the progression of immature brain diseases by promoting neuronal phagocytic activity.

P1126 Patterns of conventional therapy use and drivers of initiation for advanced therapy in Inflammatory Bowel Disease: A European situational analysis

Abstract Background Data suggest limited use of advanced therapies (AT) in inflammatory bowel disease (IBD) despite the accumulating data for the benefits of early, broader AT use.1-3 We evaluated barriers to AT use in IBD using a multimodal approach. Methods Market research was conducted June-November 2023. Quantitative survey on prescribing trends amongst gastroenterologists (GEs) prescribing ATs was conducted to capture AT initiation drivers in Germany, Italy, UK and US; questions for this survey were identified in qualitative interviews. GEs cycling an average of 0-1 and ≥2 conventional therapies (CTs) before initiating AT were classified as low and high cyclers, respectively. The COM-B (Capability, Opportunity, Motivation and Behaviour) model was used for behavioural analysis.4 Results Qualitative analysis covered responses from 62 participants (28 patients [pts], 24 GEs, 7 nurses, 3 pt organisation representatives), and quantitative analysis – from 142 GEs (Germany, 30; Italy, 30; UK, 30; US, 52) currently prescribing ATs, with 559 pt report forms (ulcerative colitis [UC], 280; Crohn’s disease [CD], 279) collected. CT cycling was more prevalent in UC vs CD; AT treatment was less likely in pts with UC vs CD (Figure). Differences among GEs by geography were only partially explained by cost containment in highly regulated markets (eg, Italy, UK). High cycling variations were linked to behavioural and belief-driven factors as illustrated by the notable use (7-20%) of 5-aminosalicylates in CD despite the lack of clinical guideline support. In behavioural analysis, AT initiation barriers included a lack of awareness of AT value and prescribing approach of exhausting all CT options before initiating AT. Low vs high cyclers placed higher importance on patient preference (UC, +16%; CD, +19%) and peer recommendations (UC, +13%; CD, +20%). In UC, the belief of exhausting all CT options before initiating AT was more important for high vs low cyclers (+9%). Qualitative research showed that the lack of clear guidelines disproportionately impacted high cyclers as they had reduced academic interest and limited capacity as contributors. Physician-relevant AT initiation drivers were the perceived CT risks and the growing recognition of the AT clinical value and long-term impact on the quality of life. Pt-relevant drivers included improved AT awareness and the ability and confidence for pts to self-advocate. Conclusion Cost containment and guideline variations could partially explain the observed geographic variations in AT use. Within any given country, clinical beliefs play a determining role. In Europe, a tangible increase in AT vs CT share could be achieved by encouraging GEs to initiate AT in pts with poor prognostic factors as per guideline suggestions.

Towards a characterization of human spatial exploration behavior

Spatial exploration is a complex behavior that can be used to gain information about developmental processes, personality traits, or mental disorders. Typically, this is done by analyzing movement throughout an unknown environment. However, in human research, until now there has been no overview on how to analyze movement trajectories with regard to exploration. In the current paper, we provide a discussion of the most common movement measures currently used in human research on spatial exploration, and suggest new indices to capture the efficiency of exploration. We additionally analyzed a large dataset (n = 409) of human participants exploring a novel virtual environment to investigate whether movement measures could be assigned to meaningful higher-order components. Hierarchical clustering of the different measures revealed three different components of exploration (exploratory behavior, spatial shape, and exploration efficiency) that in part replicate components of spatial exploratory behavior identified in animal studies. A validation of our analysis on a second dataset (n = 102) indicated that two of these clusters are stable across different contexts as well as participant samples. For the exploration efficiency cluster, our validation showed that it can be further differentiated into a goal-directed versus a general, area-directed component. By also sharing data and code for our analyses, our results provide much-needed tools for the systematic analysis of human spatial exploration behavior.

Modeling and Analysis of Mandatory Merging Decision Behavior Considering Temporal and Spatial Risks

Modeling and Analysis of Mandatory Merging Decision Behavior Considering Temporal and Spatial Risks

Experimental Investigation of Young Concrete Properties Needed for Proper Analysis of Concrete Pavements Behavior During the Hardening Process

In this paper there are presented results of experimental investigation of young concrete properties. During design process of concrete structures there is used number of mechanical properties of concrete that are not normally part of the concrete specifications. Desirable values are often related to primary parameter of concrete – mean concrete compressive strength. Some of them are poorly or even not at all described at the very beginning of concrete maturing. This paper describes results obtained from common mechanical test of compressive and tensile strength, shrinkage strain and two author’s test dealing with creep under constant compressive and tensile stresses of young concrete and also temperature development in quasi-adiabatic conditions.

Social Network Analysis of Self-Management Behavior Among Older Adults With Diabetes.

This study explores the influence of social network structures on self-management behaviors among older adults with diabetes in rural Korean villages, focusing on dietary management, physical activity, and blood glucose monitoring. Employing social network analysis (SNA), the study assessed network structures in three villages, focusing on variations in degree, closeness, and betweenness centralities to understand their impact on health behavior dissemination and adoption. The analysis identified significant differences in network configurations across the villages. Village C, characterized by the highest closeness centrality, facilitated the efficient diffusion of health-related information, thereby promoting the rapid adoption of self-management behaviors. In contrast, Village B exhibited a prominent central node with considerable influence, underscoring its potential as a strategic focal point for targeted health interventions. Social network analysis is useful for identifying influential nodes within community structures to enhance chronic disease management strategies. By tailoring interventions to the unique attributes of a community's network, significant improvements in diabetes self-care efficacy can be achieved. This approach presents a scalable and sustainable framework for community-oriented health interventions. It provides a foundation for further research into the structural and functional dynamics of diabetes self-management networks.

Deformation behavior and interface analysis of SiCp/Al composites by vibration cutting based on molecular dynamics

Deformation behavior and interface analysis of SiCp/Al composites by vibration cutting based on molecular dynamics

In-Depth Analysis of Fatal Motorcycle Accidents—Case Study in Slovenia

Motorcyclists remain a disproportionately large group of vulnerable road users, with fatality rates significantly higher than that in other road groups. Additionally, fatal accidents involving motorcyclists have a more slowly decreasing trend in comparison to that of other road users, while the number of this kind of users is growing fast. For all these reasons, there is a need to understand what the key factors leading to fatal accidents are in order to identify the possible measures to minimize the accidents themselves or at least their consequences. This would permit, indeed, to positively impact the road traffic system, leading to the creation of the safest road traffic system possible, as it is the goal of the Sustainable Safety approach. The aim of this study is to dive into the mentioned problem, analyzing fatal motorcycle accidents in Slovenia over a decade, highlighting the key factors contributing to these incidents. By integrating data from four databases, the study evaluated accident trends, infrastructural elements, and rider behavior through a multi-stage analysis. Firstly, data were collected from four national, up-to-date databases that contain information about road accidents themselves, the road infrastructure, additional police data, and media descriptions. After merging this information into one comprehensive database, where each row represents all the data available for one accident, a general analysis of accidents’ trends over the considered 10-year period was developed, considering at first all fatal road accidents, then deepening it to accidents caused by a motorcyclist, and finally to single-vehicle accidents. A statistical analysis followed, aimed at identifying a statistical correlation between the accidents and the factors leading to them. The results of the first accident analysis indicated that excessive speed, incorrect driving direction, and overtaking maneuvers are the primary causes of fatal accidents, especially on non-urban roads preferred by motorcyclists. Single-vehicle accidents frequently involve collisions with roadside objects, including safety barriers and poles, underscoring the need for targeted infrastructural improvements. The following correlation analysis revealed that a total of seven factors were statistically significant: three human factors (age, gender, experience)—which were the ones with the strongest correlations—one infrastructural factor (pavement width), and three factors belonging to external conditions (accident type, cause, and location). Of these, four were positively correlated to the causer, while three, i.e., pavement width, causes, and road location, were negatively correlated. This study provides a foundation for future research on less severe accidents and proactive risk behavior analysis, aiming to improve motorcyclist safety comprehensively.