Model For Prediction Of Behavior Research Articles

Triboinformatic Machine Learning Model for Frictional Behavior and Wear Volume Loss Prediction of SS316L Alloy Clad with WC/NiCrBSi

Triboinformatic Machine Learning Model for Frictional Behavior and Wear Volume Loss Prediction of SS316L Alloy Clad with WC/NiCrBSi

Physicians' Perceptions and Experiences Regarding Leadership: A Link Between Beliefs and Identity Formation.

Despite the development of national recommendations and training programs for effective leadership, junior and senior medical leaders often find themselves ill-prepared to take on these new responsibilities. This study aimed to explore physicians' perceptions, feelings, and beliefs regarding leadership and to provide recommendations regarding appropriate training and institutional post-training support. We conducted a qualitative study at the Geneva University Hospitals. A purposeful sample of residents (R), fellows (F), attending physicians (A), and chairpersons (CP) were invited to participate in focus groups (or semi-structured interviews) between April and June 2021. We investigated their understanding of leadership, self-perception as leaders, difficulties, and paths to improvement in their leadership skills. Focus groups were transcribed verbatim and analyzed both inductively and deductively using Fishbein's model of behavior prediction and Irby's professional identity formation framework. We conducted ten focus groups (R=3; F=4, A=2, and CP=1) and one interview (CP). Physicians expressed poor self-efficacy at all hierarchical levels: feelings of insecurity and confusion (R and F), frustration (A), and feeling stuck between divisional and institutional governance (CP). Such negative feelings were nurtured by personal beliefs with an intuitive and idealized representation of leadership. Beliefs focused more on personal characteristics rather than on skills, processes, or perceived institutional norms. Unclear expectations regarding physicians' role as leaders, overemphasis on academic achievement, and silo professional organizations fueled their feelings. Participants reported developing their leadership through trial and error, observing role models, and turning to personal resources rather than formal training. Our results show that physicians' leadership skills are still mainly acquired intuitively and that institutional norms do not encourage clarification of leadership roles and processes. Physician training in leadership skills, together with more explicit and clear institutional processes, may help to improve physicians' self-efficacy and develop their identity as leaders.

Concept Mapping STI/HIV Prevention and Condom Use among Young African American Adults.

Theory-based HIV prevention programs have resulted in increased condom use, which remains the best method for the prevention of sexually transmitted infections (STIs) among sexually active heterosexual individuals. Particularly, the integrative model of behavior prediction theorizes that attitudes, norms, self-efficacy, and socioenvironmental factors influence intention and behavior and has been useful in understanding STI risk among adolescents. However, more research is needed regarding young African American adults. Given the increased freedom and decision-making independence afforded to young adults compared to adolescents, it is important to consider the STI/human immunodeficiency virus (HIV) prevention messages that would resonate with them, particularly regarding condom use. The present study sought to explore how attitudes, subjective norms, self-efficacies, and socioenvironmental factors may influence condom use and STI/HIV prevention, as conceptualized by the participants. We conducted a group-based concept map, a systems-thinking mixed methodology that resulted in a geospatial map reflecting the conceptualizations of the participants. Self-identified young heterosexual African American adults (N = 43) aged 20-26 engaged in an interactive concept mapping procedure in order to "map out" their overarching concepts about STI/HIV risk and condom use. Seven overall conceptual domains emerged: self-efficacy for partner communication, condom use self-efficacy, social media/sociocultural influences, condom use/STI knowledge, condom use cons, condom use pros, and subjective and social norms about condom use. We presented the concept map and discussed the conceptual interpretations and the relationships among the overarching concepts. We also discussed how the social environment, including the social media environment, was conceptualized regarding STI/HIV risk and prevention among young African American adults. Concept mapping can be viewed as a way to determine worthwhile messages for intervention development. The findings may provide information for prevention programs aimed at reducing the incidence of STIs among young adult heterosexual persons within African American communities.

Navigating virtual english learning horizons: a study on students’ use of 360° videos in self-directed learning

Abstract360° videos have yielded satisfactory outcomes in English learning. However, there exists a dearth of research that delves into the efficacy of these videos for self-directed learning of English. This study aimed to investigate the factors that influence students’ use of 360° videos in self-directed learning of English by using the integrative model of behavior prediction as the theoretical background. The study sample consisted of 457 university students from China. A questionnaire was used to collect data. The questionnaire entailed few open questions and 26 items distributed among six factors: Attitudes, self-efficacy, subjective norms, behavioral intention, self-regulated skills, and actual use. SPSS 22.0, JASP, and SmartPLS 4 were used for data analysis. The results revealed significant direct effects of attitudes and self-efficacy on behavioral intention and direct effects of behavioral intention and self-regulated skills on actual behavior. However, the results showed that subjective norms and self-regulated skills did not have a direct effect on the behavioral intention, and that behavioral intention did not affect actual behavior through self-regulated skills. The implementation of 360° videos garnered interest among students for self-directed learning of English. The influence of self-regulatory skills on the utilization of 360° videos is noteworthy, as it may contribute to fostering a sense of ease and comfort among students and other stakeholders.

Synergistic effect of Al2O3 and MoS2 on the corrosion behaviour of plasma sprayed aluminium matrix composite coating

In this study, the incorporation of alumina (Al2O3) and molybdenum disulfide (MoS2) in Aluminium (Al) was successfully fabricated through shroud plasma spray. This strategic incorporation of MoS2 and Al2O3 effectively fills voids, leading to a remarkable reduction in inherent porosity within the aluminium coating, achieving an impressive ∼76.54 % reduction. These composite coatings exhibited outstanding hydrophobicity (contact angle ∼119°), signifying remarkable water repellency and anti-corrosion properties compared to bare aluminium (contact angle ∼81.7°). During the electrochemical corrosion test, the corrosion rates (CR) of Al coating (CR = 9.14 mpy) was notably around 23 times higher compared to MoS2 and Al2O3 reinforced aluminium composite coating (CR = 0.372 mpy) in a 3.5 wt % NaCl electrolyte. The subsequent post-corrosion analysis further confirmed the composite coating's efficiency in mitigating the impact of corrosive salts, underlining the effectiveness of the hybrid coating in combating corrosion attacks. A corrosion behaviour prediction model was established based on computational and experimental data.

Enhanced Pet Behavior Prediction via S2GAN-Based Heterogeneous Data Synthesis

Heterogeneous data have been used to enhance behavior prediction performance; however, it involves issues such as missing data, which need to be addressed. This paper proposes enhanced pet behavior prediction via Sensor to Skeleton Generative Adversarial Networks (S2GAN)-based heterogeneous data synthesis. The S2GAN model synthesizes the key features of video skeletons based on collected nine-axis sensor data and replaces missing data, thereby enhancing the accuracy of behavior prediction. In this study, data collected from 10 pets in a real-life-like environment were used to conduct recognition experiments on 9 commonly occurring types of indoor behavior. Experimental results confirmed that the proposed S2GAN-based synthesis method effectively resolves possible missing data issues in real environments and significantly improves the performance of the pet behavior prediction model. Additionally, by utilizing data collected under conditions similar to the real environment, the method enables more accurate and reliable behavior prediction. This research demonstrates the importance and utility of synthesizing heterogeneous data in behavior prediction, laying the groundwork for applications in various fields such as abnormal behavior detection and monitoring.

A probabilistic fatigue life assessment method for wind turbine blade based on Bayesian GPR with the effects of pitch angle

AbstractThe fatigue behavior of large wind turbine blades is complex and stochastic due to their complex structure and operating environment. This paper focuses on developing a probabilistic fatigue life assessment method for wind turbine blades considering the uncertainties from wind velocity, material mechanical properties, pitch angle, and layer thickness. To improve the efficiency of stochastic fatigue behavior analysis of wind turbine blade, unidirectional fluid‐structure coupling (UFSC) and bidirectional fluid‐structure coupling (BFSC) analysis are employed to analyze the stochastic response. Then, Gaussian process regression (GPR) and Bayesian updating are combined to establish the stochastic fatigue behavior prediction model for wind turbine blade. On this basis, a modified S‐N curve formulation is proposed, and the fatigue life of wind turbine blade is analyzed by the modified S‐N curve and compared with the three‐parameter Weibull model. The results indicate that the proposed method for fatigue life assessment has better accuracy. The proposed probabilistic fatigue life assessment method with high accuracy and high efficiency, which is beneficial for the fatigue reliability design of wind turbine blades.

Media Influence on Bystander Intervention for Health Protective Norms: The Integrative Model of Behavioral Prediction Perspective

Drawing upon the Integrative Model of Behavioral Prediction (IM) and the Bystander Intervention Model, this study investigates the impact of media health information on individuals’ intentions to address violations of health norms, specifically noncompliance with mask-wearing during the COVID-19 pandemic. Our survey results (n=1,426) indicate a positive correlation between seeking health information from the media and the intention to confront norm violators regarding mask-wearing. This correlation is mediated through three intermediary pathways: attitudes, normative beliefs, and perceived behavioral control. These discoveries address a previously unexplored area concerning pro-social health behaviors, bystander intervention, and contribute to the field of health communication by linking them to research on media influences. Combining media and peer interventions could lead to more effective health outcomes. The discussion covers both theoretical and practical implications.

Tribo-informatics approach to predict wear and friction coefficient of Mg/Si3N4 composites using machine learning techniques

The demand for lightweight, high-performance materials has driven significant advancements in magnesium-based materials. However, their practical implementation faces challenges, primarily due to their low wear resistance, especially in industries like automotive, where weight reduction is vital for fuel efficiency. Furthermore, the process of fabricating and assessing wear behavior incurs time and cost constraints. In light of this, machine learning techniques have emerged as a crucial tool for predicting the mechanical properties, wear characteristics, and tribological performance of diverse materials, including magnesium and its composites. Accordingly, the present study aims to integrate experimental results with machine learning techniques. The objective is to predict the wear rate and friction coefficient of Mg/Si3N4 nanocomposites, thus optimizing material design and manufacturing for superior wear performance. Nanocomposites are fabricated through ultrasonic-assisted stir casting, and a dataset of 120 data points is collected using a pin-on-disc tribometer under dry sliding conditions. Five supervised machine learning regression models are employed to predict wear rate and coefficient of friction, with hyperparameter tuning for a fair comparison. Results are evaluated using various statistical metrics, identifying the most effective model for accurate wear behavior prediction. The study also demonstrates improved wear resistance and lower friction coefficients in nanocomposites compared to pure magnesium. This is attributed to the even distribution of Si3N4 nanoparticles and strong interfacial bonding with the matrix. The presence of a mechanically mixed layer further enhances wear resistance under high loads and speeds. Five wear modes are identified, including abrasion, oxidation, adhesion, delamination, and plastic deformation, providing valuable insights into the wear mechanisms. A comprehensive wear map facilitates a deeper understanding of wear behavior.

A prediction model for rumor user propagation behavior based on sparse representation and transfer learning

This paper introduces a prediction model rooted in sparse representation and transfer learning, with the primary objective of predicting user behavior during rumor propagation. Users' behavior is dynamic, and rumor, rumor-refuting, and rumor-promoting messages interact dynamically, according to the model. Firstly, this paper proposes to compensate for the low performance of propagation prediction models due to data sparsity at the beginning of rumor topics' lifes. Transfer learning is used to compensate for the data sparsity problem at the beginning of the topic. To map the rumor topic space effectively, a low-rank dense vectorization algorithm based on sparse representation is proposed. Finally, to mine the potential impact of multiple types of information on users, a model based on three-party game theory is constructed. It considers the complex interaction between rumor, rumor-refuting, and rumor-promoting information in the propagation of rumor. Additionally, this paper develops a model of dynamic user behavior prediction using Rumor-Attention-Mechanism-Graph-Attention (RAM-GAT) to predict rumor propagation. Experiments demonstrate that our model can effectively mine the interaction influence between multiple types of information. It can accurately predict user behavior when initial data is insufficient. In addition, rumor propagation patterns and trends are revealed.

Rethink data-driven human behavior prediction: A Psychology-powered Explainable Neural Network

Human behavior prediction is a rapidly evolving field with numerous applications in various domains. The rapid development of machine learning facilitates research on data-driven behavior prediction. At the same time, in human factors domains, researchers still focus on modeling psychological decision-making processes from the psychological perspectives of view based on subjective data. However, it is difficult to obtain one’s psychological states in real-world scenarios, given that the collection of subjective data in real-world settings is difficult and intrusive. Thus, psychology theories can hardly be applied to behavior prediction directly. To bridge the gap between data-driven behavior prediction and psychological decision-making models, we proposed a novel Psychology-powered Explainable Neural network (PEN). In PEN, for the first time, the psychological factors of individuals (i.e., human-side features such as attitudes to technologies) are modeled and recovered explicitly based on one’s historical behaviors in certain scenarios. The multi-task optimization ensures each component in PEN to be optimized with proper gradient. Assessment results over three evaluation protocols demonstrate that, based on the historical behaviors of an individual only, the PEN can outperform the best existing behavior prediction models by 1.87% and 4.68% on two datasets when a 1:9 training–testing data partition was adopted. The framework in PEN has provided a new solution for explainable human behavior prediction when psychological information is not directly available.

Big Data Analysis and User Behavior Prediction of Social Networks Based on Artificial Neural Network

The prediction of user behavior in social networks is of great significance for understanding user dynamics, personalized recommendation, and information dissemination. With the development of artificial intelligence (AI) technology, especially the application of artificial neural networks in big data analysis, new solutions and technical means have been provided for the analysis of user behavior in social networks. This study constructs a social network user behavior prediction model based on artificial neural networks. The article first reviews related research, establishes a research framework, and then describes in detail the functional structure of the user behavior prediction system. The key data structure design is meticulously constructed and the model is built through steps such as data preprocessing module, 3D feature frame construction module, feature mapping module, and feature prediction module. In addition, the article also constructs a group behavior theme probability prediction model based on an improved encoder-decoder model, which further enhances the understanding of group behavior of users in social networks. Through comparative experiments, the model proposed in this paper demonstrates its effectiveness in predicting individual and group user behaviors. The experimental results show that the model can accurately predict the behavior patterns of users in social networks and is superior to existing methods in terms of prediction accuracy and computational efficiency. This research shows that artificial neural networks are a powerful tool for analyzing big data in social networks and for predicting user behavior. The success of the study verifies the effectiveness of the model, provides a new technical path for future analysis of user behavior in social networks, and is expected to be widely applied in areas such as personalized recommendation and information dissemination analysis.

From Information Seeking and Scanning to the Practice of Healthy Habits: A Longitudinal Test of the Integrative Model of Behavioral Prediction in the Context of Fruit and Vegetable Consumption

Fruit and vegetable intake is essential for health, but global adherence to recommended levels remains insufficient. Health information exposure positively influences consumption, yet the underlying mechanisms are not fully understood. This study aims to explore the relationships between information seeking and scanning, attitudes, norms, perceived behavioral control (PBC), intentions, and fruit and vegetable intake, following the main tenets of the Integrative Model of Behavioral Prediction (IM). Data were collected through face-to-face surveys in Santiago, Chile, with a representative sample of individuals aged 25 and older in two waves. Findings revealed that intentions in Wave 1 predicted fruit and vegetable consumption in Wave 2, with positive associations between attitudes, norms, PBC, and intentions. Information seeking was positively associated with attitudes, norms, and PBC, and it had indirect effects on fruit and vegetable consumption through attitudes, norms, PBC, and intentions. Information scanning did not show significant indirect effects on fruit and vegetable consumption, even though the path between scanning and attitudes was significant. The study provides support for the IM and highlights the importance of information seeking in promoting fruit and vegetable consumption through its influence on attitudes, norms, and PBC. The findings have practical implications for health campaigns, educational programs, healthcare interactions, and public policies targeting healthier dietary habits.

How to Evaluate Behavioral Models

Researchers building behavioral models, such as behavioral game theorists, use experimental data to evaluate predictive models of human behavior. However, there is little agreement about which loss function should be used in evaluations, with error rate, negative log-likelihood, cross-entropy, Brier score, and squared L2 error all being common choices. We attempt to offer a principled answer to the question of which loss functions should be used for this task, formalizing axioms that we argue loss functions should satisfy. We construct a family of loss functions, which we dub ``diagonal bounded Bregman divergences'', that satisfy all of these axioms. These rule out many loss functions used in practice, but notably include squared L2 error; we thus recommend its use for evaluating behavioral models.

An effective lane changing behaviour prediction model using optimized CNN and game theory

Accurately predicting lane changes, a crucial driving activity for preventing accidents and ensuring driver safety, is addressed in this study. An innovative predictive model that integrates game theory for precise lane change intention detection and an optimized Convolutional Neural Network (CNN) for trajectory prediction is proposed in this study. The CNN's efficiency is enhanced through metaheuristic optimization of both the convolution and fully connected layers using the Whale Optimization Algorithm (WOA). Emphasizing robust data processing, a Wiener filter is applied for pre-processing, and the Cascaded Fuzzy C means (CFCM) technique is employed for segmentation. The resulting Whale Optimization Algorithm-based CNN (WOA-CNN) effectively forecasts the trajectory of lane-changing vehicles. Validation of the proposed approach in Python demonstrates exceptional accuracy, reaching 96.5%. This study showcases the effectiveness of the WOA-CNN model in advancing the prediction accuracy of lane-changing behaviour, contributing to enhanced driver safety and accident prevention.

Pushing toward systemic change in the Capitalocene: Investigating the efficacy of existing behavior prediction models on individual and collective pro-environmental actions in high school students

Environmental education often advocates for individual pro-environmental behavior—which, while beneficial to a degree, fails to match the large scale of today’s capitalism-fueled socio-ecological challenges. Rather, collective action holds promise as a means for the large-scale changes required in the Capitalocene. These actions can take the shape of encouraging group uptake of behaviors (collective non-activist behaviors) or through fostering system change (collective activist behaviors). This study works to understand how well the environmental literacy framework combined with the theory of planned behavior predicts collective environmental behaviors, using North Carolina high school students as a study population. We find that, while these models effectively predict student engagement in individual environmental behaviors (adjusted R2 = 0.39), they are less accurate at predicting engagement in collective non-activist behaviors (adjusted R2 = 0.17) and collective activist behaviors (adjusted R2 = 0.10). Thus, more research and theory-building are needed to understand what drives collective behavior among youth and beyond.

Towards a system for real-time prevention of drowsiness-related accidents

<p>Traffic accidents always result in great human and material losses. One of the main causes of accidents is the human factor, which usually results from driver’s fatigue or drowsiness. To address this issue, several methods for predicting the driver’s state and behavior have been proposed. Some approaches are based on the measurement of the driver’s behavior such as: head movement, blinking time, mouth expression note, while others are based on physiological measurements to obtain information about the internal state of the driver. Several works used machine learning/deep learning to train models for driver behavior prediction. In this paper, we propose a new deep learning architecture based on residual and feature pyramid networks (FPN) for driver drowsiness detection. The trained model is integrated into a system that aims to prevent drowsinessrelated accidents in real-time. The system can detect drivers’ drowsiness in real time and alert the driver in case of danger. Experiment results on benchmarking datasets shows that our proposed architecture achieves high detection accuracy compared to baseline approaches.</p>

Time-aware multi-behavior graph network model for complex group behavior prediction

In the multifaceted landscape of social networks, user behaviors manifest in various patterns, contributing to the diversity of group behaviors. Current research on group behavior modeling often limits its focus to single behavioral types, overlooking the interplay among different behaviors. To bridge this gap, we introduce Time-aware Multi-behavior Graph Network (TMGN) model. This model integrates heterogeneous graph representation learning to discern patterns in user-item interactions across multiple behaviors, capitalizing on dynamic user preferences through time encoding strategy. Additionally, TMGN harnesses a self-attention multi-behavior fusion network to effectively amalgamate characteristics of diverse behaviors, which can tackle the complex hierarchical dependences among distinct group behaviors. Empirical validation on Yelp and the MovieLens 10M datasets demonstrates that TMGN outperforms the leading baseline model, KHGT, by 5.9 %, 23.93 %, and 8.57 % in HR@5, NDCG@5, and Recall@5 metrics, respectively. The findings offer substantial theoretical and practical insights for predicting group behavior on online platforms.

Erosion rate of AA6082-T6 aluminum alloy subjected to erosive wear determined by the meta-heuristic (SCA) based ANFIS method

Abstract AA6082-T6 aluminum alloy is used in different engineering applications. The erosive wear takes places in many machine parts. The prediction of wear amounts for aluminum alloy materials is complicated and nonlinear phenomena. The fuzzy inference systems (FIS) and the artificial neural networks (ANNs) have a series of properties on modeling nonlinear systems. In this study, it was aimed to determine the optimum erosive wear parameters in terms of wear resistance. This study suggests a meta-heuristic (sine–cosine algorithm-SCA) Based ANFIS prediction model for prediction of wear behavior of AA6082-T6 aluminum alloy within various impingement pressure, impact velocity, impingement angle and particle sizes. In this study, a model is developed that determines the optimum erosive wear parameters to achieve the minimum wear rate. The erosion rate-SCA Based ANFIS prediction model extracted reasonable results. Estimation capability has been achieved to 99.81 % by the proposed model.

Research on Customer Behavior Prediction Model for Cultural Industry Combined with Deep Learning

Abstract In recent years, as deep learning has demonstrated powerful characterization capabilities in the fields of speech, image, and text, researchers have begun to apply it to the field of prediction, i.e., predicting customer behaviors through current interaction records and features. This paper proposes a deep wandering-based customer behavior prediction model that combines deep learning techniques to forecast customer behavioral trends in the cultural industry. The model randomly wanders from the social network graph structure of the customer’s purchase of goods to generate a new behavioral sequence. We regard the user’s behavioral sequence as a word, and we pre-train all the behavioral sequence documents using the Word2vec algorithm model. The experimental comparison revealed that the model, which incorporates the depth-wandering technique, outperforms other models on the test set in terms of predictiveness. The website uses the deep wandering user behavior prediction model to forecast sales and adapts its sales strategy based on the customer’s behavior. 31% of customers were content with the books they bought from the website, while 52% were extremely content. By comparing the book sales before and after applying the model, it was found that the book sales increased significantly after adjusting the sales strategy, indicating that the customer behavior prediction model constructed in this paper can be used practically.