Model Of Behavior Research Articles

A Spreadsheet-Based Redox Flow Battery Cell Cycling Model Enabled by Closed-Form Approximations

Abstract The complex interplay between numerous parasitic processes—voltage losses, crossover, decay—challenges interpretation of cycling characteristics in redox flow batteries (RFBs). Mathematical models offer a means to predict cell performance prior to testing and to interpret experimentally measured cycling data, however most implementations require extensive domain expertise, programming knowledge, and/or computational resources. Here, we expand on our previously developed zero-dimensional modeling framework by deriving closed-form expressions for key performance metrics. The resulting closed-form model streamlines the computational structure and allows for spreadsheet modeling of cell cycling behavior, which we highlight by developing a simulation package in Microsoft® Excel®. We then apply this model to analyze previously published experimental data from our group and others, highlighting its utility in numerous diagnostic configurations—bulk electrolysis, compositionally unbalanced symmetric cell cycling, and full cell cycling. Given the accessibility of this modeling toolkit, it has potential to be a widely deployable tool for RFB research and education, aiding in data interpretation and performance prediction.

Closure to “Numerical Modeling of the Monotonic Lateral Behavior of XL and XXL Monopiles in Kaolin”

Closure to “Numerical Modeling of the Monotonic Lateral Behavior of XL and XXL Monopiles in Kaolin”

Discussion of “Numerical Modeling of the Monotonic Lateral Behavior of XL and XXL Monopiles in Kaolin”

Discussion of “Numerical Modeling of the Monotonic Lateral Behavior of XL and XXL Monopiles in Kaolin”

Stochastic bulk density in the fire spread model

AbstractIn this study, we investigate the impact of inherent randomness on fire spread rates, with a particular focus on the stochastic parameter ε, representing the ratio of effective to actual fuel bulk density. By employing various probability distributions, such as the beta random distribution model ε, we explore the complex interplay between wind and slope factors in determining fire behavior. Our 3D visualizations and density plots illuminate the multidimensional nature of fire spread, emphasizing the significant role of stochastic variability in enhancing the accuracy of fire behavior models. This approach not only deepens our theoretical understanding but also provides valuable insights for improving fire management strategies.

A systematic model for the mechanical behavior of thin-walled composite FGM pipelines subjected to strike-slip faults in geohazard area

This paper focuses on the mechanical performances of the buried pipelines with functionally graded materials (FGM) subjected to strike-slip faults. A refined displacement function is introduced to derive the fault displacement and the strain distributions. The yield fault displacement is obtained by using the classical beam theory and the first yielding scheme. A finite element model is developed to verify the present derivations, and the verification indicates that the numerical results are in close agreement with the present analytical predictions characterized by the yield displacement and the strains, respectively. Finally, a parametric analysis is carried out to assess the factors that impact the performances of FGM pipelines, such as the diameter-to-thickness ratio of the pipeline, volume fraction exponent, fault inclination angles, the properties of the soil, and so on.

Discovering online Chinese consumers’ impulse buying in live streaming by the theory of planned behavior

<p class="MsoNormal" style="margin-top: 8pt; text-align: justify;"><span lang="EN-US" style="font-family: 'times new roman', times, serif; color: black; font-size: 12pt;">The study explores online consumers’ impulse buying intentions and behaviors on live streaming platforms. Unlike traditional shopping modes, the development of real-time video streaming provides online consumers with a distinct approach to interacting with live streamers and browsing online products in real-time, potentially causing their impulse buying intentions. To understand online consumers’ impulse buying intentions and behaviors, the paper establishes the research model based on the theory of planned behavior (TPB) model and analyses influencing factors from attitude, subject norm and perceived control aspects. Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the research results show that, three factors positively affect online consumers’ impulse buying intentions and lead to their final behaviors. Meanwhile, control variables, including gender, age, and income level, demonstrate insignificant effects across the model. Unlike existing literature, the current study displays the distinct features of live streaming platforms and discovers online consumers’ impulse buying intention based on the TPB model. The results are helpful for related scholars and departments to pay more attention to the live shopping environment and understand online consumers’ impulse buying issues.</span></p>

Insights into nearshore sandbar dynamics through process-based numerical and logistic regression modeling

Process-based nearshore morphodynamic models are commonly used tools by coastal engineers and planners to predict the nearshore morphology change of sandy beaches across various spatiotemporal scales. Accurate modeling of the morphological response on medium and long time scales is imperative for quantitative assessments of coastal infrastructure over a project’s intended life-span. However, most previous modeling applications have focused on single/sub-seasonal storm events and are often limited to an assessment of the subaerial beach (i.e. berm and dune). This not only leaves uncertainty concerning the quality of morphology predictions on extended (> weeks) time scales, but also the capacity of process-based models to emulate realistic nearshore sandbar dynamics and the corresponding exchange of sediment between the nearshore-beach system. To shed light on these meso-scale dynamics, CSHORE, a 1D phase-averaged, process-based nearshore morphodynamic model, was applied on an annual scale to a multi-barred, dissipative beach in Oysterville, WA, USA. Thousands of unique sediment transport and hydrodynamic parameter combinations were executed during model calibration. A large portion of these simulations displayed physically realistic sandbar dynamics, including the growth, decay, and migration of intertidal and subtidal sandbars. To explore the model mechanisms enabling realistic bar behavior, the binary and multinomial logistic regression model were used to quantify the relationship between model parameter selection and the probability of various categorical bar configurations occurring in the final predicted profile. The results indicate the most sensitive parameters associated with barred morphology, in this study, and support the use of separate sediment transport parameters for low and high wave energy conditions. The co-utilization of numerical and statistical modeling outlined in this publication is generalizable to future exploratory modeling and/or calibration routines concerned with categorical outcomes.

Optimizing Sensor-Controlled Systems with Minimal Intervention: A Fuzzy Relational Calculus Approach

This article describes an approach for optimizing sensor-controlled systems through minimal intervention, utilizing fuzzy linear systems of equations (FLSEs). Starting with a generalized model of the system behavior, we incorporate an array of control units, environmental sensors, and an expert knowledge base. The described problems of detecting the level of intervention needed to change the system state to another is handled with the help of developed methods for solving the inverse problem faced by FLSEs. By achieving minimal intervention, we ensure that the system adjustments are effective, economically optimal, and non-intrusive. A MATLAB-based implementation is presented.

Factors Mediating the Association between Financial Socialization and Well-Being: An African American Perspective

Abstract This study examines the relationship between financial socialization and well-being (financial and subjective) mediated by three motivations: financial knowledge, goal setting, and self-control, either directly on financial behaviors or indirectly through financial skills on financial behaviors of African Americans compared with European Americans. Researchers used an integrated concept derived from Gudmunson and Dane’s (2011) Financial Socialization framework and Fisher and Fisher’s (1992) Information Motivation Behavior Model to examine National Financial Well-Being Survey data. Researchers found a significant difference between African Americans and European Americans when “self-control” mediates directly through financial behaviors and indirectly through financial skills. There is also a significant difference in the relationship when “goal setting” mediates the relationship indirectly through skills in financial behaviors. Finally, there is a significant difference when “financial knowledge” directly mediates financial behaviors. Specifically, the findings imply that self-control is one of the most impactful factors on African Americans’ well-being.

Research on the Propagation Model of Unsafe Behaviors among Construction Workers Based on a Two-Layer NAN-SIRS Network

Unsafe behaviors among construction workers are a leading cause of safety accidents in the construction industry, and studying the mechanism of unsafe behavior propagation among construction workers is essential for reducing the occurrence of safety accidents. Safety attitude plays a pivotal role in predicting workers’ behavioral intentions. We propose a propagation model of unsafe behaviors based on a two-layer complex network, in which the upper layer depicts the change in construction workers’ safety attitudes, and the lower layer represents the propagation of unsafe behaviors. In this model, we consider the impact of individual heterogeneity and herd mentality on the transmission rate, establishing a partial mapping relationship based on behavioral feedback. After that, by building a probability transition tree, we establish the risk state transition equation in detail using the microscopic Markov chain approach (MMCA) and analyze the established equations to deduce the propagation threshold of unsafe behaviors analytically. The results show that enhancing the influence of individual heterogeneity and behavioral feedback increases the threshold for the spread of unsafe behaviors, thereby reducing its scale, while herd mentality amplifies the spread. Furthermore, the coexistence of safety education and behavioral feedback may lead to one of the mechanisms fails. This research enhances understanding of the propagation mechanism of unsafe behaviors and provides a foundation for managers to implement effective measures to suppress the propagation of unsafe behaviors among construction workers.

Perceived meaningfulness or ego depletion? A two-path model of servant leadership behaviors influence on intention to leave

ABSTRACT Based on the conservation of resources theory, this study integrates the positive and negative aspects of the impact of servant leadership behaviors in the hospitality industry on leaders themselves. Using leaders’ and followers’ paired data from 346 employees nested in 101 teams and analyzed using path regression methods. The results reveal that servant leadership behaviors were positively related to the leader’s ego depletion, and the leader’s ego depletion was positively associated with the leader’s intention to leave. Servant leadership behaviors were positively related to leaders’ perceived work meaningfulness, and followers’ agreeableness significantly moderated the relationship between servant leadership behaviors and leaders’ ego depletion and the relationship between servant leadership behaviors and leaders’ perceived work meaningfulness. These findings broaden our understanding of the effects of servant leadership behaviors and provide new ideas on how to intervene in the negative effects of servant leadership behaviors.

DinoDroid: Testing Android Apps Using Deep Q-Networks

The large demand of mobile devices creates significant concerns about the quality of mobile applications (apps). Developers need to guarantee the quality of mobile apps before it is released to the market. There have been many approaches using different strategies to test the GUI of mobile apps. However, they still need improvement due to their limited effectiveness. In this article, we propose DinoDroid, an approach based on deep Q-networks to automate testing of Android apps. DinoDroid learns a behavior model from a set of existing apps and the learned model can be used to explore and generate tests for new apps. DinoDroid is able to capture the fine-grained details of GUI events (e.g., the content of GUI widgets) and use them as features that are fed into deep neural network, which acts as the agent to guide app exploration. DinoDroid automatically adapts the learned model during the exploration without the need of any modeling strategies or pre-defined rules. We conduct experiments on 64 open-source Android apps. The results showed that DinoDroid outperforms existing Android testing tools in terms of code coverage and bug detection.

A Cognitive Approach for Modeling Customer Demand Dynamics for Optimal Product Release Strategies

Abstract As agile processes are increasingly adopted for product design, and as consumer preferences are rapidly evolving with increasing information available from digital media, there is a need for a demand model that can accommodate the dynamics of product development. However, existing models of demand estimation, such as the discrete-choice models, do not capture the dynamics of product development and decision-making processes and thus are unable to effectively capture the effect of product updates and the release of information. To address this gap, we present a dynamic demand model and demonstrate how it can be used to determine the optimal time to release product updates. The demand model is based on decision field theory (DFT), which enables the modeling of the dynamic behavior of human decision makers. The contributions of this article are as follows. First, we formulate a computational model for demand modeling built on DFT and demonstrate the viability of using the model to determine product release strategies. Second, we provide analytical approximations of the demand model and compare the accuracy of the approximated demand against the demand predicted by the dynamics model. Third, we show an example of a game played by competitors trying to optimize demand for their products by choosing the optimal update time relative to each other. Finally, we demonstrate the feasibility of parameter estimation using only the demand data.

Time-dependent Kohn-Sham electron dynamics coupled with nonequilibrium plasmonic response via atomistic electromagnetic model.

Computational modeling of plasmon-mediated molecular photophysical and photochemical behaviors can help us better understand and tune the bound molecular properties and reactivity and make better decisions to design and control nanostructures. However, computational investigations of coupled plasmon-molecule systems are challenging due to the lack of accurate and efficient protocols to simulate these systems. Here, we present a hybrid scheme by combining the real-time time-dependent density functional theory (RT-TDDFT) approach with the time-domain frequency dependent fluctuating charge (TD-ωFQ) model. At first, we transform ωFQ in the frequency-domain, an atomistic electromagnetic model for the plasmonic response of plasmonic metal nanoparticles (PMNPs), into the time-domain and derive its equation-of-motion formulation. The TD-ωFQ introduces the nonequilibrium plasmonic response of PMNPs and atomistic interactions to the electronic excitation of the quantum mechanical (QM) region. Then, we combine TD-ωFQ with RT-TDDFT. The derived RT-TDDFT/TD-ωFQ scheme allows us to effectively simulate the plasmon-mediated "real-time" electronic dynamics and even the coupled electron-nuclear dynamics by combining them with the nuclear dynamics approaches. As a first application of the RT-TDDFT/TD-ωFQ method, we study the nonradiative decay rate and plasmon-enhanced absorption spectra of two small molecules in the proximity of sodium MNPs. Thanks to the atomistic nature of the ωFQ model, the edge effect of MNP on absorption enhancement has also been investigated and unveiled.

Modified Johnson-Cook Constitutive Model for Dynamic Compressive Behaviors of C250 Maraging Steel at Different Temperatures

Modified Johnson-Cook Constitutive Model for Dynamic Compressive Behaviors of C250 Maraging Steel at Different Temperatures

Prediction Models of Collaborative Behaviors in Dyadic Interactions: An Application for Inclusive Teamwork Training in Virtual Environments

Collaborative virtual environment (CVE)-based teamwork training offers a promising avenue for inclusive teamwork training. The incorporation of a feedback mechanism within virtual training environments can enhance the training experience by scaffolding learning and promoting active collaboration. However, an effective feedback mechanism requires a robust prediction model of collaborative behaviors. This paper presents a novel approach using hidden Markov models (HMMs) to predict human behavior in collaborative interactions based on multimodal signals collected from a CVE-based teamwork training simulator. The HMM was trained using k-fold cross-validation, achieving an accuracy of 97.77%. The HMM was evaluated against expert-labeled data and compared against a rule-based prediction model, demonstrating the superior predictive capabilities of the HMM, with the HMM achieving 90.59% accuracy compared to 76.53% for the rule-based model. These results highlight the potential of HMMs to predict collaborative behaviors that could be used in a feedback mechanism to enhance teamwork training experiences despite the complexity of these behaviors. This research contributes to advancing inclusive and supportive virtual learning environments, bridging gaps in cross-neurotype collaborations.

Three-dimensional modeling of the mechanical tensile behavior of metal weft-knitted fabric made from NiTi wire

In this work, the reasons for the difference between the deformation curves of TiNi wire and the metal knitted fabric made of it under uniaxial tension in the load-unload cycle have been investigated. Experimental stress-strain diagrams of NiTi wire under uniaxial tension to rupture and in the load-unload cycle have been obtained. The phase composition of the wire at each loading stage was investigated. The load curves of the NiTi wire under uniaxial tension has three linear sections corresponding to the following evolution of phase composition: up to 2% there is a section of elastic deformation of 91% austenite; in the strain range from 2% to 11% there is a plateau section associated with direct phase transformation from austenite to monoclinic martensite; the strain range from 11% to 18% is a linear hardening section associated with deformation of 69% monoclinic martensite. An accurate geometric model was developed and the stress-strain state of the metal weft-knitted fabric was evaluated to understand the mechanism of the transition from superelastic to hyperelastic deformation behavior. Direct phase transformation in metal weft-knitted web starts at a relative strain of 8% at a stress of 278 MPa. The stress reaches the ultimate strength of the wire of 684 MPa at a strain of 16.3%, and increasing the strain up to 20% causes the formation of new defect areas, mainly where the loops are in contact with each other. The calculated cyclic curve of the periodic unit cell of the metal weft knitted fabric is typical of hyperelastic materials and differs from the deformation curve of the titanium nickelide wire.

Temporal Diversity-Aware Micro-Video Recommendation with Long- and Short-Term Interests Modeling

Recommender systems have become indispensable for addressing information overload for micro-video services. They are used to characterize users’ preferences from their historical interactions and recommend micro-videos accordingly. Existing works largely leverage the multi-modal contents of micro-videos to enhance recommendation performance. However, limited efforts have been made to understand users’ complex behavior patterns, including their long- and short-term interests, as well as their temporal diversity preferences. In micro-video recommendation scenarios, users tend to have both stable long-term interests and dynamic short-term interests, and may feel tired after incessantly receiving numerous similar recommendations. In this paper, we propose a Temporal Diversity-aware micro-videorecommender (TD-VideoRec) for user behavior modeling, simultaneously capturing users’ long- and short-term preferences. Specifically, we first adopt a user-centric attention mechanism to cope with long-term interests. Then, we utilize an attention network on top of a long-short term memory network to obtain users’ short-term interests. Finally, a temporal diversity coefficient is introduced to characterize the temporal diversity preferences of users’ click behaviors. The value of the coefficient depends on the distinction between users’ long- and short-term interests extracted by vector orthogonal projection. Extensive experiments on two real-world datasets demonstrate that TD-VideoRec outperforms state-of-the-art methods.

Cone and fruit impacts on understory flammability depend on traits and forest floor coverage

BackgroundUnderstory flammability is affected by abscised plant tissue. Extensive research has shown how interspecific differences in leaf litter traits affect flammability; however, leaves represent only one component of the litter layer. Cones and fruit are also common constituents of the forest floor, yet surprisingly little is known about how flammability is affected by their presence. In this study, we ask how flammability is affected by cones and fruit trait differences, coverage differences, and varying species and coverage combinations. To address these questions, we compared cone and fruit morphological and chemical traits among longleaf pine, loblolly pine, shortleaf pine, sweetgum, post oak, and water oak. We also used burn trials to compare fire behavior of single and mixed-species treatments at three coverage levels (10% of plot area (low), 30% (medium), and 50% (high)) integrated within a common mixed-litter layer under field conditions in central Alabama, USA.ResultsLike other plant tissues, cone/fruit dry matter, carbon, and lignin content promote fuel consumption and flame height, while nitrogen suppresses flammability. Single-species treatments produced distinct patterns in fire behavior, with longleaf pine cones consistently showing higher percent fuel consumption, flame height, and maximum smoldering temperature than sweetgum capsular heads. Mixed-species treatment results were less consistent; however, at high coverage, a representative upland three-way mixture (longleaf pine + sweetgum + post oak) showed significantly greater fuel consumption and flame height relative to a bottomland three-way mixture (loblolly pine + sweetgum + water oak) at high coverage. Medium cone/fruit coverage maximized flammability in most single and multi-species treatments and produced non-additive fuel consumption in mixtures containing longleaf pine and sweetgum.ConclusionOur results confirm that individual species’ cone and fruit flammability often parallels that of litter. Fire behavior in mixture is generally driven by the most flammable constituent species, but this result changes with cone and fruit coverage. Collectively, these results indicate that cones/fruit identity and coverage play an important role in understory flammability and should be integrated into fire behavior modeling efforts in monocultures and mixtures.

Wildland fire fuels database for Corsican - Mediterranean Forest stand types

Wildfires represent a major concern for the forests in the Mediterranean basin regions since global climate change enhances the frequency and the severity of this natural ecological disturbance agent. Wildfire models (fire spread or fire behavior models) can be of valuable help for fire risk assessment as well as for planning mitigation actions which mainly concern forest management. These models need generalized vegetation (fuel) data sets at the scales which are significant for fire/forest management. However, despite of the efforts to develop methods for generating global fuel data sets, there is a lack of fine resolution regional scale fuel data sets for the Mediterranean basin. The aim of this work is to build a fuel data set based on existing data sources for fire/forest management, risk assessment and decision-support purposes. Thus, this paper presents a database (building methods and data) of wildland fuels for Mediterranean basin vegetation stands types and in particular for those in Corsica, which gathers together most common input parameters needed by wildfire’s models at several vegetation scales (i.e., stand, elements, particles). Moreover, an example is provided where simulations are run with a specific wildfire model by using the fuel input data extracted from the database. Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National (French) Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The harmonization of European NFIs generates a reliable data source available to extend and generalize the methodology presented herein to other regions or countries.