Unfolding Model Research Articles

Введение в естественные науки: взгляд со стороны культурной истории человечества

We construct an introductory Natural Sciences curriculum that meets the demands of the «cultural-historical school». Our main task was to find the conditions that contribute to the formation of model mediation of text understanding within the Natural Sciences course, the content of which represents a purposeful transformation of natural materials in accordance with the cultural-historical specifics of human activity. To explore how schoolchildren acquire orienting functions of learning texts and models supporting the logical-genetic reconstruction of the natural scientific knowledge, we conducted an experimental teaching course in Natural Sciences for 2nd-graders (21 students, 102 academic hours). Our lessons proved a principal possibility of unfolding subject and model components of the course through the organization of joint actions consistent with the tasks of learning activity development in primary school children. Also, we assessed model-building skills in text understanding and task solving in children of two schools: the first group (103 students, 68 academic hours) studied the course we had developed, and the second group (80 students, 68 academic hours) received standard school courses. A significant improvement of model-building in the experimental group as compared to the control group suggests that cultural-activity-based introduction to Natural Sciences may have a certain potential in promoting the acquisition of orienting functions of learning models, which is vital for future advancement in middle and high school.

How peers’ updates on social media influence job search

PurposeThe purpose of this paper is to investigate the ways in which social media content via social contagion may affect the job behaviors of employed individuals. Specifically, by integrating the unfolding model of voluntary turnover and social comparison theory, this paper explores whether receiving an update about a peer’s career advancement on professional social networking sites (SNSs) increases an individual’s propensity to engage in job search.Design/methodology/approachIn this analysis, the authors matched individuals’ survey data (n=125) with information received from a recruiting agency on employees’ subsequent job search behavior (i.e., sending a resume to the agency).FindingsThe results indicate that the relationship between career advancement updates on SNSs and job search behavior was stronger for employees with higher perceived employability and, contrary to our hypothesis, for those more embedded within the organization.Practical implicationsMore employable and more embedded individuals perceive social cues from social media, and these cues positively relate to their job search behaviors. To address this trend, organizations could develop a social media strategy and implement retention measures to prevent the job search (and thus potential turnover) of employable and embedded individuals.Originality/valueThis research contributes to the job search literature by examining the role of professional SNSs in driving job search behavior among employed individuals.

Deep Coupled-Representation Learning for Sparse Linear Inverse Problems With Side Information

In linear inverse problems, the goal is to recover a target signal from undersampled, incomplete or noisy linear measurements. Typically, the recovery relies on complex numerical optimization methods; recent approaches perform an unfolding of a numerical algorithm into a neural network form, resulting in a substantial reduction of the computational complexity. In this letter, we consider the recovery of a target signal with the aid of a correlated signal, the so-called side information (SI), and propose a deep unfolding model that incorporates SI. The proposed model is used to learn coupled representations of correlated signals from different modalities, enabling the recovery of multi-modal data at a low computational cost. As such, our work introduces the first deep unfolding method with SI, which actually comes from a different modality. We apply our model to reconstruct near-infrared images from undersampled measurements given RGB images as SI. Experimental results demonstrate the superior performance of the proposed framework against single-modal deep learning methods that do not use SI, multi-modal deep learning designs, and optimization algorithms.

Discontinuities in the Value of Relational Capital: The Effects on Employee Entrepreneurship and Mobility

We examine how a discontinuous increase in the value of an employee’s relational capital influences the employee’s mobility and entrepreneurship decisions in professional and business service contexts. Drawing on the unfolding model of voluntary turnover, we develop a theory proposing that positive shocks to external relational capital will catalyze employees to consider alternative employment options, thereby increasing the probability of exit. We further maintain that exit decisions in response to such shocks will be driven by a desire to appropriate more value, making these shocks strong predictors of employee entrepreneurship, especially when the employee works in an area that is peripheral to the firm’s core capabilities. Empirically, we construct a unique employee-employer linked database that tracks employment of lobbyists in the United States federal lobbying industry. Leveraging plausibly exogenous shocks to the value of an employee’s relational capital and a novel market-based measure of the employee’s position in the firm’s knowledge space, we report two sets of findings. First, an increase in the value of relational capital has a positive effect on the likelihood of mobility to established firms and employee entrepreneurship, with the effect for the latter stronger than the former. Second, the magnitude of the effect on employee entrepreneurship becomes stronger when the employee is peripheral to the firm’s core knowledge. Together, our results are consistent with a value creation-value appropriation rationale, where sudden increases in the value of an employee’s relational capital drive exit as a means to appropriate a greater portion of value created.

The Chlamydomonas deg1c Mutant Accumulates Proteins Involved in High Light Acclimation.

Degradation of periplasmic proteins (Deg)/high temperature requirement A (HtrA) proteases are ATP-independent Ser endopeptidases that perform key aspects of protein quality control in all domains of life. Here, we characterized Chlamydomonas reinhardtii DEG1C, which together with DEG1A and DEG1B is orthologous to Arabidopsis (Arabidopsis thaliana) Deg1 in the thylakoid lumen. We show that DEG1C is localized to the stroma and the periphery of thylakoid membranes. Purified DEG1C exhibited high proteolytic activity against unfolded model substrates and its activity increased with temperature and pH. DEG1C forms monomers, trimers, and hexamers that are in dynamic equilibrium. DEG1C protein levels increased upon nitrogen, sulfur, and phosphorus starvation; under heat, oxidative, and high light stress; and when Sec-mediated protein translocation was impaired. DEG1C depletion was not associated with any obvious aberrant phenotypes under nonstress conditions, high light exposure, or heat stress. However, quantitative shotgun proteomics revealed differences in the abundance of 307 proteins between a deg1c knock-out mutant and the wild type under nonstress conditions. Among the 115 upregulated proteins are PSII biogenesis factors, FtsH proteases, and proteins normally involved in high light responses, including the carbon dioxide concentrating mechanism, photorespiration, antioxidant defense, and photoprotection. We propose that the lack of DEG1C activity leads to a physiological state of the cells resembling that induced by high light intensities and therefore triggers high light protection responses.

Accurate Calculation of Barnase and SNase Folding Energetics Using Short Molecular Dynamics Simulations and an Atomistic Model of the Unfolded Ensemble: Evaluation of Force Fields and Water Models.

As proteins perform most cellular functions, quantitative understanding of protein energetics is required to gain control of biological phenomena. Accurate models of native proteins can be obtained experimentally, but the lack of equally fine models of unfolded ensembles impedes the calculation of protein folding energetics from first principles. Here, we show that an atomistic unfolded ensemble model, consisting of a few dozen conformations built from a protein sequence, can be used in conjunction with an X-ray structure of its native state to calculate accurately by difference the changes in enthalpy and heat capacity of the polypeptide upon folding. The calculation is done using molecular dynamics simulations, popular force fields, and water models, and for the two model proteins studied (barnase and SNase), the results agree within error or are very close to their experimentally determined properties. The enthalpy sampling of the unfolded ensemble is done through short 2 ns simulations that do not significantly modify the representative distribution of Rg of the starting conformations. The impressive accuracy obtained opens the possibility to investigate quantitatively systems or phenomena not amenable to experiment and paves the way for addressing the calculation of protein conformational stability (i.e., the change in Gibbs energy upon folding), a central goal of structural biology. So far, these calculated enthalpy and heat capacity changes, combined with the experimentally determined melting temperatures of the corresponding protein, allow us to reproduce the stability curves of both barnase and SNase.

Work Values and Facet Theory: From Intercorrelations to Individuals

The article proposes a new mapping sentence (MS) for values, including negative values and moral virtues. The MS allows defining work values and discriminating them from work attitudes. Previous studies on work values proposed essentially two facets, namely, type of work outcome and performance relatedness, to explain their structure, but these facets are unreliable when classifying work values. It is suggested to utilize job facets from job satisfaction research as an alternative for the work outcome facet. Using a sample of 471 employees who rated the 21 work values of the Munster Work Value Measure (MWVM), the item intercorrelations reflect such job facets as sectors of a circumplex that approximates a circle in multidimensional scaling (MDS) space. The principal components of the items can be represented as vectors in the same space. An unfolding model is then proposed that allows testing the extent to which the work value circle also holds within, and not just across, individuals. We show that this model holds for almost all individuals. The scatter of the persons in unfolding space is related to gender, educational level, and age of the respondents. For HR practitioners, these findings offer various diagnostic springboards, such as an economical way of diagnosing a person’s main work values and identifying individuals with typical and unusual work value profiles.

Understanding Team Learning Dynamics Over Time.

Organizations depend on the learning capabilities of teams in order to be competitive in today’s information-laden business landscape. Hence, it is not surprising that there have been tremendous efforts made to understand team learning within the past two decades. These efforts, however, have produced a cluttered literature-base that overlooks a fundamental aspect of team learning: How do teams learn over time? In this paper, we first synthesize the literature to develop a shared vocabulary to understand team learning dynamics. We then leverage research investigating how teams operate within the context of time (e.g., team development, performance cycles, emergent state development) and combine it with the extant team learning literature in developing an unfolding model of team learning. This comprehensive model addresses a noticeable gap in the extant literature by illustrating how teams learn over time. Finally, we put forth three grand challenges for the future of team learning research.

Should I stay or should I go? understanding employees’ decisions to leave after mergers in Hong Kong’s banking industry

Recently, Chinese firms are increasingly acquiring firms overseas. This is often followed by high levels of employee exit from the acquired firm, which can be problematic. To better understand this phenomenon, we use the unfolding model of voluntary turnover to examine why and how individual employees of Hong Kong-based banks make the decision to either stay or go after their bank is taken over by a Chinese bank. We obtain data from three major acquisitions of banks in Hong Kong by Chinese state-owned banks from 2004 to 2008. Our sample consists of 406 employees from the acquired banks as well as non-acquired banks. We find that employees that have left after being acquired are more likely to report experiencing a “shock” than are employees of non-merged banks and that this influences their decision to leave. We also find that in the merger context, male employees, front-line employees and officer-level employees have different leaving decision paths from their female, back-office, and non-officer-level counterparts respectively. No such differences are found in the non-merger context. In addition, we find that a retention bonus delays departure after the merger. These findings contribute to research and practice of managing employee turnover after cross-border acquisitions by Chinese firms.

A Study on the Relationship between Emotional Intelligence and Employee Turnover

Retaining talent continues to be an organizational issue, and surviving employees of a layoff are more likely to leave an organization voluntarily for alternative work. A quantitative method was used to explore emotional-intelligence levels of individuals, voluntary turnover, and voluntary turnover intention using Schutte et al.’s questionnaire. A significant statistical relationship was found between emotional intelligence and one of four components of the unfolding model of voluntary turnover.

Though Forced, Still Valid: Psychometric Equivalence of Forced-Choice and Single-Statement Measures

Forced-choice (FC) measures are gaining popularity as an alternative assessment format to single-statement (SS) measures. However, a fundamental question remains to be answered: Do FC and SS instruments measure the same underlying constructs? In addition, FC measures are theorized to be more cognitively challenging, so how would this feature influence respondents’ reactions to FC measures compared to SS? We used both between- and within-subjects designs to examine the equivalence of the FC format and the SS format. As the results illustrate, FC measures scored by the multi-unidimensional pairwise preference (MUPP) model and SS measures scored with the generalized graded unfolding model (GGUM) showed strong equivalence. Specifically, both formats demonstrated similar marginal reliabilities and test-retest reliabilities, high convergent validities, good discriminant validities, and similar criterion-related validities with theoretically relevant criteria. In addition, the formats had little differential impact on respondents’ general emotional and cognitive reactions except that the FC format was perceived to be slightly more difficult and more time-saving.

Go your own way: Exploring the causes of top executive turnover

Research SummaryWhy do top executives leave their firms? Research on executive turnover has either focused on CEO dismissal or on group‐level top management team (TMT) departure rates, mostly ignoring individual‐level factors that would predict why non‐CEO executives exit. Here, we extend the shock perspective of the unfolding model of turnover used in organizational behavior research to show how relational and reputational shocks influence turnover at the executive level. Our sample includes over 4,000 executives from S&P 1500 firms over 11 years. We hypothesize and find that relational and reputational shocks increase the likelihood of top executive exit. We also consider the moderating influence of pay disparity on these relationships, which impacts how each type of shock influences executive turnover.Managerial SummaryReplacing top executives can be extremely costly for firms. Consequently, understanding the reasons behind top executive exit are important. We examine the effect of different types of shock on the likelihood a top executive will exit their firm. We find that both relational shocks (e.g., other members of the TMT leaving), as well as reputational shocks (e.g., litigation or shareholder activism against the firm), increase turnover. However, we find that higher status executives experience these shocks differently than lower status executives.

Exploring the Impersonal Judgments and Personal Preferences of Raters in Rater-Mediated Assessments With Unfolding Models.

The purpose of this study is to explore the use of unfolding models for evaluating the quality of ratings obtained in rater-mediated assessments. Two different judgmental processes can be used to conceptualize ratings: impersonal judgments and personal preferences. Impersonal judgments are typically expected in rater-mediated assessments, and these ratings reflect a cumulative response process. However, raters may also be influenced by their personal preferences in providing ratings, and these ratings may reflect a noncumulative or unfolding response process. The goal of rater training in rater-mediated assessments is to stress impersonal judgments represented by scoring rubrics and to minimize the personal preferences that may represent construct-irrelevant variance in the assessment system. In this study, we explore the use of unfolding models as a framework for evaluating the quality of ratings in rater-mediated assessments. Data from a large-scale assessment of writing in the United States are used to illustrate our approach. The results suggest that unfolding models offer a useful way to evaluate rater-mediated assessments in order to initially explore the judgmental processes underlying the ratings. The data also indicate that there are significant relationships between some essay features (e.g., word count, syntactic simplicity, word concreteness, and verb cohesion) and essay orderings based on the personal preferences of raters. The implications of unfolding models for theory and practice in rater-mediated assessments are discussed.

The key role played by intermediaries in the retail insurance distribution

PurposeThe insurance market has high churn rates because customers’ purchase decision-making process and claims management rely heavily on intermediaries. The purpose of this paper is to investigate the role played by insurers and intermediaries in customer satisfaction, as well as in the preferences of customers regarding the purchase decision-making process.Design/methodology/approachThe first step was to select the most important attributes for Portuguese insurance customers. Three focus groups were conducted (using B2C and B2B markets), and data from Portuguese car insurance customers were gathered through anad hocquestionnaire. Structural equation models and the multidimensional scaling unfolding model were applied.FindingsIntermediaries play a key role in the retail insurance distribution channels by influencing customer satisfaction, claims management and the purchasing process (premium acceptance).Practical implicationsBecause of the influence that intermediaries have on customer satisfaction, insurers should improve their partnerships (back office support) with intermediaries.Originality/valueThe study contributes to the retail distribution literature of the insurance sector by providing empirical evidence of the impact of intermediaries on customers’ satisfaction.

The unfolding action model of initiation times, movement times, and movement paths.

Converging evidence has led to a consensus in favor of computational models of behavior implementing continuous information flow and parallel processing between cognitive processing stages. Yet, such models still typically implement a discrete step between the last cognitive stage and motor implementation. This discrete step is implemented as a fixed decision bound that activation in the last cognitive stage needs to cross before action can be initiated. Such an implementation is questionable as it cannot account for two important features of behavior. First, it does not allow to select an action while withholding it until the moment is appropriate for executing it. Second, it cannot account for recent evidence that cognition is not confined prior to movement initiation, but consistently leaks into movement. To address these two features, we propose a novel neurocomputational model of cognition-action interactions, namely the unfolding action model (UAM). Crucially, the model implements adaptive information flow between the last cognitive processing stage and motor implementation. We show that the UAM addresses the two abovementioned features. Empirically, the UAM accounts for traditional response time data, including positively skewed initiation time distribution, functionally fixed decision bounds and speed-accuracy trade-offs in button-press experimental designs. Moreover, it accounts for movement times, movement paths, and how they are influenced by cognitive-experimental manipulations. This move should close the current gap between abstract decision-making models and behavior observed in natural habitats. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Methodology for creating 3D paper unfolded models with complex geometry using open-source software and resources with free personal and commercial license

The creation of 3D paper unfolded models with complex geometry requires precision in the design process. Achieving this is through the use of accessible resources and modern technological means which possess the necessary technical and functional capabilities. The successful realization of the models is through the use of a structured methodology containing design stages that are defined in the exact sequence. The present study aims to create the unfoldings of complex three-dimensional models by covering the stages of providing accessible resurses with a personal and commercial license that are transformed in sequence until the ready-made variants.

Transitory effects of personality on employee turnover

Purpose – examining transitory effects of extraversion and openness to experience on employee turnover.
 Design/Method/Approach. Fully observed recursive mixed process model.
 Findings. Results show that (i) extraversion positively predicts turnover and that (ii) openness does not predict turnover. Moreover, comparing size effects between studies reveals that only extraversion has significantly more positive effect on employee turnover, which is in contradiction with previous meta-analysis.
 Theoretical implications. This research identifies a plausible boundary condition – national culture – in examining how a person’s personality impact employee turnover in organizations. It highlights the shortcomings of previous meta-analysis that failed to incorporate differences in societal values and business contexts and identifies.
 Practical implications. In studying cultural contexts and value congruencies, this study contributes to the international human resources literature by identifying boundary conditions that explain how personality impacts employee turnover.
 Originality/Value. This study is the first to analyze the effects of personality on turnover using a within-individual unfolding and holistic model.
 Research limitations/Future research. The current study incorporates only a sample from a single country. Future research that analyzes the moderating effects of societal and business values in cross-national samples could corroborate and extend on the findings from this study.
 Paper type – empirical.

CalFitter: a web server for analysis of protein thermal denaturation data.

Despite significant advances in the understanding of protein structure-function relationships, revealing protein folding pathways still poses a challenge due to a limited number of relevant experimental tools. Widely-used experimental techniques, such as calorimetry or spectroscopy, critically depend on a proper data analysis. Currently, there are only separate data analysis tools available for each type of experiment with a limited model selection. To address this problem, we have developed the CalFitter web server to be a unified platform for comprehensive data fitting and analysis of protein thermal denaturation data. The server allows simultaneous global data fitting using any combination of input data types and offers 12 protein unfolding pathway models for selection, including irreversible transitions often missing from other tools. The data fitting produces optimal parameter values, their confidence intervals, and statistical information to define unfolding pathways. The server provides an interactive and easy-to-use interface that allows users to directly analyse input datasets and simulate modelled output based on the model parameters. CalFitter web server is available free at https://loschmidt.chemi.muni.cz/calfitter/.

GGUM: An R Package for Fitting the Generalized Graded Unfolding Model.

In this article, the newly created GGUM R package is presented. This package finally brings the generalized graded unfolding model (GGUM) to the front stage for practitioners and researchers. It expands the possibilities of fitting this type of item response theory (IRT) model to settings that, up to now, were not possible (thus, beyond the limitations imposed by the widespread GGUM2004 software). The outcome is therefore a unique software, not limited by the dimensions of the data matrix or the operating system used. It includes various routines that allow fitting the model, checking model fit, plotting the results, and also interacting with GGUM2004 for those interested. The software should be of interest to all those who are interested in IRT in general or to ideal point models in particular.

Amino Acid-Based Advanced Liquid Formulation Development for Highly Concentrated Therapeutic Antibodies Balances Physical and Chemical Stability and Low Viscosity.

To develop highly concentrated therapeutic antibodies enabling convenient subcutaneous application, well stabilizing pharmaceutical formulations with low viscosities are considered to be key. The purpose of this study is to select specific amino acid combinations that reduce and balance aggregation, fragmentation and chemical degradation, and also lower viscosity of highly concentrated liquid antibodies. As a model, the therapeutically well-established antibody trastuzumab (25->200 mg mL-1 ) in liquid formulation is used. Pre-testing of formulations based on a stabilizing and protecting solutions (SPS®) platform is conducted in a thermal unfolding model using differential scanning fluorimetry (DSF) and accelerated aging at 37 and 45 °C. Pre-selected amino acid combinations are further iteratively adjusted to obtain stable highly concentrated antibody formulations with low viscosity. Size exclusion chromatography (SE-HPLC) reveals significantly lower aggregation and fragmentation at specific amino acid:sugar and protein:excipient ratios. Dynamic viscosities <20 mPa * s of highly concentrated trastuzumab (≥200 mg mL-1 ) are measured by falling ball viscosimetry. Moreover, less chemical degradation is found by cationic exchange chromatography (CEX-HPLC) even after 6 months liquid storage at 25 °C. In conclusion, specifically tailored and advanced amino acid-based liquid formulations avoid aggregation and enable the development of stable and low viscous highly concentrated biopharmaceuticals.