Unfolding Model Research Articles

Development of an unfolding model of procedures for programming learning of novice programmers

AbstractThis study reduces the difficulties experienced by novice computer programmers when establishing a procedure for problem‐solving in a computing environment. It also provides guidelines to detail the work process. For this purpose, we developed an “unfolding model of procedures” consisting of the recognition of input and output, decomposition of operation, and expansion of branching based on its condition in work. To identify the effect of applying the unfolding model of procedures on the programming learning curve, we conducted an experiment with 110 college freshers randomly assigned to an experimental group and a control group. The unfolding model was applied to the experimental group; however, both groups received introductory programming classes for 5 weeks. The experimental group showed a trend of significant improvement in deriving the input and output processes and expanded their judgment on conditions. The evaluation of flowcharts for the same task conveyed that the experimental group was more competent than the control group, and further analysis by sex revealed no difference between the sexes in the achievement of learning within either group. The experimental group showed an advantage over the control group irrespective of sex. In conclusion, we expect that the unfolding model of procedures developed in this study could be used as a tool for novice programmers to derive work procedures completely and systematically when interpreting human work in a computing environment at the early stages of learning to program.

Unraveling Iterative Control Structures from Business Processes

Iterative control structures allow the repeated execution of tasks, activities or sub-processes according to the given conditions in a process model. Iterative control structures can significantly increase the risk of triggering temporal exceptions since activities within the scope of these control structures could be repeatedly executed until a predefined condition is met. In this paper, we propose two approaches to unravel iterative control structures from process models. The first approach unravels loops based on zero-one principle. The second approach unravels loops based on branching probabilities assigned at split gateways. The proposed methods can be used to unfold structured loops, nested loops and crossing loops. Since the unfolded model does not contain any iterative control structures, it can be used for further analysis by process designers during the modeling phase. The proposed methods are implemented based on workflow graphs, and therefore they are compatible with modeling languages such as Business Process Modelling Notation (BPMN). In the experiments, the execution behavior of unfolded process models is compared against the original models based on the concept of runs. Experimental results reveal that runs generated from the original models can be correctly executed in the unfolded BPMN models that do not contain any loops.

Designing Interpretable Recurrent Neural Networks for Video Reconstruction via Deep Unfolding.

Deep unfolding methods design deep neural networks as learned variations of optimization algorithms through the unrolling of their iterations. These networks have been shown to achieve faster convergence and higher accuracy than the original optimization methods. In this line of research, this paper presents novel interpretable deep recurrent neural networks (RNNs), designed by the unfolding of iterative algorithms that solve the task of sequential signal reconstruction (in particular, video reconstruction). The proposed networks are designed by accounting that video frames' patches have a sparse representation and the temporal difference between consecutive representations is also sparse. Specifically, we design an interpretable deep RNN (coined reweighted-RNN) by unrolling the iterations of a proximal method that solves a reweighted version of the l1 - l1 minimization problem. Due to the underlying minimization model, our reweighted-RNN has a different thresholding function (alias, different activation function) for each hidden unit in each layer. In this way, it has higher network expressivity than existing deep unfolding RNN models. We also present the derivative l1 - l1 -RNN model, which is obtained by unfolding a proximal method for the l1 - l1 minimization problem. We apply the proposed interpretable RNNs to the task of video frame reconstruction from low-dimensional measurements, that is, sequential video frame reconstruction. The experimental results on various datasets demonstrate that the proposed deep RNNs outperform various RNN models.

Preliminary Design Method and Prototype Testing of a Novel Rotors Retractable Hybrid VTOL UAV

Conventional hybrid VTOL (Vertical Take-Off and Landing) aircraft are always designed by directly mounting multicopter rotors to a fixed-wing plane. This type of VTOL aircraft may suffer from terrible drag due to the copter rotors. To tackle this problem, this paper describes a novel rotors retractable hybrid VTOL UAV (RR VTOL) that combines the hovering capability of multicopters and the high cruise velocity property of pure FW aircraft. Computational fluid dynamic (CFD) analysis on rotors folding and unfolding models proves that the retractable rotors can increase the lift-to-drag ratio by approximately 2 times compared to that common hybrid VTOL UAV. Moreover, some aerodynamic performance data of the rotors folded model are also acquired. A preliminary design method of this creative UAV is proposed with some new empirical equations derived from many hybrid VTOL UAVs’ data. Emphasis is given on the method for determining the center of gravity position and horizontal tail dimensions as they are the unique aspects of the RR VTOL configuration. Subsequently, the manufacturing process and rotor retraction mechanism of two UAV generations are reviewed and a rapid prototyping method for RR VTOL is summarized. Finally, a successful flight test confirms that the proposed VTOL concept is practical and the drag reduction effect of rotors retracting function is remarkable.

AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing.

Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding methods have the good interpretation of model-based methods and the high speed of classical deep network methods. In this article, to solve the visual image CS problem, we propose a deep unfolding model dubbed AMP-Net. Rather than learning regularization terms, it is established by unfolding the iterative denoising process of the well-known approximate message passing algorithm. Furthermore, AMP-Net integrates deblocking modules in order to eliminate the blocking artifacts that usually appear in CS of visual images. In addition, the sampling matrix is jointly trained with other network parameters to enhance the reconstruction performance. Experimental results show that the proposed AMP-Net has better reconstruction accuracy than other state-of-the-art methods with high reconstruction speed and a small number of network parameters.

Modeling Self-Determination Theory Motivation Data by Using Unfolding IRT

Abstract. Self-determination theory (SDT) suggests that the degree of autonomous behavior regulation is a characteristic of distinct motivation types which thus can be ordered on the so-called Autonomy-Control Continuum (ACC). The present study employs an item response theory (IRT) model under the ideal point response/unfolding paradigm in order to model the response process to SDT motivation items in theoretical accordance with the ACC. Using data from two independent student samples (measuring SDT motivation for the academic subjects of Mathematics and German as a native language), it was found that an unfolding model exhibited a relatively better fit compared to a dominance model. The item location parameters under the unfolding paradigm showed clusters of items representing the different regulation types on the ACC to be (almost perfectly) empirically separable, as suggested by SDT. Besides theoretical implications, perspectives for the application of ideal point response/unfolding models in the development of measures for non-cognitive constructs are addressed.

Multi-domain unfolding of the Fab fragment of a humanized anti-cocaine mAb characterized by non-reducing SDS-PAGE

Monoclonal antibodies and their fragments are widely used for research and therapy. Fab fragments are useful since they retain antigen binding specificity, but being smaller proteins, are better able to penetrate biological compartments and tumors, and do not induce Fc-dependent immunological system activation. Our laboratory developed an anti-cocaine mAb (named h2E2) for the treatment of cocaine use disorders, which is currently in advanced pre-clinical development. We are also interested in the Fab fragment of this mAb for potential therapy of acute cocaine overdose. Previously, we showed that this mAb and its F(ab’)2 and Fab fragments undergo discrete domain unfolding, as detected by non-reducing SDS-PAGE, and that ligand-induced protein thermal stabilization can be quantitated utilizing differential scanning fluorimetry in the absence of SDS. Here, we demonstrate that multiple Fab protein gel bands observed using non-reducing SDS-PAGE in the presence and absence of cocaine and its metabolites can be explained and interpreted based on the differential stabilization of two unfolding domains in the Fab fragment. The variable domain is stabilized by ligands against SDS unfolding, while the constant domain is not. This data and its interpretation are also supported by differential scanning fluorimetry data for the Fab fragment in SDS. It is likely that these non-reducing SDS-PAGE results and the gel band domain unfolding model will be applicable to other small molecule binding antibodies. Thus, non-reducing SDS-PAGE is a widely available and simple method for assessing domain stability and multi-step unfolding of Fab fragments.

Momentary well-being matters: Daily fluctuations in hotel employees’ turnover intention

Drawing on affective events theory and the unfolding model of voluntary turnover, the dynamic nature of emotional dissonance, job autonomy, subjective well-being (SWB), and turnover intention are examined in a daily diary study. The study's design employs an experience sampling method. Participants are full-time employees in guest-facing positions in full-service or luxury hotels in the United States. Sixty-five participants completed a one-time baseline survey and a daily diary study twice per day for at least five working days, resulting in a total of 416 day-level observations. Within-person variance in turnover intention is predicted by day-level within-person SWB and emotional dissonance. This study shows that SWB and turnover intention are not stable phenomena among frontline hotel employees. Moreover, the study emphasizes the important moderating role of daily job autonomy and the moderating effects of person-level trait resilience. The theoretical and practical implications of these results are also discussed. • A daily diary study was conducted to explore the dynamic nature of subjective well-being (SWB) and turnover intention. • Emotional dissonance, job autonomy, SWB, and turnover intention fluctuate daily among frontline hotel employees. • Daily emotional dissonance influences both daily SWB and turnover intention. • Daily job autonomy reduces the negative influence of daily emotional dissonance on daily SWB and turnover intention. • Trait resilience mitigates the negative influence of daily emotional dissonance on SWB and turnover intention.

Thriving in the Golden Age of Algorithms: Gig Workers’ Learning, Adaptation, and Success

The widespread algorithms have deeply reshaped the nature of work, causing new challenges for organizations and individuals. Less studied, however, is the impact of algorithmic management - defined as using self-learning algorithms to make and execute labor-related managerial decisions - on individuals beyond producing economic value. Our symposium aims to explore the questions: (1) what is the role of algorithms in talent management in the gig economy? (2) How do gig workers learn and adapt to certain algorithms to form work norms? (3) How do gig workers handle the challenges associated with algorithms to achieve objective and subjective career success? (4) What are the ups and downs of algorithmic management in the eyes of gig workers and why? Our symposium covers a variety of research methods (a conceptual paper, a big-data driven study, a lab simulation study, and a qualitative study), types of gig work (offline routine work and online non-routine work), samples (web doctors, ridesharing drivers), and theoretical lenses (talent management, norm, emotion, justice). It aims to facilitate cross-division dialogue among Organizational Behavior, Human Resources, and Careers scholars. Talent Management in the Gig Economy? A Conceptual Framework Presenter: Jeroen Meijerink; U. of Twente Presenter: Sandra Fisher; Münster U. of Applied Sciences Presenter: Sharna Lee Wiblen; Sydney Business School, U. of Wollongong Web Doctor’s Compassion: An Unfolding Model of Norm Formation Presenter: Lan Wang; U. of Science and Technology of China Presenter: Chen Chen; Boston U. Driven by High Ratings: Injustice and Emotional Labor in the Ridesharing Context Presenter: Xue Lei; George Mason U. Presenter: John Cliburn; George Mason U. Presenter: Cedric Portea; George Mason U. A Qualitative Study on On-Demand Workers’ Justice Perceptions of Algorithmic Management Presenter: Lian Zhou; Guangdong U. of Technology Presenter: Xue Lei; George Mason U. Presenter: Rui Hou; Guangdong U. of Technology

Multi-Step Unfolding and Rearrangement of α-Lactalbumin by SDS Revealed by Stopped-Flow SAXS.

Interactions between proteins and surfactants are both of fundamental interest and relevant for applications in food, cosmetics and detergency. The anionic surfactant sodium dodecyl sulfate (SDS) denatures essentially all proteins. Denaturation typically involves a number of distinct steps where growing numbers of SDS molecules bind to the protein, as seen in multidisciplinary approaches combining several complementary techniques. We adopt this approach to study the SDS-induced unfolding of Ca2+-depleted α-lactalbumin (aLA), a protein particularly sensitive toward denaturation by surfactants. By combining stopped-flow mixing of protein and surfactant solutions with stopped-flow synchrotron small-angle X-ray scattering (SAXS), circular dichroism (CD) and Trp fluorescence, together with information from previous calorimetric studies, we construct a detailed picture of the unfolding process at the level of both protein and surfactant. A protein-surfactant complex is formed within the dead time of mixing (2.5 ms). Initially a cluster of SDS molecules binds asymmetrically, i.e., to one side of the protein, after which aLA redistributes around the SDS cluster. This occurs in two kinetic steps where the complex grows in number of both SDS and protein molecules, concomitant with protein unfolding. During these steps, the core-shell complex undergoes changes in shell thickness as well as core shape and radius. The entire process is very sensitive to SDS concentration and completes within 10 s at an SDS:aLA ratio of 9, decreasing to 0.2 s at 60 SDS:aLA. The number of aLA molecules per SDS complex drops from 1.9 to 1.0 over this range of ratios. While both CD and Trp kinetics reveal a fast and a slow conformational transition, only the slow transition is observed by SAXS, indicating that the protein-SDS complex (which is monitored by SAXS) adjusts to the presence of the unfolded protein. We attribute the rapid unfolding of aLA to its predominantly α-helical structure, which persists in SDS (albeit as isolated helices), enabling aLA to unfold without undergoing major secondary structural changes unlike β-sheet rich proteins. Nevertheless, the overall unfolding steps are broadly similar to those of the more β-rich protein β-lactoglobulin, suggesting that this unfolding model is representative of the general process of SDS-unfolding of proteins.

Thalassa, "Confusion of the tongues", "The unwelcome child and his death-instinct": Sándor Ferenczi's unfolding model of trauma

In this article, I trace the evolution and unfolding meanings and use of Ferenczi's model of trauma as it appears in his work on sexual abuse, on war trauma, and on early neglect. Some are works of quite long gestation and some written and published in the context of immediate circumstances, for instance, his paper on war neurosis. Ferenczi's work is seen as an influence on the psychoanalytic study of somatic states, on early gaps in psychic structure and in early and adult trauma.

Unfolding persons' personal values in ideal-point and vector models

Similarities and differences of individuals' ratings of the importance of personal values have recently been explained by the ideal-point unfolding (IPU) model. The IPU assumes that there is a geometric configuration of value points, shared by all individuals, and an ideal point that is special for each person. The distances between the ideal points and the value points correspond to the observed importance ratings. The IPU is psychologically conclusive but difficult to fit. We here investigate whether an alternative individual differences model, the vector model of unfolding (VMU), is a viable alternative to the IPU. The VMU represents individuals as vectors whose lengths and directions in value space explain the observed preferences. The VMU has an analytic solution, robust scaling algorithms, and unique solutions if properly normalized. The psychological assumptions of the VMU are simpler than those of the IPU. This makes the VMU attractive as a model for fast and frugal judgments such as responding to an anonymous questionnaire and for quickly diagnosing inter-individual differences.

Spectral Mixture Model Inspired Network Architectures for Hyperspectral Unmixing

In many statistical hyperspectral unmixing approaches, the unmixing task is essentially an optimization problem given a defined linear or nonlinear spectral mixture model. However, most of the model inference algorithms require a time-consuming iterative procedure. On the other hand, neural networks have been recently used to estimate abundances given some training samples, or directly estimate endmembers and abundances simultaneously in an unsupervised setting. However, their disadvantages are clear: lack of interpretability and reliance on the large training set. Model-inspired neural networks are constructed by the problem model and its corresponding inference algorithm. It incorporates the prior knowledge of physical model and algorithm into network architecture, combining the advantages of model-based and learning-based methods. This article deeply unfolds the linear mixture model and the corresponding iterative shrinkage-thresholding algorithm (ISTA) to build two unmixing network architectures. The first assumes that the set of endmembers are known, and the deep unfolded ISTA model is only for abundance estimation; and the second is used for blind unmixing to estimate both endmembers and abundances at the same time. The networks can be trained by supervised and unsupervised schemes, respectively, with a small-size training set, and then, unmixing becomes a feedforward process, which is very fast since no iteration is required. The experimental results show their competitive performance compared with the state-of-the-art unmixing approaches.

Improving the Accuracy of Protein Thermostability Predictions for Single Point Mutations

Accurately predicting the protein thermostability changes upon single point mutations in silico is a challenge that has implications for understanding diseases as well as industrial applications of protein engineering. Free energy perturbation (FEP) has been applied to predict the effect of single point mutations on protein stability for over 40 years and emerged as a potentially reliable prediction method with reasonable throughput. However, applications of FEP in protein stability calculations in industrial settings have been hindered by a number of limitations, including the inability to model mutations to and from prolines in which the bonded topology of the backbone is modified and the complexity in modeling charge-changing mutations. In this study, we have extended the FEP+ protocol to enable the accurate modeling of the effects on protein stability from proline mutations and from charge-changing mutations. We also evaluated the influence of the unfolded model in the stability calculations using increasingly longer peptides with native sequence and conformations. With the abovementioned improvements, the accuracy of FEP predictions of protein stability over a data set of 87 mutations on five different proteins has drastically improved compared with previous studies, with a mean unsigned error of 0.86 kcal/mol and root mean square error of 1.11 kcal/mol, comparable with the accuracy of previously published state-of-the-art small-molecule relative binding affinity calculations, which have been shown to be capable of driving discovery projects.

Monitoring Framework Based on Generalized Tensor PCA for Three-Dimensional Batch Process Data

Unfolding is a pretreating operation in most existing batch process modeling methods, but it would destroy the essential structure of the raw data. Also, the number of estimated parameters in the unfolding model is prohibitively large. In this work, a generalized tensor PCA (GTPCA) method is proposed for batch process monitoring. It can be performed on the 3-D batch process data directly to avoid the “curse of dimensionality” problem and potential information loss caused by data unfolding. Also, the uneven-length problem can be solved naturally without batch trajectory synchronization to prevent distorting data, which guarantees better quality models and monitoring performances. Moreover, within-batch detection can be easily performed using the tensor-based modeling scheme. The advantages and effectiveness of the proposed method are illustrated through a numerical case and an injection molding process in comparison with the traditional MPCA, PARAFAC, and Tucker3 methods.

Impact of abusive supervision on intention to leave: a moderated mediation model of organizational-based self esteem and emotional exhaustion

This study was aimed to offer and test a novel model explaining how abusive supervision enhances employees’ intention to leave in a high-power distance society of Pakistan. Drawing from the unfolding model of voluntary turnover as an overarching theory, we proposed that emotional exhaustion will strengthen the relationship between abusive supervision and intention to leave, and organizational-based self esteem (OBSE) will mitigate this relationship. Data were collected from 277 respondents working in the banking sector of Pakistan at two points in time. Results showed that abusive supervision has both direct and indirect impact on intention to leave such that emotional exhaustion strengthens this relationship. It was also found that OBSE mitigates the mediated relationship between abusive supervision and intention to leave. The study brings evidence of abusive supervision and its impacts from a high-power distance-oriented country i.e., Pakistan.

Thermodynamic Unfolding and Aggregation Fingerprints of Monoclonal Antibodies Using Thermal Profiling.

Predicting thermal protein stability is of major interest in the development of protein-based biopharmaceuticals. Therefore, this study provides a predictive tool for determining transition enthalpies, which can be used for ranking different proteins according to their thermal stability. Unfolding and aggregation profiles of eight different therapeutic monoclonal antibodies (mAbs) of type G, isotype 1 were investigated. The unfolding profiles were determined by intrinsic fluorescence (IF) spectroscopy and differential scanning calorimetry (DSC). A three-state unfolding fitting model was used to determine thermodynamic parameters for macromolecular multi-domain mAbs in IF experiments, like the van't Hoff enthalpy change (∆Hvh) and the entropy change (∆S) of the unfolding event. The derived values were compared to thermodynamic parameters obtained directly by calorimetry. Moreover, differences in the Fab enthalpies were used to predict aggregation behavior and protein thermal stabilities. To do so, the liquid-formulated mAbs were investigated exemplarily by size exclusion chromatography (SEC) after accelerated thermal-induced stress conditions. Comparing the thermodynamic parameters derived from IF spectroscopy and DSC resulted in similar values. Data generated by thermal-induced stress at 40°C show similar stability ranking as postulated through the Fab enthalpies for mAbs in two different formulations, while at 25°C a meaningful ranking is not possible, because distinct differences in the thermal stability cannot be observed. The additional consideration of Fab enthalpies to predict the 40 °C SEC ranking seems to be more reliable compared to the use of exclusively the melting temperatures or aggregation onset temperatures and times. We show that thermodynamic profiling can help predicting unfolding and aggregation properties of therapeutic mAbs at 40°C. Therefore, analyzing thermodynamic unfolding parameters is a useful and supportive tool discriminating thermal stability profiles of mAbs for further pharmaceutical development and clinical studies.

The evolutionarily conserved HtrA is associated with stress tolerance and protein homeostasis in the halotolerant cyanobacterium Halothece sp. PCC7418.

The HtrA protein family represents an important class of serine proteases that are widely distributed across taxa. These evolutionarily conserved proteins are crucial for survival and function as monitors of protein synthesis during various stresses. Here, we performed gene expression analysis of the entire set of putative serine protease genes in Halothece sp. PCC7418 under salt stress conditions. The gene-encoding HtrA2 (H3553) was highly upregulated. This gene was cloned and functionally characterized, and its sub-cellular localization was determined. The recombinant H3553 protein (rH3553) displayed a pH optimum of 8.0, remained stable at 45°C, and its proteolytic activity was not affected by salts. H3553 completely degraded the unfolded model protein, β-casein. In contrast, the folded model substrates (lysozyme or BSA) were not degraded by rH3553. Denaturation of BSA at a high temperature significantly increased its degradation by rH3553. H3553 was detected in the soluble protein fraction as well as the plasma membrane and thylakoid membrane fractions. Interestingly, the majority of H3553 was present in the plasma membrane under salt and heat stress conditions. Thus, H3553 resides in multiple sub-cellular locations and its localization drastically changes after exposure to stresses. Taken together, H3553 underpins protein quality-control process and is involved in the response and adaptation to salinity and heat stresses.

Testing research on friction and wear performance of microstructure surface of unfolding wheel for bearing steel ball detection

The unfolding wheel of AVIKO series steel ball surface quality automatic detector is expensive and easy to wear, which seriously restricts its application and development. In this paper, the wear reason and motion form of the unfolding wheel are analyzed, the unfolding contact model is simplified reasonably, and the corresponding experimental equipment and scheme are designed. The microstructures with different shape parameters are machined on the test pieces surface by laser machining technology, and the theoretical calculation formula for the weight of wear is obtained through friction and wear tests and data fitting. The test results show that compared with the smooth test piece, the test pieces with microstructure surface can increase the friction coefficient and reduce the degree of wear, and the diamond microstructure has the best effect on reducing the wear weight. The fitted formula provides theoretical and experimental basis for the follow-up research and the design of microstructure parameters of unfolding wheel.

An Item-Level Analysis for Detecting Faking on Personality Tests: Appropriateness of Ideal Point Item Response Theory Models.

How to detect faking on personality measures has been investigated using various methods and procedures. As previous findings are mixed and rarely based on ideal point item response theory models, additional research is needed for further exploration. This study modeled the responses of personality tests using ideal point method across instructed faking and honest responding conditions. A sample of undergraduate students participated the within-subjects measures to examine how the item location parameter derived from the generalized graded unfolding model changed, and how individuals’ perception about items changed when faked. The mean test scores of faking group was positively correlated to the magnitude of within-subjects score change. The item-level analysis revealed both conscientiousness items (18.8%) and neuroticism items (50.0%) appeared significant shifts on item parameters, suggesting that response pattern changed from honest to faking conditions. The direction of the change appeared both in positive and negative way, demonstrating that faking could increase or decrease personality factor scores. The results indicated that the changes of perceptions on items could be operated by faking, offering some support for the ideal point model to be an adequate measure for detecting faking. However, the findings of diagnostic accuracy analysis also implied that the appropriateness of ideal point models for detecting faking should be under consideration, also be used with caution. Implications, further research directions, and limitations are discussed.