3D Aligned Nanofiber Scaffold Fabrication with Trench-Guided Electrospinning for Cardiac Tissue Engineering.

Constructing three-dimensional (3D) aligned nanofiber scaffolds is significant for the development of cardiac tissue engineering, which is promising in the field of drug discovery and disease mechanism study. However, the current nanofiber scaffold preparation strategy, which mainly includes manual assembly and hybrid 3D printing, faces the challenge of integrated fabrication of morphology-controllable nanofibers due to its cross-scale structural feature. In this research, a trench-guided electrospinning (ES) strategy was proposed to directly fabricate 3D aligned nanofiber scaffolds with alternative ES and a direct ink writing (DIW) process. The electric field effect of DIW poly(dimethylsiloxane) (PDMS) side walls on guiding whipping ES nanofibers was investigated to construct trench design rules. It was found that the width/height ratio of trenches greatly affected the nanofiber alignment, and the trench width/height ratio of 1.5 provided the nanofiber alignment degree over 60%. As a proof of principle, 3D nanofiber scaffolds with controllable porosity (60-80%) and alignment (30-60%) were fabricated. The effect of the scaffolds was verified by culturing human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), which resulted in the uniform 3D distribution of aligned hiPSC-CMs with ∼1000 µm thickness. Therefore, this printing strategy shows great potential for the efficient engineered tissue construction.

Estimation of nonlinear mixed-effects continuous-time models using the continuous-discrete extended Kalman filter.

Many intensive longitudinal measurements are collected at irregularly spaced time intervals, and involve complex, possibly nonlinear and heterogeneous patterns of change. Effective modelling of such change processes requires continuous-time differential equation models that may be nonlinear and include mixed effects in the parameters. One approach of fitting such models is to define random effect variables as additional latent variables in a stochastic differential equation (SDE) model of choice, and use estimation algorithms designed for fitting SDE models, such as the continuous-discrete extended Kalman filter (CDEKF) approach implemented in the dynr R package, to estimate the random effect variables as latent variables. However, this approach's efficacy and identification constraints in handling mixed-effects SDE models have not been investigated. In the current study, we analytically inspect the identification constraints of using the CDEKF approach to fit nonlinear mixed-effects SDE models; extend a published model of emotions to a nonlinear mixed-effects SDE model as an example, and fit it to a set of irregularly spaced ecological momentary assessment data; and evaluate the feasibility of the proposed approach to fit the model through a Monte Carlo simulation study. Results show that the proposed approach produces reasonable parameter and standard error estimates when some identification constraint is met. We address the effects of sample size, process noise variance, and data spacing conditions on estimation results.

A knowledge graph based question answering method for medical domain.

Question answering (QA) is a hot field of research in Natural Language Processing. A big challenge in this field is to answer questions from knowledge-dependable domain. Since traditional QA hardly satisfies some knowledge-dependable situations, such as disease diagnosis, drug recommendation, etc. In recent years, researches focus on knowledge-based question answering (KBQA). However, there still exist some problems in KBQA, traditional KBQA is limited by a range of historical cases and takes too much human labor. To address the problems, in this paper, we propose an approach of knowledge graph based question answering (KGQA) method for medical domain, which firstly constructs a medical knowledge graph by extracting named entities and relations between the entities from medical documents. Then, in order to understand a question, it extracts the key information in the question according to the named entities, and meanwhile, it recognizes the questions' intentions by adopting information gain. The next an inference method based on weighted path ranking on the knowledge graph is proposed to score the related entities according to the key information and intention of a given question. Finally, it extracts the inferred candidate entities to construct answers. Our approach can understand questions, connect the questions to the knowledge graph and inference the answers on the knowledge graph. Theoretical analysis and real-life experimental results show the efficiency of our approach.

Dynamic Mixture Modeling with dynr.

Mixture modeling is commonly used to model sample heterogeneity by identifying unobserved classes of individuals with similar characteristics. Despite abundance of evidence in the literature suggesting that individuals are often characterized by different dynamic processes underlying their physiological, cognitive, psychological, and behavioral states, applications of dynamic mixture modeling are surprisingly lacking. We present here a proof-of-concept example of dynamic mixture modeling, where latent groups of individuals were identified based on different dynamic patterns in their time series. Our sample consists of 192 men who were in a heterosexual relationship. They were asked to complete a daily questionnaire involving emotions related to their relationship. Two latent groups were identified based on the strength of association between positive (e.g., loving) and negative (e.g., doubtful) affect. Men in the group characterized by a strong negative association (ß=-.67) tended to be younger and had higher levels of anxiety toward their relationship than men in the other group, which was characterized by a weaker negative association (ß=-.31). We illustrate the specification and estimation of dynamic mixture model using "dynr," an R package capable of handling a broad class of linear and nonlinear discrete- and continuous-time models with regime-switching properties.

Identifying Qualitative Between-Subject and Within-Subject Variability: A Method for Clustering Regime-Switching Dynamics.

Technological advancement provides an unprecedented amount of high-frequency data of human dynamic processes. In this paper, we introduce an approach for characterizing qualitative between and within-subject variability from quantitative changes in the multi-subject time-series data. We present the statistical model and examine the strengths and limitations of the approach in potential applications using Monte Carlo simulations. We illustrate its usage in characterizing clusters of dynamics with phase transitions with real-time hand movement data collected on an embodied learning platform designed to foster mathematical learning.

Effects of Process Parameters on the Thickness Uniformity in Two-Point Incremental Forming (TPIF) with a Positive Die for an Irregular Stepped Part.

Incremental sheet forming (ISF) is a novel flexible forming technology with advantages, such as a low forming force, low-energy-consuming equipment, and good forming performance. The lack of available information about the formability of the two-point incremental forming (TPIF) process makes it limited for practical applications. Taking an irregular stepped part as the target part, the effects of process parameters on the thickness uniformity when using TPIF with a positive die for AA1060 aluminum alloy sheets were investigated. First, the set of optimal parameters regarding the diameter of the tool head, feed rate, and the step size were obtained through orthogonal experiments. Furthermore, the optimal parameter set of the number of forming passes, the direction of movement of the forming tool, and the forming angle was determined and the optimal forming result was numerically and experimentally verified. This demonstrated that the parameters affecting the thickness uniformity of the irregular stepped parts were, in descending order, the diameter of the forming tool, the feed rate, and the step size, with corresponding optimal values of 12 mm, 15,000 mm/min, and 0.4 mm, respectively. With an increase of the number of passes and a decrease of the forming angle between adjacent passes, and adopting an alternating clockwise and counterclockwise toolpath, the thickness uniformity of the formed parts was effectively improved.

What Technology Can and Cannot Do to Support Assessment of Non-cognitive Skills.

Advances in technology hold great promise for expanding what assessments may achieve across domains. We focus on non-cognitive skills as our domain, but lessons can be extended to other domains for both the advantages and drawbacks of new technological approaches for different types of assessments. We first briefly review the limitations of traditional assessments of non-cognitive skills. Next, we discuss specific examples of technological advances, considering whether and how they can address such limitations, followed by remaining and new challenges introduced by incorporating technology into non-cognitive assessments. We conclude by noting that technology will not always improve assessments over traditional methods and that careful consideration must be given to the advantages and limitations of each type of assessment relative to the goals and needs of the assessor. The domain of non-cognitive assessments in particular remains limited by lack of agreement and clarity on some constructs and their relations to observable behavior (e.g., self-control versus -regulation versus -discipline), and until these theoretical limitations must be overcome to realize the full benefit of incorporating technology into assessments.

How Do Test Takers Interact With Simulation-Based Tasks? A Response-Time Perspective.

Many traditional educational assessments use multiple-choice items and constructed-response items to measure fundamental skills. Virtual performance assessments, such as game- or simulation-based assessments, are designed recently in the field of educational measurement to measure more integrated skills through the test takers' interactive behaviors within an assessment in a virtual environment. This paper presents a systematic timing study based on data collected from a simulation-based task designed recently at Educational Testing Service. The study is intended to understand the response times in complex simulation-based tasks so as to shed light on possible ways of leveraging response time information in designing, assembling, and scoring of simulation-based tasks. To achieve this objective, a series of five analyses were conducted to first understand the statistical properties of the timing data, and then investigate the relationship between the timing patterns and the test takers' performance on the items/task, demographics, motivation level, personality, and test-taking behaviors through use of different statistical approaches. We found that the five analyses complemented each other and revealed different useful timing aspects of this test-taker sample's behavioral features in the simulation-based task. The findings were also compared with notable existing results in the literature related to timing data.

Understanding Test Takers' Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data.

With the rise of more interactive assessments, such as simulation- and game-based assessment, process data are available to learn about students' cognitive processes as well as motivational aspects. Since process data can be complicated due to interdependencies in time, our traditional psychometric models may not necessarily fit, and we need to look for additional ways to analyze such data. In this study, we draw process data from a study on self-adapted test under different goal conditions (Arieli-Attali, 2016) and use hidden Markov models to learn about test takers' choice making behavior. Self-adapted test is designed to allow test takers to choose the level of difficulty of the items they receive. The data includes test results from two conditions of goal orientation (performance goal and learning goal), as well as confidence ratings on each question. We show that using HMM we can learn about transition probabilities from one state to another as dependent on the goal orientation, the accumulated score and accumulated confidence, and the interactions therein. The implications of such insights are discussed.

What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R.

Intensive longitudinal data in the behavioral sciences are often noisy, multivariate in nature, and may involve multiple units undergoing regime switches by showing discontinuities interspersed with continuous dynamics. Despite increasing interest in using linear and nonlinear differential/difference equation models with regime switches, there has been a scarcity of software packages that are fast and freely accessible. We have created an R package called dynr that can handle a broad class of linear and nonlinear discrete- and continuous-time models, with regime-switching properties and linear Gaussian measurement functions, in C, while maintaining simple and easy-to-learn model specification functions in R. We present the mathematical and computational bases used by the dynr R package, and present two illustrative examples to demonstrate the unique features of dynr.

Representing Sudden Shifts in Intensive Dyadic Interaction Data Using Differential Equation Models with Regime Switching.

A growing number of social scientists have turned to differential equations as a tool for capturing the dynamic interdependence among a system of variables. Current tools for fitting differential equation models do not provide a straightforward mechanism for diagnosing evidence for qualitative shifts in dynamics, nor do they provide ways of identifying the timing and possible determinants of such shifts. In this paper, we discuss regime-switching differential equation models, a novel modeling framework for representing abrupt changes in a system of differential equation models. Estimation was performed by combining the Kim filter (Kim and Nelson State-space models with regime switching: classical and Gibbs-sampling approaches with applications, MIT Press, Cambridge, 1999) and a numerical differential equation solver that can handle both ordinary and stochastic differential equations. The proposed approach was motivated by the need to represent discrete shifts in the movement dynamics of [Formula: see text] mother-infant dyads during the Strange Situation Procedure (SSP), a behavioral assessment where the infant is separated from and reunited with the mother twice. We illustrate the utility of a novel regime-switching differential equation model in representing children's tendency to exhibit shifts between the goal of staying close to their mothers and intermittent interest in moving away from their mothers to explore the room during the SSP. Results from empirical model fitting were supplemented with a Monte Carlo simulation study to evaluate the use of information criterion measures to diagnose sudden shifts in dynamics.

(Re)evaluating the Implications of the Autoregressive Latent Trajectory Model Through Likelihood Ratio Tests of Its Initial Conditions.

The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.

Cs sorption to potential host rock of low-level radioactive waste repository in Taiwan: experiments and numerical fitting study.

A reliable performance assessment of radioactive waste repository depends on better knowledge of interactions between nuclides and geological substances. Numerical fitting of acquired experimental results by the surface complexation model enables us to interpret sorption behavior at molecular scale and thus to build a solid basis for simulation study. A lack of consensus on a standard set of assessment criteria (such as determination of sorption site concentration, reaction formula) during numerical fitting, on the other hand, makes lower case comparison between various studies difficult. In this study we explored the sorption of cesium to argillite by conducting experiments under different pH and solid/liquid ratio (s/l) with two specific initial Cs concentrations (100mg/L, 7.5 × 10(-4)mol/L and 0.01 mg/L, 7.5 × 10(-8)mol/L). After this, numerical fitting was performed, focusing on assessment criteria and their consequences. It was found that both ion exchange and electrostatic interactions governed Cs sorption on argillite. At higher initial Cs concentration the Cs sorption showed an increasing dependence on pH as the solid/liquid ratio was lowered. In contrast at trace Cs levels, the Cs sorption was neither s/l dependent nor pH sensitive. It is therefore proposed that ion exchange mechanism dominates Cs sorption when the concentration of surface sorption site exceeds that of Cs, whereas surface complexation is attributed to Cs uptake under alkaline environments. Numerical fitting was conducted using two different strategies to determine concentration of surface sorption sites: the clay model (based on the cation exchange capacity plus surface titration results) and the iron oxide model (where the concentration of sorption sites is proportional to the surface area of argillite). It was found that the clay model led to better fitting than the iron oxide model, which is attributed to more amenable sorption sites (two specific sorption sites along with larger site density) when using clay model. Moreover, increasing s/l ratio would produce more sorption sites, which helps to suppress the impact of heterogeneous surface on Cs sorption behavior under high pH environments.