bluuebunny/crossref_metadata_2025_split huggingface dataset:
A quadrilateral shell element incorporating thickness‐stretch for nearly incompressible hyperelasticity
Authors: Yamamoto Takeki , Yamada Takahiro 
Published: December 2019

SummaryThe authors proposed a quadrilateral shell element enriched with degrees of freedom to represent thickness‐stretch. The quadrilateral shell element can be utilized to consider large deformations for nearly incompressible materials, and its performance is demonstrated in small and large deformation analyses of hyperelastic materials in this study. Formulation of the proposed shell element is based on extension of the MITC4 shell element. A displacement variation in the thickness direction is introduced to evaluate the change in thickness. In the proposed approach, the thickness direction is defined using the director vectors at each midsurface node. The thickness‐stretch is approximated by the movements of additional nodes, which are placed along the thickness direction from the bottom to the top surface. The transverse normal strain is calculated using these additional nodes without assuming the plane stress condition; hence, a three‐dimensional constitutive equation can be employed without any modification. In this work, the authors apply an assumed strain technique to the special shell element to alleviate volumetric locking for nearly incompressible materials. Several numerical examples are presented to examine the effectiveness of the proposed element.
Teacher Skill Development: Evidence from Performance Ratings by Principals
Authors: Kraft Matthew A. , Papay John P. , Chi Olivia L. 
Published: December 2019

AbstractWe examine the dynamic nature of teacher skill development using panel data on principals’ subjective performance ratings of teachers. Past research on teacher productivity improvement has focused primarily on one important but narrow measure of performance: teachers’ value‐added to student achievement on standardized tests. Unlike value‐added, subjective performance ratings provide detailed information about specific skill dimensions and are available for teachers in non‐tested grades and subjects. Using a within‐teacher returns‐to‐experience framework, we find, on average, large and rapid improvements in teachers’ instructional practices throughout their first 10 years on the job as well as substantial differences in improvement rates across individual teachers. We also document that subjective performance ratings contain important information about teacher effectiveness. In the district we study, principals appear to differentiate teacher performance throughout the full distribution instead of just in the tails. Furthermore, prior performance ratings and gains in these ratings provide additional information about teachers’ ability to improve test scores that is not captured by prior value‐added scores. Taken together, our study provides new insights on teacher performance improvement and variation in teacher development across instructional skills and individual teachers.
Childhood homelessness, resilience, and adolescent mental health: A prospective, person‐centered approach
Authors: Herbers Janette E. , Cutuli J. J. , Keane Joanna N. , Leonard Jake A. 
Published: December 2019

AbstractSchool districts and other service providers are increasingly aware of the substantial mental health needs of students experiencing family homelessness. Past findings are mixed regarding whether homelessness conveys unique risk beyond the risks associated with extreme poverty. With prospective longitudinal data on homelessness experiences across childhood, we utilized latent profile analysis as a person‐centered approach to conceptualizing mental health outcomes in adolescence for 3,778 youth. We considered literal family homelessness as well as families living doubled‐up, and we employed propensity score matching to identify a comparison group of nonhomeless students balanced across a range of covariates to address systematic bias. Results indicated that students who experienced literal homelessness during childhood were significantly less likely to demonstrate profiles of resilience in mental health functioning. We considered our approach and findings in light of challenges and opportunities particularly relevant to the school context.
Inhibition of noncanonical Wnt pathway overcomes enzalutamide resistance in castration‐resistant prostate cancer
Authors: Chen Xiaoliang , Liu Jinghui , Cheng Lijun , Li Chaohao , Zhang Zhuangzhuang , Bai Yunfeng , Wang Ruixin , Han Tao , Huang Changkun , Kong Yifan , Feng Feng , Liu Xiaoqi 
Published: December 2019

AbstractBackgroundBecause androgen receptor (AR) signaling is essential for prostate cancer (PCa) initiation and progression, castration is the main approach for treatment. Unfortunately, patients tend to enter a stage called castration‐resistant prostate cancer (CRPC) despite the initial response to castration. For various reasons, AR signaling is reactivated in CRPC. As such, AR signaling inhibitors, such as enzalutamide, has been approved by the Food and Drug Administration to treat CRPC in the clinic. However, the limited success of these new drugs suggests an immediate unmet need to understand the underlying mechanisms for resistance so novel targets can be identified to enhance their efficacy.MethodsAn unbiased bioinformatics analysis was performed with the existing human patient dataset and RNA‐seq results of in‐house PCa cell lines to identify new targets to overcome enzalutamide resistance. Cell viability and growth were detected by 3‐(4,5‐dimethylthiazol‐2‐yl)‐2, 5‐diphenyltetrazolium bromide and colony formation assay. Cell invasion and migration were detected by transwell assay. Protein levels were detected by Western blot or immunofluorescence.ResultsWe found that the noncanonical Wnt signaling was activated in enzalutamide‐resistant PCa cells and that the activation of noncanonical Wnt signaling was correlated with AR expression and disease progression. This was validated by the elevated expression of noncanonical Wnt pathway members such as Wnt5a, RhoA, and ROCK in enzalutamide‐resistant PCa cells in comparison to their enzalutamide‐sensitive counterparts. And, both Y27632, an inhibitor of ROCK, and depletion of ROCK enhanced the efficacy of enzalutamide in enzalutamide‐resistant PCa cells. Of significance, a combination of Y27632 and enzalutamide inhibited 22RV1‐derived xenograft tumor growth synergistically. Finally, ROCK depletion plus enzalutamide treatment inhibited invasion and migration of enzalutamide‐resistant PCa cells via inhibition of epithelial‐mesenchymal transition.ConclusionsThe noncanonical Wnt pathway is activated in enzalutamide‐resistant PCa and inhibition of noncanonical Wnt pathway overcomes enzalutamide resistance and enhances its efficacy in CRPC.
Probability analysis of damages to subsea pipeline
Authors: Sulaiman Nurul S. , Tan Henry 
Published: December 2019

AbstractPipeline failure due to various threats may contribute to significant adverse impact on human safety, environment, and economy. In order to mitigate the severity of pipeline failure consequences, maintaining the integrity of the vast and aging pipeline structure is crucial. The main concern in offshore risk analysis is the unpredictable and uncertain pipeline conditions. In probability theory, Bayesian network is known as a powerful tool for knowledge presentation and condition inference under uncertainty. Probability analysis of pipeline damages is necessary to prevent unwanted incidents that may cause catastrophic accidents. In this paper, a Bayesian network model was developed to identify and analyze the probability of subsea pipeline condition subjected to corrosion, third party, operational, and material damages. Statistical data and experts' knowledge were integrated in addressing data limitation. Attaining the proposed network model, diagnostic analysis, mutual analysis, and sensitivity analysis were performed to validate and provide a substantial amount of confidence on the outcomes of the proposed model. The analyses have demonstrated that estimation of the developed model is reliable. The outcome obtained can be used to assist the decision maker to prepare preventive safety measures and allocate proper resources to significantly minimize the occurrence probability of the risk factors.
Migration trajectories and their relationship to mental health among internal migrants in urban China: A sequence alignment approach
Authors: Yang Min , Dijst Martin , Helbich Marco 
Published: December 2019

AbstractAlthough migration trajectories over people's life courses seem to be associated with mental health outcomes, previous studies have considered migration at only one point in time when correlating migration with mental health. However, people can migrate multiple times during their life courses. The decision to migrate can be triggered by several life course development events, such as education, entry to the labour market, marriage, or retirement. The present study addressed this research gap by focusing on the trajectories of migration and their relationship to mental health among internal migrants in China. Data were collected from a cross‐sectional survey (N = 534) in Shenzhen, China, in 2017. People's migration trajectories were aligned into migration groups using sequence alignment method. Binary logistic regression models were estimated to assess the associations between each migration trajectory group and the prevalence of mental health problems, controlling for sociodemographics and self‐reported physical health. The results show that migration trajectories—namely, the sequence of multiple migrations between migrants' places of origin and their final destinations—are significantly related to mental health outcomes. Our findings suggest that treating migration as a one‐time transition could be problematic because many migrants undertake multiple migration trips.
Tracking and parameter identification for model reference adaptive control
Authors: Malisoff Michael 
Published: December 2019

SummaryWe provide barrier Lyapunov functions for model reference adaptive control algorithms, allowing us to prove robustness in the input‐to‐state stability framework and to compute rates of exponential convergence of the tracking and parameter identification errors to zero. Our results ensure identification of all entries of the unknown weight and control effectiveness matrices. We provide easily checked sufficient conditions for our relaxed persistency of excitation conditions to hold. Our illustrative numerical example demonstrates the performance of the control methods.
Multigradient recursive reinforcement learning NN control for affine nonlinear systems with unmodeled dynamics
Authors: Bai Weiwei , Zhang Bin , Zhou Qi , Lu Renquan 
Published: December 2019

SummaryIn this paper, an adaptive reinforcement learning approach is developed for a class of discrete‐time affine nonlinear systems with unmodeled dynamics. The multigradient recursive (MGR) algorithm is employed to solve the local optimal problem, which is inherent in gradient descent method. The MGR radial basis function neural network approximates the utility functions and unmodeled dynamics, which has a faster rate of convergence than that of the gradient descent method. A novel strategic utility function and cost function are defined for the affine systems. Finally, it concludes that all the signals in the closed‐loop system are semiglobal uniformly ultimately bounded through differential Lyapunov function method, and two simulation examples are presented to demonstrate the effectiveness of the proposed scheme.
Adult Vocabulary Modulates Speed of Word Integration Into Preceding Text Across Sentence Boundaries: Evidence From Self‐Paced Reading
Authors: Guerra Ernesto , Kronmüller Edmundo 
Published: December 2019

AbstractAn important component of reading comprehension is the reader's capacity to make inferences that can maintain the coherence between propositions within the text. However, the cognitive and linguistic skills that underlie online inference making remain elusive. The authors aimed to clarify the effects of vocabulary and text comprehension on word‐to‐text integration during reading. In a self‐paced reading study, the authors measured participants’ integration of words across sentence boundaries by comparing reading times at a critical word and the subsequent region, when preceded by the same critical word (repetition condition), by a lexically associated word (association condition), or by a word with no direct lexical link (inference condition). Furthermore, the authors assessed vocabulary knowledge, text comprehension, working memory, decoding, and fluid intelligence in the sample. Results showed that richer vocabulary was associated with immediate integration, as evidenced by reading time differences between the critical word and the subsequent word in the inference condition. Other individual differences did not predict self‐paced reading times. Findings suggest that lexical knowledge plays a stronger role relative to other individual skills in online inference making and contributes to refining understanding of the mechanisms that underlie efficient reading.
The Effect of Orthographic Neighbors on Second‐Grade Students’ Spelling Acquisition
Authors: Chen Yi‐Jui Iva , Cunningham Anne E. , Rabe‐Hesketh Sophia , Hinshaw Stephen P. , Irey Robin C. 
Published: December 2019

ABSTRACTMany words in English resemble one another in multiple ways. Words with similar spellings are referred to as orthographic neighbors. The purpose of this within‐subject experimental study was to examine the effect of orthographic neighbors on the spelling acquisition of second‐grade students. In each of five sessions of a computer‐based experiment, 71 participants were presented with two prime words and prompted to learn the spelling of seven novel words. The latter seven words were control words (no meaningful connection with the corresponding prime word) and neighbor words (words representing various types of connections with the corresponding prime word). Spelling tasks were administered twice: immediately after the experiment and two days later. The findings suggest that spelling acquisition depends on two critical factors: orthographic neighbor type and orthographic processing ability. Students’ spelling acquisition was supported by analogizing rime neighbors (e.g., rain/vain). However, facilitative effects were not found for substitution neighbors (e.g., rain/ruin) and transposition neighbors (e.g., clam/calm). Additionally, a student’s level of orthographic processing was an important determinant of spelling acquisition; students with well‐developed orthographic processing ability correctly learned the novel words regardless of the presence of rime neighbors. In contrast, acquiring spelling was far more difficult for students with less developed orthographic processing ability, but their spelling acquisition was strongly facilitated by the presence of rime neighbors. Implications of this research are (a) learners’ orthographic processing skills should be considered when designing spelling instruction and (b) early elementary students need instructional support when making orthographic analogies for substitution and transposition neighbors.
Surveying the reach and maturity of machine learning and artificial intelligence in astronomy
Authors: Fluke Christopher J. , Jacobs Colin 
Published: December 2019

AbstractMachine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established.This article is categorized under:
Application Areas > Science and Technology
Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining
Technologies > Machine Learning
Dynamical constraints on RG flows and cosmology
Authors: Baumann Daniel , Green Daniel , Hartman Thomas 
Published: December 2019

AbstractSum rules connecting low-energy observables to high-energy physics are an interesting way to probe the mechanism of inflation and its ultraviolet origin. Unfortunately, such sum rules have proven difficult to study in a cosmological setting. Motivated by this problem, we investigate a precise analogue of inflation in anti-de Sitter spacetime, where it becomes dual to a slow renormalization group flow in the boundary quantum field theory. This dual description provides a firm footing for exploring the constraints of unitarity, analyticity, and causality on the bulk effective field theory. We derive a sum rule that constrains the bulk coupling constants in this theory. In the bulk, the sum rule is related to the speed of radial propagation, while on the boundary, it governs the spreading of nonlocal operators. When the spreading speed approaches the speed of light, the sum rule is saturated, suggesting that the theory becomes free in this limit. We also discuss whether similar results apply to inflation, where an analogous sum rule exists for the propagation speed of inflationary fluctuations.
A new surrogate model–based method for individualized spot welding sequence optimization with respect to geometrical quality
Authors: Tabar Roham Sadeghi , Wärmefjord Kristina , Söderberg Rikard 
Published: December 2019

AbstractIn an individualized shee metal assembly line, form and dimensional variation of the in-going parts and different disturbances from the assembly process result in the final geometrical deviations. Securing the final geometrical requirements in the sheet metal assemblies is of importance for achieving aesthetic and functional quality. Spot welding sequence is one of the influential contributors to the final geometrical deviation. Evaluating spot welding sequences to retrieve lower geometrical deviations is computationally expensive. In a geometry assurance digital twin, where assembly parameters are set to reach an optimal geometrical outcome, a limited time is available for performing this computation. Building a surrogate model based on the physical experiment data for each assembly is time-consuming. Performing heuristic search algorithms, together with the FEM simulation, requires extensive evaluations times. In this paper, a neural network approach is introduced for building surrogate models of the individual assemblies. The surrogate model builds the relationship between the spot welding sequence and geometrical deviation. The approach results in a drastic reduction in evaluation time, up to 90%, compared to the genetic algorithm, while reaching a geometrical deviation with marginal error from the global optimum after welding in a sequence.
Speech changes in patients with a full rehabilitation for severe tooth wear, a first evaluation study
Authors: Sterenborg Bernadette A.M.M. , Kalaykova Stanimira I. , Knuijt Simone , Loomans Bas A.C. , Huysmans Marie-Charlotte D. N. J. M. 
Published: December 2019

Abstract
Objective
The aim of this study was to evaluate changes in speech characteristics and self-perceived quality of speech in tooth wear patients, after occlusal rehabilitation.

Materials and methods
Patients with tooth wear were included in this study after informed consent. The amount of tooth wear was scored with Tooth Wear Evaluation System (TWES). To assess the perspective of the patient, the Dutch Speech Handicap Index was used (SHI). Acoustic analysis was performed to evaluate changes with the use of voice recordings. These were made before treatment, T0; directly after treatment, T1; 1 month after treatment, T2. With the use of PRAAT software, the spectral characteristic centre of gravity (COG) was evaluated for the sounds /s/, /f/, /v/, /d/, /t/, /m/.

Results
Recordings of 17 patients (14 men, 3 women, mean age 41.2 ± 10.4 years) were included. SHI scores did not change significantly between T0 and T2 (p = 0.054). A multiple regression model showed that for all sounds the intercept was negative, but statistically significant only for /s/ and /f/ between T0 and T1. The effect of the initial change (between T0 and T1) on the change between T1 and T2 was clearly negative for all sounds (p < 0.001), showing a rebound effect ranging between 29 and 68% of the initial change.

Conclusion
Tooth wear patients perceive improvement in speech function after treatment.

Clinical significance
Clinicians may explain to patients that speech is likely to alter for a short period due to treatment but that there will be a good adaption to the new situation.
What do Australian patients with inflammatory arthritis value in treatment? A discrete choice experiment
Authors: Ho Kerrie-Anne , Acar Mustafa , Puig Andrea , Hutas Gabor , Fifer Simon 
Published: December 2019

Abstract
Background and objectives
The purpose of this study was to develop an understanding of treatment preferences in patients with inflammatory arthritis (IA) [rheumatoid arthritis (RA), ankylosing spondylitis (AS) and psoriatic arthritis (PsA)] focussing on treatment attributes that patients’ value, their relative importance, and the risk-benefit trade-offs that characterise patients’ choices around treatment.

Methods
A discrete choice experiment (DCE) approach was used. Attributes of interest were clinical efficacy; slowing of disease progression; risk of mild-moderate side effects; risk of severe side effects; frequency of administration; real-world product evidence; management of related conditions; and availability of a patient support programme. Using data from the DCE component, a restricted latent class model (LCM) was estimated to determine discrete ‘classes’ of treatment preferences.

Results
In this analysis, 206 participants were included (AS n = 59; PsA n = 62; RA n = 85). Two classes were identified. For ‘class 1’ (59.9%), the most important attributes (across all treatment modalities) were preventing disease progression, clinical efficacy and risk of mild-to-moderate side effects. For ‘class 2’ (40.1%), clinical and non-clinical attributes were important, and attribute importance depended on treatment modality. Patient demographic and treatment characteristics did not predict class membership.

Conclusion
For most patients with IA, clinical efficacy, stopping disease progression and risks of mild-to-moderate side effects are important treatment attributes. Patients with prior biologic DMARD experience had greater preference for injection treatments. For a subset of patients, patient support programmes and the frequency of administration were important. Clinicians should be mindful of preferences when prescribing treatment to patients with IA.Key Points• Most patients consider clinical efficacy, stopping disease progression and the risk of mild-to-moderate side effects as important treatment attributes• Patients with prior biologic DMARD experience have greater preference for injection treatments.• For a subset of patients, patient support programmes, and the frequency of administration were important.• Clinicians should be mindful of preferences when prescribing treatment to patients with IA.