Miracle-dz/UniFAR-FTUs huggingface dataset:
A Computer‐Aided Model for the Simulation of Railway Ballast by Random Sequential Adsorption Process

This article presents a computer‐aided multistage methodology for the simulation of railway ballasts using the Random Sequential Adsorption (RSA – 2D domain) paradigm. The primary stage in this endeavor is the numerical generation of a synthetic sample by a “particle sizing and positioning” process followed by a “compaction” process. The synthetic samples of ballast are then visualized in the Computer‐Aided Design (CAD) environment. The outcomes of the simulation are analyzed by comparison with the results of an experimental investigation carried out using a methacrylate container in which real samples of railway ballast are formed. A test of model reliability is carried out between the aggregates number and the grading curves of the synthetic sample and the real one. A validation is therefore performed using the ground‐penetrating radar (GPR) nondestructive testing (NDT) method and the finite‐difference time‐domain (FDTD) simulation developed in a computer‐aided environment. The results prove the viability and the applicability of the proposed modeling for the assessment of railway ballast conditions.
Kids SIPsmartER: A Feasibility Study to Reduce Sugar-Sweetened Beverage Consumption Among Middle School Youth in Central Appalachia

Purpose: To test the feasibility of Kids SIPsmartER, a school-based intervention to reduce consumption of sugar-sweetened beverages (SSBs). Design: Matched-contact randomized crossover study with mixed-methods analysis. Setting: One middle school in rural, Appalachian Virginia. Participants: Seventy-four sixth and seventh graders (5 classrooms) received Kids SIPsmartER in random order over 2 intervention periods. Feasibility outcomes were assessed among 2 teachers. Intervention: Kids SIPsmartER consisted of 6 lessons grounded in the Theory of Planned Behavior, media literacy, and public health literacy and aimed to improve individual SSB behaviors and understanding of media literacy and prevalent regional disparities. The matched-contact intervention promoted physical activity. Measures: Beverage Intake Questionnaire-15 (SSB consumption), validated theory questionnaires, feasibility questionnaires (student and teacher), student focus groups, teacher interviews, and process data (eg, attendance). Analysis: Repeated measures analysis of variances across 3 time points, descriptive statistics, and deductive analysis of qualitative data. Results: During the first intervention period, students receiving Kids SIPsmartER (n = 43) significantly reduced SSBs by 11 ounces/day (P = .01) and improved media (P < .001) and public health literacy (P < .01) understanding; however, only media literacy showed between-group differences (P < .01). Students and teachers found Kids SIPsmartER acceptable, in-demand, practical, and implementable within existing resources. Conclusion: Kids SIPsmartER is feasible in an underresourced, rural school setting. Results will inform further development and large-scale testing of Kids SIPsmartER to reduce SSBs among rural adolescents.
Classifying the Fault Type in Underground Distribution System Based on Fuzzy Logic Algorithm

This paper exhibits the best possible information example of fluffy rationale calculation for blame sort characterization in underground link. The proposed calculation utilizing mix of discrete wavelet changes (DWT) and fluffy rationale. The DWT is connected to concentrate high recurrence segment from blame current waveform utilizing mother wavelet daubechies4 (db4). The most extreme coefficients detail of DWT and greatest proportion of DWT, acquired from stage A, B, C and zero succession of blame current waveforms have been utilized as an info factors for choice calculation. The acquired outcomes in term of normal exactness have demonstrated that the most extreme proportion of DWT can accomplished tasteful precision in blame sort order.
Cooperative Path-Planning for Multi-Vehicle Systems

In this paper, we propose a collision avoidance algorithm for multi-vehicle systems, which is a common problem in many areas, including navigation and robotics. In dynamic environments, vehicles may become involved in potential collisions with each other, particularly when the vehicle density is high and the direction of travel is unrestricted. Cooperatively planning vehicle movement can effectively reduce and fairly distribute the detour inconvenience before subsequently returning vehicles to their intended paths. We present a novel method of cooperative path planning for multi-vehicle systems based on reinforcement learning to address this problem as a decision process. A dynamic system is described as a multi-dimensional space formed by vectors as states to represent all participating vehicles’ position and orientation, whilst considering the kinematic constraints of the vehicles. Actions are defined for the system to transit from one state to another. In order to select appropriate actions whilst satisfying the constraints of path smoothness, constant speed and complying with a minimum distance between vehicles, an approximate value function is iteratively developed to indicate the desirability of every state-action pair from the continuous state space and action space. The proposed scheme comprises two phases. The convergence of the value function takes place in the former learning phase, and it is then used as a path planning guideline in the subsequent action phase. This paper summarizes the concept and methodologies used to implement this online cooperative collision avoidance algorithm and presents results and analysis regarding how this cooperative scheme improves upon two baseline schemes where vehicles make movement decisions independently.
A Coupled Magnetomechanical Model for Magnetostrictive Transducers and its Application to Villari-Effect Sensors

A coupled magnetomechanical model for the design and control of Villarieffect magnetostrictive sensors is presented. The model quantifies the magnetization changes that a magnetostrictive material undergoes when subjected to a dc excitation field and variable stresses. The magnetic behavior is characterized by considering the Jiles-Atherton mean field theory for ferromagnetic hysteresis. This theory is constructed from a thermodynamic balance between the energy available for magnetic moment rotation and the energy lost as domain walls attach to and detach from pinning sites. The effect of stress on magnetization is quantified through a law of approach to the anhysteretic magnetization. Elastic properties are incorporated by means of a wave equation that quantifies the strains and stresses which arise in magnetostrictive materials in response to moment rotations. This yields a nonlinear PDE system for the strains, stresses and magnetization state of a magnetostrictive transducer as it drives or is driven by external loads. Because the model addresses the magnetomechanical coupling, it is applicable to both magnetostrictive sensors and actuators. Properties of the model and approximation method are illustrated by comparison with experimental data collected from a Terfenol-D sensor.
Intraday value-at-risk estimation for directional change events and investment strategies

Accurate risk measurement is important for making and assessing investment decisions. Recently, directional change representations of returns are proposed as a new method for analyzing and forecasting intra-day price movements and for creating investment strategies. This paper presents an FGARCH model for intraday Value-at-Risk (IVaR) estimation, for assessing risk properties in time series represented as directional change events, and for predicting the market risk for investment strategies based on directional changes. We apply the proposed method to 5-minute intraday data and report the time-varying risk based on IVaR estimates. We study the accuracy of these estimates and report the robustness of the risk estimates to the choice of the threshold parameter for directional change representations. Furthermore, we apply the proposed methodology to compare the risk properties of two investment strategies based on directional changes with a baseline moving window investment strategy. For these data, we find that the directional change strategies lead to higher returns but no substantial increase in risk compared to the baseline strategy. The proposed methodology is applicable to other intra-day data frequencies and it is generalizable to other risk metrics.
Linking Demography and Consumption of Henosepilachna vigintioctopunctata (Coleoptera: Coccinellidae) Fed on Solanum photeinocarpum (Solanales: Solanaceae): With a New Method to Project the Uncertainty of Population Growth and Consumption

Abstract Because life tables are capable of providing the most comprehensive description on the survival, stage differentiation, and the reproduction of animal populations, they can be considered as the bases of population ecology and pest management. Researchers concerned with studies involving life tables inevitably face the problem of describing the variabilities that occur in the survival, stage differentiation, and fecundity data. Finding a means to include these variabilities in population projections concerning pest management may be problematic. Henosepilachna vigintioctopunctata (F.) (Coleoptera: Coccinellidae) is a pest of many plant species in Asia, including cultivated crops, ornamentals, and wild plants. The raw life history data (survival, stage differentiation, and fecundity) and consumption rate of both sexes of H. vigintioctopunctata reared on Solanum photeinocarpum Nakamura et Odashima (Solanales: Solanaceae) were collected in the laboratory and analyzed based on the age-stage, two-sex life table theory.The intrinsic rate of increase (r), finite rate of increase (λ ), net reproductive rate (R0), mean generation time (T), and net consumption rate (C0) of H. vigintioctopunctata were 0.1312 d–1, 1.1402 d–1, 603.5 offspring, 48.8 d, and 77.8 cm2, respectively. By using the bootstrap technique with 100,000 samples, we demonstrated that the life tables constructed based on the 2.5th and 97.5th percentiles of R0 and λ can be used to describe the variabilities found in the survival and fecundity curves and to project the uncertainty of population growth.
Controlling the impact response of a one-link flexible robotic arm

A finite-element model of a single-link flexible robotic arm is derived, and the initial conditions required to represent impact loading are determined. An open-loop control system is designed using frequency-domain techniques to compute a desired hub torque profile. The motion of the arm after tip impact and including hub actuation is simulated. Simulation results are compared to experimental data. It is shown that the controller damps vibration caused by the impact and allows the steady-state tip deflection to be predetermined. <<ETX>>
Structured Mathematical Modeling of Industrial Boiler

As a major utility system in industry, boilers consume a large portion of the total energy and costs. Significant reduction of boiler cost operation can be gained through improvements in efficiency. In accomplishing such a goal, an adequate dynamic model that comprehensively reflects boiler characteristics is required. This paper outlines the idea of developing a mathematical model of a water-tube industrial boiler based on first principles guided by the bond graph method in its derivation. The model describes the temperature dynamics of the boiler subsystems such as economizer, steam drum, desuperheater, and superheater. The mathematical model was examined using industrial boiler performance test data.It can be used to build a boiler simulator or help operators run a boiler effectively.
From Process to Practice: Towards a Practice-based Model of Digital Innovation

The ongoing digitalization of many corporate functions, including the innovation process, brings about fundamental changes that urge us to rethink established theories. Facilitating digital innovation requires a deep understanding of the actual practices that are carried out by innovating people with the help of artifacts. In this paper, we study the use of artifacts and illustrate their different roles in the underlying innovation practices to provide rich insights into digital innovation from a practice perspective. Grounded in a nearly three year-long, qualitative case study at two Swiss software companies and an extensive set of empirical data, this paper conceptualizes four interrelated digital innovation practices, namely making sense of an idea, aligning mental models, negotiating solution paths, and crafting an idea. We suggest a practice-based model of digital innovation, specify a set of practices for enabling digital innovation in organizations, and clarify the role of artifacts in digital innovation practices.
Prevalence of tobacco use among junior high and senior high school students in Taiwan.

BACKGROUND Tobacco use is a major preventable cause of death in the world. This article describes and compares tobacco use prevalence for students attending junior high schools and senior high schools in Taiwan. METHODS This report uses data from the Global Youth Tobacco Survey (GYTS) completed among 4689 junior high school students and 4426 senior high school students in Taiwan in 2004-2005. The GYTS uses a 2-stage sampling design to produce nationally representative data for junior and senior high students in general and vocational schools. RESULTS Higher smoking prevalence was observed among senior high (10.1% general schools and 15.9% vocational schools) than junior high (5.5%) school students. Smoking prevalence of girls in junior high (3.2%) and senior high schools (4.6% general and 11.1% vocational) was almost as high or higher than adult females' (4.3%) smoking rates. The pattern of smoking intensity across school years and type of school shows that the percentage of smokers who were experimenters (47.1%) was higher in junior high school and the percentage of smokers who were regular/established smokers (over 50%) was higher in senior high school. CONCLUSIONS Smoking prevalence described in this report shows that there are challenges facing the tobacco prevention and control program in Taiwan. The findings suggest that schools should increase their smoking initiation prevention efforts and make available cessation programs and counseling to help students quit smoking. If effective youth tobacco control programs are not developed and implemented in Taiwan, future morbidity and mortality attributed to tobacco will surely increase, especially among women.
Predictors of all-cause 30 day readmission among Medicare patients with type 2 diabetes

Abstract Objective: Readmission is costly among patients with type 2 diabetes (T2DM) in Medicare Advantage Prescription Drug Plans; identifying high-risk patients is necessary for targeting reduction programs. The objective of this study was to develop a claims-based algorithm to predict all-cause 30 day readmission among patients with T2DM. Methods: This study used administrative data from 1 January 2012 through 31 January 2014. The cohort included hospitalized T2DM patients, aged 18–90 with ≥12 months’ continuous enrollment before an unplanned hospital admission and ≥1 month of enrollment post-discharge, excluding patients in long-term care >30 days pre-index. Multivariate logistic regression predicted the likelihood of readmission following hospitalization in 2013. The analytic file was randomly split into training and test datasets to build and validate the model. Candidate variables included physician and patient demographics, baseline clinical conditions, and healthcare utilization metrics. Clinical conditions were classified using the Healthcare Cost and Utilization Project clinical classification system for ICD-9-CM. Results: Of 63,237 individuals, 17.1% experienced a readmission. Of nearly 200 candidate variables, 14 were predictors of readmission, including total cumulative number of days for inpatient stays and the number of emergency department visits in the baseline period. Male gender, older age, and certain comorbidities were associated with higher likelihood of readmission. The final model demonstrated good discriminant ability (c-statistic = 0.82). Conclusions: This study provided evidence that certain patient characteristics and healthcare utilization are predictive of readmission. An algorithm with good discriminant ability was developed which could be used to target readmission reduction programs. Physician gender, specialty, and ownership status did not appear to influence the likelihood of readmission.
Neumann Optimizer: A Practical Optimization Algorithm for Deep Neural Networks

Progress in deep learning is slowed by the days or weeks it takes to train large models. The natural solution of using more hardware is limited by diminishing returns, and leads to inefficient use of additional resources. In this paper, we present a large batch, stochastic optimization algorithm that is both faster than widely used algorithms for fixed amounts of computation, and also scales up substantially better as more computational resources become available. Our algorithm implicitly computes the inverse Hessian of each mini-batch to produce descent directions; we do so without either an explicit approximation to the Hessian or Hessian-vector products. We demonstrate the effectiveness of our algorithm by successfully training large ImageNet models (Inception-V3, Resnet-50, Resnet-101 and Inception-Resnet-V2) with mini-batch sizes of up to 32000 with no loss in validation error relative to current baselines, and no increase in the total number of steps. At smaller mini-batch sizes, our optimizer improves the validation error in these models by 0.8-0.9%. Alternatively, we can trade off this accuracy to reduce the number of training steps needed by roughly 10-30%. Our work is practical and easily usable by others -- only one hyperparameter (learning rate) needs tuning, and furthermore, the algorithm is as computationally cheap as the commonly used Adam optimizer.
A mixed methods assessment of the barriers and readiness for meeting the SNAP depth of stock requirements in Baltimore’s small food stores

ABSTRACT We sought to understand Baltimore corner store owners’ awareness of and readiness for the then-approved Supplemental Nutrition Assistance Program depth of stock requirements and assess potential barriers and solutions. In-depth interviews and stocking observations were conducted in 17 corner stores in low-income food deserts of Baltimore City. Corner store owners conveyed little to no awareness of the pending depth of stock changes. Only two stores were currently ready for the requirements. Low customer demand, high amounts of potential spoilage, and unfair pricing at the wholesaler were identified by store owners as barriers to stocking required foods.
Methods to improve rehabilitation of patients following breast cancer surgery: a review of systematic reviews

Context Breast cancer is the most prevalent cancer amongst women but it has the highest survival rates amongst all cancer. Rehabilitation therapy of post-treatment effects from cancer and its treatment is needed to improve functioning and quality of life. This review investigated the range of methods for improving physical, psychosocial, occupational, and social wellbeing in women with breast cancer after receiving breast cancer surgery. Method A search for articles published in English between the years 2009 and 2014 was carried out using The Cochrane Database of Systematic Reviews, the Database of Abstracts of Reviews of Effects, PubMed, and ScienceDirect. Search terms included: ‘breast cancer’, ‘breast carcinoma’, ‘surgery’, ‘mastectomy’, ‘lumpectomy’, ‘breast conservation’, ‘axillary lymph node dissection’, ‘rehabilitation’, ‘therapy’, ‘physiotherapy’, ‘occupational therapy’, ‘psychological’, ‘psychosocial’, ‘psychotherapy’, ‘exercise’, ‘physical activity’, ‘cognitive’, ‘occupational’, ‘alternative’, ‘complementary’, and ‘systematic review’. Study selection Systematic reviews on the effectiveness of rehabilitation methods in improving post-operative physical, and psychological outcomes for breast cancer were selected. Sixteen articles met all the eligibility criteria and were included in the review. Data extraction Included review year, study aim, total number of participants included, and results. Data synthesis Evidence for exercise rehabilitation is predominantly in the improvement of shoulder mobility and limb strength. Inconclusive results exist for a range of rehabilitation methods (physical, psycho-education, nutritional, alternative-complementary methods) for addressing the domains of psychosocial, cognitive, and occupational outcomes. Conclusion There is good evidence for narrowly-focused exercise rehabilitation in improving physical outcome particularly for shoulder mobility and lymphedema. There were inconclusive results for methods to improve psychosocial, cognitive, and occupational outcomes. There were no reviews on broader performance areas and lifestyle factors to enable effective living after treatment. The review suggests that comprehensiveness and effectiveness of post-operative breast cancer rehabilitation should consider patients’ self-management approaches towards lifestyle redesign, and incorporate health promotion aspects, in light of the fact that breast cancer is now taking the form of a chronic illness with longer survivorship years.
Intra and inter-site reproducibility of myelin water volume fraction values derived using McDESPOT.

INTRODUCTION: Investigation of neuronal tissue microstructure, integrity and biochemical composition has wide neuroscience application. Multicomponent relaxometry, which aims to decompose the measured MR signal into contributions from discrete micro-anatomical water components, provides specific information related to tissue micro-structure and has demonstrated utility in white matter disorders, such as multiple sclerosis, and psychiatric disorders, such as schizophrenia. Recently introduced, mcDESPOT (multi-component Driven Equilibrium Single Pulse Observation of T1 and T2) , offers a promising alternative to conventional T2-based multi-component relaxometry techniques. To be of broad applicability, however, the method should provide consistent estimates or have high intra-site reproducibility, of relaxation time and volume fraction parameters. Further, values should agree across imaging centres (high inter-site reproducibility) in order to permit meaningful comparisons across data from different groups, and to allow application of the technique to increasingly common multi-centre investigations. The aim of this work, therefore, was to establish the intraand inter-site reproducibility of mcDESPOT-derived myelin water volume fraction (fMW) estimates.
Chemical characterization of neuroendocrine targets for progesterone in the female rat brain and pituitary.

The secretory products of some of the cell types which respond directly to actions of progesterone in the female rat brain and pituitary were determined by combining immunocytochemistry with autoradiography following systemic administration of the synthetic progestin ligand [3H]-R5020. Four major findings are reported: (1) Approximately 90% of the tyrosine hydroxylase (TH)-immunoreactive neurons in the hypothalamic arcuate nucleus have progesterone receptors, while TH-immunoreactive neurons in other portions of the hypothalamus (e.g. the periventricular region and the zona incerta) do not. (2) Approximately 30% of the beta-endorphin neurons in the hypothalamus have progesterone receptors. (3) None of the luteinizing hormone-releasing hormone neurons examined have progesterone receptors. (4) Approximately 98% of the cells in the anterior pituitary that have progesterone receptors contain luteinizing hormone. Lactotrophs do not contain progesterone receptors. Many progestin targets in the brain remain to be characterized chemically. The implications for progesterone-inducible genes and neuroendocrine control systems are discussed.