Ionization cross sections for low energy electron transport

Two models for the calculation of ionization cross sections by electron impact on atoms, the Binary-Encouter-Bethe and the Deutsch-Maerk models, have been implemented; they are intended to extend and improve Geant4 simulation capabilities in the energy range below 1 keV. The physics features of the implementation of the models are described, and their differences with respect to the original formulations are discussed. Results of the verification with respect to the original theoretical sources and of extensive validation with respect to experimental data are reported. The validation process also concerns the ionization cross sections included in the Evaluated Electron Data Library used by Geant4 for low energy electron transport. Among the three cross section options, the Deutsch-Maerk model is identified as the most accurate at reproducing experimental data over the energy range subject to test.Comment: To be published in IEEE Trans. Nucl. Sci., Dec. 201

Physics data management tools: computational evolutions and benchmarks

The development of a package for the management of physics data is described: its design, implementation and computational benchmarks. This package improves the data management tools originally developed for Geant4 physics models based on the EADL, EEDL and EPDL97 data libraries. The implementation exploits recent evolutions of the C++ libraries appearing in the C++0x draft, which are intended for inclusion in the next C++ ISO Standard. The new tools improve the computational performance of physics data management.Comment: 6 pages, to appear in proceedings of the Joint International Conference on Supercomputing in Nuclear Applications and Monte Carlo 2010 (SNA + MC2010

Optimizing the Mixing Proportion with Neural Networks Based on Genetic Algorithms for Recycled Aggregate Concrete

This research aims to optimize the mixing proportion of recycled aggregate concrete (RAC) using neural networks (NNs) based on genetic algorithms (GAs) for increasing the use of recycled aggregate (RA). NN and GA were used to predict the compressive strength of the concrete at 28 days. And sensitivity analysis of the NN based on GA was used to find the mixing ratio of RAC. The mixing criteria for RAC were determined and the replacement ratio of RAs was identified. This research reveal that the proposed method, which is NN based on GA, is proper for optimizing appropriate mixing proportion of RAC. Also, this method would help the construction engineers to utilize the recycled aggregate and reduce the concrete waste in construction process

Increased Risk of Ischemic Stroke during Sleep in Apneic Patients.

BACKGROUND AND PURPOSE:The literature indicates that obstructive sleep apnea (OSA) increases the risk of ischemic stroke. However, the causal relationship between OSA and ischemic stroke is not well established. This study examined whether preexisting OSA symptoms affect the onset of acute ischemic stroke. METHODS:We investigated consecutive patients who were admitted with acute ischemic stroke, using a standardized protocol including the Berlin Questionnaire on symptoms of OSA prior to stroke. The collected stroke data included the time of the stroke onset, risk factors, and etiologic subtypes. The association between preceding OSA symptoms and wake-up stroke (WUS) was assessed using multivariate logistic regression analysis. RESULTS:We identified 260 subjects with acute ischemic strokes with a definite onset time, of which 25.8% were WUS. The presence of preexisting witnessed or self-recognized sleep apnea was the only risk factor for WUS (adjusted odds ratio=2.055, 95% confidence interval=1.035-4.083, p=0.040). CONCLUSIONS:Preexisting symptoms suggestive of OSA were associated with the occurrence of WUS. This suggests that OSA contributes to ischemic stroke not only as a predisposing risk factor but also as a triggering factor. Treating OSA might therefore be beneficial in preventing stroke, particularly that occurring during sleep

Feature Selection for Very Short-Term Heavy Rainfall Prediction Using Evolutionary Computation

We developed a method to predict heavy rainfall in South Korea with a lead time of one to six hours. We modified the AWS data for the recent four years to perform efficient prediction, through normalizing them to numeric values between 0 and 1 and undersampling them by adjusting the sampling sizes of no-heavy-rain to be equal to the size of heavy-rain. Evolutionary algorithms were used to select important features. Discriminant functions, such as support vector machine (SVM), k-nearest neighbors algorithm (k-NN), and variant k-NN (k-VNN), were adopted in discriminant analysis. We divided our modified AWS data into three parts: the training set, ranging from 2007 to 2008, the validation set, 2009, and the test set, 2010. The validation set was used to select an important subset from input features. The main features selected were precipitation sensing and accumulated precipitation for 24 hours. In comparative SVM tests using evolutionary algorithms, the results showed that genetic algorithm was considerably superior to differential evolution. The equitable treatment score of SVM with polynomial kernel was the highest among our experiments on average. k-VNN outperformed k-NN, but it was dominated by SVM with polynomial kernel

Classification of Fermionic Topological Orders from Congruence Representations

The fusion rules and braiding statistics of anyons in $(2+1)$D fermionic topological orders are characterized by the modular data of a super-modular category. On the other hand, the modular data of a super-modular category form a congruence representation of the $\Gamma_\theta$ subgroup of the modular group $\mathrm{SL}_2(\mathbb{Z})$. We provide a method to classify the modular data of super-modular categories by first obtaining the congruence representations of $\Gamma_\theta$ and then building candidate modular data out of those representations. We carry out this classification up to rank $10$. We obtain both unitary and non-unitary modular data, including all previously known unitary modular data, and also discover new classes of modular data of rank $10$. We also determine the central charges of all these modular data, without explicitly computing their modular extensions.Comment: 32 pages, 2 figures, 6 table

Fabrication of Subretinal 3D Microelectrodes with Hexagonal Arrangement

This study presents the fabrication of three-dimensional (3D) microelectrodes for subretinal stimulation, to accommodate adjacent return electrodes surrounding a stimulating electrode. For retinal prosthetic devices, the arrangement of return electrodes, the electrode size and spacing should be considered together, to reduce the undesired dissipation of electric currents. Here, we applied the hexagonal arrangement to the microelectrode array for the localized activation of retinal cells and better visual acuity. To provide stimuli more efficiently to non-spiking neurons, a 3D structure was created through a customized pressing process, utilizing the elastic property of the materials used in the fabrication processes. The diameter and pitch of the Pt-coated electrodes were 150 μm and 350 μm, respectively, and the height of the protruded electrodes was around 20 μm. The array consisted of 98 hexagonally arranged electrodes, supported by a flexible and transparent polydimethylsiloxane (PDMS) base, with a thickness of 140 μm. Also, the array was coated with 2 μm-thick parylene-C, except the active electrode sites, for more focused stimulation. Finally, the electrochemical properties of the fabricated microelectrodes were characterized, resulting in the mean impedance of 384.87 kω at 1 kHz and the charge storage capacity (CSC) of 2.83 mC cm-2. The fabricated microelectrodes are to be combined with an integrated circuit (IC) for additional in vitro and in vivo experiments. © 2020 by the authors.1

What are the phases of change in exercise behaviors (EB), and factors affecting exercise behaviors (EB) of male workers in a workplace setting?

The purpose of this study was to evaluate the phases of change of exercise among male workers and to analyze the factors affecting their EB using Information-Motivation-Behavioral skill-Revealed Related Variables (IMBR) model. The study included 163 male workers from a major Hyundai Transys company, Seosan city. Data were analyzed using Pearson’s correlation coefficients, and hierarchical regression etc. Regarding the phases of change in exercise, 135 individuals (82.8%) were classified into phase 3 (preparation phase), phase 4 (action phase), and phase 5 (maintenance phase). In the first step, factors such as health status (β = 0.26 , p < 0.001), smoking (β = 0.16, p = 0.005), number of exercises per week (β = 0.35, p < 0.001), times of each exercise (β = 0.17, p = 0.005), and phases of change in exercise (β = 0.17, p = 0.014) were identified as significant factors affecting EB. In the second step, health status (β = 0.19, p = 0.001), smoking (β = −0.13, p = 0.019), number of exercises per week (β = 0.31, p < 0.001), phases of change in exercise (β = 0.13, p = 0.034), and sport commitment (β = 0.16, p = 0.019) were identified as significant factors. In the third step, health status (β = 0.27, p < 0.001), number of exercises per week (β = 0.14, p = 0.005), and exercise self-efficacy (β = 0.39, p < 0.001) were identified as significant factors, explaining 68.3% of the variance in EB. To promote EB, it’s important to assess the phases of change in exercise and consider factors such as health status, smoking, the number of exercises per week and the duration of each exercise. Interventions that enhance sport commitment and exercise self-efficacy should be considered. It’s recommended to apply IMBR model in exercise studies for workers

Development of software for computing forming information using a component based approach

ABSTRACTIn shipbuilding industry, the manufacturing technology has advanced at an unprecedented pace for the last decade. As a result, many automatic systems for cutting, welding, etc. have been developed and employed in the manufacturing process and accordingly the productivity has been increased drastically. Despite such improvement in the manufacturing technology, however, development of an automatic system for fabricating a curved hull plate remains at the beginning stage since hardware and software for the automation of the curved hull fabrication process should be developed differently depending on the dimensions of plates, forming methods and manufacturing processes of each shipyard. To deal with this problem, it is necessary to create a “plug-in” framework, which can adopt various kinds of hardware and software to construct a full automatic fabrication system. In this paper, a framework for automatic fabrication of curved hull plates is proposed, which consists of four components and related software. In particular the software module for computing fabrication information is developed by using the ooCBD development methodology, which can interface with other hardware and software with minimum effort. Examples of the proposed framework applied to medium and large shipyards are presented