Prediction of gas-liquid two-phase flow regime in microgravity

An attempt is made to predict gas-liquid two-phase flow regime in a pipe in a microgravity environment through scaling analysis based on dominant physical mechanisms. Simple inlet geometry is adopted in the analysis to see the effect of inlet configuration on flow regime transitions. Comparison of the prediction with the existing experimental data shows good agreement, though more work is required to better define some physical parameters. The analysis clarifies much of the physics involved in this problem and can be applied to other configurations

An Agent Based Market Design Methodology for Combinatorial Auctions

Auction mechanisms have attracted a great deal of interest and have been used in diverse e-marketplaces. In particular, combinatorial auctions have the potential to play an important role in electronic transactions. Therefore, diverse combinatorial auction market types have been proposed to satisfy market needs. These combinatorial auction types have diverse market characteristics, which require an effective market design approach. This study proposes a comprehensive and systematic market design methodology for combinatorial auctions based on three phases: market architecture design, auction rule design, and winner determination design. A market architecture design is for designing market architecture types by Backward Chain Reasoning. Auction rules design is to design transaction rules for auctions. The specific auction process type is identified by the Backward Chain Reasoning process. Winner determination design is about determining the decision model for selecting optimal bids and auctioneers. Optimization models are identified by Forward Chain Reasoning. Also, we propose an agent based combinatorial auction market design system using Backward and Forward Chain Reasoning. Then we illustrate a design process for the general n-bilateral combinatorial auction market. This study serves as a guideline for practical implementation of combinatorial auction markets design.Combinatorial Auction, Market Design Methodology, Market Architecture Design, Auction Rule Design, Winner Determination Design, Agent-Based System

A Study on the Qualitative Data Analysis for Customer Experience Management Strategy

Securing customer experience data that creates positive emotions for customers and differentiates them from products and services from competitors is becoming important to a company's growth engine. In particular, an important factor in the management of experience data requires a qualitative-based experience data processing method to secure good experience data different from the quantitative data collection such as big data and processing method. With the emergence of the experience economy, it is very important for companies to collect and process experience data in the existing big data processing method. However, the experience data processing method based on big data that analyses the current quantitative data is difficult to provide good experience data from a corporate data strategic point of view. In particular, for corporate customer experience management, mix studies are required for analysis method of qualitative experience data to meaningfully interpret the expansive quantitative experience data of big data and phenomena and context in social science. This is because it is possible to discover the meaning of experience data by reading the context of phenomena by collecting experiences through ethnography methods such as observation or interviewing the context that could not be read in the process of processing the vast quantitative experience data of the big data method. In this study, the first processing was performed as an affinity diagram through a method of collecting experience data using ethnography method. Secondly, the effect of the qualitative experience data processing method on customer experience management, customer loyalty reinforcement, and enterprise value creation was studied. As a result, only the research hypothesis that there was a direct relationship between the affinity method and the utilization of experience data was rejected, and all the research settings set for the remaining qualitative experience data processing and utilization model were adopted

Classification of Radiology Reports Using Neural Attention Models

The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value. Distinguished from previous studies, our approach embraces a double-annotated dataset and strays away from obscure "black-box" models to comprehensive deep learning models. In this paper, we present a novel neural attention mechanism that not only classifies clinically important findings. Specifically, convolutional neural networks (CNN) with attention analysis are used to classify radiology head computed tomography reports based on five categories that radiologists would account for in assessing acute and communicable findings in daily practice. The experiments show that our CNN attention models outperform non-neural models, especially when trained on a larger dataset. Our attention analysis demonstrates the intuition behind the classifier's decision by generating a heatmap that highlights attended terms used by the CNN model; this is valuable when potential downstream medical decisions are to be performed by human experts or the classifier information is to be used in cohort construction such as for epidemiological studies

Analytic spatial and temporal temperature profile in a finite laser rod with input laser pulses

In this communication, we present an analytic expression of the thermal load in a cylindrical laser rod. We consider a pump beam with Gaussian temporal and spatial profile, which permits, using superposition of the single pulse solution, an explicit calculation of the optical path length difference across the radial direction of the rod and of the transient thermal focal length changes for a variable pump repetition rate and pulse width. We have chosen to model Ti:A_l2O_3 as a specific example, however our solution is completely general and can be applied to any materials with cylindrical geometry employing a stable laser cavity design

Facile and time-resolved chemical growth of nanoporous CaxCoO2 thin films for flexible and thermoelectric applications

CaxCoO2 thin films can be promising for widespread flexible thermoelectric applications in a wide temperature range from room-temperature self-powered wearable applications (by harvesting power from body heat) to energy harvesting from hot surfaces (e.g., hot pipes) if a cost-effective and facile growth technique is developed. Here, we demonstrate a time resolved, facile and ligand-free soft chemical method for the growth of nanoporous Ca0.35CoO2 thin films on sapphire and mica substrates from a water-based precursor ink, composed of in-situ prepared Ca2+-DMF and Co2+-DMF complexes. Mica serves as flexible substrate as well as sacrificial layer for film transfer. The grown films are oriented and can sustain bending stress until a bending radius of 15 mm. Despite the presence of nanopores, the power factor of Ca0.35CoO2 film is found to be as high as 0.50 x 10-4 Wm-1K-2 near room temperature. The present technique, being simple and fast to be potentially suitable for cost-effective industrial upscaling.Comment: 16 pages, 5 figure

Robust face anti-spoofing framework with Convolutional Vision Transformer

Owing to the advances in image processing technology and large-scale datasets, companies have implemented facial authentication processes, thereby stimulating increased focus on face anti-spoofing (FAS) against realistic presentation attacks. Recently, various attempts have been made to improve face recognition performance using both global and local learning on face images; however, to the best of our knowledge, this is the first study to investigate whether the robustness of FAS against domain shifts is improved by considering global information and local cues in face images captured using self-attention and convolutional layers. This study proposes a convolutional vision transformer-based framework that achieves robust performance for various unseen domain data. Our model resulted in 7.3%$p$ and 12.9%$p$ increases in FAS performance compared to models using only a convolutional neural network or vision transformer, respectively. It also shows the highest average rank in sub-protocols of cross-dataset setting over the other nine benchmark models for domain generalization.Comment: ICIP 202