Autism Spectrum Disorder is a neurological condition that affects 1 in 160 children worldwide. To date, this disorder does not yet have a standardized cure, and not being treated early can affect the child's quali...
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This work is based upon the results of an evaluation process applied over data mining techniques, in order to find the most adequate ones to extract classification rules from first-year students' academic and demo...
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This chapter describes the removal of microorganisms and particulates from indoor air. Granular bed filters may be used for particulate removal from air. These systems have been developed mainly for use in industrial ...
This chapter describes the removal of microorganisms and particulates from indoor air. Granular bed filters may be used for particulate removal from air. These systems have been developed mainly for use in industrial processes and are not suitable for use in indoor settings. However, if desiccant materials are used as the filtering media, these types of units may be used for cleaning indoor air. Desiccant-based air conditioning systems are used for cleaning indoor air. In desiccant based systems, moist air is passed through a bed containing desiccants, where moisture is removed from air by adsorption. The dry air from the bed is further conditioned to adjust its temperature and humidity. Silica gel, molecular sieves, and activated alumina are primarily used as desiccants because of their excellent water adsorption capacity. Particulate removal systems work on the principle that as a gas stream containing particle flows through a filtration device the particles are acted on by various external forces that cause their separation from the gas stream. The mechanisms that cause the separation of the particles from the gas stream include sedimentation electrostatic precipitation, inertial deposition, and Brownian diffusion.
Low-pressure membrane filtration is an important technology for water treatment but suffers from the inefficient removal of low-molecular-weight pollutants (such as antibiotics) and severe membrane fouling. Herein, an...
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The adoption of omni-channel strategy has changed the relation between retailers and customers and brought more complexity to the retailing supply chains. To address the increasing complexity, it is necessary to adopt...
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Liquid crystals have proven to provide a versatile experimental and theoretical platform for studying topological objects such as vortices, skyrmions, and hopfions. In parallel, in hard condensed matter physics, the c...
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In this paper, we present a distributed algorithm utilizing the proximal alternating direction method of multipliers (ADMM) in conjunction with sequential constraint tightening to address mixed-integer quadratic progr...
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This study aimed to develop an interaction modeling method for high-dimensional industrial data with sparsity. Particularly, we discussed the potential and limitations of Sparse Factorization Machines (SFM) with featu...
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This study aimed to develop an interaction modeling method for high-dimensional industrial data with sparsity. Particularly, we discussed the potential and limitations of Sparse Factorization Machines (SFM) with feature selection capabilities after examining the applicability of Factorization Machines (FMs) to numerical and categorical mixed data like industrial data. FMs has been already a major recommendation engine outperforms SVM because of robustness to the sparse data. However, conventional FMs and SFM based on the L2 norm regularization tolerate huge False-Positives (FPs), which is fatal in the application to real data to which the oracle model is unknown. Therefore, in this study, we focus on the way to automatically reduce the several millions of FPs in interactions while keeping high True-Positives (TPs). For the purpose, SFM with trigonometric inequality (TI) upper boundaries (Atarashi et al., 2021) is improved by two directions. The first is the development of TI_SFM (L1) with an L1 norm for selection of main factors, particularly for FPs reduction of the main factors. The second is the application of adaptive technique for reducing FPs of interactions (combinatorial features). We newly developed "Adaptive SFM" with adaptive technique to introduce data-driven penalty of the interaction term. As the result of numerical evaluations using a mass production oracle interaction model and several simulation data, False-Positives of the main factors (F) and the interactions (F) are significantly reduced, while keeping high level of True-Positives of the main factor () and the interactions (). Concretely, our proposed Adaptive SFM (L1) outperforms the original TI_SFM (L2) as much reducing F and F by over 99% when applying our proposed penalty considering not only the relationship between the explanatory variable and the objective variable, as basic adaptive technique, but also the factor loading indicating the relationship between the latent vector internally opt
By adjusting the analytic mass matrix or stiffness parameters, the correlation between measured and computed modal data can be improved. This article proposes a simple method for model optimization. Numerical examples...
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By adjusting the analytic mass matrix or stiffness parameters, the correlation between measured and computed modal data can be improved. This article proposes a simple method for model optimization. Numerical examples will be included to illustrate the proposed approach. (C) 1997 John Wiley & Sons, Inc.
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