• Coupled systems mechanics
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• v.6 no.1
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• pp.1-15
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• 2017

Despite the exceptional mechanical properties of individual carbon nanotubes (CNTs), the effective properties of CNT-reinforced composites remain below expectations. The composite's microstructure has been identified as a key factor in explaining this discrepancy. In this contribution, a method for generating representative volume elements of aligned CNT sheets is presented. The model captures material characteristics such as random waviness and entanglement of individual nanotubes. Thus it allows studying microstructural effects on the composite's effective properties. Simulations investigating the strengthening effect of the application of a pre-stretch on the CNTs are carried out and found to be in very good agreement with experimental values. They highlight the importance of the nanotube's waviness and entanglement for the mechanical behavior of the composite. The presented representative volume elements are the first to accurately capture the waviness and entanglement of CNT sheets for realistically high volume fractions.

• Advances in aircraft and spacecraft science
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• v.3 no.1
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• pp.77-93
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• 2016

In this paper, a zero-dimensional mathematical formulation for rapid and explosive decompression analyses of pressurized aircraft is developed. Air flows between two compartments and between the damaged compartment and external ambient are modeled by assuming an adiabatic, reversible transformation. Both supercritical and subcritical decompressions are considered, and the attention focuses on intercompartment venting systems. In particular, passive and active vents are addressed, and mathematical models of both swinging and translational blowout panels are provided. A numerical procedure based on an explicit Euler integration scheme is also discussed for multi-compartment aircraft analysis. Various numerical solutions are presented, which highlight the importance of considering the opening dynamics of blowout panels. The comparisons with the results from the literature demonstrate the validity of the proposed methodology, which can be also applied, with no lack of accuracy, to the decompression analysis of spacecraft.

• Clinical and Experimental Pediatrics
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• v.64 no.8
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• pp.393-399
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• 2021

Food allergies and food-induced anaphylaxis are important health problems. Several cofactors modulating the onset of anaphylaxis have been identified. In the presence of cofactors, allergic reactions may be induced at lower doses of food allergens and/or become severe. Exercise and concomitant infections are well-documented cofactors of anaphylaxis in children. Other factors such as consumption of nonsteroidal anti-inflammatory drugs, alcohol ingestion, and stress have been reported. Cofactors reportedly play a role in approximately 30% of anaphylactic reactions in adults and 14%-18.3% in children. Food-dependent exercise-induced anaphylaxis (FDEIA) is the best-studied model of cofactor-induced anaphylaxis. Wheat-dependent exercise-induced anaphylaxis, the most common FDEIA condition, has been studied the most. The mechanisms of action of cofactors have not yet been fully identified. This review aims to educate clinicians on recent developments in the role of cofactors and highlight the importance of recognizing cofactors in food allergies and food-induced anaphylaxis.

• Steel and Composite Structures
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• v.32 no.1
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• pp.111-126
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• 2019

This paper deals with experimental investigation and modeling of the static behavior of a novel RCS beam-column exterior joint. The studied joint detail is a through-column type in which an H steel profile totally embedded inside RC column is directly welded to the steel beam. The H steel profile was covered by two supplementary plates in the joint area in order to avoid the stirrups resisting shear in the joint area. Two full-scale through-column-type RCS joints were tested under static loading. The objectives of the tests were to examine the connection performance and to highlight the contribution of two supplementary plates on the shear resistance of the joint. A reliable nonlinear 3D finite element model was developed using ABAQUS software to predict the response and behavior of the studied RCS joint. An extensive parametric study was performed to investigate the influences of the stirrups, the encased profile length and supplementary plate length on the behavior of the studied RCS joint.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.1738-1764
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• 2019

With the advent of big data, deep learning technology has become an important research direction in the field of machine learning, which has been widely applied in the image processing, natural language processing, speech recognition and online advertising and so on. This paper introduces deep learning techniques from various aspects, including common models of deep learning and their optimization methods, commonly used open source frameworks, existing problems and future research directions. Firstly, we introduce the applications of deep learning; Secondly, we introduce several common models of deep learning and optimization methods; Thirdly, we describe several common frameworks and platforms of deep learning; Finally, we introduce the latest acceleration technology of deep learning and highlight the future work of deep learning.

• Geomechanics and Engineering
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• v.29 no.2
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• pp.171-182
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• 2022

Reusing the used pile has not yet been implemented due to the unpredictability of the bearing capacity evolution. This paper presents an analytic approach to estimate the sides shear setup after the dissipation of pore pressure. Long-term evolution of adjacent soil is simulated by viscoelastic-plastic constitutive model. Then, an innovative concept of quasi-overconsolidation is proposed to estimate the strength changes of surrounding soil. Total stress method (α method) is employed to evaluate the long term bearing capacity. Measured data of test piles in Louisiana and semi-logarithmic time function are cited to validate the effectiveness of the presented method. Comparisons illustrate that the presented approach gives a reasonably prediction of the side shear setup. Both the presented method and experiment show the shaft resistance increase by 30%-50%, and this highlight the potential benefit of piles reutilization.

• BMB Reports
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• v.54 no.5
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• pp.253-259
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• 2021

Aging is characterized by a functional decline in most physiological processes, including alterations in cellular metabolism and defense mechanisms. Increasing evidence suggests that caloric restriction extends longevity and retards age-related diseases at least in part by reducing metabolic rate and oxidative stress in a variety of species, including yeast, worms, flies, and mice. Moreover, recent studies in invertebrates - worms and flies, highlight the intricate interrelation between reproductive longevity and somatic aging (known as disposable soma theory of aging), which appears to be conserved in vertebrates. This review is specifically focused on how the reproductive system modulates somatic aging and vice versa in genetic model systems. Since many signaling pathways governing the aging process are evolutionarily conserved, similar mechanisms may be involved in controlling soma and reproductive aging in vertebrates.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.29-29
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• 2023

To mitigate the damaging impacts of floods, accurate prediction of runoff, streamflow and flood inundation is needed. Conventional approach of simulating hydrology and hydraulics using loosely coupled models cannot capture the complex dynamics of surface and sub-surface processes. Additionally, the scarcity of data in ungauged basins and quality of data in gauged basins add uncertainty to model predictions, which need to be quantified. In this presentation, first the role of integrated modeling on creating accurate flood simulations and inundation maps will be presented with specific focus on urban environments. Next, the use of machine learning in producing streamflow predictions will be presented with specific focus on incorporating covariate shift and the application of theory guided machine learning. Finally, a framework to quantify the uncertainty in flood models using Hierarchical Bayesian Modeling Averaging will be presented. Overall, this presentation will highlight that creating accurate information on flood magnitude and extent requires innovation and advancement in different aspects related to hydrologic predictions.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.16-22
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• 2024

Robotic grasping in unstructured environments poses a significant challenge, demanding precise estimation of gripping positions for diverse and unknown objects. Generative Grasping Convolution Neural Network (GG-CNN) can estimate the position and direction that can be gripped by a robot gripper for an unknown object based on a three-dimensional depth map. Since GG-CNN uses only a depth map as an input, the precision of the depth map is the most critical factor affecting the result. To address the challenge of depth map precision, we integrate the Segment Anything Model renowned for its robust zero-shot performance across various segmentation tasks. We adjust the components corresponding to the segmented areas in the depth map aligned through external calibration. The proposed method was validated on the Cornell dataset and SurgicalKit dataset. Quantitative analysis compared to existing methods showed a 49.8% improvement with the dataset including surgical instruments. The results highlight the practical importance of our approach, especially in scenarios involving thin and metallic objects.

• Journal of Information Technology Applications and Management
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• v.30 no.5
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• pp.59-82
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• 2023

The present research aims to examine whether learning from the supervisor influences readiness for change with the mediating impact of planfulness. Drawing upon the theory of planned behavior, it is hypothesized that learning from the supervisor positively impacts planfulness ability in individuals, which in turn enhances the readiness for change. Through using convenience sampling, the sample of 451 was collected from employees working full-time in the manufacturing and I.T. service organizations in India. Structural equation modeling and regression analysis indicate that learning from the supervisor is positively associated with readiness for change and planfulness. Additionally, planfulness fully mediated the relationship between learning from the supervisor and readiness to change. The findings of the present research highlight that continuous support and learning from the supervisor enhances the planfulness ability of the individual and consequently enhances individual readiness for change. The current research is pioneering in testing the hypothetical model associating learning from the supervisor, planfulness, and readiness for change.