• 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.

• Journal of the Korean Society of Visualization
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• v.22 no.2
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• pp.90-95
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• 2024

This study examined changes in airway airflow characteristics before and after extensive surgery for tongue cancer, which includes neck dissection and reconstruction. Pre- and post-operative CBCT scans were used to model 3D upper airways. Computational fluid dynamics (CFD) simulations analyzed airflow and pressure variations. Results showed a significant reduction in airway volume post-surgery, especially in the posterior tongue and epiglottis areas, leading to increased airflow velocity and complex vortex formations. Pressure drop analysis revealed that post-surgery, higher negative pressure is required for inhalation, indicating increased breathing effort. This suggests that the surgical removal of cancerous tissues and lymph nodes, along with reconstruction, alters airway geometry significantly, potentially impacting respiratory function. The findings highlight the clinical importance of assessing airway changes in tongue cancer surgery to anticipate and mitigate postoperative respiratory complications.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.