• Computers and Concrete
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• v.24 no.5
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• pp.469-488
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• 2019

In this research study, the artificial neural networks approach is used to estimate the ultimate shear capacity of reinforced concrete beams with transverse reinforcement. More specifically, surrogate approaches, such as artificial neural network models, have been examined for predicting the shear capacity of concrete beams, based on experimental test results available in the pertinent literature. The comparison of the predicted values with the corresponding experimental ones, as well as with available formulas from previous research studies or code provisions highlight the ability of artificial neural networks to evaluate the shear capacity of reinforced concrete beams in a trustworthy and effective manner. Furthermore, for the first time, the (quantitative) values of weights for the proposed neural network model, are provided, so that the proposed model can be readily implemented in a spreadsheet and accessible to everyone interested in the procedure of simulation.

• Molecules and Cells
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• v.45 no.1
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• pp.6-15
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• 2022

Phase separation is a thermodynamic process leading to the formation of compositionally distinct phases. For the past few years, numerous works have shown that biomolecular phase separation serves as biogenesis mechanisms of diverse intracellular condensates, and aberrant phase transitions are associated with disease states such as neurodegenerative diseases and cancers. Condensates exhibit rich phase behaviors including multiphase internal structuring, noise buffering, and compositional tunability. Recent studies have begun to uncover how a network of intermolecular interactions can give rise to various biophysical features of condensates. Here, we review phase behaviors of biomolecules, particularly with regard to regular solution models of binary and ternary mixtures. We discuss how these theoretical frameworks explain many aspects of the assembly, composition, and miscibility of diverse biomolecular phases, and highlight how a model-based approach can help elucidate the detailed thermodynamic principle for multicomponent intracellular phase separation.

• Advances in nano research
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• v.15 no.2
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• pp.141-153
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• 2023

The main objective of this paper is to develop the finite element study on the nonlinear free vibration of functionally graded nanocomposite spherical shells reinforced with graphene platelets under the first-order shear deformation shell theory and von Kármán nonlinear kinematic relations. The governing equations are presented by introducing the full asymmetric nonlinear strain-displacement relations followed by the constitutive relations and energy functional. The extended Halpin-Tsai model is utilized to specify the overall Young's modulus of the nanocomposite. Then, the finite element formulation is derived and the quadrilateral 8-node shell element is implemented for finite element discretization. The nonlinear sets of dynamic equations are solved by the use of the harmonic balance technique and iterative method to find the nonlinear frequency response. Several numerical examples are represented to highlight the impact of involved factors on the large-amplitude vibration responses of nanocomposite spherical shells. One of the main findings is that for some geometrical and material parameters, the fundamental vibrational mode shape is asymmetric and the axisymmetric formulation cannot be appropriately employed to model the nonlinear dynamic behavior of nanocomposite spherical shells.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.30-45
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• 2024

This study introduces an effective method for predicting individual local tax delinquencies using prevalent machine learning and deep learning algorithms. The evaluation of credit risk holds great significance in the financial realm, impacting both companies and individuals. While credit risk prediction has been explored using statistical and machine learning techniques, their application to tax arrears prediction remains underexplored. We forecast individual local tax defaults in Republic of Korea using machine and deep learning algorithms, including convolutional neural networks (CNN), long short-term memory (LSTM), and sequence-to-sequence (seq2seq). Our model incorporates diverse credit and public information like loan history, delinquency records, credit card usage, and public taxation data, offering richer insights than prior studies. The results highlight the superior predictive accuracy of the CNN model. Anticipating local tax arrears more effectively could lead to efficient allocation of administrative resources. By leveraging advanced machine learning, this research offers a promising avenue for refining tax collection strategies and resource management.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.28 no.1
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• pp.1-21
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• 2024

This review comprehensively explores the evolution, theoretical underpinnings, variations, and applications of diffusion models. Originating as a generative framework, diffusion models have rapidly ascended to the forefront of machine learning research, owing to their exceptional capability, stability, and versatility. We dissect the core principles driving diffusion processes, elucidating their mathematical foundations and the mechanisms by which they iteratively refine noise into structured data. We highlight pivotal advancements and the integration of auxiliary techniques that have significantly enhanced their efficiency and stability. Variants such as bridges that broaden the applicability of diffusion models to wider domains are introduced. We put special emphasis on the ability of diffusion models as a crucial foundation model, with modalities ranging from image, 3D assets, and video. The role of diffusion models as a general foundation model leads to its versatility in many of the downstream tasks such as solving inverse problems and image editing. Through this review, we aim to provide a thorough and accessible compendium for both newcomers and seasoned researchers in the field.

• BMB Reports
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• v.57 no.8
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• pp.352-362
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• 2024

Hematopoietic stem cell transplantation (HSCT) remains an indispensable therapeutic strategy for various hematological diseases. This review discusses the pivotal role of bone marrow (BM) niches in influencing the efficacy of HSCT and evaluates the current animal models, emphasizing their limitations and the need for alternative models. Traditional animal models, mainly murine xenograft, have provided significant insights, but due to species-specific differences, are often constrained from accurately mimicking human physiological responses. These limitations highlight the importance of developing alternative models that can more realistically replicate human hematopoiesis. Emerging models that include BM organoids and BM-on-a-chip microfluidic systems promise enhanced understanding of HSCT dynamics. These models aim to provide more accurate simulations of the human BM microenvironment, potentially leading to improved preclinical assessments and therapeutic outcomes. This review highlights the complexities of the BM niche, discusses the limitations of current models, and suggests directions for future research using advanced model systems.

• Steel and Composite Structures
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• v.29 no.3
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• pp.363-377
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• 2018

An efficient and free of shear locking finite element model is developed and employed to study free vibration of tapered bidirectional functionally graded material (BFGM) beams. The beam material is assumed to be formed from four distinct constituent materials whose volume fraction continuously varies along the longitudinal and thickness directions by power-law functions. The finite element formulation based on the first-order shear deformation theory is derived by using hierarchical functions to interpolate the displacement field. In order to improve efficiency and accuracy of the formulation, the shear strain is constrained to constant and the exact variation of the cross-sectional profile is employed to compute the element stiffness and mass matrices. A comprehensive parametric study is carried out to highlight the influence of the material distribution, the taper and aspect ratios as well as the boundary conditions on the vibration characteristics. Numerical investigation reveals that the proposed model is efficient, and it is capable to evaluate the natural frequencies of BFGM beams by using a small number of the elements. It is also shown that the effect of the taper ratio on the fundamental frequency of the BFGM beams is significantly influenced by the boundary conditions. The present results are of benefit to optimum design of tapered FGM beam structures.

• KIEAE Journal
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• v.13 no.6
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• pp.83-91
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• 2013

Cool roofs are currently being emerged as one of important mechanism to save energy in relation to the building. It is specifically proposed that the changing trends of rooftop surface temperature in the flat roof building model could be used effectively as an indicator to reduced cooling load reduced by cool roof since it can present stable temperature record, that is not influenced according to the nearby physical as well as human variables. The temperature of cool roof in summer was lower around $20^{\circ}C$, compared to the general roofs. Such a seasonal or daily comparative study for rooftop temperature in the building model will highlight that the cool roof efficiency could be calculated in much area-wide context according to rooftop color distribution in urban residential area. It is anticipated that this research output could be used as a valuable reference in identifying energy saving by cool roof since an objective monitoring has been proposed based on the rooftop temperature in the building model, fully quantitative performance of thermal infrared image.

• Journal of Korea Multimedia Society
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• v.7 no.11
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• pp.1540-1549
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• 2004

In this paper, we propose an algorithm to detect semantic events in golf video using Hidden Markov Model. The purpose of this paper is to identify and classify the golf events to facilitate highlight-based video indexing and summarization. In this paper we first define 4 semantic events, and then design HMM model with states made up of each event. We also use 10 multiple visual features based on MPEG-7 visual descriptors to acquire parameters of HMM for each event. Experimental results showed that the proposed algorithm provided reasonable detection performance for identifying a variety of golf events.

• Wind and Structures
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• v.5 no.1
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• pp.61-80
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• 2002

Wind tunnel aeroelastic model tests of the Commonwealth Advisory Aeronautical Research Council (CAARC) standard tall building were conducted using a three-degree-of-freedom base hinged aeroelastic(BHA) model. Experimental investigation into the effects of coupled translational-torsional motion, cross-wind/torsional frequency ratio and eccentricity between centre of mass and centre of stiffness on the wind-induced response characteristics and wind excitation mechanisms was carried out. The wind tunnel test results highlight the significant effects of coupled translational-torsional motion, and eccentricity between centre of mass and centre of stiffness, on both the normalised along-wind and cross-wind acceleration responses for reduced wind velocities ranging from 4 to 20. Coupled translational-torsional motion and eccentricity between centre of mass and centre of stiffness also have significant impacts on the amplitude-dependent effect caused by the vortex resonant process, and the transfer of vibrational energy between the along-wind and cross-wind directions. These resulted in either an increase or decrease of each response component, in particular at reduced wind velocities close to a critical value of 10. In addition, the contribution of vibrational energy from the torsional motion to the cross-wind response of the building model can be greatly amplified by the effect of resonance between the vortex shedding frequency and the torsional natural frequency of the building model.