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First-Principle Study of Heterojunction Electronic Structure for Optoelectronic Applications
No full textDepartment of Chemistryclos
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No full textDepartment of Chemistryclos
Practical synthesis of solar hydrogen
No full textIt remains a significant challenge to develop a photocatalytic water splitting system to produce solar hydrogen (H-2) with greater than 10% efficiency. Recently, a practical water splitting approach has been demonstrated that produces H-2 fuel using natural sunlight, pure water, and photocatalysts
On the master space for brane brick models
No full textWe systematically study the master space of brane brick models that represent a large class of 2d (0, 2) quiver gauge theories. These 2d (0, 2) theories are worldvolume theories of D1-branes that probe singular toric Calabi-Yau 4-folds. The master space is the freely generated space of chiral fields subject to the J- and E-terms and the non-abelian part of the gauge symmetry. We investigate several properties of the master space for abelian brane brick models with U(1) gauge groups. For example, we calculate the Hilbert series, which allows us by using the plethystic programme to identify the generators and defining relations of the master space. By studying several explicit examples, we also show that the Hilbert series of the master space can be expressed in terms of characters of irreducible representations of the full global symmetry of the master space
Numerical and Machine Learning Approach for Fe3O4-Au/Blood Hybrid Nanofluid Flow in a Melting/Non-Melting Heat Transfer Surface with Entropy Generation
No full textThe physiological system loses thermal energy to nearby cells via the bloodstream. Such energy loss can result in sudden death, severe hypothermia, anemia, high or low blood pressure, and heart surgery. Gold and iron oxide nanoparticles are significant in cancer treatment. Thus, there is a growing interest among biomedical engineers and clinicians in the study of entropy production as a means of quantifying energy dissipation in biological systems. The present study provides a novel implementation of an intelligent numerical computing solver based on an MLP feed-forward backpropagation ANN with the Levenberg-Marquard algorithm to interpret the Cattaneo-Christov heat flux model and demonstrate the effect of entropy production and melting heat transfer on the ferrohydrodynamic flow of the Fe3O4-Au/blood Powell-Eyring hybrid nanofluid. Similarity transformation studies symmetry and simplifies PDEs to ODEs. The MATLAB program bvp4c is used to solve the nonlinear coupled ordinary differential equations. Graphs illustrate the impact of a wide range of physical factors on variables, including velocity, temperature, entropy generation, local skin friction coefficient, and heat transfer rate. The artificial neural network model engages in a process of data selection, network construction, training, and evaluation through the use of mean square error. The ferromagnetic parameter, porosity parameter, distance from origin to magnetic dipole, inertia coefficient, dimensionless Curie temperature ratio, fluid parameters, Eckert number, thermal radiation, heat source, thermal relaxation parameter, and latent heat of the fluid parameter are taken as input data, and the skin friction coefficient and heat transfer rate are taken as output data. A total of sixty data collections were used for the purpose of testing, certifying, and training the ANN model. From the results, it is found that the fluid temperature declines when the thermal relaxation parameter is improved. The latent heat of the fluid parameter impacts the entropy generation and Bejan number. There is a less significant impact on the heat transfer rate of the hybrid nanofluid over the sheet on the melting heat transfer parameter
Self-Assembly of Copper Oxide Interfaced MnO2 for Oxygen Evolution Reaction
No full textDesigning efficient electrocatalytic systems through facile synthesis remains a formidable task. To address this issue, this paper presents the design of a combination material comprising two transition metal oxides (copper oxide and manganese oxide (CuO/MnO2)), synthesized using a conventional microwave technique to efficiently engage as an active oxygen evolution reaction (OER) catalyst. The structural and morphological properties of the composite were confirmed by the aid of X-ray diffraction (XRD) studies, field emission scanning electron microscopy (FESEM), X-ray photoelectron spectroscopy (XPS), and energy-dispersive spectrometry (EDS). FESEM clearly indicated well-aligned interlacing of CuO with MnO2. The OER performance was carried out in 1 M KOH. The assembled CuO/MnO2 delivered a benchmark current density (j = 10 mA cm(-2)) at a minimal overpotential (? = 294 mV), while pristine CuO required a high ? (316 mV). Additionally, the CuO/MnO2 electrocatalyst exhibited stability for more than 15 h. These enhanced electrochemical performances were attributed to the large volume and expanded diameter of the pores, which offer ample surface area for catalytic reactions to boost OER. Furthermore, the rate kinetics of the OER are favored in composite due to low Tafel slope (77 mV/dec) compared to CuO (80 mV/dec)
A surrogate model-based framework for seismic resilience estimation of bridge transportation networks
No full textA bridge transportation network supplies products from various source nodes to destination nodes through bridge structures in a target region. However, recent frequent earthquakes have caused damage to bridge structures, resulting in extreme direct damage to the target area as well as indirect damage to other lifeline structures. Therefore, in this study, a surrogate model -based comprehensive framework to estimate the seismic resilience of bridge transportation networks is proposed. For this purpose, total system travel time (TSTT) is introduced for accurate performance indicator of the bridge transportation network, and an artificial neural network (ANN)-based surrogate model is constructed to reduce traffic analysis time for high-dimensional TSTT computation. The proposed framework includes procedures for constructing an ANN-based surrogate model to accelerate network performance computation, as well as conventional procedures such as direct Monte Carlo simulation (MCS) calculation and bridge restoration calculation. To demonstrate the proposed framework, Pohang bridge transportation network is reconstructed based on geographic information system (GIS) data, and an ANN model is constructed with the damage states of the transportation network and TSTT using the representative earthquake epicenter in the target area. For obtaining the seismic resilience curve of the Pohang region, five epicenters are considered, with earthquake magnitudes 6.0 to 8.0, and the direct and indirect damages of the bridge transportation network are evaluated. Thus, it is concluded that the proposed surrogate model -based framework can efficiently evaluate the seismic resilience of a high-dimensional bridge transportation network, and also it can be used for decision-making to minimize damage
BoIR: Box-Supervised Instance Representation for Multi Person Pose Estimation
No full textSingle-stage multi-person human pose estimation (MPPE) methods have shown great performance improvements, but existing methods fail to disentangle features by individual instances under crowded scenes. In this paper, we propose a bounding box-level instance representation learning called BoIR, which simultaneously solves instance detection, instance disentanglement, and instance-keypoint association problems. Our new instance embedding loss provides a learning signal on the entire area of the image with bounding box annotations, achieving globally consistent and disentangled instance representation. Our method exploits multi-task learning of bottom-up keypoint estimation, bounding box regression, and contrastive instance embedding learning, without additional computational cost during inference. BoIR is effective for crowded scenes, outperforming state-of-the-art on COCO val (0.8 AP), COCO test-dev (0.5 AP), CrowdPose (4.9 AP), and OCHuman (3.5 AP). Code will be available at https://github.com/uyoung-jeong/BoI
