56016 research outputs found
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No full textBrand Exploration in Metaverse: Effects of User-Avatar Resemblance on Engagement and Brand Attitude
No full textBrand metaverse, which refers to the brand in a virtual world, has become an important medium for brands to communicate with customers. In this study, we investigate the influence of user-avatar resemblance on brand metaverse engagement and brand attitude. Specifically, we propose that user-avatar resemblance affects brand attitude by virtue of the engagement with the brand metaverse. Furthermore, we posit that copresence, the simultaneous presence of multiple avatars in the brand metaverse, acts as a moderator that strengthens the mediation. We conducted an experiment using a fashion brand???s virtual world in a popular metaverse platform. Our hypotheses were supported for the main and interaction effects. The findings provide meaningful implications for marketing practitioners who have intentions to implement ???metaverse marketing.??
Investigating the role of interstitial water molecules in copper hexacyanoferrate for sodium-ion battery cathodes
No full textPrussian blue analogues (PBAs) are one of the most promising cathode materials for sodium (Na)-ion batteries owing to their large channel size and stability in aqueous and organic electrolytes. However, the impact of interstitial water molecules within PBA channels has not yet been adequately investigated. Herein, by comparing the electrochemical performance of PBAs in aqueous and organic electrolytes, we demonstrate that water molecules depending on their number can inhibit the insertion of hydrated Na+ ions. As a result, CuHCFe-1.4H(2)O with fewer interstitial water molecules possesses a higher specific capacity in an aqueous electrolyte compared to CuHCFe-1.8H(2)O, which has a higher number of interstitial water molecules. In addition, we found that interstitial water molecules can obstruct Na+ ion diffusion, leading to poor kinetic properties. We believe that the newly found roles of interstitial water molecules could shed light on the design of high-performance PBAs for Na+-ion battery cathodes
Effect of high replacement ratio of lime mud as a filler on mechanical and hydration properties of high-strength mortar
No full textLime mud (LM) is a solid industrial byproduct with high alkalinity and calcium carbonate (CaCO3) content produced by the paper industry. LM has been used and investigated as a supplementary cementitious material (SCM). However, using LM with a high replacement ratio causes agglomeration, which reduces the material's strength and has a detrimental effect on other properties. In addition to agglomeration, LM is unreactive; thus, in this work, a high replacement ratio (70%) of sand with raw LM was investigated in a mortar mixture containing silica fume (SF), considering the fast SF hydration properties of cement in a high alkaline environment, and river sand is used to reduce the agglomeration of LM. The effects of the replacement were then evaluated using compressive strength, X-ray diffraction, thermogravimetric analysis, mercury intrusion porosimetry (MIP), and scanning electron microscopy (SEM). The hydration kinetics were investigated using isothermal calorimetry. The experimental results showed that combining SF with a high replacement ratio (70%) of LM improves compressive strength and flexural strength by 16% and 59.7%, respectively. This is due to the filler nature of LM, which refines the pore structure. Furthermore, the highly alkaline environment provided by LM increased the secondary hydration of SF with cement, which also improved the strength
Paper-based electrochemical immunosensor for detection of multiple respiratory disease viruses and bacteria
No full textPose-Guided 3D Human Generation in Indoor Scene
No full textIn this work, we address the problem of scene-aware 3D human avatar generation based on human-scene interactions. In particular, we pay attention to the fact that physical contact between a 3D human and a scene (i.e., physical human-scene interactions) requires a geometrical alignment to generate natural 3D human avatar. Motivated by this fact, we present a new 3D human generation framework that considers geometric alignment on potential contact areas between 3D human avatars and their surroundings. In addition, we introduce a compact yet effective human pose classifier that classifies the human pose and provides potential contact areas of the 3D human avatar. It allows us to adaptively use geometric alignment loss according to the classified human pose. Compared to state-of-the-art method, our method can generate physically and semantically plausible 3D humans that interact naturally with 3D scenes without additional post-processing. In our evaluations, we achieve the improvements with more plausible interactions and more variety of poses than prior research in qualitative and quantitative analysis. Project page: https://bupyeonghealer.github.io/phin/
Development of Novel Epigenetic Anti-Cancer Therapy Targeting TET Proteins
No full textEpigenetic dysregulation, particularly alterations in DNA methylation and hydroxymethylation, plays a pivotal role in cancer initiation and progression. Ten-eleven translocation (TET) proteins catalyze the successive oxidation of 5-methylcytosine (5mC) to 5-hydroxymethylcytosine (5hmC) and further oxidized methylcytosines in DNA, thereby serving as central modulators of DNA methylation???demethylation dynamics. TET loss of function is causally related to neoplastic transformation across various cell types while its genetic or pharmacological activation exhibits anti-cancer effects, making TET proteins promising targets for epigenetic cancer therapy. Here, we developed a robust cell-based screening system to identify novel TET activators and evaluated their potential as anti-cancer agents. Using a carefully curated library of 4533 compounds provided by the National Cancer Institute, Bethesda, MD, USA, we identified mitoxantrone as a potent TET agonist. Through rigorous validation employing various assays, including immunohistochemistry and dot blot studies, we demonstrated that mitoxantrone significantly elevated 5hmC levels. Notably, this elevation manifested only in wild-type (WT) but not TET-deficient mouse embryonic fibroblasts, primary bone marrow-derived macrophages, and leukemia cell lines. Furthermore, mitoxantrone-induced cell death in leukemia cell lines occurred in a TET-dependent manner, indicating the critical role of TET proteins in mediating its anti-cancer effects. Our findings highlight mitoxantrone???s potential to induce tumor cell death via a novel mechanism involving the restoration of TET activity, paving the way for targeted epigenetic therapies in cancer treatment
Physics-Guided Deep Scatter Estimation by Weak Supervision for Quantitative SPECT
No full textAccurate scatter estimation is important in quantitative SPECT for improving image contrast and accuracy. With a large number of photon histories, Monte-Carlo (MC) simulation can yield accurate scatter estimation, but is computationally expensive. Recent deep learning-based approaches can yield accurate scatter estimates quickly, yet full MC simulation is still required to generate scatter estimates as ground truth labels for all training data. Here we propose a physics-guided weakly supervised training framework for fast and accurate scatter estimation in quantitative SPECT by using a 100 x shorter MC simulation as weak labels and enhancing them with deep neural networks. Our weakly supervised approach also allows quick fine-tuning of the trained network to any new test data for further improved performance with an additional short MC simulation (weak label) for patient-specific scatter modelling. Our method was trained with 18 XCAT phantoms with diverse anatomies / activities and then was evaluated on 6 XCAT phantoms, 4 realistic virtual patient phantoms, 1 torso phantom and 3 clinical scans from 2 patients for Lu-177 SPECT with single / dual photopeaks (113, 208 keV). Our proposed weakly supervised method yielded comparable performance to the supervised counterpart in phantom experiments, but with significantly reduced computation in labeling. Our proposed method with patient-specific fine-tuning achieved more accurate scatter estimates than the supervised method in clinical scans. Our method with physics-guided weak supervision enables accurate deep scatter estimation in quantitative SPECT, while requiring much lower computation in labeling, enabling patient-specific fine-tuning capability in testing
