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When we perform particle-based water simulation, water particles are often increased dramatically because of particle splitting around breaking holes to maintain the thin fluid sheets. Because most of the existing approaches do not consider the volume of the water particles, the water particles must have a very low mass to satisfy the law of the conservation of mass. This phenomenon smears the motion of the water, which would otherwise result in splashing, thereby resulting in artifacts such as numerical dissipation. Thus, we propose a new fluid-implicit, particle-based framework for maintaining and representing the thin sheets and turbulent flows of water. After splitting the water particles, the proposed method uses the ghost density and ghost mass to redistribute the difference in mass based on the volume of the water particles. Next, small-scale turbulent flows are formed in local regions and transferred in a smooth manner to the global flow field. Our results show us the turbulence details as well as the thin sheets of water, thereby obtaining an aesthetically pleasing improvement compared with existing methods. [ABSTRACT FROM AUTHOR]

Photobiomodulation therapy based on micro light-emitting diodes (µLEDs) holds remarkable potential for the beauty industry. Here, a cosmetically effective face-fit surface-lighting µLED mask for skin anti-aging is introduced. The face-conformable mask enables deep tissue treatment through proximal light irradiation, with a 3D origami structure capable of adapting to complex facial contours with closed adherence. A blister-assisted laser transfer achieves rapid and accurate µLEDs transfer at a high throughput of 50 chips per second, facilitating a mass-producible and large-area process. Finally, clinical trials demonstrate significant improvements in elasticity, sagging, and wrinkles across six facial areas, with a maximum enhancement of 340% in deep skin elasticity of the perioral area compared to the conventional LED mask group., (© 2024 Wiley‐VCH GmbH.)

Ethnopharmacological Relevance: Artemisia argyi possesses pharmacological activities against various immunopathological conditions associated with inflammation., Aim of the Study: This study explored the inhibitory role of Artemisia argyi methanol extract (Aa-ME) in inflammatory responses and the underlying mechanism in macrophages., Materials and Methods: Caspase-11 non-canonical inflammasome was activated in J774A.1 macrophage by Pam3CSK4 treatment and lipopolysaccharide (LPS) transfection. Aa-ME-mediated in vitro anti-inflammatory action was examined using MTT assay, lactate dehydrogenase (LDH) activity assay, enzyme-linked immunosorbent assay (ELISA), nitric oxide (NO) generation assay, and quantitative real-time polymerase chain reaction (qPCR). Aa-ME-mediated in vivo anti-inflammatory action was examined in LPS-stimulated lethal septic mice., Results: Aa-ME inhibited caspase-11 non-canonical inflammasome-stimulated pyroptosis and the secretion of IL-1β and IL-18 in J774A.1 macrophages. Aa-ME also inhibited NO generation by downregulating inducible NO synthase (iNOS) expression in LPS-primed and caspase-11 non-canonical inflammasome-triggered J774A.1 cells. The mechanism study revealed Aa-ME suppressed the auto-proteolytic activation of caspase-11 and gasdermin D (GSDMD) in J774A.1 cells and also interfered with caspase-11-mediated direct recognition of LPS. Moreover, Aa-ME alleviated LPS-induced lethal sepsis in mice by increasing their survival rate without significant toxicity., Conclusion: These results suggest a novel mechanism by which Aa-ME alleviates inflammatory responses by deactivating caspase-11 non-canonical inflammasome in macrophages., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

In optics, the refractive index of a material and its spatial distribution determine the characteristics of light propagation. Therefore, exploring both low- and high-index materials/structures is an important consideration in this regard. Hollow cavities, which are defined as low-index bases, exhibit a variety of unusual or even unexplored optical characteristics and are used in numerous functionalities including diffraction gratings, localised optical antennas and low-loss resonators. In this report, we discuss the fabrication of hollow cavities of various sizes (0.2-5 μm in diameter) that are supported by conformal dielectric/metal shells, as well as their specific applications in the ultraviolet (photodetectors), visible (light-emitting diodes, solar cells and metalenses), near-infrared (thermophotovoltaics) and mid-infrared (radiative coolers) regions. Our findings demonstrate that hollow cavities tailored to specific spectra and applications can serve as versatile optical platforms to address the limitations of current optoelectronic devices. Furthermore, hollow cavity embedded structures are highly elastic and can minimise the thermal stress caused by high temperatures. As such, future applications will likely include high-temperature devices such as thermophotovoltaics and concentrator photovoltaics., (© 2022. The Author(s).)

We have recently seen many successful applications of recurrent neural networks (RNNs) on electronic medical records (EMRs), which contain histories of patients' diagnoses, medications, and other various events, in order to predict the current and future states of patients. Despite the strong performance of RNNs, it is often challenging for users to understand why the model makes a particular prediction. Such black-box nature of RNNs can impede its wide adoption in clinical practice. Furthermore, we have no established methods to interactively leverage users' domain expertise and prior knowledge as inputs for steering the model. Therefore, our design study aims to provide a visual analytics solution to increase interpretability and interactivity of RNNs via a joint effort of medical experts, artificial intelligence scientists, and visual analytics researchers. Following the iterative design process between the experts, we design, implement, and evaluate a visual analytics tool called RetainVis, which couples a newly improved, interpretable, and interactive RNN-based model called RetainEX and visualizations for users' exploration of EMR data in the context of prediction tasks. Our study shows the effective use of RetainVis for gaining insights into how individual medical codes contribute to making risk predictions, using EMRs of patients with heart failure and cataract symptoms. Our study also demonstrates how we made substantial changes to the state-of-the-art RNN model called RETAIN in order to make use of temporal information and increase interactivity. This study will provide a useful guideline for researchers that aim to design an interpretable and interactive visual analytics tool for RNNs.