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Due to excellent performance, hydrogen is treated as the most promising energy carrier. However, during the storage of liquid hydrogen, there are still some thorny issues that need to be addressed urgently, such as fluid thermal stratification and sloshing phenomenon. To efficiently grasp fluid sloshing mechanical characteristics, a simple visual sloshing experiment rig was established by using a rectangle transparent water vessel. The variations of the interface shape and the impact force during sloshing were monitored and analyzed. The effects of sloshing frequency, horizontal acceleration, and initial liquid height on fluid sloshing mechanical mechanism were investigated. The results show that when the external sloshing excitation is close to the first order natural frequency, obvious interface fluctuation and large amplitude sloshing force variation are observed. The present work is of significance to strengthen the understanding of fluid sloshing mechanical performance and may lay a solid foundation for fluid sloshing suppression and long‐term storage of cryogenic fuels. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to address the challenge of designing finite-time fault-tolerant control mechanisms for nonlinear flexible spacecraft systems, which are particularly vulnerable to randomized actuator faults. Diverging from traditional methodologies, our research harnesses the capabilities of the Takagi–Sugeno (T–S) fuzzy framework. A unique feature of our model is the representation of actuator failures as stochastic signals following a Markov process, thereby offering a robust solution for addressing timeliness concerns. In this paper, we introduce a generalized reciprocally convex inequality that includes adjustable parameters, broadening the scope of previous results by accommodating them as special cases. Through the amalgamation of this enhanced inequality and flexible independent parameters, we propose an innovative controller design strategy. This approach establishes a stability standard that guarantees mean-square H ∞ performance. In order to validate the efficacy of the suggested strategy, we present a numerical illustration involving a nonlinear spacecraft system, showcasing the practical advantages and feasibility of our proposed technique. [ABSTRACT FROM AUTHOR]

The advantages of machine-learning methods have been widely explored in Raman spectroscopy analysis. In this study, a lightweight network model for mineral analysis based on Raman spectral feature visualization is proposed. The model, called the fire module convolutional neural network (FMCNN.), was based on a convolutional neural network, and a fire-module was introduced to increase the width of the network, while also ensuring fewer trainable parameters in the network and reducing the model's computational complexity. The visualization process is based on a deconvolution network, which maps the features of the middle layer back to the feature space. While fully exploring the features of the Raman spectral data, it also transparently displays the neural network feature extraction results. Experiments show that the classification accuracy of the model reaches 0.988. This method can accurately classify Raman spectra of minerals with less reliance on human participation. Combined with the analysis of the results of feature visualization, our method has high reliability and good application prospects in mineral classification., Raman spectroscopy is based on the Raman scattering effect discovered by the Indian scientist C.V. Raman. It analyzes the scattering spectrum with a frequency different from that of the incident [...]

Introduction: Lysosomes are degradative organelles that maintain cellular homeostasis and protein quality control. Transcription factor EB (TFEB)–mediated lysosome biogenesis enhances lysosome‐dependent degradation and alleviates neurodegenerative diseases, but the mechanisms underlying TFEB regulation and modification are still poorly understood. Methods: By screening novel small‐molecule compounds, we identified a group of lysosome‐enhancing compounds (LYECs) that promote TFEB activation and lysosome biogenesis. Results: One of these compounds, LH2‐051, significantly inhibited the function of the dopamine transporter (DAT) and subsequently promoted lysosome biogenesis. We uncovered cyclin‐dependent kinase 9 (CDK9) as a novel regulator of DAT‐mediated lysosome biogenesis and identified six novel CDK9‐phosphorylated sites on TFEB. We observed that signal transduction by the DAT‐CDK9‐TFEB axis occurs on lysosomes. Finally, we found that LH2‐051 enhanced the degradation of amyloid beta plaques and improved the memory of amyloid precursor protein (APP)/Presenilin 1 (PS1) mice. Discussion: We identified the DAT‐CDK9‐TFEB signaling axis as a novel regulator of lysosome biogenesis. Our study sheds light on the mechanisms of protein quality control under pathophysiological conditions. [ABSTRACT FROM AUTHOR]

Millions of people are suffering from Alzheimer's disease globally, but there is still no effective treatment for this neurodegenerative disease. Thus, novel therapeutic approaches for Alzheimer's disease are needed, which requires further evaluation of the regulatory mechanisms of protein aggregate degradation. Lysosomes are crucial degradative organelles that maintain cellular homeostasis. Transcription factor EB-mediated lysosome biogenesis enhances autolysosome-dependent degradation, which subsequently alleviates neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, and Huntington's disease. In this review, we start by describing the key features of lysosomes, including their roles in nutrient sensing and degradation, and their functional impairments in different neurodegenerative diseases. We also explain the mechanisms - especially the post-translational modifications - which impact transcription factor EB and regulate lysosome biogenesis. Next, we discuss strategies for promoting the degradation of toxic protein aggregates. We describe Proteolysis-Targeting Chimera and related technologies for the targeted degradation of specific proteins. We also introduce a group of LYsosome-Enhancing Compounds, which promote transcription factor EB-mediated lysosome biogenesis and improve learning, memory, and cognitive function in APP-PSEN1 mice. In summary, this review highlights the key aspects of lysosome biology, the mechanisms of transcription factor EB activation and lysosome biogenesis, and the promising strategies which are emerging to alleviate the pathogenesis of neurodegenerative diseases., Competing Interests: None

Introduction: Lysosomes are degradative organelles that maintain cellular homeostasis and protein quality control. Transcription factor EB (TFEB)-mediated lysosome biogenesis enhances lysosome-dependent degradation and alleviates neurodegenerative diseases, but the mechanisms underlying TFEB regulation and modification are still poorly understood., Methods: By screening novel small-molecule compounds, we identified a group of lysosome-enhancing compounds (LYECs) that promote TFEB activation and lysosome biogenesis., Results: One of these compounds, LH2-051, significantly inhibited the function of the dopamine transporter (DAT) and subsequently promoted lysosome biogenesis. We uncovered cyclin-dependent kinase 9 (CDK9) as a novel regulator of DAT-mediated lysosome biogenesis and identified six novel CDK9-phosphorylated sites on TFEB. We observed that signal transduction by the DAT-CDK9-TFEB axis occurs on lysosomes. Finally, we found that LH2-051 enhanced the degradation of amyloid beta plaques and improved the memory of amyloid precursor protein (APP)/Presenilin 1 (PS1) mice., Discussion: We identified the DAT-CDK9-TFEB signaling axis as a novel regulator of lysosome biogenesis. Our study sheds light on the mechanisms of protein quality control under pathophysiological conditions., (© 2022 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)