A review of research on acoustic detection of heat exchanger tube

Leakage in heat exchanger tubes can result in unreliable products and dangerous situations, which could cause great economic losses. Along with fast development of modern acoustic detection technology, using acoustic signals to detect leakage in heat exchange tube has been gradually accepted and considered with great potential by both industrial and research societies. In order to further advance the development of acoustic signal detection technology and investigate better methods for leakage detection in heat exchange tube, in this paper, firstly, we conduct a short overview of the theory of acoustic signal detection on heat exchanger tube, which had already been continuously developed for a few decades by researchers worldwide. Thereafter, we further expound the advantages and limitations of acoustic signal detection technology on heat exchanger tube in four aspects: 1) principles of acoustic signal detection, 2) characteristics of sound wave propagation in heat exchanger tube, 3) methods of leakage detection, and 4) leakage localization in heat exchanger tube

Electron on solid neon -- a new solid-state single-electron qubit platform

The promise of quantum computing has driven a persistent quest for new qubit platforms with long coherence, fast operation, and large scalability. Electrons, ubiquitous elementary particles of nonzero charge, spin, and mass, have commonly been perceived as paradigmatic local quantum information carriers. Despite superior controllability and configurability, their practical performance as qubits via either motional or spin states depends critically on their material environment. Here we report our experimental realization of a new qubit platform based upon isolated single electrons trapped on an ultraclean solid neon surface in vacuum. By integrating an electron trap in a circuit quantum electrodynamics architecture, we achieve strong coupling between the motional states of a single electron and microwave photons in an on-chip superconducting resonator. Qubit gate operations and dispersive readout are used to measure the energy relaxation time $T_1$ of $15~\mu$s and phase coherence time $T_2$ over 200 ns, indicating that the electron-on-solid-neon qubit already performs near the state of the art as a charge qubit.Comment: 7 pages, 3 figure

Photoacoustic Identification of Laser-induced Microbubbles as Light Scattering Centers for Optical Limiting in Liquid Suspension of Graphene Nanosheets

Liquid suspensions of carbon nanotubes, graphene and transition metal dichalcogenides have exhibited excellent performance in optical limiting. However, the underlying mechanism has remained elusive and is generally ascribed to their superior nonlinear optical properties such as nonlinear absorption or nonlinear scattering. Using graphene as an example, we show that photo-thermal microbubbles are responsible for the optical limiting as strong light scattering centers: graphene sheets absorb incident light and become heated up above the boiling point of water, resulting in vapor and microbubble generation. This conclusion is based on direct observation of bubbles above the laser beam as well as a strong correlation between laser-induced ultrasound and optical limiting. In-situ Raman scattering of graphene further confirms that the temperature of graphene under laser pulses rises above the boiling point of water but still remains too low to vaporize graphene and create graphene plasma bubbles. Photo-thermal bubble scattering is not a nonlinear optical process and requires very low laser intensity. This understanding helps us to design more efficient optical limiting materials and understand the intrinsic nonlinear optical properties of nanomaterials

Classification of Alzheimer's disease using robust TabNet neural networks on genetic data

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases and its onset is significantly associated with genetic factors. Being the capabilities of high specificity and accuracy, genetic testing has been considered as an important technique for AD diagnosis. In this paper, we presented an improved deep learning (DL) algorithm, namely differential genes screening TabNet (DGS-TabNet) for AD binary and multi-class classifications. For performance evaluation, our proposed approach was compared with three novel DLs of multi-layer perceptron (MLP), neural oblivious decision ensembles (NODE), TabNet as well as five classical machine learnings (MLs) including decision tree (DT), random forests (RF), gradient boosting decision tree (GBDT), light gradient boosting machine (LGBM) and support vector machine (SVM) on the public data set of gene expression omnibus (GEO). Moreover, the biological interpretability of global important genetic features implemented for AD classification was revealed by the Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO). The results demonstrated that our proposed DGS-TabNet achieved the best performance with an accuracy of 93.80% for binary classification, and with an accuracy of 88.27% for multi-class classification. Meanwhile, the gene pathway analyses demonstrated that there existed two most important global genetic features of AVIL and NDUFS4 and those obtained 22 feature genes were partially correlated with AD pathogenesis. It was concluded that the proposed DGS-TabNet could be used to detect AD-susceptible genes and the biological interpretability of susceptible genes also revealed the potential possibility of being AD biomarkers

Localized concentration reversal of lithium during intercalation into nanoparticles.

Nanoparticulate electrodes, such as Li x FePO4, have unique advantages over their microparticulate counterparts for the applications in Li-ion batteries because of the shortened diffusion path and access to nonequilibrium routes for fast Li incorporation, thus radically boosting power density of the electrodes. However, how Li intercalation occurs locally in a single nanoparticle of such materials remains unresolved because real-time observation at such a fine scale is still lacking. We report visualization of local Li intercalation via solid-solution transformation in individual Li x FePO4 nanoparticles, enabled by probing sub-angstrom changes in the lattice spacing in situ. The real-time observation reveals inhomogeneous intercalation, accompanied with an unexpected reversal of Li concentration at the nanometer scale. The origin of the reversal phenomenon is elucidated through phase-field simulations, and it is attributed to the presence of structurally different regions that have distinct chemical potential functions. The findings from this study provide a new perspective on the local intercalation dynamics in battery electrodes

The crosstalk between microbiota and metabolites in AP mice: an analysis based on metagenomics and untargeted metabolomics

Background and purposeMicrobiome dysfunction is known to aggravate acute pancreatitis (AP); however, the relationship between this dysfunction and metabolite alterations is not fully understood. This study explored the crosstalk between the microbiome and metabolites in AP mice.MethodsExperimental AP models were established by injecting C57/BL mice with seven doses of cerulein and one dose of lipopolysaccharide (LPS). Metagenomics and untargeted metabolomics were used to identify systemic disturbances in the microbiome and metabolites, respectively, during the progression of AP.ResultsThe gut microbiome of AP mice primarily included Firmicutes, Bacteroidetes, Actinobacteria, and Proteobacteria, and “core microbiota” characterized by an increase in Proteobacteria and a decrease in Actinobacteria. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis found that significantly different microbes were involved in several signaling networks. Untargeted metabolomics identified 872 metabolites, of which lipids and lipid-like molecules were the most impacted. An integrated analysis of metagenomics and metabolomics indicated that acetate kinase (ackA) gene expression was associated with various gut microbiota, including Alistipes, Butyricimonas, and Lactobacillus, and was strongly correlated with the metabolite daphnoretin. The functional gene, O-acetyl-L-serine sulfhydrylase (cysK), was associated with Alistipes, Jeotgalicoccus, and Lactobacillus, and linked to bufalin and phlorobenzophenone metabolite production.ConclusionThis study identified the relationship between the gut microbiome and metabolite levels during AP, especially the Lactobacillus-, Alistipes-, and Butyricimonas-associated functional genes, ackA and cysK. Expression of these genes was significantly correlated to the production of the anti-inflammatory and antitumor metabolites daphnoretin and bufalin

GLS as a diagnostic biomarker in breast cancer: in-silico, in-situ, and in-vitro insights

BackgroundRecently, a novel programmed cell death mechanism, Cuproptosis, has been discovered and found to play an important role in the development and progression of diverse tumors. In the present study, we comprehensively investigated the core gene of this mechanism, GLS, in breast cancer.Materials and methodsBulk RNA sequencing data were curated from the TCGA repository to investigate the aberrant expression of GLS over diverse cancer types. Then, we examined its efficacy as a diagnostic biomarker in breast cancer by Area Under Curve (AUC) of the Receiver Operative Characteristic (ROC) curve. Furthermore, by applying siRNA technique, we knocked down the GLS expression level in cancerous cell lines, measuring the corresponding effects on cell proliferation and metastasis. Afterward, we explored the potential implications of GLS expression in the tumor immune microenvironment quantitatively by using several R packages and algorithms, including ESTIMATE, CIBERSORT, etc.ResultsPan-cancer analysis suggested that GLS was aberrantly over-expressed in many cancer types, with breast cancer being typical. More in-depth analyses revealed the expression of GLS exerted a high ROC-AUC value in breast cancer diagnosis. Through the knock-down of GLS expression, it was found that GLS expression was strongly relevant to the growth and metastasis of tumor. Furthermore, it was also found to be correlated with the immune tumor microenvironment.ConclusionWe highlighted that GLS expression might be applicable as a diagnostic biomarker in breast cancer and possess significant implications in the growth and metastasis of tumor and the immune tumor microenvironment, sharing new insights into ontological and personalized medicine