The thermal and electrical properties of the promising semiconductor MXene Hf2CO2

In this work, we investigate the thermal and electrical properties of oxygen-functionalized M2CO2 (M = Ti, Zr, Hf) MXenes using first-principles calculations. Hf2CO2 is found to exhibit a thermal conductivity better than MoS2 and phosphorene. The room temperature thermal conductivity along the armchair direction is determined to be 86.25-131.2 Wm-1K-1 with a flake length of 5-100 um, and the corresponding value in the zigzag direction is approximately 42% of that in the armchair direction. Other important thermal properties of M2CO2 are also considered, including their specific heat and thermal expansion coefficients. The theoretical room temperature thermal expansion coefficient of Hf2CO2 is 6.094x10-6 K-1, which is lower than that of most metals. Moreover, Hf2CO2 is determined to be a semiconductor with a band gap of 1.657 eV and to have high and anisotropic carrier mobility. At room temperature, the Hf2CO2 hole mobility in the armchair direction (in the zigzag direction) is determined to be as high as 13.5x103 cm2V-1s-1 (17.6x103 cm2V-1s-1), which is comparable to that of phosphorene. Broader utilization of Hf2CO2 as a material for nanoelectronics is likely because of its moderate band gap, satisfactory thermal conductivity, low thermal expansion coefficient, and excellent carrier mobility. The corresponding thermal and electrical properties of Ti2CO2 and Zr2CO2 are also provided here for comparison. Notably, Ti2CO2 presents relatively low thermal conductivity and much higher carrier mobility than Hf2CO2, which is an indication that Ti2CO2 may be used as an efficient thermoelectric material.Comment: 26 pages, 5 figures, 2 table

Use of the shearlet energy entropy and of the support vector machine classifier to process weak microseismic and desert seismic signals

Low-amplitude signal detection is a key procedure in borehole microseismic and desert seismic exploration. Usually, signals are difficult to detect due to their low amplitude and noise contamination. To solve this problem, we propose a method combining shearlet energy entropy with a support vector machine (SVM) to detect low-amplitude signals. In the proposed method, the signal feature is extracted using shearlet energy entropy. The signal is more sparsely represented in the shearlet domain because of the multi-scale and multi-direction characteristic of the shearlet transform, which favours signal feature extraction. Furthermore, in calculating shearlet energy entropy, we use the correlation of shearlet coefficients to enhance the difference between signal and noise in the shearlet domain. Shearlet energy entropy makes the SVM achieve a more accurate classification result compared with other traditional features such as amplitude and energy. The results of synthetic and field data show that our method is more effective than the STA/LTA and the convolutional neural network for low-amplitude microseismic signal and desert seismic signal detection

Use of the shearlet energy entropy and of the support vector machine classifier to process weak microseismic and desert seismic signals

Low-amplitude signal detection is a key procedure in borehole microseismic and desert seismic exploration. Usually, signals are difficult to detect due to their low amplitude and noise contamination. To solve this problem, we propose a method combining shearlet energy entropy with a support vector machine (SVM) to detect low-amplitude signals. In the proposed method, the signal feature is extracted using shearlet energy entropy. The signal is more sparsely represented in the shearlet domain because of the multi-scale and multi-direction characteristic of the shearlet transform, which favours signal feature extraction. Furthermore, in calculating shearlet energy entropy, we use the correlation of shearlet coefficients to enhance the difference between signal and noise in the shearlet domain. Shearlet energy entropy makes the SVM achieve a more accurate classification result compared with other traditional features such as amplitude and energy. The results of synthetic and field data show that our method is more effective than the STA/LTA and the convolutional neural network for low-amplitude microseismic signal and desert seismic signal detection

A model local interpretation routine for deep learning based radio galaxy classification

Radio galaxy morphological classification is one of the critical steps when producing source catalogues for large-scale radio continuum surveys. While many recent studies attempted to classify source radio morphology from survey image data using deep learning algorithms (i.e., Convolutional Neural Networks), they concentrated on model robustness most time. It is unclear whether a model similarly makes predictions as radio astronomers did. In this work, we used Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art eXplainable Artificial Intelligence (XAI) technique to explain model prediction behaviour and thus examine the hypothesis in a proof-of-concept manner. In what follows, we describe how \textbf{LIME} generally works and early results about how it helped explain predictions of a radio galaxy classification model using this technique.Comment: 4 pages, 1 figure, accepted summary paper for URSI GASS 2023 J0

Colonic mucosal biopsy location can not affect the results of mucosal metabolomics and mucosal microbiota analysis in IBS

ObjectiveTo compare and analyze the mucosal metabolites and mucosal microbiota of different parts of colon in patients with IBS.MethodsA total of 10 patients with IBS-D and six healthy controls (HC) were enrolled. All enrolled participants underwent two biopsies of the ileocecal and sigmoid colon during colonoscopy. Metabolomic profiling of one piece of tissue was conducted using desorption electrospray ionization-mass spectrometry (DESI-MS), and the gut flora of the other piece was examined using 16S rRNA sequencing. The metabolic profiles and flora of the ileocecal and sigmoid colonic mucosa in each group were further analyzed in this study.Results(1) Principal components analysis (PCA) indicated that mucosal metabolites did not differ in different parts of the colon in either the IBS-D or HC groups. (2) In the mucosal microbiome analyses, no differences between the microbiota of the two parts of the colon were found by using Principal Co-ordinates Analysis (PCoA). In IBS group, comparing with sigmoid mucosa, the chao1 richness indice was higher and the Shannon index was lower in the ileocecal mucosa (p = 0.40, p = 0.22). However, in the HC group, microbiome analysis of the ileocecal mucosa showed lower values for Chao 1 and Shannon indices than those of the sigmoid colon mucosa (p = 0.06, p = 0.86). (3) Compared with the HC group, 1,113 metabolic signal peaks were upregulated, whereas 594 metabolites were downregulated in the IBS-D samples. Moreover, the PCA of the metabolites showed significant separation between the IBS-D and HC groups. (4) Chao1 expression was significantly higher in the mucosal microbiota with IBS-D than in the HC (p = 0.03). The Shannon index was lower in IBS-D, but the difference was not statistically significant (p = 0.53). PCoA revealed a significant difference in the microflora structure between the IBS-D and HC groups.ConclusionThe mucosal metabolic profile and mucosal flora structure of the colon were similar, despite different locations in IBS and healthy subjects. IBS had abnormal colonic mucosal metabolism and flora disturbances

Exploratory Factor Analysis for Validating Traditional Chinese Syndrome Patterns of Chronic Atrophic Gastritis

Background. Traditional Chinese medicine (TCM) has long been used to treat chronic atrophic gastritis (CAG). The aim of the present study was to evaluate the TCM syndrome characteristics of CAG and its core pathogenesis so as to promote optimization of treatment strategies. Methods. This study was based on a participant survey conducted in 4 hospitals in China. Patients diagnosed with CAG were recruited by simple random sampling. Exploratory factor analysis (EFA) was conducted on syndrome extraction. Results. Common factors extracted were assigned to six syndrome patterns: qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, and yang deficiency. Distribution frequency of all syndrome patterns showed that qi deficiency, qi stagnation, blood stasis, phlegm turbidity, and heat excess were higher (76.7%–84.2%) compared with yang deficiency (42.5%). Distribution of main syndrome patterns showed that frequencies of qi deficiency, qi stagnation, phlegm turbidity, heat, and yang deficiency were higher (15.8%–20.8%) compared with blood stasis (8.3%). Conclusions. The core pathogenesis of CAG is combination of qi deficiency, qi stagnation, blood stasis, phlegm turbidity, heat, and yang deficiency. Therefore, treatment strategy of herbal prescriptions for CAG should include herbs that regulate qi, activate blood, resolve turbidity, clear heat, remove toxin, and warm yang

2018-2019 Dean\u27s Showcase No. 4

https://spiral.lynn.edu/conservatory_deansshowcase/1071/thumbnail.jp

2018-2019 Master Class - Emerson Quartet (Chamber Music)

https://spiral.lynn.edu/conservatory_masterclasses/1182/thumbnail.jp