Development genetic and stability classification of seasonal glacial lakes in a tectonically active area—A case study in Niangmuco, east margin of the Eastern Himalayan Syntaxis

The Niangmuco region on the east margin of the Eastern Himalayan Syntaxis features a large number of glacial lakes. The development process and stability classification of glacial lakes is of great significance to the study of seasonal glaciers in the eastern Himalayan margin, with implications for economic development and disaster prevention. Based on Landsat remote sensing image data from 2000 to 2021, this study analyzed the development and change characteristics of glacial lakes in the Niangmuco region during the past 21 years, and classified the stability of lakes with areas >0.02 km2 using the fuzzy consistent matrix method. In this area, 126 glacial lakes were identified within an elevation range of 3044–4156 m with a total area of 10.94 km2. These lakes primarily included glacial erosion lakes, valley lakes, tectonic lakes, and landslide dam lakes. Specifically, glacial erosion lakes accounted for 88.9% of the total number of lakes and 60.3% of the total lake area, followed by valley lakes with 6.3% and 23.7%, respectively. From 2000 to 2010, the total area of glacial lakes decreased from 10.53 km2 to 10.09 km2, which may be attributable to climate fluctuations. Subsequently, the area of lakes increased significantly to 10.94 km2 in 2021, an increase of 0.41 km2. Compared with 2000, among the lakes with a growth rate of 0.019 km2/a in 21 years, glacial erosion lakes exhibited the largest change. Among the classified glacial lakes in the study area, 95.7% were stable and relatively stable, mostly comprising glacial erosion lakes at high altitudes between 3468 and 4156 m. Only 4 unstable and extremely unstable glacial lakes were identified, and they were located near a fault zone. The findings show that the development and the change of glacial lakes in the area are primarily controlled by temperature and precipitation, and the topography and fault activity have important influences on the stability of glacial lakes

Nonlinear Modeling and Analysis of Pressure Wave inside CEUP Fuel Pipeline

Operating conditions dependent large pressure variations are one of the working characteristics of combination electronic unit pump (CEUP) fuel injection system for diesel engines. We propose a precise and accurate nonlinear numerical model of pressure inside HP fuel pipeline of CEUP using wave equation (WE) including both viscous and frequency dependent frictions. We have proved that developed hyperbolic approximation gives more realistic description of pressure wave as compared to classical viscous damped wave equation. Frictional effects of various frequencies on pressure wave have been averaged out across valid frequencies to represent the combined effect of all frequencies on pressure wave. Dynamic variations of key fuel properties including density, acoustic wave speed, and bulk modulus with varying pressures have also been incorporated. Based on developed model we present analysis on effect of fuel pipeline length on pressure wave propagation and variation of key fuel properties with both conventional diesel and alternate fuel rapeseed methyl ester (RME) for CEUP pipeline

Expression profiling of the RPE in zebrafish smarca4 mutant revealed altered signals that potentially affect RPE and retinal differentiation

Purpose The purpose of this study was to develop a framework for analyzing retinal pigment epithelium (RPE) expression profiles from zebrafish eye mutants. Methods: The fish model we used was SWI/SNF-related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 (smarca4), a retinal dystrophic mutant with a previously described retinal phenotype and expression profiles. Histological and Affymetrix GeneChip analyses were conducted to characterize the RPE defects and underlying differential expression, respectively. Results: Histological analysis revealed that smarca4 RPE was formed, but its differentiation was abnormal. In particular, ultrastructural analysis of smarca4 RPE by transmission electron microscopy demonstrated several defects in melanogenesis. The nature of these defects also suggests that the cytoskeletal dynamics, which are tightly linked with melanogenesis, were impaired in smarca4 RPE. To compare the expression profile of normal wild-type (WT) and smarca4 RPE, the gene expression profiles of microdissected retinas and RPE-attached retinas were measured with Affymetrix GeneChip analysis. The RPE expression values were then estimated from these samples by subtracting the retinal expression values from the expression values of the RPE-attached retinas. A factorial analysis was conducted using the expression values of the RPE, retinal, and whole-embryo samples. Specific rules (contrasts) were built using the coefficients of the resulting fitted models to select for three groups of genes: 1) smarca4-regulated RPE genes, 2) smarca4-regulated retinal genes, and 3) smarca4-regulated RPE genes that are not differentially expressed in the retina. Interestingly, the third group consists of 39 genes that are highly related to cytoskeletal dynamics, melanogenesis, and paracrine and intracellular signal transduction. Conclusions: Our analytical framework provides an experimental approach to identify differentially-regulated genes in the retina and the RPE of zebrafish mutants in which both of these tissues are affected by the underlying mutation. Specifically, we have used the method to identify a group of 39 genes that can potentially explain the melanogenesis defect in the smarca4 RPE. In addition, several genes in this group are secreted signaling molecules. Thus, this observation further implicates that the smarca4 RPE might play a role in the retinal dystrophic phenotype in smarca4

ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification

Despite achieving state-of-the-art performance, deep learning methods generally require a large amount of labeled data during training and may suffer from overfitting when the sample size is small. To ensure good generalizability of deep networks under small sample sizes, learning discriminative features is crucial. To this end, several loss functions have been proposed to encourage large intra-class compactness and inter-class separability. In this paper, we propose to enhance the discriminative power of features from a new perspective by introducing a novel neural network termed Relation-and-Margin learning Network (ReMarNet). Our method assembles two networks of different backbones so as to learn the features that can perform excellently in both of the aforementioned two classification mechanisms. Specifically, a relation network is used to learn the features that can support classification based on the similarity between a sample and a class prototype; at the meantime, a fully connected network with the cross entropy loss is used for classification via the decision boundary. Experiments on four image datasets demonstrate that our approach is effective in learning discriminative features from a small set of labeled samples and achieves competitive performance against state-of-the-art methods. Codes are available at https://github.com/liyunyu08/ReMarNet.Comment: IEEE TCSVT 202

Investigation on Electromagnetic Models of High-Speed Solenoid Valve for Common Rail Injector

A novel formula easily applied with high precision is proposed in this paper to fit the B-H curve of soft magnetic materials, and it is validated by comparison with predicted and experimental results. It can accurately describe the nonlinear magnetization process and magnetic saturation characteristics of soft magnetic materials. Based on the electromagnetic transient coupling principle, an electromagnetic mathematical model of a high-speed solenoid valve (HSV) is developed in Fortran language that takes the saturation phenomena of the electromagnetic force into consideration. The accuracy of the model is validated by the comparison of the simulated and experimental static electromagnetic forces. Through experiment, it is concluded that the increase of the drive current is conducive to improving the electromagnetic energy conversion efficiency of the HSV at a low drive current, but it has little effect at a high drive current. Through simulation, it is discovered that the electromagnetic energy conversion characteristics of the HSV are affected by the drive current and the total reluctance, consisting of the gap reluctance and the reluctance of the iron core and armature soft magnetic materials. These two influence factors, within the scope of the different drive currents, have different contribution rates to the electromagnetic energy conversion efficiency

Extracting lignin-SiO2 composites from Si-rich biomass to prepare Si/C anode materials for lithium ions batteries

The comprehensive utilization of Si-rich biomass is restrained by macromolecular lignin and a large amount of ash. In this study, rice husks (RHs) are treated as a representative by alkali extraction and acid precipitation, and the obtained lignin-SiO2 composite is modified by carbonazation, ball milling, magnesiothermic reduction and additives. Through these processes, a Si/C composite with excellent electrochemical properties is obtained and performs stable cycling performance with high specific capacity retention of 572 mA h g−1 at 1 A g−1 after 1000 cycles. This introduced method provides a potential for utilizing Si-rich biomass comprehensively and preparing desirable Si/C anode materials from Si-rich biomass derived lignin-SiO2 composites

An efficient disposable and flexible electrochemical sensor based on a novel and stable metal carbon composite derived from cocoon silk

Abstract(#br)The present work reports cocoon silk fibroin (SF)as a unique precursor for the in-situ fabrication of well-engineered, stable and leach free gold nanoparticle doped carbonaceous materials (AuNPs@NSC). In principle, at the molecular level, SF has a singular structure that can be converted to a N-doped aromatic carbon structure by heat treatment. The electrochemical properties of the prepared nanocomposite were examined by cyclic voltammetry and differential pulse voltammetry. A flexible three electrode sensor system with AuNPs@NSC-modified working electrodes has been developed, to achieve easy operation and quick and accurate responses. The electrochemical results showed that the sensor made by the AuNPs@NSC-modified working electrode demonstrated high sensitivity for the detection of rutin, which is attributed to the good distribution of the AuNPs on the carbon matrix. Using differential pulse voltammetry (DPV), the AuNPs@NSC electrode was found to have a linear response in the range of 0.11–250 μM and a comparably low limit of detection of 0.02 μM (S/N = 3). To ensure the accuracy and applicability of the sensors, the concentration of rutin in the commodity (rutin capsule, 10 mg/capsule) was examined, and the sensor provided high precision with a minimum relative error (RE) of 3.3%. These findings suggest that AuNPs@NSC can be considered to be a potential electrode material for the development of electrochemical devices and has great potential in extending their application to the flexible sensor field