Interpreting What Typical Fault Signals Look Like via Prototype-matching

Neural networks, with powerful nonlinear mapping and classification capabilities, are widely applied in mechanical fault diagnosis to ensure safety. However, being typical black-box models, their application is limited in high-reliability-required scenarios. To understand the classification logic and explain what typical fault signals look like, the prototype matching network (PMN) is proposed by combining the human-inherent prototype-matching with autoencoder (AE). The PMN matches AE-extracted feature with each prototype and selects the most similar prototype as the prediction result. It has three interpreting paths on classification logic, fault prototypes, and matching contributions. Conventional diagnosis and domain generalization experiments demonstrate its competitive diagnostic performance and distinguished advantages in representation learning. Besides, the learned typical fault signals (i.e., sample-level prototypes) showcase the ability for denoising and extracting subtle key features that experts find challenging to capture. This ability broadens human understanding and provides a promising solution from interpretability research to AI-for-Science.Comment: 17 pages, 12 figures, 6 table

Key Interplay between the Co-Opted Sorting Nexin-BAR Proteins and PI3P Phosphoinositide in the Formation of the Tombusvirus Replicase

Positive-strand RNA viruses replicate in host cells by forming large viral replication organelles, which harbor numerous membrane-bound viral replicase complexes (VRCs). In spite of its essential role in viral replication, the biogenesis of the VRCs is not fully understood. The authors identified critical roles of cellular membrane-shaping proteins and PI(3)P (phosphatidylinositol 3-phosphate) phosphoinositide, a minor lipid with key functions in endosomal vesicle trafficking and autophagosome biogenesis, in VRC formation for tomato bushy stunt virus (TBSV). The authors show that TBSV co-opts the endosomal SNX-BAR (sorting nexin with Bin/Amphiphysin/Rvs- BAR domain) proteins, which bind to PI(3)P and have membrane-reshaping function during retromer tubular vesicle formation, directly into the VRCs to boost progeny viral RNA synthesis. We find that the viral replication protein-guided recruitment and pro-viral function of the SNX-BAR proteins depends on enrichment of PI(3)P at the site of viral replication. Depletion of SNX-BAR proteins or PI(3)P renders the viral double-stranded (ds)RNA replication intermediate RNAi-sensitive within the VRCs in the surrogate host yeast and in planta and ribonuclease-sensitive in cell-free replicase reconstitution assays in yeast cell extracts or giant unilamellar vesicles (GUVs). Based on our results, we propose that PI(3)P and the co-opted SNX-BAR proteins are coordinately exploited by tombusviruses to promote VRC formation and to play structural roles and stabilize the VRCs during viral replication. Altogether, the interplay between the co-opted SNX-BAR membrane-shaping proteins, PI(3)P and the viral replication proteins leads to stable VRCs, which provide the essential protection of the viral RNAs against the host antiviral responses

Does institutional quality affect the level of entrepreneurial success differently across the entrepreneurship distribution?

The phenomenon of entrepreneurship has driven much of the attention of academics, practitioners and policy makers. A correct and deep understanding of all the different conditions affecting entrepreneurship rates will advance in the establishment of useful measures that increase entrepreneurial success. The vast majority of the literature on the effect of institutional quality on entrepreneurship has been investigated based on average effects. However, how the impact of institutional quality on the level of entrepreneurship varies with the conditional distribution of entrepreneurship is still poorly understood. The present study attempts to fill the research gaps in this field. In order to examine the impact of institutional quality on entrepreneurship at different entrepreneurship levels, the fuzzy set qualitative comparative analysis approach is employed. Results show that entrepreneurial success is determined by the combination of the following conditions of institutional quality: voice accountability, political stability, regulatory quality and rule of law. Implications and future research directions are discussed. It would be interesting to analyze in future studies the motivation to become an entrepreneur, both theoretically and empirically, differentiating between opportunity and necessity entrepreneurship

Efficient Commercial Bank Customer Credit Risk Assessment Based on LightGBM and Feature Engineering

Effective control of credit risk is a key link in the steady operation of commercial banks. This paper is mainly based on the customer information dataset of a foreign commercial bank in Kaggle, and we use LightGBM algorithm to build a classifier to classify customers, to help the bank judge the possibility of customer credit default. This paper mainly deals with characteristic engineering, such as missing value processing, coding, imbalanced samples, etc., which greatly improves the machine learning effect. The main innovation of this paper is to construct new feature attributes on the basis of the original dataset so that the accuracy of the classifier reaches 0.734, and the AUC reaches 0.772, which is more than many classifiers based on the same dataset. The model can provide some reference for commercial banks' credit granting, and also provide some feature processing ideas for other similar studies

A Quantitative Study of the Hog1 MAPK Response to Fluctuating Osmotic Stress in Saccharomyces cerevisiae

Background Yeast cells live in a highly fluctuating environment with respect to temperature, nutrients, and especially osmolarity. The Hog1 mitogen-activated protein kinase (MAPK) pathway is crucial for the adaption of yeast cells to external osmotic changes. Methodology/Principal Findings To better understand the osmo-adaption mechanism in the budding yeast Saccharomyces cerevisiae, we have developed a mathematical model and quantitatively investigated the Hog1 response to osmotic stress. The model agrees well with various experimental data for the Hog1 response to different types of osmotic changes. Kinetic analyses of the model indicate that budding yeast cells have evolved to protect themselves economically: while they show almost no response to fast pulse-like changes of osmolarity, they respond periodically and are well-adapted to osmotic changes with a certain frequency. To quantify the signal transduction efficiency of the osmo-adaption network, we introduced a measure of the signal response gain, which is defined as the ratio of output change integral to input (signal) change integral. Model simulations indicate that the Hog1 response gain shows bell-shaped response curves with respect to the duration of a single osmotic pulse and to the frequency of periodic square osmotic pulses, while for up-staircase (ramp) osmotic changes, the gain depends on the slope. Conclusions/Significance The model analyses suggest that budding yeast cells have selectively evolved to be optimized to some specific types of osmotic changes. In addition, our work implies that the signaling output can be dynamically controlled by fine-tuning the signal input profiles

SBML-SAT: a systems biology markup language (SBML) based sensitivity analysis tool

<p>Abstract</p> <p>Background</p> <p>It has long been recognized that sensitivity analysis plays a key role in modeling and analyzing cellular and biochemical processes. Systems biology markup language (SBML) has become a well-known platform for coding and sharing mathematical models of such processes. However, current SBML compatible software tools are limited in their ability to perform global sensitivity analyses of these models.</p> <p>Results</p> <p>This work introduces a freely downloadable, software package, SBML-SAT, which implements algorithms for simulation, steady state analysis, robustness analysis and local and global sensitivity analysis for SBML models. This software tool extends current capabilities through its execution of global sensitivity analyses using multi-parametric sensitivity analysis, partial rank correlation coefficient, SOBOL's method, and weighted average of local sensitivity analyses in addition to its ability to handle systems with discontinuous events and intuitive graphical user interface.</p> <p>Conclusion</p> <p>SBML-SAT provides the community of systems biologists a new tool for the analysis of their SBML models of biochemical and cellular processes.</p

Forward and backward motion control of a vibro-impact capsule system.

A capsule system driven by a harmonic force applied to its inner mass is considered in this study. Four various friction models are employed to describe motion of the capsule in different environments taking into account Coulomb friction, viscous damping, Stribeck effect, pre-sliding, and frictional memory. The non-linear dynamics analysis has been conducted to identify the optimal amplitude and frequency of the applied force in order to achieve the motion in the required direction and to maximize its speed. In addition, a position feedback control method suitable for dealing with chaos control and coexisting attractors is applied for enhancing the desirable forward and backward capsule motion. The evolution of basins of attraction under control gain variation is presented and it is shown that the basin of the desired attractors could be significantly enlarged by slight adjustment of the control gain improving the probability of reaching such an attractor

Isolation, identification and Analysis of Drug Resistance of Salmonella Pul-lorum

The article provides data on the isolation and identification of the pathogen S.&nbsp;Pullorum from pathological material of chickens. With further study of tinctorial, cultural-morphological and biological properties of the pathogen. The susceptibility of Salmonella pullorum to broad-spectrum antibacterial drugs such as cephalosporins and carbopenems was further studied to determine the drug of choice for improving treatment and prevention of avian bacterial diseases. In order to diagnose Salmonella pullorum (S. Pullorum) diarrhea accurately and analyze its drug resistance. In this study, the pathogen of a chicken suspected of S. Pullorum was isolation, PCR amplification and drug sensitivity analysis of the pathogen from in chicken farm in Xinxiang, north China. The results showed that the bacteria strain was diagnosed as S. Pullorum base on isolation and identification, Gram staining and biochemical identification of the bacteria. Antibacterial drugs sensitivity test confirmed that the bacteria was sensitive to ceftiofur, ceftriaxone, meropenem and kanamycin, and the effect of sensitive antibiotics was obvious in clinical treatment. Altogether, the present experiment revealed a detailed measure for S. Pullorum prevention and control and that achieved good clinical results, which laid a fundamental information for farmers and veterinary workers on eradication of S. Pullorum