The Minimum Rank of Sign Pattern Matrices with a 1-Separation

Given a sign pattern matrix M composed of two sub-patterns A and B connected by a 1-separation, we provide a formula that relates the minimum rank of M to the minimum rank of some small variations of A and B

How Service Guarantee Induces Customer Opportunism Behavior in Online Environment —The Moderating Role of Customers\u27 Personal Characteristics and Reference Group’s Relationship Strength

On the internet, the enterprise provides service guarantee, such as return without reason in seven days , to reduce the perceived risk of online customers effectively. Meanwhile, such service guarantee leads some customer opportunistic behavior. Taking the customers\u27 personal characteristics and reference group’s relationship strength as moderator variables, we conduct an empirical research to study the major factor and it’s effect paths on customer opportunistic behavior by using the scenario role-playing approach. The result shows that higher service guarantee is more likely to induce customer opportunism behavior. And customers’ personality (Machiavellianism) has nothing to do with the relationship. On the contrary, the relationship strength has a significantly moderating role in the impact of service guarantee strength on customers’ opportunistic behavior. Knowing friends of strong relationship have opportunistic behaviors, customer is more likely to choose the similar behavior when they face the higher service guarantee

Comprehensive Benefit Evaluation and Regional Difference Research of State-owned Mixed-ownership Enterprises

Scientific and accurate assessment of the comprehensive benefits and regional differences of state-owned mixed-owned enterprises is an important link in promoting the steady progress of mixed-ownership reform of state-owned enterprises and achieving high-quality development of the state-owned economy. Using factor analysis method and GA-BP neural network model to evaluate the comprehensive benefits of listed state-owned mixed enterprises, the results show that, first, the main factors affecting the comprehensive benefits of state-owned mixed enterprises are profitability factor, sustainability factor and solvency factor, and the effect is as follows: profitability factor\u3esustainability factor\u3esolvency factor; second, factor analysis effectively improves the evaluation performance of the GA-BP neural network model. Further analysis of regional differences, it is found that the average comprehensive benefit of regional state-owned mixed enterprises is: eastern region \u3e central region \u3e western region. Among them, the profitability factor and solvency factor are as follows: eastern region\u3ecentral region\u3ewestern region, and the size of sustainability factor is: eastern region\u3ewestern region\u3ecentral region. Therefore, we can start from deepening the classification reform of state-owned enterprises, increasing investment in innovation, reducing financial leverage, and improving regional linkages, so as to improve the profitability, solvency and sustainability of enterprises, narrow regional differences, and achieve coordinated development between regions

Genetic Evolution and Molecular Selection of the HE Gene of Influenza C Virus

Influenza C virus (ICV) was first identified in humans and swine, but recently also in cattle, indicating a wider host range and potential threat to both the livestock industry and public health than was originally anticipated. The ICV hemagglutinin-esterase (HE) glycoprotein has multiple functions in the viral replication cycle and is the major determinant of antigenicity. Here, we developed a comparative approach integrating genetics, molecular selection analysis, and structural biology to identify the codon usage and adaptive evolution of ICV. We show that ICV can be classified into six lineages, consistent with previous studies. The HE gene has a low codon usage bias, which may facilitate ICV replication by reducing competition during evolution. Natural selection, dinucleotide composition, and mutation pressure shape the codon usage patterns of the ICV HE gene, with natural selection being the most important factor. Codon adaptation index (CAI) and relative codon deoptimization index (RCDI) analysis revealed that the greatest adaption of ICV was to humans, followed by cattle and swine. Additionally, similarity index (SiD) analysis revealed that swine exerted a stronger evolutionary pressure on ICV than humans, which is considered the primary reservoir. Furthermore, a similar tendency was also observed in the M gene. Of note, we found HE residues 176, 194, and 198 to be under positive selection, which may be the result of escape from antibody responses. Our study provides useful information on the genetic evolution of ICV from a new perspective that can help devise prevention and control strategies

The Noninvasive Measurement of Central Aortic Blood Pressure Waveform

Central aortic pressure (CAP) is a potential surrogate of brachial blood pressure in both clinical practice and routine health screening. It directly reflects the status of the central aorta. Noninvasive measurement of CAP becomes a crucial technique of great interest. There have been advances in recent years, including the proposal of novel methods and commercialization of several instruments. This chapter briefly introduces the clinical importance of CAP and the theoretical basis for the generation of CAP in the first and second sections. The third section describes and discusses the measurement of peripheral blood pressure waveforms, which is employed to estimate CAP. We then review the proposed methods for the measurement of CAP. The calibration of blood pressure waveforms is discussed in the fourth section. After a brief discussion of the technical limitations, we give suggestions for perspectives and future challenges

Auto-Focus Contrastive Learning for Image Manipulation Detection

Generally, current image manipulation detection models are simply built on manipulation traces. However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy information within the entire image, and 2) ignore the trace relations among the pixels of each manipulated region and its surroundings. To overcome these limitations, we propose an Auto-Focus Contrastive Learning (AF-CL) network for image manipulation detection. It contains two main ideas, i.e., multi-scale view generation (MSVG) and trace relation modeling (TRM). Specifically, MSVG aims to generate a pair of views, each of which contains the manipulated region and its surroundings at a different scale, while TRM plays a role in modeling the trace relations among the pixels of each manipulated region and its surroundings for learning the discriminative representation. After learning the AF-CL network by minimizing the distance between the representations of corresponding views, the learned network is able to automatically focus on the manipulated region and its surroundings and sufficiently explore their trace relations for accurate manipulation detection. Extensive experiments demonstrate that, compared to the state-of-the-arts, AF-CL provides significant performance improvements, i.e., up to 2.5%, 7.5%, and 0.8% F1 score, on CAISA, NIST, and Coverage datasets, respectively

Study on Tensile Properties of Nanoreinforced Epoxy Polymer: Macroscopic Experiments and Nanoscale FEM Simulation Prediction

The effect of nanosilica contents on mechanical properties of the epoxy matrix with some nanoparticle aggregations was studied in macroscopic experiments and nanoscale simulation, particularly with regard to the effective modulus and ultimate stress. Three analytical models were used to obtain the effective elastic modulus of nanoparticle-reinforced composites. Based on Monte-Carlo method, the special program for the automatic generation of 2D random distribution particles without overlapping was developed for nanocomposite modeling. Weight fractions of nanoparticles were converted to volume fractions, in order to coordinate the content unit in the simulation. In numerical analysis, the weak interface strengthening and toughening mechanism was adopted. Virtual crack closure technique (VCCT) and extended finite element method (XFEM) were used to simulate phenomena of nanoparticle debonding and matrix crack growth. Experimental and simulation results show a good agreement with each other. By way of simulation, the weak interface toughening and strengthening mechanism of nanocomposites is confirmed