Quantifying immediate price impact of trades based on the kk-shell decomposition of stock trading networks

Traders in a stock market exchange stock shares and form a stock trading network. Trades at different positions of the stock trading network may contain different information. We construct stock trading networks based on the limit order book data and classify traders into $k$ classes using the $k$-shell decomposition method. We investigate the influences of trading behaviors on the price impact by comparing a closed national market (A-shares) with an international market (B-shares), individuals and institutions, partially filled and filled trades, buyer-initiated and seller-initiated trades, and trades at different positions of a trading network. Institutional traders professionally use some trading strategies to reduce the price impact and individuals at the same positions in the trading network have a higher price impact than institutions. We also find that trades in the core have higher price impacts than those in the peripheral shell.Comment: 6 pages including 3 figures and 1 tabl

Novel potential therapeutic targets of alopecia areata

Alopecia areata (AA) is a non-scarring hair loss disorder caused by autoimmunity. The immune collapse of the hair follicle, where interferon-gamma (IFN-γ) and CD8+ T cells accumulate, is a key factor in AA. However, the exact functional mechanism remains unclear. Therefore, AA treatment has poor efficacy maintenance and high relapse rate after drug withdrawal. Recent studies show that immune-related cells and molecules affect AA. These cells communicate through autocrine and paracrine signals. Various cytokines, chemokines and growth factors mediate this crosstalk. In addition, adipose-derived stem cells (ADSCs), gut microbiota, hair follicle melanocytes, non-coding RNAs and specific regulatory factors have crucial roles in intercellular communication without a clear cause, suggesting potential new targets for AA therapy. This review discusses the latest research on the possible pathogenesis and therapeutic targets of AA

Identification CCL2,CXCR2,S100A9 of the immune-related gene markers and immune infiltration characteristics of inflammatory bowel disease and heart failure via bioinformatics analysis and machine learning

BackgroundRecently, heart failure (HF) and inflammatory bowel disease (IBD) have been considered to be related diseases with increasing incidence rates; both diseases are related to immunity. This study aims to analyze and identify immune-related gene (IRG) markers of HF and IBD through bioinformatics and machine learning (ML) methods and to explore their immune infiltration characteristics.MethodsThis study used gene expressiondata (GSE120895, GSE21610, GSE4183) from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs) and compare them with IRGs from the ImmPort database to obtain differentially expressed immune-related genes (DIRGs). Functional enrichment analysis of IRGs was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, three machine models and protein–protein interactions (PPIs) were established to identify diagnostic biomarkers. The receiver operating characteristic (ROC) curves were applied to evaluate the diagnostic value of the candidate biomarkersin the validation set (GSE1145, GSE36807) and obtain their correlations with immune cells through the Spearman algorithm. Finally, the CIBERSORT algorithm was used to evaluate the immune cell infiltration of the two diseases.ResultsThirty-four DIRGs were screened and GO and KEGG analysis results showed that these genes are mainly related to inflammatory and immune responses. CCL2, CXCR2 and S100A9 were identified as biomarkers.The immune correlation results indicated in both diseases that CCL2 is positively correlated with mast cell activation, CXCR2 is positively correlated with neutrophils and S100A9 is positively correlated with neutrophils and mast cell activation. Analysis of immune characteristics showed that macrophages M2, macrophages M0 and neutrophils were present in both diseases.ConclusionsCCL2, CXCR2 and S100A9 are promising biomarkers that will become potential immunogenetic biomarkers for diagnosing comorbidities of HF and IBD. macrophages M2, macrophages M0, neutrophil-mediated inflammation and immune regulation play important roles in the development of HF and IBD and may become diagnostic and therapeutic targets

Living in a Simulation? An Empirical Investigation of a Smart Driving-Simulation Testing System

The internet of things (IoT) generally refers to the embedding of computing and communication devices in various types of physical objects (e.g., automobiles) used in people’s daily lives. This paper draws on feedback intervention theory to investigate the impact of IoT-enabled immediate feedback interventions on individual task performance. Our research context is a smart test-simulation service based on internet-of-vehicles (IoV) technology that was implemented by a large driver-training service provider in China. This system captures and analyzes data streams from onboard sensors and cameras installed in vehicles in real time and immediately provides individual students with information about errors made during simulation tests. We postulate that the focal smart service functions as a feedback intervention (FI) that can improve task performance. We also hypothesize that student training schedules moderate this effect and propose an interaction effect on student performance based on feedback timing and the number of FI cues. We collected data about students’ demographics, their training session records, and information about their simulation test(s) and/or their official driving skills field tests and used a quasi-experimental method along with propensity score matching to empirically validate our research model. Difference-in-difference analysis and multiple regression results support the significant impact of the simulation test as an FI on student performance on the official driving skills field test. Our results also supported the interaction effect between feedback timing and the number of corrective FI cues on official test performance. This paper concludes with a discussion of the theoretical contributions and practical significance of our research

Finite element modelling of a reflection differential split-D Eddy current probe scanning surface notches

Differential eddy current probes are commonly used to detect shallow surface cracks in conductive materials. In recent years, a growing number of research works on their numerical modelling was conducted since the development of analytical or semi-analytical models for such a sensor may be prone to intractable complications. In this paper finite element modelling (FEM) has been employed to simulate the interaction of a reflection differential split-D probe with surface electrical discharge machined (EDM) notches in 3 dimensional (3-D) half space. In order to attain a better insight into the correct setup of the FEM parameters, a simple multi-turn cylindrical absolute coil has also been modelled. The outcome generated through the simulated scan of this absolute coil over a surface notch in aluminum is validated with existing experimental impedance data taken from the literature. Parameters contributing to reliable FEM simulation results, such as maximum mesh size, mesh distribution, extent of the surrounding air domain and conductivity of the air are investigated for the 3-D modelling of both absolute and differential probes. This study shows that the simulation results on a commercial reflection differential split-D surface pencil probe closely estimate the experimental measurements of the probe’s impedance variations as it scans three EDM notches having different depths in aluminum. The simulation results, generated by Comsol Multiphysics FEM package, for the cases of absolute and differential probes are checked for their extent of validity