Dynamic structure of stock communities: A comparative study between stock returns and turnover rates

The detection of community structure in stock market is of theoretical and practical significance for the study of financial dynamics and portfolio risk estimation. We here study the community structures in Chinese stock markets from the aspects of both price returns and turnover rates, by using a combination of the PMFG and infomap methods based on a distance matrix. We find that a few of the largest communities are composed of certain specific industry or conceptional sectors and the correlation inside a sector is generally larger than the correlation between different sectors. In comparison with returns, the community structure for turnover rates is more complex and the sector effect is relatively weaker. The financial dynamics is further studied by analyzing the community structures over five sub-periods. Sectors like banks, real estate, health care and New Shanghai take turns to compose a few of the largest communities for both returns and turnover rates in different sub-periods. Several specific sectors appear in the communities with different rank orders for the two time series even in the same sub-period. A comparison between the evolution of prices and turnover rates of stocks from these sectors is conducted to better understand their differences. We find that stock prices only had large changes around some important events while turnover rates surged after each of these events relevant to specific sectors, which may offer a possible explanation for the complexity of stock communities for turnover rates

Linking motion-induced blindness to perceptual filling-in

Abstract“Motion-induced blindness” and “perceptual filing-in” are two phenomena in which perceptually salient stimuli repeatedly disappear and reappear after prolonged viewing. Despite the many similarities between MIB and PFI, two differences suggest that they could be unrelated phenomena: (1) An area surrounded by background stimuli can be perceived to disappear completely in PFI but not in MIB and (2) high contrast stimuli are perceived to disappear less easily in PFI but, remarkably enough, more easily in MIB. In this article we show that the apparent differences between MIB and PFI disappear when eccentricity, contrast, and perceptual grouping are taken into account and that both are most likely caused by the same underlying mechanism

Isolation of AhDHNs from Arachis hypogaea L. and evaluation of AhDHNs expression under exogenous abscisic acid (ABA) and water stress

The peanut (Arachis hypogaea L.) is an important oil and cash crop all over the world. It is mostly planted in arid and semi-arid regions. To determine the mechanism by which dehydrins (DHNs) are regulated by abscisic acid (ABA) in peanuts, three Arachis hypogaea L. dehydrins (AhDHNs) were isolated from peanut plants and sequenced. By blasting the protein sequences of these AhDHNs, AhDHN1 was found belonging to the YnSKn subfamily. AhDHN2 and AhDHN3 were found belonging to the SKn and YnKn types, respectively. 100 μM ABA enhanced AhDHNs expression in peanut leaves. When peanut plants were treated with ABA and then with the ABA synthesis inhibitor sodium tungstate 12 h later, AhDHN expression was suppressed. However, AhDHN2 was inhibited by sodium tungstate at 2 h, though other AhDHNs were not. AhDHNs expressions increased greatly in peanut leaves treated with 30% polyethylene glycol (PEG). Sodium tungstate along with PEG inhibited the expression of AhDHNs. This study found that exogenous and endogenous ABA can both affect the expression of AhDHN independently. The differential expression of AhDHNs to exogenous ABA may be because of differences in the structure of different AhDHNs.Keywords: Arachis hypogaea L. dehydrins (AhDHNs), peanut, abscisic acid (ABA), expression, sodium tungstate, water stres

Sleep Apnea Detection Using Multi-Error-Reduction Classification System with Multiple Bio-Signals.

INTRODUCTION: Obstructive sleep apnea (OSA) can cause serious health problems such as hypertension or cardiovascular disease. The manual detection of apnea is a time-consuming task, and automatic diagnosis is much more desirable. The contribution of this work is to detect OSA using a multi-error-reduction (MER) classification system with multi-domain features from bio-signals. METHODS: Time-domain, frequency-domain, and non-linear analysis features are extracted from oxygen saturation (SaO2), ECG, airflow, thoracic, and abdominal signals. To analyse the significance of each feature, we design a two-stage feature selection. Stage 1 is the statistical analysis stage, and Stage 2 is the final feature subset selection stage using machine learning methods. In Stage 1, two statistical analyses (the one-way analysis of variance (ANOVA) and the rank-sum test) provide a list of the significance level of each kind of feature. Then, in Stage 2, the support vector machine (SVM) algorithm is used to select a final feature subset based on the significance list. Next, an MER classification system is constructed, which applies a stacking with a structure that consists of base learners and an artificial neural network (ANN) meta-learner. RESULTS: The Sleep Heart Health Study (SHHS) database is used to provide bio-signals. A total of 66 features are extracted. In the experiment that involves a duration parameter, 19 features are selected as the final feature subset because they provide a better and more stable performance. The SVM model shows good performance (accuracy = 81.68%, sensitivity = 97.05%, and specificity = 66.54%). It is also found that classifiers have poor performance when they predict normal events in less than 60 s. In the next experiment stage, the time-window segmentation method with a length of 60s is used. After the above two-stage feature selection procedure, 48 features are selected as the final feature subset that give good performance (accuracy = 90.80%, sensitivity = 93.95%, and specificity = 83.82%). To conduct the classification, Gradient Boosting, CatBoost, Light GBM, and XGBoost are used as base learners, and the ANN is used as the meta-learner. The performance of this MER classification system has the accuracy of 94.66%, the sensitivity of 96.37%, and the specificity of 90.83%

Technological Innovation: A Case Study of Mobile Internet Information Technology Applications in Community Management

The Mobile Internet Information Technology MIIT has been widely accepted as one of the most promising technologies in the next decades, having various applications and different value positions. However, few published studies explore and examine the effects of MIIT on community management. Based on the Dramaturgical Theory, this article uses a case study method to get an insightful understanding of MIIT. This article found that the MIIT was used by grid organizations to realize technological innovation and change organizational routines and structures, but eventually it was shaped by them, so this new technology was only able to embed itself into the public service model as a secondary or complementary role. Copyright: © 2018 IGA Globa

A new design methodology of highly reliable TFT based integrated circuits in display applications

Thin-film transistors (TFTs) technology is currently the dominant technology for pixel switching in display application. The new consumer electronics requires higher resolution and brightness, lower power consumption, multi-functional with new features such as flexible and foldable display. This drives TFT devices to deliver more complex functions. Owing to a sustained, enormous effort in TFT research and development and a continuous capital investment from the display industry around the world for the past three decades, the performance of TFT has not only surpassed the display requirements in most areas, but also go beyond the simply switch to more complex digital and analogue integrated circuits, for example, the flexible and narrow bezel displays integrated row drivers with TFT technology next to the pixel array. Such integrated circuits comprise thousands of switches operating together, requires an accurate analysis during design. In the recent years, new display technologies, such as organic light-emitting diode (OLED) display and light-emitting diode (LED) displays have been emerging and become commercial reality due to certain advantages like self-luminous, high contrast, and etc. However, the OLED device has relative shorted lifetime and the current driving TFTs typically suffer from the electrical instability issue under high temperature and long-time stress condition. Thus, the reliability concerns in display have generated a considerable number of experimental studies and require careful analysis for the design of its pixel and integrated drivers. Particularly, individual TFTs are exposed to various stress condition in display operation with different degradation such as threshold voltage shift (ΔVth) or mobility (μ) decreasing result in a failure of display operation, given that the performance of an aging TFT might deviate from expectation of original design, and moreover, it might influence its neighboring TFTs. Traditional design method considering device performance variation and device-level aging approach of ΔVth and μ may not appropriate given that the traditional design of display pixel and driver circuit did not consider the evolutionary effects to each TFTs and different aging rate under various stress condition. Please click Additional Files below to see the full abstract