Dynamic Analysis and Chaos of the 4D Fractional-Order Power System

Now dealing with power system became the most important arts. In this paper we report the dynamic analysis of a fractional-order power system with parameter Q1. To the best know of our knowledge, that was the first report about bifurcation analysis of the fractional order power system. So first we discuss the dynamic analysis with different fractional order and different parameters. Furthermore we will establish its numerical simulations which are provided to demonstrate the feasibility and efficacy of our analysis

Empirical insights into industrial policy’s influence on phytoprotection innovation

Intelligent Phytoprotection is an important direction for the modern development of plant protection related disciplines, and its essence is the innovative application of new generation information technology industry, high-end equipment manufacturing industry, and digital industry related technologies in the traditional plant protection field. This article first identifies 224 International Patent Classification (IPC) Main groups in the field of intelligent phytoprotection technology based on the International Patent Classification System. And then combines with China’s industrial policy practice, we explore the impact of industrial policy on the application number of invention patents in the field of intelligent phytoprotection technology using the Difference-in-difference (DID) method and the Synthetic DID method. The study results showed that the implementation of industrial policy can significantly promote the patent application activities in the intelligent phytoprotection treatment group, with an average increase of 517 invention patent applications compared to the control group that is not affected by the policy. The research conclusion of this article suggests that for countries and regions, industrial policies are an important tool for promoting the innovation and development of intelligent phytoprotection related technologies

Seasonality in the cross section of stock returns: Advanced markets versus emerging markets

We extend the studies of stock return seasonality by Heston and Sadka (2008, 2010) to a comprehensive sample of 42 international markets, including 21 advanced markets and 21 emerging markets. The empirical results show a large variation in stock seasonality across markets and suggest that this phenomenon exists primarily in advanced markets. A winner-loser portfolio approach shows that return seasonality is economically significant in advanced markets but not in emerging markets. We conduct statistical, rational and behavioral analyses to explore the potential reasons for the seasonality observed in advanced markets and find that regression bias, the January effect, and the Fama-French-Carhart type risk premium all can partially explain this seasonality difference

A Reputation-based Mechanism to Stimulate Cooperation in Wireless Sensor Networks

In wireless sensor networks, the sensor nodes need to collaborate with each other to transmit packets to the destination. However, some malicious nodes are not cooperative. The paper introduces a new reputation-based mechanism to stimulate nodes to forward packets for other nodes and enforce the security of the networks. All nodes are encouraged to maintain a good reputation so that their packets can be forwarded by other nodes, and a node will be isolated and punished if it acts maliciously. The impact of collisions and interference on nodes' reputation is reduced, and nodes can have chance to restore cooperation after being mistaken for the selfish ones. The low competitive nodes that do not have enough energy to help other nodes can also be treated well. While searching a route to the destination, the factors of reputation, remaining energy and the distance to the destination are taken into consideration. Simulation results show that our strategy can achieve relatively high throughput even when there are malicious nodes in the networks

A measurement model for real estate bubble size based on the panel data analysis : an empirical case study

Employing the fundamental value of real estate determined by the economic fundamentals, a measurement model for real estate bubble size is established based on the panel data analysis. Using this model, real estate bubble sizes in various regions in Japan in the late 1980s and in recent China are examined. Two panel models for Japan provide results, which are consistent with the reality in the 1980s where a commercial land price bubble appeared in most area and was much larger than that of residential land. This provides evidence of the reliability of our model, overcoming the limit of existing literature with this method. The same models for housing prices in China at both the provincial and city levels show that contrary to the concern of serious housing price bubble in China, over-valuing in recent China is much smaller than that in 1980s Japan.S1 File. Details of Data Sources.We acknowledge the support from the following research projects: ªA Study on Spatial Effects of Regional Systemic Financial Risks Led by Real Estate Market in Jiangsu Provinceº (15EYD004) from Social Science Scientific Fund of Jiangsu Province; ªA Study on Regional Systemic Financial Risks Led by Real Estate in Chinaº (2016M591948) from China Post-doctor Scientific Fund; ªMethodology of Functional Data Mining with Its Application in Financial Marketº (15YJCZH162) from Humanities and Social Science Foundation of Ministry of Education of China; ªSailing Planº from China University of Mining & Technology.http://www.plosone.orgam2017Electrical, Electronic and Computer Engineerin

CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation

To improve instance-level detection/segmentation performance, existing self-supervised and semi-supervised methods extract either task-unrelated or task-specific training signals from unlabeled data. We show that these two approaches, at the two extreme ends of the task-specificity spectrum, are suboptimal for the task performance. Utilizing too little task-specific training signals causes underfitting to the ground-truth labels of downstream tasks, while the opposite causes overfitting to the ground-truth labels. To this end, we propose a novel Class-Agnostic Semi-Supervised Learning (CA-SSL) framework to achieve a more favorable task-specificity balance in extracting training signals from unlabeled data. CA-SSL has three training stages that act on either ground-truth labels (labeled data) or pseudo labels (unlabeled data). This decoupling strategy avoids the complicated scheme in traditional SSL methods that balances the contributions from both data types. Especially, we introduce a warmup training stage to achieve a more optimal balance in task specificity by ignoring class information in the pseudo labels, while preserving localization training signals. As a result, our warmup model can better avoid underfitting/overfitting when fine-tuned on the ground-truth labels in detection and segmentation tasks. Using 3.6M unlabeled data, we achieve a significant performance gain of 4.7% over ImageNet-pretrained baseline on FCOS object detection. In addition, our warmup model demonstrates excellent transferability to other detection and segmentation frameworks.Comment: Appeared in ECCV202

A nomogram based on genotypic and clinicopathologic factors to predict the non-sentinel lymph node metastasis in Chinese women breast cancer patients

BackgroundSentinel lymph node biopsy (SLNB) is the standard treatment for breast cancer patients with clinically negative axilla. However, axillary lymph node dissection (ALND) is still the standard care for sentinel lymph node (SLN) positive patients. Clinical data reveals about 40-75% of patients without non-sentinel lymph node (NSLN) metastasis after ALND. Unnecessary ALND increases the risk of complications and detracts from quality of life. In this study, we expect to develop a nomogram based on genotypic and clinicopathologic factors to predict the risk of NSLN metastasis in SLN-positive Chinese women breast cancer patients.MethodsThis retrospective study collected data from 1,879 women breast cancer patients enrolled from multiple centers. Genotypic features contain 96 single nucleotide polymorphisms (SNPs) associated with breast cancer susceptibility, therapy and prognosis. SNP genotyping was identified by the quantitative PCR detection platform. The genetic features were divided into two clusters by the mutational stability. The normalized polygenic risk score (PRS) was used to evaluate the combined effect of each SNP cluster. Recursive feature elimination (RFE) based on linear discriminant analysis (LDA) was adopted to select the most useful predictive features, and RFE based on support vector machine (SVM) was used to reduce the number of SNPs. Multivariable logistic regression models (i.e., nomogram) were built for predicting NSLN metastasis. The predictive abilities of three types of model (based on only clinicopathologic information, the integrated clinicopathologic and all SNPs information, and integrated clinicopathologic and significant SNPs information) were compared. Internal and external validations were performed and the area under ROC curves (AUCs) as well as a series of evaluation indicators were assessed.Results229 patients underwent SLNB followed by ALND and without any neo-adjuvant therapy, 79 among them (34%) had a positive axillary NSLN metastasis. The LDA-RFE identified the characteristics including lymphovascular invasion, number of positive SLNs, number of negative SLNs and two SNP clusters as significant predictors of NSLN metastasis. Furthermore, the SVM-RFE selected 29 significant SNPs in the prediction of NSLN metastasis. In internal validation, the median AUCs of the clinical and all SNPs combining model, the clinical and 29 significant SNPs combining model, and the clinical model were 0.837, 0.795 and 0.708 respectively. Meanwhile, in external validation, the AUCs of the three models were 0.817, 0.815 and 0.745 respectively.ConclusionWe present a new nomogram by combining genotypic and clinicopathologic factors to achieve higher sensitivity and specificity comparing with traditional clinicopathologic factors to predict NSLN metastasis in Chinese women breast cancer. It is recommended that more validations are required in prospective studies among different patient populations

MiR-650 represses high-risk non-metastatic colorectal cancer progression via inhibition of AKT2/GSK3β/E-cadherin pathway

Although 5-year survival rate of non-metastatic colorectal cancer (CRC) is high, about 10% of patients in stage I and II still develop into metastatic CRC and eventually die after resection. Currently, there is no effective biomarker for predicting the prognosis of non-metastatic CRC in clinical practice. In this study, we identified miR-650 as a biomarker for prognosis prediction. We observed that the expression of miR-650 in tumor tissues had a positive association with overall survival. MiR-650 inhibited cell growth and invasion in vitro and in vivo. Furthermore, miR-650 targeted AKT2 and repressed the activation of the AKT pathway (AKT2/GSK3β/E-cadherin). Thus it induced the translocation of E-cadherin and β-catenin in cancer cells. Our results highlight the potential of miR-650 as a prognostic prediction biomarker and therapeutic target in non-metastatic CRC via inhibition of the AKT2/GSK3β/E-cadherin pathway

Policy Review: Addressing the Complex Challenges of Regulating Biotherapeutics

The advancing industry of biotherapeutics is providing the public with new promising and innovative drugs which may pose risks if their production, distribution, and marketing are not directly governed by legislation. Apart from international agreements, such as the Cartagena Protocol, there are no specific and direct laws or regulations governing manipulated cell-based therapeutics in Canada. The introduction of these laws and regulations in Canada will allow for the safe research and use of biotherapeutics in a proactive manner

Power scaling of high-power linearly polarized fiber lasers with <10 GHz linewidth

In this work, an all-fiberized polarization-maintained (PM) fiber laser has been demonstrated with a near-top-hat-shaped spectrum. By optimizing the modulation signal to generate near-top-hat-shaped spectrums, a 3-kW PM fiber laser has been achieved at &lt;10 GHz linewidth with the polarization extinction ratio of 96% and beam quality of 1.156, which is the highest output power ever reported with approximately 10 GHz linewidth, and further scaling of output power is limited by stimulated Brillouin scattering. By decomposing the mode content, the proportion of the fundamental mode in the output laser is above 97%. The stimulated Raman scattering suppression ratio reaches 62 dB at the maximal output power