Research on dynamic robust planning method for active distribution network considering correlation

The universality of load subjects in distribution network brings challenges to the reliability of distribution network planning results. In this paper, a two-stage dynamic robust distribution network planning method considering correlation is proposed. The method evaluates the correlation between random variables using the Spearman rank correlation coefficient, and converts the correlated random variables into mutually independent random variables by Cholesky decomposition and independent transformation; expresses the source-load uncertainty by a bounded interval without distribution, and describes the active distribution network planning as a dynamic zero-sum game problem by combining with the two-phase dynamic robust planning; use the Benders decomposition approach to tackle the issue; mathematical simulation is used to confirm the accuracy and efficacy of the method. The results show that the dynamic robustness planning method of active distribution network taking into account the correlation can accurately simulate the operation of active distribution network with uncertain boundaries, which enhances the reliability and economy of the active distribution network planning results

Attenuation of epigenetic regulator SMARCA4 and ERK-ETS signaling suppresses aging-related dopaminergic degeneration

How complex interactions of genetic, environmental factors and aging jointly contribute to dopaminergic degeneration in Parkinson's disease (PD) is largely unclear. Here, we applied frequent gene co‐expression analysis on human patient substantia nigra‐specific microarray datasets to identify potential novel disease‐related genes. In vivo Drosophila studies validated two of 32 candidate genes, a chromatin‐remodeling factor SMARCA4 and a biliverdin reductase BLVRA. Inhibition of SMARCA4 was able to prevent aging‐dependent dopaminergic degeneration not only caused by overexpression of BLVRA but also in four most common Drosophila PD models. Furthermore, down‐regulation of SMARCA4 specifically in the dopaminergic neurons prevented shortening of life span caused by α‐synuclein and LRRK2. Mechanistically, aberrant SMARCA4 and BLVRA converged on elevated ERK‐ETS activity, attenuation of which by either genetic or pharmacological manipulation effectively suppressed dopaminergic degeneration in Drosophila in vivo. Down‐regulation of SMARCA4 or drug inhibition of MEK/ERK also mitigated mitochondrial defects in PINK1 (a PD‐associated gene)‐deficient human cells. Our findings underscore the important role of epigenetic regulators and implicate a common signaling axis for therapeutic intervention in normal aging and a broad range of age‐related disorders including PD

Fracture behavior and self-sharpening mechanisms of polycrystalline cubic boron nitride in grinding based on cohesive element method

Unlike monocrystalline cubic boron nitride (CBN), polycrystalline CBN (PCBN) shows not only higher fracture resistance induced by tool-workpiece interaction but also better self-sharpening capability; therefore, efforts have been devoted to the study of PCBN applications in manufacturing engineering. Most of the studies, however, remain qualitative due to difficulties in experimental observations and theoretical modeling and provide limited in-depth understanding of the self-sharpening behavior/mechanism. To fill this research gap, the present study investigates the self-sharpening process of PCBN abrasives in grinding and analyzes the macro-scale fracture behavior and highly localized micro-scale crack propagation in detail. The widely employed finite element (FE) method, together with the classic Voronoi diagram and cohesive element technique, is used considering the pronounced success of FE applications in polycrystalline material modeling. Grinding trials with careful observation of the PCBN abrasive morphologies are performed to validate the proposed method. The self-sharpening details, including fracture morphology, grinding force, strain energy, and damage dissipation energy, are studied. The effects of maximum grain cut depths (MGCDs) and grinding speeds on the PCBN fracture behavior are discussed, and their optimum ranges for preferable PCBN self-sharpening performance are suggested

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A Deep Multiview Active Learning for Large-Scale Image Classification

Multiview active learning (MAL) is a technique which can achieve a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. In this paper, we present a new deep multiview active learning (DMAL) framework which is the first to combine multiview active learning and deep learning for annotation effort reduction. In this framework, our approach advances the existing active learning methods in two aspects. First, we incorporate two different deep convolutional neural networks into active learning which uses multiview complementary information to improve the feature learnings. Second, through the properly designed framework, the feature representation and the classifier can be simultaneously updated with progressively annotated informative samples. The experiments with two challenging image datasets demonstrate that our proposed DMAL algorithm can achieve promising results than several state-of-the-art active learning algorithms

Multiview Active Learning for Scene Classification with High-Level Semantic-Based Hypothesis Generation

Multiview active learning (MVAL) is a technique which can result in a large decrease in the size of the version space than traditional active learning and has great potential applications in large-scale data analysis. This paper made research on MVAL-based scene classification for helping the computer accurately understand diverse and complex environments macroscopically, which has been widely used in many fields such as image retrieval and autonomous driving. The main contribution of this paper is that different high-level image semantics are used for replacing the traditional low-level features to generate more independent and diverse hypotheses in MVAL. First, our algorithm uses different object detectors to achieve local object responses in the scenes. Furthermore, we design a cascaded online LDA model for mining the theme semantic of an image. The experimental results demonstrate that our proposed theme modeling strategy fits the large-scale data learning, and our MVAL algorithm with both high-level semantic views can achieve significant improvement in the scene classification than traditional active learning-based algorithms

Gene Set Enrichment Analysis and Genetic Experiment Reveal Changes in Cell Signaling Pathways Induced by α-Synuclein Overexpression

Abnormal accumulation of alpha synuclein (α-Syn) in sporadic and familial Parkinson’s disease (PD) may be a key step in its pathogenesis. In this study, the expression matrix of the GSE95427 dataset after α-Syn overexpression in human glioma cell line H4 was obtained from the GEO database. We used the Gene Set Enrichment Analysis (GSEA) method to reanalyze this dataset to evaluate the possible functions of α-Syn. The results showed that the tumor necrosis factor alpha (TNF-α) signal was significantly activated in α-Syn-overexpressing cells, and oxidative phosphorylation signal, extracellular matrix signal, cell cycle related signal and fatty acid metabolism signal were significantly inhibited. Moreover, we employed the α-Syn-expressing transgenic Drosophila model of Parkinson’s disease and knocked-down eiger, a TNF superfamily ligand homologue, indicating that the TNF-α pathway plays a role in the common pathogenesis of synucleinopathies. Our analysis based on GSEA data provides more clues for a better understanding of α-Syn function

Fault Diagnosis Method for Rotating Machinery Based on Multi-scale Features

Abstract The vibration signals of rotating machinery usually contain various natural oscillation modes, exhibiting multi-scale features. This paper proposes a Multi-Branch one-dimensional deep Convolutional Neural Network model (MBCNN) that can extract multi-scale features from raw data hierarchically, thereby improving the diagnostic accuracy of gearbox faults in noisy environments. Meanwhile, the algorithms for multi-branch generation and algorithms of the convolution and pooling for each branch are deducted. The MBCNN integrates multiple branches with interrelated convolution kernels of different widths, and each branch can extract the high-level features of the signal. The network parameters of each branch are adjusted by the loss function, which makes the features of the branches complementary. Through the design of MBCNN, the local, global, deep layer and comprehensive information can be obtained from the raw data. On the widely used Case Western Reserve University Bearing Dataset, this paper conducted a performance comparison between the proposed MBCNN and other baselines including the shallow learning methods, 1D-CNN, and multi-scale feature learning methods. Moreover, our gearbox dataset was conducted on a fault diagnosis platform, and a series of experiments were conducted to verify the effectiveness and superiority of the MBCNN. The results indicate that the MBCNN can identify the faults in the gearbox with an accuracy of higher than 92%, and the average validation time per sample is less than 3.2 ms. In a noisy environment, the diagnostic accuracy can reach 90%. The proposed MBCNN provides an effective and intelligent detection method to identify the faults of rotating machinery in the manufacturing processes