An evaluation of artificial intelligent technologiesfor fault diagnosis in power network

The artificial intelligent technology provides an advanced tool for fault diagnosis in powel networks, and has madegreat progress in the past thirty years. This paper briefly introduces the basic concepts of the intelligent technologies for faultdiagnosis. including expert system, artificial neural network, fuzzy theory, genetic algorithms and Petri net. Based on theseconcepts. the corresponding methods for power network fault diagnosis in the literature are reviewed. Their features andmain problems are discussed for further research and development. 簡要介紹了相關的人工智能技術 ,如專家系統 ( expert system) ,人工神經網絡 ( artificialneural network) ,模糊理論 ( fuzzy theory) ,遺傳算法 ( genetic algorithms)及 Petri網絡 ( Petri net)等的基本概念 ,并在此基礎上對文獻中提出的相應的輸電網絡故障診斷方法進行述評 ,分析它們在輸電網絡故障診斷中應用的特點以及存在的主要問題 ,以促進該研究領域的進一步發

Robustness and Enhancement of Neural Synchronization by Activity-Dependent Coupling

We study the synchronization of two model neurons coupled through a synapse having an activity-dependent strength. Our synapse follows the rules of Spike-Timing Dependent Plasticity (STDP). We show that this plasticity of the coupling between neurons produces enlarged frequency locking zones and results in synchronization that is more rapid and much more robust against noise than classical synchronization arising from connections with constant strength. We also present a simple discrete map model that demonstrates the generality of the phenomenon.Comment: 4 pages, accepted for publication in PR

Minimum degree reordering based graph partitioning method for distributed fault section estimation system in power networks

In order to make fault section estimation (FSE) in large-scale power networks use distributed artificial intelligence approach, we have to develop an efficient way to partition the large-scale power network into desired number of connected sub-networks such that each sub-network should have quasi-balanced working burden in performing FSE. In this paper, an efficient minimum degree reordering based graph partitioning method is suggested for the partitioning task. The method consists of two basic steps: partitioning the power network into connected, quasi-balanced and frontier minimized sub-networks based on minimum degree reordering and minimizing the number of the frontier nodes of the sub-networks through iterations so as to reduce the interaction of FSE in adjacent sub-networks. The partitioning procedure and characteristic analysis is presented. The method has been implemented with sparse storage technique and tested in the IEEE 14-bus, 30-bus and 118-bus systems respectively. Computer simulation results show that the proposed multiple-way graph partitioning method is suitable for FSE in large-scale power networks and is compared favorably with other graph partitioning methods suggested in references.published_or_final_versio

Contributions of advection and melting processes to the decline in sea ice in the Pacific sector of the Arctic Ocean

The Pacific sector of the Arctic Ocean (PA, hereafter) is a region sensitive to climate change. Given the alarming changes in sea ice cover during recent years, knowledge of sea ice loss with respect to ice advection and melting processes has become critical. With satellite-derived products from the National Snow and Ice Center (NSIDC), a 38-year record (1979–2016) of the loss in sea ice area in summer within the Pacific-Arctic (PA) sector due to the two processes is obtained. The average sea ice outflow from the PA to the Atlantic-Arctic (AA) Ocean during the summer season (June–September) reaches 0.173×106&thinsp;km2, which corresponds to approximately 34&thinsp;% of the mean annual export (October to September). Over the investigated period, a positive trend of 0.004×106&thinsp;km2&thinsp;yr−1 is also observed for the outflow field in summer. The mean estimate of sea ice retreat within the PA associated with summer melting is 1.66×106&thinsp;km2, with a positive trend of 0.053×106&thinsp;km2&thinsp;yr−1. As a result, the increasing trends of ice retreat caused by outflow and melting together contribute to a stronger decrease in sea ice coverage within the PA (0.057×106&thinsp;km2&thinsp;yr−1) in summer. In percentage terms, the melting process accounts for 90.4&thinsp;% of the sea ice retreat in the PA in summer, whereas the remaining 9.6&thinsp;% is explained by the outflow process, on average. Moreover, our analysis suggests that the connections are relatively strong (R=0.63), moderate (R=-0.46), and weak (R=-0.24) between retreat of sea ice and the winds associated with the dipole anomaly (DA), North Atlantic Oscillation (NAO), and Arctic Oscillation (AO), respectively. The DA participates by impacting both the advection (R=0.74) and melting (R=0.55) processes, whereas the NAO affects the melting process (R=-0.46).</p

Investigation of dietary fiber, protein, vitamin E and other nutritional compounds of banana flower of two cultivars grown in China

The nutritional composition of banana flowers of two cultivars [cvs. Baxijiao (AAA) and Paradisical (AAB)] grown in Hainan of China has been studied. Flower samples were collected and extracted according to methods of Association of Official Analytical Chemists (AOAC). Results showed thatbanana flowers contained abundant dietary fiber (4.96-5.74 g/100g) and proteins (1.62-2.07 g/100 g). The major amino acids are glycine, leucine, alanine, and aspartic acid. Lysine had a lowest chemical score of 64% among the essential amino acids. In both species, flowers contained a higher composition of unsaturated fatty acids (65-66%), mainly the linoleic acid, while saturated fatty acids (mainly palmitic acid) is low. The contents of vitamin E, total saponin and flavonoids were 0.87-1.07, 0.12 and 5.27&#8211;5.90mg/100 g, respectively. This study provides a fundamental nutritional data of banana flowers which can be essential in food science

Characterization of brain blood flow and the amplitude of low-frequency fluctuations in major depressive disorder: A multimodal meta-analysis.

Background In healthy subjects, there is an association between amplitude of low-frequency fluctuations (ALFF) and regional cerebral blood flow (rCBF). To date, no published meta-analysis has investigated changes in the regional ALFF in medication-free depressed patients. Methods In this study, we aimed to explore whether resting-state rCBF and ALFF changes co-occur in the depressed brain without the potential confound of medication. Using signed differential mapping (SDM), we conducted two meta-analyses, one of rCBF studies and one of ALFF studies, involving medication-free patients with major depressive disorder (MDD). In addition, we conducted a multimodal meta-analysis to identify brain regions that showed abnormalities in both rCBF and ALFF. Results A total of 16 studies were included in this series. We identified abnormalities in resting-state rCBF and ALFF in the left insula in medication-free MDD patients compared with healthy controls (HC). In addition, we observed altered resting-state rCBF in the limbic-subcortical-cortical circuit and altered ALFF in the default mode network (DMN) and some motor-related brain regions. Limitations The analysis techniques, patient characteristics and clinical variables of the included studies were heterogeneous. Conclusions The conjoint alterations in ALFF and rCBF in the left insula may represent core neuropathological changes in medication-free patients with MDD and merit further studying

Physical activity and sedentary behaviour among children and adolescents with intellectual disabilities during the COVID-19 lockdown in China

Background: In the wake of the COVID-19 pandemic, preliminary research has reported a significant decline in physical activity (PA) and an increase in sedentary behaviour (SB) among typically developed children and adolescents. Limited research has looked at the current situation of PA and SB during this pandemic among children and adolescents with intellectual disabilities (ID). This study investigated the situations about PA and SB among school-aged children and adolescents with ID on China’s mainland during the COVID-19 outbreak. Methods: In total, 837 parents of children and adolescents (ages 6–18 years) with ID from 15 special education schools of Shandong Province in China were recruited through convenience sampling in the study. Parents reported PA and SB among children and adolescents with ID through the Children’s Leisure Activities Study Survey-Chinese version (CLASS-C) online questionnaires. Results: From parents’ reports, Chinese children and adolescents with ID during the COVID-19 pandemic participated in approximately 10 min of moderate to-vigorous physical activity, and engaged in approximately 530 min of SB every day. Meanwhile, only 17.4% of children and adolescents with ID were able to achieve the recommendation of 60 min of daily moderate-to-vigorous physical activity and 76.1% of children and adolescents with ID spent more than 2 h on SB per day. Additionally, the problems of decrease PA and excessive SB were more prominent in older adolescents with ID compared with younger children with ID. Conclusion: In China, the low level of PA and high level of SB is particularly evident in children and adolescents with ID during the outbreak of COVID-19. The great majority of children and adolescents with ID did not meet the recommended amount of PA while undergoing excessive SB under the long-term home quarantine environment. Therefore, immediate attention and great effort should be made to deal with this severe situation among this vulnerable population in the mainland of China

Energy detection-based spectrum sensing with constraint region in cognitive LTE systems

EPSRC TV White Space Testbed with Robust Spectrum Sensing Algorithms Project. Grant Number: EP/L024241/1 State Major Science and Technology Special Project. Grant Number: 2015ZX0300200