3D Simulation of Partial Discharge in High Voltage Power Networks

Open accessPartial discharge (PD) events arise inside power cables due to defects of cable’s insulation material, characterized by a lower electrical breakdown strength than the surrounding dielectric material. These electrical discharges cause signals to propagate along the cable, manifesting as noise phenomena. More significantly, they contribute to insulation degradation and can produce a disruptive effect with a consequent interruption of power network operation. PD events are, therefore, one of the best ‘early warning’ indicators of insulation degradation and, for this reason, the modeling and studying of such phenomena, together with the development of on-line PDs location methods, are important topics for network integrity assessment, and to define methods to improve the power networks’ Electricity Security. This paper presents a 3D model of PD events inside a void in epoxy-resin insulation cables for High Voltage (HV) power networks. The 3D model has been developed using the High Frequency (HF) Solver of CST Studio Suite® software. PD events of a few µs duration have been modelled and analyzed. The PD behavior has been investigated using varying electrical stress. A first study of the PD signal propagation in a power network is described

Contribution of histone variants to aneuploidy: a cancer perspective

Data availability statement: The original contributions presented in the study are included in the article's Supplementary Material, available online at: https://www.frontiersin.org/articles/10.3389/fgene.2023.1290903/full#supplementary-material, further inquiries can be directed to the corresponding authors.Copyright © 2023 Ragusa and Vagnarelli. Histone variants, which generally differ in few amino acid residues, can replace core histones (H1, H2A, H2B, and H3) to confer specific structural and functional features to regulate cellular functions. In addition to their role in DNA packaging, histones modulate key processes such as gene expression regulation and chromosome segregation, which are frequently dysregulated in cancer cells. During the years, histones variants have gained significant attention as gatekeepers of chromosome stability, raising interest in understanding how structural and functional alterations can contribute to tumourigenesis. Beside the well-established role of the histone H3 variant CENP-A in centromere specification and maintenance, a growing body of literature has described mutations, aberrant expression patterns and post-translational modifications of a variety of histone variants in several cancers, also coining the term “oncohistones.” At the molecular level, mechanistic studies have been dissecting the biological mechanisms behind histones and missegregation events, with the potential to uncover novel clinically-relevant targets. In this review, we focus on the current understanding and highlight knowledge gaps of the contribution of histone variants to aneuploidy, and we have compiled a database (HistoPloidyDB) of histone gene alterations linked to aneuploidy in cancers of the The Cancer Genome Atlas project.BBSRC grant BB/V013920/1 to PV

A Computationally Light Pruning Strategy for Single Layer Neural Networks based on Threshold Function

Embedded machine learning relies on inference functions that can fit resource-constrained, low-power computing devices. The literature proves that single layer neural networks using threshold functions can provide a suitable trade off between classification accuracy and computational cost. In this regard, the number of neurons directly impacts both on computational complexity and on resources allocation. Thus, the present research aims at designing an efficient pruning technique that can take into account the peculiarities of the threshold function. The paper shows that feature selection criteria based on filter models can effectively be applied to neuron selection. In particular, valuable outcomes can be obtained by designing ad-hoc objective functions for the selection process. An extensive experimental campaign confirms that the proposed objective function compares favourably with state-of-the-art pruning techniques

A survey on deep learning in image polarity detection: Balancing generalization performances and computational costs

Deep convolutional neural networks (CNNs) provide an effective tool to extract complex information from images. In the area of image polarity detection, CNNs are customarily utilized in combination with transfer learning techniques to tackle a major problem: the unavailability of large sets of labeled data. Thus, polarity predictors in general exploit a pre-trained CNN as the feature extractor that in turn feeds a classification unit. While the latter unit is trained from scratch, the pre-trained CNN is subject to fine-tuning. As a result, the specific CNN architecture employed as the feature extractor strongly affects the overall performance of the model. This paper analyses state-of-the-art literature on image polarity detection and identifies the most reliable CNN architectures. Moreover, the paper provides an experimental protocol that should allow assessing the role played by the baseline architecture in the polarity detection task. Performance is evaluated in terms of both generalization abilities and computational complexity. The latter attribute becomes critical as polarity predictors, in the era of social networks, might need to be updated within hours or even minutes. In this regard, the paper gives practical hints on the advantages and disadvantages of the examined architectures both in terms of generalization and computational cost

Estrutura do sub-bosque em manchas florestais no Pantanal da Nhecolândia: efeitos da presença de gado.

bitstream/CPAP-2009-09/56932/1/COT74.pd

Ocupação de manchas florestais por espécies de pica-paus e arapaçus no Pantanal.

Neste estudo avaliamos a influência de algumas variáveis de paisagem e habitat nas probabildades de ocupação de manchas florestais naturais por seis espécies deste tipo de aves no Pantanal da Nhecolândia. O estudo foi realizado em 2008 na fazenda Nhumirim e arredores, nas estações chuvosa e seca