SIMP-ALL: a generalized SIMP method based on the topological derivative concept

Topology optimization has emerged in the last years as a promising research fieldwith a wide range of applications. One of the most successful approaches, theSIMP method, is based on regularizing the problem and proposing a penaliza-tion interpolation function. In this work, we propose an alternative interpolationfunction, the SIMP-ALL method that is based on the topological derivative con-cept. First, we show the strong relation in plane linear elasticity between theHashin-Shtrikman (H-S) bounds and the topological derivative, providing anew interpretation of the last one. Then, we show that the SIMP-ALL interpo-lation remains always in between the H-S bounds regardless the materials tobe interpolated. This result allows us to interpret intermediate values as realmicrostructures. Finally, we verify numerically this result and we show the con-venience of the proposed SIMP-ALL interpolation for obtaining auto-penalizedoptimal design in a wider range of cases. A MATLAB code of the SIMP-ALLinterpolation function is also provide

A Review on Multi-Terminal High Voltage Direct Current Networks for Wind Power Integration

With the growing pressure to substitute fossil fuel-based generation, Renewable Energy Sources (RES) have become one of the main solutions from the power sector in the fight against climate change. Offshore wind farms, for example, are an interesting alternative to increase renewable power production, but they represent a challenge when being interconnected to the grid, since new installations are being pushed further off the coast due to noise and visual pollution restrictions. In this context, Multi-Terminal High Voltage Direct Current (MT-HVDC) networks are the most preferred technology for this purpose and for onshore grid reinforcements. They also enable the delivery of power from the shore to offshore Oil and Gas (O&G) production platforms, which can help lower the emissions in the transition away from fossil fuels. In this work, we review relevant aspects of the operation and control of MT-HVDC networks for wind power integration. The review approaches topics such as the main characteristics of MT-HVDC projects under discussion/commissioned around the world, rising challenges in the control and the operation of MT-HVDC networks and the modeling and the control of the Modular Multilevel Converter (MMC) stations. To illustrate the challenges on designing the control system of a MT-HVDC network and to corroborate the technical discussions, a simulation of a three-terminal MT-HVDC network integrating wind power generation and offshore O&G production units to the onshore grid is performed in Matlab's Simscape Electrical toolbox. The results highlight the main differences between two alternatives to design the control system for an MT-HVDC network

kNN and SVM classification for EEG: a review

This paper review the classification method of EEG signal based on k-nearest neighbor (kNN) and support vector machine (SVM) algorithm. For instance, a classifier learns an input features from a dataset using specific approach and tuning parameters, develop a classification model, and use the model to predict the corresponding class of new input in an unseen dataset. EEG signals contaminated with various noises and artefacts, non-stationary and poor in signal-to-noise ratio (SNR). Moreover, most EEG applications involve high dimensional feature vector. kNN and SVM were used in EEG classification and has been proven successfully in discriminating features in EEG dataset. However, different results were observed between different EEG applications. Hence, this paper reviews the used of kNN and SVM classifier on various EEG applications, identifying their advantages and disadvantages, and also their overall performances

Notes for genera: basal clades of Fungi (including Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota)

Compared to the higher fungi (Dikarya), taxonomic and evolutionary studies on the basal clades of fungi are fewer in number. Thus, the generic boundaries and higher ranks in the basal clades of fungi are poorly known. Recent DNA based taxonomic studies have provided reliable and accurate information. It is therefore necessary to compile all available information since basal clades genera lack updated checklists or outlines. Recently, Tedersoo et al. (MycoKeys 13:1--20, 2016) accepted Aphelidiomycota and Rozellomycota in Fungal clade. Thus, we regard both these phyla as members in Kingdom Fungi. We accept 16 phyla in basal clades viz. Aphelidiomycota, Basidiobolomycota, Blastocladiomycota, Calcarisporiellomycota, Caulochytriomycota, Chytridiomycota, Entomophthoromycota, Glomeromycota, Kickxellomycota, Monoblepharomycota, Mortierellomycota, Mucoromycota, Neocallimastigomycota, Olpidiomycota, Rozellomycota and Zoopagomycota. Thus, 611 genera in 153 families, 43 orders and 18 classes are provided with details of classification, synonyms, life modes, distribution, recent literature and genomic data. Moreover, Catenariaceae Couch is proposed to be conserved, Cladochytriales Mozl.-Standr. is emended and the family Nephridiophagaceae is introduced

One-step HPLC purification procedure for porcine brain 90-kDa heat shock protein

[[abstract]]The 90-kDa heat shock protein (HSP90) was purified from porcine brain by a novel single-step purification procedure using diethylaminoethyl high-performance liquid chromatography (HPLC). About 4.8 mg of HSP90 was isolated from 25 g wet wt porcine brain tissue. The purified protein possessed a single moiety on one- and two-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis followed by silver staining. Western blotting using monoclonal antibody prepared against human HSP90 confirmed its identity as HSP90. These results indicate that small-scale HPLC purification of HSP90 from porcine brain tissue can be readily accomplished, with high yield, using a convenient one-step purification method. The procedure described in this paper represents a significant improvement in current purification methods for the isolation of HSP90 from porcine brain