A Smart Deep Convolutional Neural Network for Real-Time Surface Inspection

A proper detection and classification of defects in steel sheets in real time have become a requirement for manufacturing these products, largely used in many industrial sectors. However, computers used in the production line of small to medium size companies, in general, lack performance to attend real-time inspection with high processing demands. In this paper, a smart deep convolutional neural network for using in real-time surface inspection of steel rolling sheets is proposed. The architecture is based on the state-of-the-art SqueezeNet approach, which was originally developed for usage with autonomous vehicles. The main features of the proposed model are: small size and low computational burden. The model is 10 to 20 times smaller when compared to other networks designed for the same task, and more than 700 times smaller than general networks. Also, the number of floating-point operations for a prediction is about 50 times lower than the ones used for similar tasks. Despite its small size, the proposed model achieved near-perfect accuracy on a public dataset of 1800 images of six types of steel rolling defects.     

Extracellular Vesicles in CNS Developmental Disorders

The central nervous system (CNS) is the most complex structure in the body, consisting of multiple cell types with distinct morphology and function. Development of the neuronal circuit and its function rely on a continuous crosstalk between neurons and non-neural cells. It has been widely accepted that extracellular vesicles (EVs), mainly exosomes, are effective entities responsible for intercellular CNS communication. They contain membrane and cytoplasmic proteins, lipids, non-coding RNAs, microRNAs and mRNAs. Their cargo modulates gene and protein expression in recipient cells. Several lines of evidence indicate that EVs play a role in modifying signal transduction with subsequent physiological changes in neurogenesis, gliogenesis, synaptogenesis and network circuit formation and activity, as well as synaptic pruning and myelination. Several studies demonstrate that neural and non-neural EVs play an important role in physiological and pathological neurodevelopment. The present review discusses the role of EVs in various neurodevelopmental disorders and the prospects of using EVs as disease biomarkers and therapeutics.     

Production of hydrogen from methanol steam reforming using CuPd/ZrO2 catalysts – Influence of the catalytic surface on methanol conversion and CO selectivity

Electricity generation for mobile applications by proton exchange membrane fuel cells (PEMFCs) is typically hindered by the low volumetric energy density of hydrogen. Nevertheless, nearly pure hydrogen can be generated in-situ from methanol steam reforming (MSR), with Cu-based catalysts being the most common MSR catalysts. Cu-based catalysts display high catalytic performance, even at low temperatures (ca. 250 °C), but are easily deactivated. On the other hand, Pd-based catalysts are very stable but show poor MSR selectivity, producing high concentrations of CO as by-product. This work studies bimetallic catalysts where Cu was added as a promoter to increase MSR selectivity of Pd. Specifically, the surface composition was tuned by different sequences of Cu and Pd impregnation on a monoclinic ZrO₂ support. Both methanol conversion and MSR selectivity were higher for the catalyst with a CuPd-rich surface compared to the catalyst with a Pd-rich surface. Characterization analysis indicate that the higher MSR selectivity results from a strong interaction between the two metals when Pd is impregnated first (likely an alloy). This sequence also resulted in better metallic dispersion on the support, leading to higher methanol conversion. A H₂ production rate of 86.3 mmol h^?1 g^?1 was achieved at low temperature (220 °C) for the best performing catalyst.     

Using Twitter® as source of information for dietary market research: a study on veganism and plant-based diets

Social media has experienced rapid growth in recent years and has been subjected of discussions on a wide range of topics including new lifestyles and eating habits, providing a great opportunity to obtain spontaneous consumer information. In this sense, the present work aimed to understand the perception of Twitter^® users about themes of veganism and plant-based diets. The social networking data mining methodology was applied to measure the relationship between both terms. The significant differences found were analysed using the global chi-square (χ²) test, and their sources of variation were investigated by the chi-square per cell. The results indicate that the vegan group's posts are more related to the categories of recipes, trends, criticism and negative comments about veganism, being more often citing sources when compared to the other group. The results of the plant-based diet group are more significantly related to the impacts of nutrition, physical activity and consumer health. In conclusion, Twitter^® has proved to be an interesting tool for obtaining data on (re) produced food publications on social media and their results can guide the market and the academic environment in creating new products, services and marketing strategies to answer the needs of specific consumers.     

The rapid synthesis of nanostructured orthorhombic KNbO3 particles by a microwave-assisted hydrothermal method and their characterization

We studied the molar ratio effects of niobium and potassium precursors on the structure and morphology of potassium niobate powders prepared via microwave-assisted hydrothermal synthesis (MaHS). KNbO₃ nanostructures in the form of nanotowers and nanocubes were obtained at reduced synthesis times (30–240 min). The products were characterized via XRD, Raman spectroscopy, SEM, and TEM; band gap calculations used diffuse reflectance data. The results indicate that KNbO₃ nanostructures were obtained with crystallite sizes ranging from 33 to 52 nm. An orthorhombic crystalline structure was formed from the increase of KOH at a molar ratio Nb₂O₅:KOH (1:8 to 1:16 M). The band-gap of 3.1–3.3 eV has potential use in photodegradation applications.     

OWL‐based acquisition and editing of computer‐interpretable guidelines with the CompGuide editor

Computer‐Interpretable Guidelines (CIGs) are the dominant medium for the delivery of clinical decision support, given the evidence‐based nature of their source material. Therefore, these machine‐readable versions have the ability to improve practitioner performance and conformance to standards, with availability at the point and time of care. The formalisation of Clinical Practice Guideline knowledge in a machine‐readable format is a crucial task to make it suitable for the integration in Clinical Decision Support Systems. However, the current tools for this purpose reveal shortcomings with respect to their ease of use and the support offered during CIG acquisition and editing. In this work, we characterise the current landscape of CIG acquisition tools based on the properties of guideline visualisation, organisation, simplicity, automation, manipulation of knowledge elements, and guideline storage and dissemination. Additionally, we describe the CompGuide Editor, a tool for the acquisition of CIGs in the CompGuide model for Clinical Practice Guidelines that also allows the editing of previously encoded guidelines. The Editor guides the users throughout the process of guideline encoding and does not require proficiency in any programming language. The features of the CIG encoding process are revealed through a comparison with already established tools for CIG acquisition.     

Selective Sharing of Load Current Components Among Parallel Power Electronic Interfaces in Three-phase Four-wire Stand-alone Microgrid

This paper investigates selective sharing of load current components among the parallel operation of distributed generators (DGs) in three-phase four-wire stand-alone microgrids. The proposed control method is based on master-slave operation of DGs, and the goal of selective sharing of load current components is to have DGs located in close proximity of the load operating in slave mode, in order to inject their available energy and also compensate the non-active load current components, while the distant DGs might operate in master mode to share the remaining load autonomously. Droop control is employed due to impracticality of communication at remote nodes, and resistive line impedance compensation is adopted to decouple active and reactive power controllers and ensure proper active power sharing among master DGs, irrespective of the mitigation of non-active current components by the slave inverters. The sharing factors for each current component are determined by a higher level control. The Conservative Power Theory (CPT) decompositions provide decoupled power and current references for the inverters, resulting in a selective sharing strategy. The principles supporting the developed control strategy are discussed, and the effectiveness of the control is demonstrated through computational simulations using PSIM software.