Corrosion and mechanical properties of plasma electrolytic oxidation‐coated AZ80 magnesium alloy

Plasma electrolytic oxidation (PEO) coatings were produced on AZ80 magnesium alloy in a solution containing silicates and phosphates and working at high current densities with short treatment times. The effect of a sealing treatment in boiling water on corrosion and mechanical properties of the coatings were investigated. Moreover, the corrosion mechanism of the samples with and without the sealing treatment was evaluated. The microstructure of the coatings was characterized with scanning electron microscope observation and X‐ray diffraction analysis. The mechanical properties were evaluated with nanoindentation tests and the corrosion resistance was studied by potentiodynamic polarization, electrochemical impedance spectroscopy, and scanning vibrating electrode technique. The results showed that the sealing did not influence the microstructure and the mechanical properties of the samples and instead produced a remarkable increase in the corrosion resistance. The crevice corrosion, present in the sample without the sealing, was avoided with the treatment in boiling water.     

Cachexia,a Systemic Disease beyond Muscle Atrophy

Cachexia is a complication of dismal prognosis, which often represents the last step of several chronic diseases. For this reason, the comprehension of the molecular drivers of such a condition is crucial for the development of management approaches. Importantly, cachexia is a syndrome affecting various organs, which often results in systemic complications. To date, the majority of the research on cachexia has been focused on skeletal muscle, muscle atrophy being a pivotal cause of weight loss and the major feature associated with the steep reduction in quality of life. Nevertheless, defining the impact of cachexia on other organs is essential to properly comprehend the complexity of such a condition and potentially develop novel therapeutic approaches.     

Different Stages of Quiescence,Senescence, and Cell Stress Identified by Molecular Algorithm Based on the Expression of Ki67, RPS6, and Beta-Galactosidase Activity

During their life span, cells have two possible states: a non-cycling, quiescent state (G0) and a cycling, activated state. Cells may enter a reversible G0 state of quiescence or, alternatively, they may undergo an irreversible G0 state. The latter may be a physiological differentiation or, following a stress event, a senescent status. Discrimination among the several G0 states represents a significant investigation, since quiescence, differentiation, and senescence are progressive phenomena with intermediate transitional stages. We used the expression of Ki67, RPS6, and beta-galactosidase to identify healthy cells that progressively enter and leave quiescence through G0-entry, G0 and G0-alert states. We then evaluated how cells may enter senescence following a genotoxic stressful event. We identified an initial stress stage with the expression of beta-galactosidase and Ki67 proliferation marker. Cells may recover from stress events or become senescent passing through early and late senescence states. Discrimination between quiescence and senescence was based on the expression of RPS6, a marker of active protein synthesis that is present in senescent cells but absent in quiescent cells. Even taking into account that fixed G0 states do not exist, our molecular algorithm may represent a method for identifying turning points of G0 transitional states that continuously change.     

Plant Growth Promotion Function of Bacillus sp. Strains Isolated from Salt-Pan Rhizosphere and Their Biocontrol Potential against Macrophomina phaseolina

In recent decades, intensive crop management has involved excessive use of pesticides or fertilizers, compromising environmental integrity and public health. Accordingly, there has been worldwide pressure to find an eco-friendly and safe strategy to ensure agricultural productivity. Among alternative approaches, Plant Growth-Promoting (PGP) rhizobacteria are receiving increasing attention as suitable biocontrol agents against agricultural pests. In the present study, 22 spore-forming bacteria were selected among a salt-pan rhizobacteria collection for their PGP traits and their antagonistic activity against the plant pathogen fungus Macrophomina phaseolina. Based on the higher antifungal activity, strain RHFS10, identified as Bacillus vallismortis, was further examined and cell-free supernatant assays, column purification, and tandem mass spectrometry were employed to purify and preliminarily identify the antifungal metabolites. Interestingly, the minimum inhibitory concentration assessed for the fractions active against M. phaseolina was 10 times lower and more stable than the one estimated for the commercial fungicide pentachloronitrobenzene. These results suggest the use of B. vallismortis strain RHFS10 as a potential plant growth-promoting rhizobacteria as an alternative to chemical pesticides to efficiently control the phytopathogenic fungus M. phaseolina.     

Modelling of electricity savings in the Danish households sector: from the energy system to the end-user

In this paper, we examine the value of investing in energy-efficient household appliances from both an energy system and end-user perspectives. We consider a set of appliance categories constituting the majority of the electricity consumption in the private household sector, and focus on the stock of products which need to be replaced. First, we look at the energy system and investigate whether investing in improved energy efficiency can compete with the cost of electricity supply from existing or new power plants. To assess the analysis, Balmorel, a linear optimization model for the heat and power sectors, has been extended in order to endogenously determine the best possible investments in more efficient home appliances. Second, we propose a method to relate the optimal energy system solution to the end-user choices by incorporating consumer behaviour and electricity price addition due to taxes. The model is non-exclusively tested on the Danish energy system under different scenarios. Computational experiments show that several energy efficiency measures in the household sector should be regarded as valuable investments (e.g. an efficient lighting system) while others would require some form of support to become profitable. The analysis quantifies energy and economic savings from the consumer side and reveals the impacts on the Danish power system and surrounding countries. Compared to a business-as-usual energy scenario, the end-user attains net economic savings in the range of 30–40 EUR per year, and the system can benefit of an annual electricity demand reduction of 140–150 GWh. The paper enriches the existing literature about energy efficiency modelling in households, contributing with novel models, methods, and findings related to the Danish case.     

The Unbearable Shallow Understanding of Deep Learning

This paper analyzes the rapid and unexpected rise of deep learning within Artificial Intelligence and its applications. It tackles the possible reasons for this remarkable success, providing candidate paths towards a satisfactory explanation of why it works so well, at least in some domains. A historical account is given for the ups and downs, which have characterized neural networks research and its evolution from “shallow” to “deep” learning architectures. A precise account of “success” is given, in order to sieve out aspects pertaining to marketing or sociology of research, and the remaining aspects seem to certify a genuine value of deep learning, calling for explanation. The alleged two main propelling factors for deep learning, namely computing hardware performance and neuroscience findings, are scrutinized, and evaluated as relevant but insufficient for a comprehensive explanation. We review various attempts that have been made to provide mathematical foundations able to justify the efficiency of deep learning, and we deem this is the most promising road to follow, even if the current achievements are too scattered and relevant for very limited classes of deep neural models. The authors’ take is that most of what can explain the very nature of why deep learning works at all and even very well across so many domains of application is still to be understood and further research, which addresses the theoretical foundation of artificial learning, is still very much needed.

     

Bibliometric analysis and mapping of publications on brewing science from 1940 to 2018

Over the last 80 years, malting and brewing has evolved through increased scientific understanding, driven by numerous factors including efficiency improvement, cost reduction, quality enhancement, risk minimisation, brand marketing and image enhancement. The aim of this work was to review the historical trends in global brewing research in order to identify current opportunities and emerging trends, as well as uncover future key drivers. A bibliometric analysis was performed with English language peer reviewed literature from the Scopus database together with mapping using the VOSviewer clustering software. The bibliometric results were structured to identify which affiliations, authors, and journals have been involved in brewing research, as well as the average number of citations per paper. The bibliometric mapping yielded ‘research term maps’, where the words occurring more frequently in the research papers were grouped in coloured clusters by area of interest, and their relationships indicated. The year maps revealed which average terms tended to emerge in chronological order. The estimated average citation number per paper was about nine. Countries, including Spain, Czech Republic, Italy and Belgium displayed higher average citation scores than the world average. In the period 1940-79, brewing publications were mainly process oriented whereas between 2008-18, there was a shift in focus to the consumer (health and social responsibility) and to environmental sustainability. © 2020 The Institute of Brewing & Distilling     

Calcium phosphate-based nanosystems for advanced targeted nanomedicine

Synthetic calcium phosphates (CaPs) are the most widely accepted bioceramics for the repair and reconstruction of bone tissue defects. The recent advancements in materials science have prompted a rapid progress in the preparation of CaPs with nanometric dimensions, tailored surface characteristics, and colloidal stability opening new perspectives in their use for applications not strictly related to bone. In particular, the employment of CaPs nanoparticles as carriers of therapeutic and imaging agents has recently raised great interest in nanomedicine. CaPs nanoparticles, as well as other kinds of nanoparticles, can be engineered to specifically target the site of the disease (cells or organs), thus minimizing their dispersion in the body and undesired organism-nanoparticles interactions. The most promising and efficient approach to improve their specificity is the ‘active targeting’, where nanoparticles are conjugated with a targeting moiety able to recognize and bind with high efficacy and selectivity to receptors that are highly expressed only in the therapeutic site. The aim of this review is to give an overview on advanced targeted nanomedicine with a focus on the most recent reports on CaP nanoparticles-based systems, specifically designed for the active targeting. The distinctive characteristics of CaP nanoparticles with respect to the other kinds of nanomaterials used in nanomedicine are also discussed.     

Detection,localisation and tracking of pallets using machine learning techniques and 2D range data

The problem of autonomous transportation in industrial scenarios is receiving a renewed interest due to the way it can revolutionise internal logistics, especially in unstructured environments. This paper presents a novel architecture allowing a robot to detect, localise, and track (possibly multiple) pallets using machine learning techniques based on an on-board 2D laser rangefinder only. The architecture is composed of two main components: the first stage is a pallet detector employing a Faster Region-Based Convolutional Neural Network (Faster R-CNN) detector cascaded with a CNN-based classifier; the second stage is a Kalman filter for localising and tracking detected pallets, which we also use to defer commitment to a pallet detected in the first stage until sufficient confidence has been acquired via a sequential data acquisition process. For fine-tuning the CNNs, the architecture has been systematically evaluated using a real-world dataset containing 340 labelled 2D scans, which have been made freely available in an online repository. Detection performance has been assessed on the basis of the average accuracy over k-fold cross-validation, and it scored 99.58% in our tests. Concerning pallet localisation and tracking, experiments have been performed in a scenario where the robot is approaching the pallet to fork. Although data have been originally acquired by considering only one pallet as per specification of the use case we consider, artificial data have been generated as well to mimic the presence of multiple pallets in the robot workspace. Our experimental results confirm that the system is capable of identifying, localising and tracking pallets with a high success rate while being robust to false positives.