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     

A macro-element strategy based upon spectral finite elements and mortar elements for transient wave propagation modeling. Application to ultrasonic testing of laminate composite materials

Proposing efficient numerical modeling tools for high-frequency wave propagation in realistic configurations, such as the one appearing in ultrasonic testing experiments, is a major challenge, especially in the perspective of inversion loops or parametric studies. We propose a numerical methodology addressing this challenge and based upon the combination of the spectral finite element method and the mortar element method. From a prior decomposition of the scene of interest into “macro-elements,” we show how one can improve the performances of the standard finite element procedures in terms of memory footprint and computational load. Additionally, using this decomposition, we are able to efficiently reconstruct important modeling features on-the-fly, such as orientations of anisotropic materials or splitting directions of perfectly matched layers formulations, altogether in a robust and efficient manner. We believe that this strategy is particularly suitable for parametric studies and sensitivity analysis. We illustrate our strategy by simulating the propagation of an ultrasonic wave into an immersed and curved anisotropic laminate 3D specimen flawed with an internal circular delamination of varying size, thus showing the efficiency and the robustness of our approach.     

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.     

Numerical modeling of wave propagation in anisotropic viscoelastic laminated materials in transient regime: Application to modeling ultrasonic testing of composite structures

Composite laminate structures remain an important family of materials used in cutting-edge industrial areas. Building efficient numerical modeling tools for high-frequency wave propagation in order to represent ultrasonic testing experiments of these materials remains a major challenge. In particular, incorporating attenuation phenomena within anisotropic plies, and thin intermediate isotropic layers between the plies often represent significant obstacles for standard numerical approaches. In our work, we address both issues by proposing a systematic study of the fully discrete propagators associated to the Kelvin-Voigt, Maxwell, and Zener models, and by incorporating effective transmission conditions between plies using the mortar element method. We illustrate the soundness of our approach by proposing intermediate one-dimensional and two-dimensional numerical evidence, and we apply it to a more realistic configuration of a curved laminate composite structure in a three-dimensional setting.     

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.

     

Using normative ethics for building a good evaluation of research practices: towards the assessment of researcher’s virtues

This paper is an attempt of using co-citation analysis to sort out and to analyze the development and evolution of a latest hot area, open innovation from the perspective of network embedding. A dataset of 1437 records published between 1990 and 2019 is collected from Web of Science database. The empirical results show the latest hot topics in the open innovation study focus on innovation performance and value creation. In addition, we make a new interpretation of open innovation from four aspects: innovation and entrepreneurship, resource acquisition, knowledge sharing and innovation performance, then combines the importance of network embedding to the innovation and development of enterprises, and proposes the future research direction of open innovation. Our research in this paper is helpful to systematically sort out the knowledge context of open innovation, which is of great significance to the construction and development of open innovation knowledge system. The conclusions and implications in this paper will be particularly illuminating for both academic research and enterprises’ practice application.