Achieving DFT accuracy with a machine-learning interatomic potential: Thermomechanics and defects in bcc ferromagnetic iron

We show that the Gaussian Approximation Potential machine learning framework can describe complex magnetic potential energy surfaces, taking ferromagnetic iron as a paradigmatic challenging case. The training database includes total energies, forces, and stresses obtained from density-functional theory in the generalized-gradient approximation, and comprises approximately 150,000 local atomic environments, ranging from pristine and defected bulk configurations to surfaces and generalized stacking faults with different crystallographic orientations. We find the structural, vibrational and thermodynamic properties of the GAP model to be in excellent agreement with those obtained directly from first-principles electronic-structure calculations. There is good transferability to quantities, such as Peierls energy barriers, which are determined to a large extent by atomic configurations that were not part of the training set. We observe the benefit and the need of using highly converged electronic-structure calculations to sample a target potential energy surface. The end result is a systematically improvable potential that can achieve the same accuracy of density-functional theory calculations, but at a fraction of the computational cost

HiTrust: building cross-organizational trust relationship based on a hybrid negotiation tree

Small-world phenomena have been observed in existing peer-to-peer (P2P) networks which has proved useful in the design of P2P file-sharing systems. Most studies of constructing small world behaviours on P2P are based on the concept of clustering peer nodes into groups, communities, or clusters. However, managing additional multilayer topology increases maintenance overhead, especially in highly dynamic environments. In this paper, we present Social-like P2P systems (Social-P2Ps) for object discovery by self-managing P2P topology with human tactics in social networks. In Social-P2Ps, queries are routed intelligently even with limited cached knowledge and node connections. Unlike community-based P2P file-sharing systems, we do not intend to create and maintain peer groups or communities consciously. In contrast, each node connects to other peer nodes with the same interests spontaneously by the result of daily searches

Event-based Customization of Multi-tenant SaaS Using Microservices

Popular enterprise software such as ERP, CRM is now being made available on the Cloud in the multi-tenant Software as a Service (SaaS) model. The added values come from the ability of vendors to enable customer-specific business advantage for every different tenant who uses the same main enterprise software product. Software vendors need novel customization solutions for Cloud-based multi-tenant SaaS. In this paper, we present an event-based approach in a non-intrusive customization framework that can enable customization for multi-tenant SaaS and address the problem of too many API calls to the main software product. The experimental results on Microsoft’s eShopOnContainers show that our approach can empower an event bus with the ability to customize the flow of processing events, and integrate with tenant-specific microservices for customization. We have shown how our approach makes sure of tenant-isolation, which is crucial in practice for SaaS vendors. This direction can also reduce the number of API calls to the main software product, even when every tenant has different customization services.publishedVersio

DevOps and its Philosophy : Education Matters!

DevOps processes comply with principles and offer practices with main objective to support efficiently the evolution of IT systems. To be efficient a DevOps process relies on a set of integrated tools. DevOps is among the first competencies together with Agile method required by the industry. As a new approach it is necessary to develop and offer to the academy and to the industry training programs to prepare our engineers in the best possible way. In this chapter we present the main aspects of the educational effort made in the recent years to educate to the concepts and values of the DevOps philosophy. This includes principles, practices, tools and architectures, primarily the microservices architectural style, which shares many aspects of DevOps approaches especially the modularity and flexibility which enables continuous change and delivery. Two experiences have been made, one at academic level as a master program course and the other, as an industrial training. Based on those two experiences, we provide a comparative analysis and some proposals in order to develop and improve DevOps education for the future

Integrated cascade biorefinery processes for the production of single cell oil by Lipomyces starkeyi from Arundo donax L. hydrolysates

Giant reed (Arundo donax L.) is a promising source of carbohydrates that can be converted into single cell oil (SCO) by oleaginous yeasts. Microbial conversion of both hemicellulose and cellulose fractions represents the key step for increasing the economic sustainability for SCO production. Lipomyces starkeyi DSM 70,296 was cultivated in two xylose-rich hydrolysates, obtained by the microwave-assisted hydrolysis of hemicellulose catalysed by FeCl3 or Amberlyst-70, and in two glucose-rich hydrolysates obtained by the enzymatic hydrolysis of cellulose. L. starkeyi grew on both undetoxified and partially-detoxified hydrolysates, achieving the lipid content of 30 wt% and yield values in the range 15–24 wt%. For both integrated cascade processes the final production of about 8 g SCO from 100 g biomass was achieved. SCO production through integrated hydrolysis cascade processes represents a promising solution for the effective exploitation of lignocellulosic feedstock from perennial grasses towards new generation biodiesel and other valuable bio-based products

Remote sensing of ecosystem light use efficiency with MODIS-based PRI

Several studies sustained the possibility that a photochemical reflectance index (PRI) directly obtained from satellite data can be used as a proxy for ecosystem light use efficiency (LUE) in diagnostic models of gross primary productivity. This modelling approach would avoid the complications that are involved in using meteorological data as constraints for a fixed maximum LUE. However, no unifying model predicting LUE across climate zones and time based on MODIS PRI has been published to date. In this study, we evaluate the effectiveness with which MODIS-based PRI can be used to estimate ecosystem light use efficiency at study sites of different plant functional types and vegetation densities. Our objective is to examine if known limitations such as dependence on viewing and illumination geometry can be overcome and a single PRI-based model of LUE (i.e. based on the same reference band) can be applied under a wide range of conditions. Furthermore, we were interested in the effect of using different faPAR (fraction of absorbed photosynthetically active radiation) products on the in-situ LUE used as ground truth and thus on the whole evaluation exercise. We found that estimating LUE at site-level based on PRI reduces uncertainty compared to the approaches relying on a maximum LUE reduced by minimum temperature and vapour pressure deficit. Despite the advantages of using PRI to estimate LUE at site-level, we could not establish an universally applicable light use efficiency model based on MODIS PRI. Models that were optimised for a pool of data from several sites did not perform well

Size Matters: Microservices Research and Applications

In this chapter we offer an overview of microservices providing the introductory information that a reader should know before continuing reading this book. We introduce the idea of microservices and we discuss some of the current research challenges and real-life software applications where the microservice paradigm play a key role. We have identified a set of areas where both researcher and developer can propose new ideas and technical solutions.Comment: arXiv admin note: text overlap with arXiv:1706.0735

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information

Teaching DevOps in academia and industry: reflections and vision

This paper describes our experience of delivery educational programs in academia and in industry on DevOps, compare the two approaches and sum-up the lessons learnt. We also propose a vision to implement a shift in the Software Engineering Higher Education curricula