A Cloud-Edge Orchestration Platform for the Innovative Industrial Scenarios of the IoTwins Project

The concept of digital twins has growing more and more interest not only in the academic field but also among industrial environments thanks to the fact that the Internet of Things has enabled its cost-effective implementation. Digital twins (or digital models) refer to a virtual representation of a physical product or process that integrate data from various sources such as data APIs, historical data, embedded sensors and open data, giving to the manufacturers an unprecedented view into how their products are performing. The EU-funded IoTwins project plans to build testbeds for digital twins in order to run real-time computation as close to the data origin as possible (e.g., IoT Gateway or Edge nodes), and whilst batch-wise tasks such as Big Data analytics and Machine Learning model training are advised to run on the Cloud, where computing resources are abundant. In this paper, the basic concepts of the IoTwins project, its reference architecture, functionalities and components have been presented and discussed

Comprehensive genetic dissection of wood properties in a widely-grown tropical tree: Eucalyptus

Background: Eucalyptus is an important genus in industrial plantations throughout the world and is grown for use as timber, pulp, paper and charcoal. Several breeding programmes have been launched worldwide to concomitantly improve growth performance and wood properties (WPs). In this study, an interspecific cross between Eucalyptus urophylla and E. grandis was used to identify major genomic regions (Quantitative Trait Loci, QTL) controlling the variability of WPs. Results: Linkage maps were generated for both parent species. A total of 117 QTLs were detected for a series of wood and end-use related traits, including chemical, technological, physical, mechanical and anatomical properties. The QTLs were mainly clustered into five linkage groups. In terms of distribution of QTL effects, our result agrees with the typical L-shape reported in most QTL studies, i.e. most WP QTLs had limited effects and only a few (13) had major effects (phenotypic variance explained > 15%). The co-locations of QTLs for different WPs as well as QTLs and candidate genes are discussed in terms of phenotypic correlations between traits, and of the function of the candidate genes. The major wood property QTL harbours a gene encoding a Cinnamoyl CoA reductase (CCR), a structural enzyme of the monolignol-specific biosynthesis pathway. Conclusions: Given the number of traits analysed, this study provides a comprehensive understanding of the genetic architecture of wood properties in this Eucalyptus full-sib pedigree. At the dawn of Eucalyptus genome sequence, it will provide a framework to identify the nature of genes underlying these important quantitative traits. (Résumé d'auteur

FITeagle: A semantic testbed management framework

Approaches to share resources across administrative domains have been developed for many years. A specific field of application is the experimental evaluation of Future Internet related research within federated facilities. Testbeds that want to offer support for this experiment life cycle have to expose multiple interfaces along with mechanisms to allow handovers between them. Existing work rest upon self-contained software components that are bound to specific protocols and schematic data models that aggravate such handovers. As a result, for a testbed the process of joining one or multiple federations is an expensive process. We propose an approach that reutilizes insights of the Semantic Web research to address parts of this issue. In this paper, we describe a framework which abstracts from specific protocols and functionalities by managing heterogeneous resources based on a semantic information model. As a result, different API calls relate to the semantically same resources which allows theses handovers and further makes this approach applicable to related fields such a federated cloud computing. We have validated our work within several research projects and further show that the implementation is standard compliant

A service orchestration architecture for Fog-enabled infrastructures

The development of Fog Computing technology is crucial to address the challenges to come with the mass adoption of Internet Of Things technology, where the generation of data tends to grow at an unprecedented pace. The technology brings computing power to the surrounds of devices, to offer local processing, filtering, storage and analysis of data and control over actuators. Orchestration is a requirement of Fog Computing technology to deliver services, based on the composition of microservices. It must take into consideration the heterogeneity of the IoT environment and device's capabilities and constraints. This heterogeneity requires a different approach for orchestration, be it regarding infrastructure management, node selection and/or service placement. Orchestrations shall be manually or automatically started through event triggers. Also, the Orchestrator must be flexible enough to work in a centralized or distributed fashion. Orchestration is still a hot topic and can be seen in different areas, especially in the Service Oriented Architectures, hardware virtualization, in the Cloud, and in Network Virtualization Function. However, the architecture of these solutions is not enough to handle Fog Requirements, specially Fog's heterogeneity, and dynamics. In this paper, we propose an architecture for Orchestration for the Fog Computing environment. We developed a prototype to proof some concepts. We discuss in this paper the implementation, and the tools chose, and their roles. We end the paper with a discussion on performance indicators and future direction on the evaluation of non-functional aspects of the Architecture

Evaluating the Impacts of Geographic Cohorting and Patient Rooming on Hospital Efficiency

*Purpose*: Patient-centered care is increasingly described as patients receiving “the right care, in the right place, at the right time”. In the inpatient setting, bed placement can contribute to that goal by facilitating high-performance teams while minimizing high-cost patient boarding and the safety implications of off-service placement. Geographic cohorting is a potentially important hospital quality goal for improving efficiency, as it may reduce length of stay and improve patient safety. Geographic cohorting (GCh) aims to place patients in rooms primarily by a team-based physical location. An example of this process would be to assign a hospitalist team to one unit and restrict admissions for that hospitalist team to that physical unit whenever possible. This study investigated the current state of GCh for general medicine patients at Maine Medical Center (MMC), the impact on length of stay (LOS) and other key measures, and potential opportunities and implications for implementing GCh in the future. *Methods*: An interdepartmental working group determined the operational definition for GCh for general medicine patients at MMC (e.g. patients on the “Medicine R7” team are “cohorted” when placed on the unit R7). These rules were compared to retrospective (FY2017-August 2019) electronic health record (EHR) data for patient admissions. The data elements included admission and discharge date/time/unit, and each treatment team assigned during the patient’s stay (e.g., “Medicine R4”). Using the R programming language and Excel, data were analyzed for cohorting performance (e.g. percent of patients cohorted at admission) by unit, by team, longitudinally, and in different hospital conditions (e.g. high census). Cohorted and non-cohorted patients were compared for length of stay. Using the placement rules, unit capacities, and actual data, a simulated “patient placer” was developed using Visual Basic for Applications (VBA). Using this simulator, altered placement rule sets were compared for impact on cohorting and hospital efficiency. *Results*: Patient admissions missing crucial data elements were removed from the data set (37.65%), leaving 56,631 patient admissions for analysis; of these, 20,262 (35.78%) were general medicine patients. 52.1% of general medicine patients were cohorted on admission versus more than 60% at discharge. The Medicine P2C and Hospitalist Gibson APP teams were the most commonly cohorted, as were the P2C and R7 units. Cohorting performance did not appear to correlate with hospital census or turnover. Medicine R2/R2APP showed a decrease in median LOS between cohorted and non-cohorted patients of .27 days. The patient placer demonstrated that the prioritization of cohorting during patient placement is likely feasible within the current capacity of MMC. *Conclusions*: General medicine patients are more often cohorted on discharge than admission, likely due to internal transfers during the patient admission. Cohorting at admission appears to be correlated with a decreased length of stay in certain conditions. Improving GCh appears feasible given the results of the patient placer. This study was limited by missing data and a lack of patient-level demographic and acuity information, especially level of care. Further studies should match patients based on their characteristics in order to evaluate the impact of cohorting on outcomes