On the coset category of a skew lattice

Skew lattices are non-commutative generalizations of lattices. The coset structure decomposition is an original approach to the study of these algebras describing the relation between its rectangular classes. In this paper we will look at the category determined by these rectangular algebras and the morphisms between them, showing that not all skew lattices can determine such a category. Furthermore, we will present a class of examples of skew lattices in rings that are not strictly categorical, and present sufficient conditions for skew lattices of matrices in rings to constitute $\wedge$-distributive skew lattices.Comment: 17 pages, submitted to Demonstratio Mathematica. arXiv admin note: text overlap with arXiv:1212.649

On ideals of a skew lattice

Ideals are one of the main topics of interest to the study of the order structure of an algebra. Due to their nice properties, ideals have an important role both in lattice theory and semigroup theory. Two natural concepts of ideal can be derived, respectively, from the two concepts of order that arise in the context of skew lattices. The correspondence between the ideals of a skew lattice, derived from the preorder, and the ideals of its respective lattice image is clear. Though, skew ideals, derived from the partial order, seem to be closer to the specific nature of skew lattices. In this paper we review ideals in skew lattices and discuss the intersection of this with the study of the coset structure of a skew lattice.Comment: 16 page

SMALL AND MEDIUM PORTS' ACTIVITIES MODELLING: INTRODUCTION TO THE PIXEL APPROACH

[EN] Port activities undeniably have an impact on their environment, the city and citizens living nearby. To have a better understanding of these impacts, the ports of the future will require tools allowing suitable modelling, simulation and data analysis. This challenge is also tied to another current reality: the heterogeneous data coming from different stakeholders converging into ports are not optimally exploited due to lack of interoperability. Thus, the forthcoming research and development initiatives must address these demands from a holistic point of view. PIXEL (H2020-funded project) aims at creating the first smart, flexible and scalable solution reducing the environmental impact while enabling optimization of operations in port ecosystems. PIXEL brings the most innovative IoT and ICT technology to ports and demonstrate their capacity to take advantage of modern approaches. Using an interoperable open IoT platform, data is acquired and integrated into an information hub comprised of small, low-level sensors up to virtual sensors able to extract relevant data from high level services. Finally, this hub integrates smart models to analyse port processes for prediction and optimization purposes: (i) a model of consumption and energy production of the port with the aim of moving towards green energy production; (ii) a model of congestion of multi-modal transport networks to reduce the impact of port traffic on the network; and (iii) models of environmental pollution of the port to reduce the environmental impacts of the port on the city and its citizens. The main issue tackled by PIXEL is to provide interoperability between these models and allow real integration and communication in the context of an environmental management model. Besides that, PIXEL devotes to decouple port¿s size and its ability to deploy environmental impact mitigation specifying an innovative methodology and an integrated metric for the assessment of the overall environmental impact of ports.The PIXEL project, the results of which are presented in this paper, is being funded from the European Union s Horizon 2020 research and innovation programme under grant agreement no. 769355 Port IoT for Environmental Leverage (PIXEL)Simon, E.; Garnier, C.; Lacalle, I.; Costa, JP.; Palau Salvador, CE. (2019). SMALL AND MEDIUM PORTS' ACTIVITIES MODELLING: INTRODUCTION TO THE PIXEL APPROACH. WIT Transactions on the Built Environment (Online). 187:149-163. https://doi.org/10.2495/MT190141S14916318

NewsMeSH: a new classifier designed to annotate health news with MeSH headings

Motivation In the age of big data, the amount of scientific information available online dwarfs the ability of current tools to support researchers in locating and securing access to the necessary materials. Well-structured open data and the smart systems that make the appropriate use of it are invaluable and can help health researchers and professionals to find the appropriate information by, e.g., configuring the monitoring of information or refining a specific query on a disease. Methods We present an automated text classifier approach based on the MEDLINE/MeSH thesaurus, trained on the manual annotation of more than 26 million expert-annotated scientific abstracts. The classifier was developed tailor-fit to the public health and health research domain experts, in the light of their specific challenges and needs. We have applied the proposed methodology on three specific health domains: the Coronavirus, Mental Health and Diabetes, considering the pertinence of the first, and the known relations with the other two health topics. Results A classifier is trained on the MEDLINE dataset that can automatically annotate text, such as scientific articles, news articles or medical reports with relevant concepts from the MeSH thesaurus. Conclusions The proposed text classifier shows promising results in the evaluation of health-related news. The application of the developed classifier enables the exploration of news and extraction of health-related insights, based on the MeSH thesaurus, through a similar workflow as in the usage of PubMed, with which most health researchers are familiar

System Architecture of A European Platform for Health Policy Decision Making: MIDAS

Background: Healthcare data is a rich yet underutilized resource due to its disconnected, heterogeneous nature. A means of connecting healthcare data and integrating it with additional open and social data in a secure way can support the monumental challenge policy-makers face in safely accessing all relevant data to assist in managing the health and wellbeing of all. The goal of this study was to develop a novel health data platform within the MIDAS (Meaningful Integration of Data Analytics and Services) project, that harnesses the potential of latent healthcare data in combination with open and social data to support evidence-based health policy decision-making in a privacy-preserving manner. Methods: The MIDAS platform was developed in an iterative and collaborative way with close involvement of academia, industry, healthcare staff and policy-makers, to solve tasks including data storage, data harmonization, data analytics and visualizations, and open and social data analytics. The platform has been piloted and tested by health departments in four European countries, each focusing on different region-specific health challenges and related data sources. Results: A novel health data platform solving the needs of Public Health decision-makers was successfully implemented within the four pilot regions connecting heterogeneous healthcare datasets and open datasets and turning large amounts of previously isolated data into actionable information allowing for evidence-based health policy-making and risk stratification through the application and visualization of advanced analytics. Conclusions: The MIDAS platform delivers a secure, effective and integrated solution to deal with health data, providing support for health policy decision-making, planning of public health activities and the implementation of the Health in All Policies approach. The platform has proven transferable, sustainable and scalable across policies, data and regions