Application of Digital Ecosystem Design Methodology Within the Health Domain

We define a digital ecosystem (DES) as the dynamic and synergetic complex of digital communities consisting of interconnected, interrelated, and interdependent digital species situated in a digital environment that interact as a functional unit and are linked together through actions, information, and transaction flows. The design of DESs requires the integration of a number of different and complementary technologies, including agent-based and self-organizing systems, ontologies, swarm intelligence, ambient intelligence, data mining, genetic algorithms, etc. The integration of multiple technologies and the resulting synergetic effects contribute to the creation of highly complex, dynamic, and powerful systems. The application of DESs within different domains has the power to transform these domains by giving them a more intelligent and a more dynamic nature. In this paper, we illustrate how a DES design methodology can be used to systematically create a Digital Health Ecosystem (DHES). We address the key steps associated with the DES design and focus specifically on the use of the electronic health records within the DHES. The design methodology framework illustrated in this paper serves as a navigating tool during the design of DHESs

Application of digital ecosystems in health domain

Digital Ecosystems (DES) have recently been introduced into the computer and information societies. A Digital Ecosystem is the dynamic and synergetic complex of Digital Communities consisting of interconnected, interrelated and interdependent Digital Species situated in a Digital Environment, that intereact as a functional unit and are linked together through actions, information and transaction flows. Digital Ecosystems integrate various cutting-edge technologies including ontologies, agent-based and self-organizing systems, swarm intelligence, ambient intelligence, data mining etc. The synergetic effects of these methodologies results in a more efficient, effective, reliable and secure system.The application of DES within the health domain would transform the way in which health information is created, stored, accessed, used, managed, analyzed and shared, and would bring an innovative breakthrough within bealth domain. In this paper, we illustrate how the DES Design Methodology can be implemented within the health domain. We focus on the key factors associated with the DES design. The design methodology framework allows better control over the design process and serves as a navigating tool during the Digital Health Ecosystems design

Value-driven partner search for <i>Energy from Waste</i> projects

Energy from Waste (EfW) projects require complex value chains to operate effectively. To identify business partners, plant operators need to network with organisations whose strategic objectives are aligned with their own. Supplier organisations need to work out where they fit in the value chain. Our aim is to support people in identifying potential business partners, based on their organisation’s interpretation of value. Value for an organisation should reflect its strategy and may be interpreted using key priorities and KPIs (key performance indicators). KPIs may comprise any or all of knowledge, operational, economic, social and convenience indicators. This paper presents an ontology for modelling and prioritising connections within the business environment, and in the process provides means for defining value and mapping these to corresponding KPIs. The ontology is used to guide the design of a visual representation of the environment to aid partner search

The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Checklist for One Health Epidemiological Reporting of Evidence (COHERE).

One Health is defined as the intersection and integration of knowledge regarding humans, animals, and the environment, yet as the One Health scientific literature expands, there is considerable heterogeneity of approach and quality of reporting in One Health studies. In addition, many researchers who publish such studies do not include or integrate data from all three domains of human, animal, and environmental health. This points to a critical need to unify guidelines for One Health studies. This report details the Checklist for One Health Epidemiological Reporting of Evidence (COHERE) to guide the design and publication format of future One Health studies. COHERE was developed by a core writing team and international expert review group that represents multiple disciplines, including human medicine, veterinary medicine, public health, allied professionals, clinical laboratory science, epidemiology, the social sciences, ecohealth and environmental health. The twin aims of the COHERE standards are to 1) improve the quality of reporting of observational or interventional epidemiological studies that collect and integrate data from humans, animals and/or vectors, and their environments; and 2) promote the concept that One Health studies should integrate knowledge from these three domains. The 19 standards in the COHERE checklist address descriptions of human populations, animal populations, environmental assessment, spatial and temporal relationships of data from the three domains, integration of analyses and interpretation, and inclusion of expertise in the research team from disciplines related to human health, animal health, and environmental health

Disrupting Illicit Supply Networks: New Applications of Operations Research and Data Analytics to End Modern Slavery

Report from a 2017 National Science Foundation workshop on promising research directions for applications of operations research and data analytics toward the disruption of illicit supply networks like human trafficking. The workshop was funded by the NSF’s Operations Engineering (ENG) and the Law & Social Sciences Program (SBE) under grant # CMMI-1726895. The report addresses the opportunity to apply advances from the fields of operations research, management science, analytics, machine learning, and data science toward the development of disruptive interventions against illicit networks. Such an extension of the current research agenda for trafficking would move understanding of such dynamic systems from descriptive characterization and predictive estimation toward improved dynamic operational control.Bureau of Business Researc