This paper analyzes connectivity and efficiency of a SME network across two industries. These characteristics are likely to be different for networks of various industries. The concept of 'small worlds' is used to judge overall network efficiency. The actual network can be classified as one in which a small world is present. Visualization of the results shows a single core group in the network. It was found that non-profit as well as science actors were overrepresented in the core of the field.
This is the age of network extinction. Small is trivial. Notorious vagueness and non-commitment on the side of slackerish members killed the once cute, postmodern construct of networks. Platforms did the rest. Decentralization may be the flavour of the day, but no one is talking about networks anymore as a solution for the social media mess. Where have all the networks gone?(This essay was written in August 2019 for the INC/Transmediale co-publication The Eternal Network: The Ends and Becomings of Network Culture that came out on January 28, 2020 at the opening of the Berlin Transmediale festival. You can read and download the publication here. The essay was slightly shortened; below you will find the original text).
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This essay is based on research promoted by INDIRE, Italian NationalInstitute for Documentation, Innovation and Educational Researchin Education, and is developed under the research on ‘Professionalnetworks, Educational models and School principal’s profile in Italy’. Onthe basis of observation and analysis of research data, a new theoryis assumed and new characteristics are defined, belonging to bothprofessional networks and educational models applied to all typesof professional networks. The characteristics so far identified are:plastic nature of networks, network punctuated equilibrium, networkconnectivity, emergent behavior and sociality of network members.It is also shown how the knowledge shared in a network materializes inEvents that produce Event Capital. The theory will be complemented byan experimentation phase.
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The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
ILIAD builds on the assets resulting from two decades of investments in policies and infrastructures for the blue economy and aims at establishing an interoperable, data-intensive, and cost-effective Digital Twin of the Ocean (DTO). It capitalizes on the explosion of new data provided by many different earth sources, advanced computing infrastructures (cloud computing, HPC, Internet of Things, Big Data, social networking, and more) in an inclusive, virtual/augmented, and engaging fashion to address all Earth Data challenges. It will contribute towards a sustainable ocean economy as defined by the Centre for the Fourth Industrial Revolution and the Ocean, a hub for global, multi-stakeholder co-operation.
