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.
Objectives Most complex healthcare interventions target a network of healthcare professionals. Social network analysis (SNA) is a powerful technique to study how social relationships within a network are established and evolve. We identified in which phases of complex healthcare intervention research SNA is used and the value of SNA for developing and evaluating complex healthcare interventions. Methods A scoping review was conducted using the Arksey and O’Malley methodological framework. We included complex healthcare intervention studies using SNA to identify the study characteristics,level of complexity of the healthcare interventions, reported strengths and limitations, and reported implications of SNA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews 2018 was used to guide the reporting. Results Among 2466 identified studies, 40 studies were selected for analysis. At first, the results showed that SNA seems underused in evaluating complex intervention research. Second, SNA was not used in the development phase of the included studies. Third, the reported implications in the evaluation and implementation phase reflect the value of SNA in addressing the implementation and population complexity. Fourth, pathway complexity and contextual complexity of the included interventions were unclear or unable to access. Fifth, the use of a mixed methods approach was reported as a strength, as the combination and integration of a quantitative and qualitative method clearly establishes the results. Conclusion SNA is a widely applicable method that can be used in different phases of complex intervention research. SNA can be of value to disentangle and address the level of complexity of complex healthcare interventions. Furthermore, the routine use of SNA within a mixed method approach could yield actionable insights that would be useful in the transactional context of complex interventions.
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.
Indonesia’s peat forests remain severely threatened by forest fires, oil palm plantation development and extractive industries, which leads to biodiversity loss, increased emissions of greenhouse gases, and the marginalization of Indigenous Peoples and local communities. In 2008 the Government of Indonesia introduced the Social Forestry Programme under which Indigenous Peoples and local communities can acquire a 35-year management permit. Since then, about 10 percent of Indonesian State Forest has been designated for community-based forest conservation and restoration initiatives. The devolution of authority to the local level has created a new playing field. The Social Forestry Programme reverses more than a century of centralistic forest policy, and requires a fundamental re-orientation of all actors working in the forestry sector. The central question underlying this proposal is how Dutch civil society organizations (applied universities and NGOs) can effectively support Indigenous Peoples and local communities in the protection and restoration of peat forests in Indonesia. This project aims to set up a Living Lab in Ketapang District in West Kalimantan to study, identify and test novel ways to work together with a variety of stakeholders to effectively conserve and restore peat forest. In Ketapang District, Tropenbos Indonesia has assisted three Village Forest Management Groups (Pematang Gadung, Sungai Pelang and Sungai Besar) in securing a Social Forestry Permit. Students from three Dutch Universities (Van Hall Larenstein, Aeres Hogeschool and Inholland) will conduct research in partnership with students from Universitas Tanjungpura on the integration of local ecological knowledge and technical expertise, on the economic feasibility of community-based forestry enterprises, and on new polycentric governance structures. The results of these studies will be disseminated to policy makers and civil society groups working in Indonesia, using the extensive networks of IUCN NL and Tropenbos Indonesia.
