Energy policies are vital tools used by countries to regulate economic and social development as well as guarantee national security. To address the problems of fragmented policy objectives, conflicting tools, and overlapping initiatives, the internal logic and evolutionary trends of energy policies must be explored using the policy content. This study uses 38,277 energy policies as a database and summarizes the four energy policy objectives: clean, low-carbon, safe, and efficient. Using the TextCNN model to classify and deconstruct policies, the LDA + Word2vec theme conceptualization and similarity calculations were compared with the EISMD evolution framework to determine the energy policy theme evolution path. Results indicate that the density of energy policies has increased. Policies have become more comprehensive, barriers between objectives have gradually been broken, and low-carbon objectives have been strengthened. The evolution types are more diversified, evolution paths are more complicated, and the evolution types are often related to technology, industry, and market maturity. Traditional energy themes evolve through inheritance and merger; emerging technology and industry themes evolve through innovation, inheritance, and splitting. Moreover, this study provides a replicable analytical framework for the study of policy evolution in other sectors and evidence for optimizing energy policy design
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Copyright enforcement by private third parties – does it work uniformly across the EU? Since the inception of Napster, home copying of digital files has taken a flight. The first providers of software or infrastructure for the illegal exchange of files were held contributory or vicariously liable for copyright infringement. In response, they quickly diluted the chain of liability to such an extent that neither the software producers, nor the service providers could be held liable. Moving further down the communication chain, the rights holders are now requiring Internet Service Providers (ISPs) that provide access to end customers to help them with the enforcement of their rights. This article discusses case-law regarding the enforcement of copyright by Internet Access Providers throughout Europe. At first glance, copyright enforcement has been harmonised by means of a number of directives, and article 8(3) of the Copyright Directive (2001/29/EC) regulates that EU Member States must ensure the position of rights holders with regard to injunctions against ISPs. Problem solved? Case law from Denmark, Ireland, Belgium, Norway, England, The Netherlands, Austria and the Court of Justice of the EU was studied. In addition, the legal practice in Germany was examined. The period of time covered by case law is from 2003 to 2013, the case law gives insight into the differences that still exist after the implementation of the directive.
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Abstract: The typical structure of the healthcare sector involves (specialist) intertwined practices co-occurring in formal or informal networks. These practices must answer to the concerns and needs of all related stakeholders. Multimorbidity and the need to share knowledge for scientific development are among the driving factors for collaboration in healthcare. To establish and keep up a permanent collaborative link, it takes effort and understanding of the network characteristics that must be governed. It is not hard to find practices of Network Governance (NG) in a variety of industries. Still, there is a lack of insight in this subject, including knowledge on how to establish and maintain an effective healthcare network. Consequently, this study's research question is: How is network governance organized in the healthcare sector? A systematic literature study was performed to select 80 NG articles. Based on these publications the characteristics of NG are made explicit. The findings demonstrate that combinations of governance style (relational versus contractual governance) and governance structure (lead versus shared governance) lead to different network dynamics. Furthermore, the results show that in order to comprehend how networks in the healthcare sector emerge and can be regulated, it is vital to understand the current network type. Additionally, it informs us of the governing factors. Zie https://www.hbo-kennisbank.nl/details/sharekit_han:oai:surfsharekit.nl:e4f8fa3a-4af8-42ef-b2dd-c86d77b4cec6
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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.
