At the 5Groningen field lab, the next generation of wireless technology is being put to the test in an experiment with a prototype involving real-time decision-making using 5G edge computing. One of the applications envisioned is a smart police vest that can fully automatically detect threats like firearms and stabbing weapons.
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The development of information and communication technologies (ICT) has led to many innovative technologies. The integration of technologies such as the internet of things (IoT), cloud computing, and machine learning concepts have given rise to Industry 4.0. Fog and edge computing have stepped in to fill the areas where cloud computing is inadequate to ensure these systems work quickly and efficiently. The number of connected devices has brought about cybersecurity issues. This study reviewed the current literature regarding edge/fog-based cybersecurity in IoT to display the current state.
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Information and Communication Technologies (ICTs) affect the environment in various ways. Their energy consumption is growing exponentially, with and without the use of ‘green’ energy. Increasing environmental awareness within information science has led to discussions on sustainable development. ‘Green Computing’ has been introduced: the study and practice of environmentally sus- tainable computing. This can be defined as ‘designing, manufacturing, using, and disposing of com- puters, servers, and associated subsystems - such as monitors, printers, storage devices, and net- working and communications systems - efficiently and effectively with minimal or no impact on the en- vironment’. Nevertheless, the data deluge makes it not only necessary to pay attention to the hard- and software dimensions of ICTs but also to the value of the data stored. We explore the possibilities to use information and archival science to reduce the amount of stored data. In reducing this amount of stored data, it’s possible to curb unnecessary power consumption. The objectives of this paper are to develop a model (and test its viablility) to [1] increase awareness in organizations for the environ- mental aspects of data storage, [2] reduce the amount of stored data, and [3] reduce power consump- tion for data storage. This model integrates the theories of Green Computing, Information Value Chain (IVC) and Archival Retention Levels (ARLs). We call this combination ‘Green Archiving’. Our explora- tory research was a combination of desk research, qualitative interviews with information technology and information management experts, a focus group, and two exploratory case studies. This paper is the result of the first stage of a research project that is aimed at developing low power ICTs that will automatically appraise, select, preserve or permanently delete data based on their value. Such an ICT will automatically reduce storage capacity and curb power consumption used for data storage. At the same time, data disposal will reduce overload caused by storing the same data in different for- mats, it will lower costs and it reduces the potential for liability.
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The Northern Netherlands (NN) finds itself at the junction of all the big transitions. Digitalisation is essential to follow through with these. Considering this, our region has the potential to make sizeable progress if it can successfully roll out widespread digitalisation. As a hardcore transition economy, the NN may even join the European frontrunners and act as an example for other regions. It is from this challenge that the NN will start with the European Digital Innovation Hub (EDIH NN). We have chosen to specialise in the area of Autonomous Systems, which includes multiple digital technologies that are relevant for the four transitions in the NN: (1) Smart Agro, (2) Smart Manufacturing, (3) Life Science and Health and (4) Utilities, Built Environment and Mobility. In the first three-year EDIH NN wants to support more than 750 companies and lay the foundation for long-term support of all companies. The following building blocks for EDIH NN are: • A Brokerage network that will identify issues regarding digitalisation and relay these to Solution Providers (high TRL) and knowledge providers (low TRL). • A Test Before Invest network (test and demo facilities) comprising 20+ organisations that will invest in Autonomous Systems within their domain, and collaborate towards becoming a European testing ground. • A Smart Factory Accelerator to strengthen the digital maturity of companies. • An Empowerment programme to strengthen companies in the areas of DEP Technologies: Cyber Security and Artificial Intelligence. • An approach based on High Performance Computing to make digitalisation more accessible. • The Smart Makers Academy: A programme aimed at matching supply and demand around digital skills, based on individual learning outcomes. • A Funding Readiness programme to help companies that need to invest for their digitalisation strategy, in finding funding opportunities. • A network to stimulate supply and demand around Autonomous Systems
Het is een tijds- en kostenintensief proces om de conditie van assets in de publieke ruimte te monitoren. Nieuwe technologie in de vorm van 3D LiDAR scanning biedt nieuwe mogelijkheden voor conditiemonitoring. Het doel van deze KIEM-aanvraag is (i) om de hardware geschikt te maken voor frequente en goedkope opnames in de stedelijke omgeving, (ii) de analysetechnieken van de geproduceerde datasets verder te ontwikkelen en (iii) een geannoteerde dataset gefocust op asset management te produceren. Dit zorgt ervoor dat publieke en MKB-partijen slimmere, snellere en volledigere onderhoudsbeslissingen kunnen nemen. Het consortium van Fietskoerier.nl, Sonarski, Gemeente Amsterdam en de Hogeschool van Amsterdam heeft elkaar gevonden in de vraag: “Hoe kan (publieke) LiDAR data bijdragen aan SMART Asset Management?” Dit project bevat een unieke combinatie van twee technologieën die op dit moment in ontwikkeling zijn (i) sensor data gedreven conditiemonitoring en (ii) point cloud algoritmes op LiDAR data. Fietskoerier.nl heeft de resources om op een duurzame manier de stad in kaart te brengen. Sonarski heeft een oplossing voor het uitvoeren van de 3D scans en Gemeente Amsterdam is een belangrijke kennispartner en heeft groot scala aan assets in de publieke ruimte. De deelnemers van dit project zien deze aanvraag als een eerste stap en hebben de intentie om te groeien tot een groter consortium welke de gehele keten van onderhoud omvat.
Plastic waste is one of the largest environmental problems in the 21st century. By 2050, up to 12,000 Mt of plastic waste is estimated to be in landfills or in the natural environment. Biochemical recycling by using modified microbial enzymes have shown potentials in the back-to-monomer (BTM) recycling of polyethylene terephthalate by breaking down the polymers into re-usable monomers. These enzymes can be produced via fungal species. In order to make this biochemical BTM process viable a process integrated enzyme production is key in increasing the efficiency and reducing the cost of enzymes. For this a molecular monitoring method, such as RNA-seq (RNA-sequencing), is needed. RNA-seq can achieve a snapshot on enzyme producing process inside of the cell by semi-quantitatively measuring the volume of enzyme encoding RNAs. This information can bring hints on fungal strain improvement by promoting the desired enzymes. It also helps to instantly monitor the BTM production outcomes. However, conventional RNA-seq platforms can only be performed via service providers or startup investments reaching 2 million euros. Each round of analysis could take as long as 6 weeks turnaround time. Furthermore, the method creates huge amount of complicated datasets, only by expert skills and specialized high performance computing the data can be sorted in a comprehensive manner. To solve these problems, in this project, by combining the expertise on plastic end-of-life control, fungal enzyme production, molecular monitoring and Bioinformatics from both the UAS and SME sides, we aim to implement a novel RNA-seq based system to monitor the in-process enzyme production for plastic degradation. We will optimize the existing portable RNA-seq prototype machinery for semi-real time monitoring of the BTM recycling process. The downstream data will be handled by a tailored analysis pipeline designed with expert knowledge via an user-friendly interface.
