Many articles have been published on scale-down concepts as well as additive manufacturing techniques. However, information is scarce when miniaturization and 3D printing are applied in the fabrication of bioreactor systems. Therefore, garnering information for the interfaces between miniaturization and 3D printing becomes important and essential. The first goal is to examine the miniaturization aspects concerning bioreactor screening systems. The second goal is to review successful modalities of 3D printing and its applications in bioreactor manufacturing. This paper intends to provide information on anaerobic digestion process intensification by fusion of miniaturization technique and 3D printing technology. In particular, it gives a perspective on the challenges of 3D printing and the options of miniature bioreactor systems for process high-throughput screening.
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The project discussed in this paper is aimed at increasing people’s understanding of the existence and desired workings of ambient technology in the home by demonstrating its potential. For this purpose, an interactive dollhouse is presented. The dollhouse, a miniature model of a sensor-equipped home, was developed and used to engage elderly users in the design of an ambient monitoring system. This paper explains the design of the interactive dollhouse and the ways it was used as an elderly-centered design method for increasing understanding of the desired workings of ambient monitoring in the home.
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A novel type of application for the exploration of enclosed or otherwise difficult to access environments requires large quantities of miniaturized sensor nodes to perform measurements while they traverse the environment in a “go with the flow” approach. Examples of these are the exploration of underground cavities and the inspection of industrial pipelines or mixing tanks, all of which have in common that the environments are difficult to access and do not allow position determination using e.g. GPS or similar techniques. The sensor nodes need to be scaled down towards the millimetre range in order to physically fit through the narrowest of parts in the environments and should measure distances between each other in order to enable the reconstruction of their positions relative to each other in offline analysis. Reaching those levels of miniaturization and enabling reconstruction functionality requires: 1) novel reconstruction algorithms that can deal with the specific measurement limitations and imperfections of millimetre-sized nodes, and 2) improved understanding of the relation between the highly constraint hardware design space of the sensor nodes and the reconstruction algorithms. To this end, this work provides a novel and highly robust sensor swarm reconstruction algorithm and studies the effect of hardware design trade-offs on its performance. Our findings based on extensive simulations, which push the reconstruction algorithm to its breaking point, provide important guidelines for the future development of millimetre-sized sensor nodes.
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Management policy for protected species is currently often based on literature reviews and expert judgement, even though it requires tailor-made species knowledge on a local level. While wildlife management should preferably be evidence based, tailor-made field data is seldom used in current practices, because it is hardly available, difficult to collect and expensive. Recent development of digital technology is changing the field of wildlife management with “more, better, faster and cheaper” ways of data collection. Especially automated collection of field data with different types of sensors is promising, whereas miniaturization and low cost mass-production increase availability and use of these sensors. For collection of field data about predator-prey interactions, there is a need to develop wireless sensor networks that automatically identify different species in a community, while they record their spatially explicit data and their behaviour. Therefore, we will put together a consortium of partners that will develop a EU LIFE programme proposal, with the focus to develop a sensor network necessary to automatically monitor multiple species (i.e., species communities) for species conservation management. The consortium will consist of Van Hall Larenstein, Sovon Dutch Centre for Field Ornithology, the Dutch Mammal Society, Sensing Clues and DIKW intelligence. It will bring together a strong mix of expert knowledge on applied species conservation and wildlife management, ecological field research, wildlife intelligence, and handling and analysis of big data. This project matches the Top sector High-tech Systems & Materials, and revolves around 4 distinct phases: selection of potential consortium partners, exploration of the problem, working towards a common action perspective and writing a EU LIFE programme proposal. We will use knowledge co-creation techniques to explore the first three project phases.
Wildlife crime is an important driver of biodiversity loss and disrupts the social and economic activities of local communities. During the last decade, poaching of charismatic megafauna, such as elephant and rhino, has increased strongly, driving these species to the brink of extinction. Early detection of poachers will strengthen the necessary law enforcement of park rangers in their battle against poaching. Internationally, innovative, high tech solutions are sought after to prevent poaching, such as wireless sensor networks where animals function as sensors. Movement of individuals of widely abundant, non-threatened wildlife species, for example, can be remotely monitored ‘real time’ using GPS-sensors. Deviations in movement of these species can be used to indicate the presence of poachers and prevent poaching. However, the discriminative power of the present movement sensor networks is limited. Recent advancements in biosensors led to the development of instruments that can remotely measure animal behaviour and physiology. These biosensors contribute to the sensitivity and specificity of such early warning system. Moreover, miniaturization and low cost production of sensors have increased the possibilities to measure multiple animals in a herd at the same time. Incorporating data about within-herd spatial position, group size and group composition will improve the successful detection of poachers. Our objective is to develop a wireless network of multiple sensors for sensing alarm responses of ungulate herds to prevent poaching of rhinos and elephants.
