Reliability is a constraint of low-power wireless connectivity, commonly addressed by the deployment of mesh topology. Accordingly, power consumption becomes a major concern during the design and implementation of such networks. Thus, a mono-objective optimization was implemented in this work to decrease the total amount of power consumed by a low-power wireless mesh network based on Thread protocol. Using a genetic algorithm, the optimization procedure takes into account a pre-defined connectivity matrix, in which the possible distances between all network devices are considered. The experimental proof-of-concept shows that a mean gain of 26.45 dB is achievable in a specific scenario. Through our experimental results, we conclude that the Thread mesh protocol has much leeway to meet the low-power consumption requirement of wireless sensor networks.
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Using an optimized transformation protocol we have studied the possible interactions between transforming plasmid DNA and the Hansenula polymorpha genome. Plasmids consisting only of a pBR322 replicon, an antibiotic resistance marker for Escherichia coli and the Saccharomyces cerevisiae LEU2 gene were shown to replicate autonomously in the yeast at an approximate copy number of 6 (copies per genome equivalent). This autonomous behaviour is probably due to an H. polymorpha replicon-like sequence present on the S. cerevisiae LEU2 gene fragment. Plasmids replicated as multimers consisting of monomers connected in a head-to-tail configuration. Two out of nine transformants analysed appeared to contain plasmid multimers in which one of the monomers contained a deletion. Plasmids containing internal or flanking regions of the genomic alcohol oxidase gene were shown to integrate by homologous single or double cross-over recombination. Both single- and multi-copy (two or three) tandem integrations were observed. Targeted integration occurred in 1-22% of the cases and was only observed with plasmids linearized within the genomic sequences, indicating that homologous linear ends are recombinogenic in H. polymorpha. In the cases in which no targeted integration occurred, double-strand breaks were efficiently repaired in a homology-independent way. Repair of double-strand breaks was precise in 50-68% of the cases. Linearization within homologous as well as nonhomologous plasmid regions stimulated transformation frequencies up to 15-fold.
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and metereological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be GatedRecurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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Publicatie bij de rede, uitgesproken bij de aanvaarding van het ambt als lector Green Biotechnology aan Hogeschool Inholland te Amsterdam op 20 mei2015 door dr. C.M. Kreike
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Completeness of data is vital for the decision making and forecasting on Building Management Systems (BMS) as missing data can result in biased decision making down the line. This study creates a guideline for imputing the gaps in BMS datasets by comparing four methods: K Nearest Neighbour algorithm (KNN), Recurrent Neural Network (RNN), Hot Deck (HD) and Last Observation Carried Forward (LOCF). The guideline contains the best method per gap size and scales of measurement. The four selected methods are from various backgrounds and are tested on a real BMS and meteorological dataset. The focus of this paper is not to impute every cell as accurately as possible but to impute trends back into the missing data. The performance is characterised by a set of criteria in order to allow the user to choose the imputation method best suited for its needs. The criteria are: Variance Error (VE) and Root Mean Squared Error (RMSE). VE has been given more weight as its ability to evaluate the imputed trend is better than RMSE. From preliminary results, it was concluded that the best K‐values for KNN are 5 for the smallest gap and 100 for the larger gaps. Using a genetic algorithm the best RNN architecture for the purpose of this paper was determined to be Gated Recurrent Units (GRU). The comparison was performed using a different training dataset than the imputation dataset. The results show no consistent link between the difference in Kurtosis or Skewness and imputation performance. The results of the experiment concluded that RNN is best for interval data and HD is best for both nominal and ratio data. There was no single method that was best for all gap sizes as it was dependent on the data to be imputed.
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The evolution of emerging technologies that use Radio Frequency Electromagnetic Field (RF-EMF) has increased the interest of the scientific community and society regarding the possible adverse effects on human health and the environment. This article provides NextGEM’s vision to assure safety for EU citizens when employing existing and future EMF-based telecommunication technologies. This is accomplished by generating relevant knowledge that ascertains appropriate prevention and control/actuation actions regarding RF-EMF exposure in residential, public, and occupational settings. Fulfilling this vision, NextGEM commits to the need for a healthy living and working environment under safe RF-EMF exposure conditions that can be trusted by people and be in line with the regulations and laws developed by public authorities. NextGEM provides a framework for generating health-relevant scientific knowledge and data on new scenarios of exposure to RF-EMF in multiple frequency bands and developing and validating tools for evidence-based risk assessment. Finally, NextGEM’s Innovation and Knowledge Hub (NIKH) will offer a standardized way for European regulatory authorities and the scientific community to store and assess project outcomes and provide access to findable, accessible, interoperable, and reusable (FAIR) data.
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A modified genetic algorithm (MGA) optimization procedure, alongside time series machine learning (ML) classifiers, is proposed to minimize handovers in a digital twin-based visible light communication (VLC) system. Frequent handovers have a direct impact on the overall performance of the VLC system due to the inherent connection downtime of a handover process. The handover scheme proposed in this article considers the receiver trajectory information to minimize handovers, maintaining the system performance below the forward error correction limit. Simulation results indicate that the proposed scheme outperforms a power-based handover scheme, achieving handover reductions of 42.47%. Therefore, the MGA combined to the ML models approach is an effective means of minimizing handovers, as well as improving overall VLC system performance.
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The challenges we collectively face, such as climate change, are characterized by more complexity, interdependence, and dynamism than is common for educational practice. This presents a challenge for (university) education. These transition challenges are often described as wicked or VUCA (Volatile, Uncertain, Complex, and Ambiguous) problems. In response, educational innovations that are inspired by ecology such as living labs are starting to emerge, but little is known about how learners engage within and with these more ecological forms of education. This work is an exploratory study into how learners navigate VUCA learning environments linked to tackling sustainability transition challenges, with a focus on the positive qualities of these experiences. This is done through interpretative phenomenological analysis (IPA) of seven students (using semi-structured interviews) of the MSC Metropolitan Analysis, Design and Engineering program, a joint degree from Wageningen University and Delft University of Technology in the Netherlands. The main findings, which are both psychological and educational, of this exploration include openness to new experiences (1), flexibility (2), a process appreciation of learning (3), a desire to create a positive impact on one’s direct biophysical environment (4) and society (5). In addition, we discuss the potential limitations of the malleability of these different qualities and propose future avenues for research into ecological learning for universities. This work closes by highlighting recommendations for educators to consider when designing or engaging in ecological forms of higher education that connect students to sustainability transitions.
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Dit artikel onderzoekt immuniteit buiten de contouren van een menselijk lichaam en een biopolitiek kader in het plantwetenschappelijk materieel-discursief object van het superonkruid met zijn resistentie en tolerantie voor herbiciden. In plaats van categorisch aan te nemen dat alle vormen van immuniteit en immuunsystemen plaatsvinden binnen de abstracte categorie van het (menselijk) lichaam, besteedt het artikel aandacht aan de manier waarop het superonkruid als analytisch en synthetiserend brandpunt het concept van immuniteit gaat bevolken en erdoor bevolkt wordt. In het algemeen beweert de auteur dat de materiële dimensie van het superonkruid kan worden gezien als een uitbreiding van of aanvulling op noties van het individuele, autonome en begrensde menselijke lichaam, maar dat deze materiële dimensie ook zijn eigen subjectpositie kan ondermijnen. Door het concept van immuniteit los te koppelen van zijn 'oorsprong' in het menselijk lichaam, kunnen nieuwe ontologische gronden voor menselijke en niet-menselijke politieke ecologieën worden bedacht, met een andere vorm van belichaming, die noch negatief, noch bevestigend zijn.
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