Abstract
Epilepsy affects approximately 1% of the world's population. Semiology of epileptic seizures contain major clinical signs to classify epilepsy syndromes currently evaluated by epileptologists by simple visual inspection of video. There is a necessity to create automatic and semiautomatic methods for seizure detection and classification to better support patient monitoring management and diagnostic decisions. One of the current promising approaches are the marker-less computer-vision techniques. In this paper an end-to-end deep learning approach is proposed for binary classification of Frontal vs. Temporal Lobe Epilepsies based solely on seizure videos. The system utilizes infrared (IR) videos of the seizures as it is used 24/7 in hospitals' epilepsy monitoring units. The architecture employs transfer learning from large object detection "static" and human action recognition "dynamic" datasets such as ImageNet and Kinectics-400, to extract and classify the clinically known spatiotemporal features of seizures. The developed classification architecture achieves a 5-fold cross-validation f1-score of 0.844 +/- 0.042. This architecture has the potential to support physicians with diagnostic decisions and might be applied for online applications in epilepsy monitoring units. Furthermore, it may be jointly used in the near future with synchronized scene depth 3D information and EEG from the seizures.

Abstract
In Wireless Sensor Networks (WSN), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network lifetime and, consequently, the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Particularly, balancing the energy required to transport data efficiently across such dynamic environments poses significant challenges to routing protocol design and operation, being the trade-off of reducing data redundancy while achieving an acceptable delivery rate a fundamental research topic. In this way, this work proposes a new energy-aware epidemic protocol that uses the current state of the network energy to create a dynamic distribution topology by self-adjusting each node forwarding behavior as eager or lazy according to the local residual battery. Simulated evaluations demonstrate its efficiency in energy consumption, delivery rate, and reduced computational burden when compared with classical gossip protocols as well as with a directional protocol.

Abstract
With present day industries pressing for retrofitting of current machinery into Industry 4.0 ideas, a large effort is put into data production, storage and analysis. To be able to use such data, it is fundamental to create intelligent software for analysis and visualisation of a growing but frequently faulty amount of data, without the quality and quantity adequate for full blown data mining techniques. This article case studies a foundry company that uses the lost wax method to produce metal parts. As retrofitting is underway, modelling, simulation and smart data visualisation are proposed as methods to overcome data shortage in quantity and quality. The developed data visualisation system is demonstrated to be adapted to the requirements and needs of this company in order to approach full automation ideas. Such data visualisation system allow workers and supervisors to know in real time what is happening in the factory, or study the passage of manufacturing orders for a specific area. Data Analysts can also predict machinery problems, correct issues with slow changing deviations and gather additional knowledge on the implementation of the process itself.

Abstract
The diffusion of distributed energy resources (DERs) has changed the supply-demand balance of power systems. One option to modernize the management of the electricity distribution is to operate the distribution system with interconnected micro -grids (MGs). However, the MG participation in wholesale energy and ancillary service markets creates several challenges in the interactions among the energy market managing entities. To solve these problems, local energy markets (LEMs) have been proposed, where the MGs can trade energy with each other under the management of the LEM manager (LEMM) to minimize their operation cost. In this paper, a local energy market is modeled for multi-MGs (MMGs) to minimize the operation cost of MGs individually and their social welfare in cooperation with each other. In such model, the optimal scheduling of the DERs in each MG is done through the market clearing process. To investigate the effectiveness of the proposed approach, the local energy market is applied to a distribution network with three MGs.

Abstract
A national census is an official count of a country's population that aims to motivate and measure sustainable development. Traditionally, a census is a cumbersome manual operation that involves distributing surveys to all households in the country through field agents or by mail. Recently, some countries have utilized voluntary electronic submissions in addition to the manual work to reduce costs and increase efficiency. However, an increasing number of countries are resorting to a register-based census that uses pre-existing official registers to derive its data. This paper describes Oman's upcoming register-based census, e-Census 2020, and analyses it against the European Commission's necessary conditions that facilitate a successful transition from a traditional to a register-based census [1].

Abstract
Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.