AbstractInteractions between units in political systems often occur across multiple relational contexts. These relational systems feature interdependencies that result in inferential shortcomings and poorly-fitting models when ignored. General advancements in inferential network analysis have improved our ability to understand relational systems featuring interdependence, but developments specific to working with interdependence that cross relational contexts remain sparse. In this paper, I introduce a multilayer network approach to modeling systems comprising multiple relations using the exponential random graph model. In two substantive applications, the first a policy communication network and the second a global conflict network, I demonstrate that the multilayer approach affords inferential leverage and produces models that better fit observed data.
Purpose – In this paper we propose an iterative approach for the deployment of rural telecommunication networks. Methodology/approach/design – This approach relies heavily on the concept of locality, prioritizing small 'cells' with a considerable population density, and exploits the natural nesting of the distribution of rural communities, focusing in communities which are populous enough to justify the investment required to provide them with connectivity, and whose sheer size promotes the formation of 'satellite' communities that could be benefited from the initial investment at a marginal expense. For this approach, the concept of 'cells' is paramount, which are constructed iteratively based on the contour of a Voronoi tessellation centered on the community of interest. Once the focal community has been 'connected' with network of the previous layer, the process is repeated with less populous communities at each stage until a coverage threshold has been reached. One of the main contributions of this methodology is that it makes every calculation based on 'street distance' instead of Euclidean, giving a more realistic approximate of the length of the network and hence the amount of the investment. To test our results, we ran our experiments on two segregated communities in one of the most complicated terrains, due to the mountain chains, in the state of Chiapas, Mexico. Findings – The results suggest that the use of 'street distance' and a local approach leads to the deployment of a remarkably different network than the standard methodology would imply. Practical implications – The results of this paper might lead to a significant reduction in the costs associated with these kinds of projects and therefore make the democratization of connectivity a reality. In order to make our results reproducible, we make all our code open and publicly available on GitHub. ; Purpose – In this paper we propose an iterative approach for the deployment of rural telecommunication networks. Methodology/approach/design – This approach relies heavily on the concept of locality, prioritizing small 'cells' with a considerable population density, and exploits the natural nesting of the distribution of rural communities, focusing in communities which are populous enough to justify the investment required to provide them with connectivity, and whose sheer size promotes the formation of 'satellite' communities that could be benefited from the initial investment at a marginal expense. For this approach, the concept of 'cells' is paramount, which are constructed iteratively based on the contour of a Voronoi tessellation centered on the community of interest. Once the focal community has been 'connected' with network of the previous layer, the process is repeated with less populous communities at each stage until a coverage threshold has been reached. One of the main contributions of this methodology is that it makes every calculation based on 'street distance' instead of Euclidean, giving a more realistic approximate of the length of the network and hence the amount of the investment. To test our results, we ran our experiments on two segregated communities in one of the most complicated terrains, due to the mountain chains, in the state of Chiapas, Mexico. Findings – The results suggest that the use of 'street distance' and a local approach leads to the deployment of a remarkably different network than the standard methodology would imply. Practical implications – The results of this paper might lead to a significant reduction in the costs associated with these kinds of projects and therefore make the democratization of connectivity a reality. In order to make our results reproducible, we make all our code open and publicly available on GitHub. ; Purpose – In this paper we propose an iterative approach for the deployment of rural telecommunication networks. Methodology/approach/design – This approach relies heavily on the concept of locality, prioritizing small 'cells' with a considerable population density, and exploits the natural nesting of the distribution of rural communities, focusing in communities which are populous enough to justify the investment required to provide them with connectivity, and whose sheer size promotes the formation of 'satellite' communities that could be benefited from the initial investment at a marginal expense. For this approach, the concept of 'cells' is paramount, which are constructed iteratively based on the contour of a Voronoi tessellation centered on the community of interest. Once the focal community has been 'connected' with network of the previous layer, the process is repeated with less populous communities at each stage until a coverage threshold has been reached. One of the main contributions of this methodology is that it makes every calculation based on 'street distance' instead of Euclidean, giving a more realistic approximate of the length of the network and hence the amount of the investment. To test our results, we ran our experiments on two segregated communities in one of the most complicated terrains, due to the mountain chains, in the state of Chiapas, Mexico. Findings – The results suggest that the use of 'street distance' and a local approach leads to the deployment of a remarkably different network than the standard methodology would imply. Practical implications – The results of this paper might lead to a significant reduction in the costs associated with these kinds of projects and therefore make the democratization of connectivity a reality. In order to make our results reproducible, we make all our code open and publicly available on GitHub.
Mobile communications are growing and the number of users is constantly increasing at an accelerated rate, as well as the demand for the services they request. In the last few years, many efforts have focused on the design and deployment of the new fifth generation (5G) cellular networks. However, novel highly demanding applications, which are already emerging, need to go beyond 5G in order to meet the requirements in terms of network performance. But, at the same time, as the Earth does not allow us to increase the carbon footprint anymore, the energy consumption of the communication networks has to be critically taken into consideration. A multi-objective approach for addressing all these issues is therefore required. This work develops a cellular network framework that allows the evaluation of different system parameters over dynamic traffic patterns, as well as optimizing the different conflicting objectives simultaneously. The novelty relies on that the optimization process integrates key performance indicators from different layers of the network, namely the radio and the network layers, aiming at reaching solutions that account for the power consumption of the base stations, the total capacity provided to mobile users and also the signaling cost generated by handovers. Moreover, new metrics are needed to evaluate different solutions. Starting from the well-known energy efficiency merit factor (bits/Joule), three new merit factors are proposed to classify the network performance since they take into account several network parameters at the same time. These indicators show us the ideal working point that can be used to plan the point of operation of the network. These operation points are a medium-high power and capacity load and a low signaling load. ; Spanish National Program of Research, Development, Innovation TIN2016-75097-P RTI2018-102002-A-I00 EQC2018-004988-P ; Junta de Andalucía B-TIC-402-UGR18 ; European Union (EU) IB18003 ; Junta de Extremadura IB18003 ; FPU19/01251 ; FPU18/01965
Personal safety is a critical aspect of daily life, but also in the military. Active soldiers often have to carry heavy gear during missions, which puts pressure on their backs. Therefore, the military must come up with new technologies that allow both protection and movement. In this paper, it is explaining the development of an armored upper limb exoskeleton with three degrees of freedom. To ensure portability, it is used battery-fed DC actuators. The system was encased in a metal matrix that doubles up as a protective plate. The exoskeleton, the control system, the actuators, and the plate are integrated so that they offer protection while supporting the flexion and extension of the upper limb.
The authors greatly acknowledge the IMIS2 project of the National Reform Programme of Latvia for financial support. The publication costs of this article were covered by the Estonian Academy of Sciences and the University of Tartu. ; We investigated a promising material for hydrogen storage and sensing. The material was obtained by exfoliating recycled graphite waste and simultaneously modifying the product with metal impurities (Bi, V, Cu). As a result, graphene sheet stack (GSS) powder was obtained. The material was further processed by hydrothermal annealing and reduction. Raman spectra of the GSS materials are provided to show the presence of graphene-like structures and defects in the exfoliated material. The synthesized graphene material has good semiconductor properties with a low electrical resistance for hydrogen sensing applications. ; Tartu Ülikool, Eesti Teaduste Akadeemia, Center of Excellence has received funding from the European Union's Horizon 2020 Framework Programme H2020-WIDESPREAD-01-2016-2017-TeamingPhase2 under grant agreement No. 739508, project CAMART²
