Measuring engagement on twitter using a composite index: An application to social media influencers

El compromiso en las redes sociales es un concepto complejo, en el que interactúan muchos componentes interconectados y difíciles de evaluar. Es precisamente esta complejidad la que motivó este trabajo, que propone un índice compuesto como herramienta para medir el engagement. Utilizando TOPSIS, un método multicriterio que basa su ranking en minimizar la distancia al punto ideal y maximizar la distancia al anti-ideal, se utiliza una combinación de indicadores basados ​​en dos enfoques: el enfoque del tweet y el enfoque del seguidor. El primero refleja el compromiso basado en la producción del usuario y el segundo mide el compromiso por popularidad. Este índice se aplicó a un grupo de Social Media Influencers y se obtuvo un ranking general, así como un ranking por cada enfoque de medición del engagement. La comparación de los rankings generados por los diferentes enfoques muestra la idoneidad y pertinencia de ambos, ya que se confirma que miden aspectos diferentes, y que ambos son necesarios para ofrecer una visión holística del engagement que genera un usuario en Twitter; este es un hallazgo nuevo en comparación con estudios anteriores, que solo se centraron en un enfoque u otro.Funding for open access charge: Universidad de Málag

Selection of the Graphics Card to be used in Ethereum Mining with Linear BWM-TOPSIS

Blockchain technology is becoming more and more important and new usage areas are emerging every day. However, the most fundamental one of these usage areas is cryptocurrencies, which led to the emergence of blockchain technology. Cryptocurrency transfers are made possible with mining. Although there are many cryptocurrencies available today, a lot of them use Ethereum-based blockchain technology. The choice of the most optimal graphics card (GPU; Graphics Processing Unit) in cryptocurrency mining is very important for the efficiency and profitability of the mining operations to be performed. Since this decision problem depends on more than one criterion, it should be handled using Multiple-Criteria Decision-Making Methods (MCDM). Accordingly, the study focused on the mining of Ethereum-based cryptocurrencies and the selection of the optimal GPU to be used in mining with linear BWM-TOPSIS.  As a result of the study, a model is presented in which miners can choose the most efficient GPU for them and the optimal GPU as of January 2020 has been determined

A systematic review on multi-criteria group decision-making methods based on weights: analysis and classification scheme

Interest in group decision-making (GDM) has been increasing prominently over the last decade. Access to global databases, sophisticated sensors which can obtain multiple inputs or complex problems requiring opinions from several experts have driven interest in data aggregation. Consequently, the field has been widely studied from several viewpoints and multiple approaches have been proposed. Nevertheless, there is a lack of general framework. Moreover, this problem is exacerbated in the case of experts’ weighting methods, one of the most widely-used techniques to deal with multiple source aggregation. This lack of general classification scheme, or a guide to assist expert knowledge, leads to ambiguity or misreading for readers, who may be overwhelmed by the large amount of unclassified information currently available. To invert this situation, a general GDM framework is presented which divides and classifies all data aggregation techniques, focusing on and expanding the classification of experts’ weighting methods in terms of analysis type by carrying out an in-depth literature review. Results are not only classified but analysed and discussed regarding multiple characteristics, such as MCDMs in which they are applied, type of data used, ideal solutions considered or when they are applied. Furthermore, general requirements supplement this analysis such as initial influence, or component division considerations. As a result, this paper provides not only a general classification scheme and a detailed analysis of experts’ weighting methods but also a road map for researchers working on GDM topics or a guide for experts who use these methods. Furthermore, six significant contributions for future research pathways are provided in the conclusions.The first author acknowledges support from the Spanish Ministry of Universities [grant number FPU18/01471]. The second and third author wish to recognize their support from the Serra Hunter program. Finally, this work was supported by the Catalan agency AGAUR through its research group support program (2017SGR00227). This research is part of the R&D project IAQ4EDU, reference no. PID2020-117366RB-I00, funded by MCIN/AEI/10.13039/ 501100011033.Peer ReviewedPostprint (published version

Study on Evaluating Innovation Ability of High-tech Industry Based on Particle Swarm Synthesis Optimization

The innovation capability of high-tech industry reflects international competitiveness of a country. In order to scientifically evaluate the innovation capability, this paper proffers the comprehensive evaluation of the particle swarm synthesis optimization based on deviation maximization, principal component analysis and TOPSIS method. The evaluation index system of the innovation capability of high-tech industry is constructed; the paper conducts the empirical research through the particle swarm synthesis algorithm combined with provincial yearbook data on 30 provinces in China, and obtains the technological innovation capability results and rankings of each province\u27s high-tech industry. The empirical results also show that the overall innovation capability of China\u27s high-tech industry shows the growth trend, indicating that the implementation of an innovation-driven development strategy and other related policies have achieved suitable results. At the same time, the level of development in the eastern part of China is always higher than that of the central region and the western region. The paper further analyses reasons of differences in the development level of different regions. The particle swarm synthesis optimization has strong applicability in a comprehensive evaluation. This paper provides an effective reference for evaluating the innovation ability of the high-tech industry

Intelligent Decision Support System for Energy Management in Demand Response Programs and Residential and Industrial Sectors of the Smart Grid

This PhD thesis addresses the complexity of the energy efficiency control problem in residential and industrial customers of Smart electrical Grid, and examines the main factors that affect energy demand, and proposes an intelligent decision support system for applications of demand response. A multi criteria decision making algorithm is combined with a combinatorial optimization technique to assist energy managers to decide whether to participate in demand response programs or obtain energy from distributed energy resources

Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios

A railway system plays a significant role in countries with large territorial dimensions. The Brazilian rail cargo system (BRCS), however, is focused on solid bulk for export. This paper investigates the extreme performances of BRCS through a new hybrid model that combines TOPSIS with a genetic algorithm for estimating the weights in optimized scenarios. In a second stage, the significance of selected variables was assessed. The transport of any type of cargo, a centralized control of the operation, and sharing the railway track pushing competition, and the diversification of services are significant for high performance. Public strategies are discussed.Indisponível

Multi-criteria decision making monarch butterfly optimization for optimal distributed energy resources mix in distribution networks

The optimal integration of distributed energy resources (DERs) is a multiobjective and complex combinatorial optimization problem that conventional optimization methods cannot solve efficiently. This paper reviews the existing DER integration models, optimization and multi-criteria decision-making approaches. Further to that, a recently developed monarch butterfly optimization method is introduced to solve the problem of DER mix in distribution systems. A new multiobjective DER integration problem is formulated to find the optimal sites, sizes and mix (dispatchable and non-dispatchable) for DERs considering multiple key performance objectives. Besides, a hybrid method that combines the monarch butterfly optimization and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to solve the formulated large-scale multi-criteria decision-making problem. Whilst the meta-heuristic optimization method generates non-dominated solutions (creating Pareto-front), the TOPSIS approach selects that with the most promising outcome from a large number of alternatives. The effectiveness of this approach is verified by solving single and multiobjective dispatchable DER integration problems over the benchmark 33-bus distribution system and the performance is compared with the existing optimization methods. The proposed model of DER mix and the optimization technique significantly improve the system performance in terms of average annual energy loss reduction by 78.36%, mean node voltage deviation improvement by 9.59% and average branches loadability limits enhancement by 50%, and minimized the power fluctuation induced by 48.39% renewable penetration. The proposed optimization techniques outperform the existing methods with promising exploration and exploitation abilities to solve engineering optimization problems

Supplier selection with Shannon entropy and fuzzy TOPSIS in the context of supply chain risk management

Supplier selection is the process of finding the right suppliers, at the right price, at the right time, in the right quantities, and with the right quality. The aim of this paper, is supplier selection in the context of supply chain risk management. Thus nine criteria of quality, on time delivery and performance history and six risks in the supply chain including supply risk, demand risk, manufacturing risk, logistics risk, information risk and environmental risk considered for evaluating suppliers. Shannon entropy is used for weighing criteria and fuzzy TOPSIS is applied for ranking suppliers. Findings show that, in the spare parts supplier selection problem, demand risk is the most important factor

Under-water noise pollution sources, mitigation measures in commercial vessels: the trade-off analysis in the case of study for trans mountain project, Port of Vancouver, Canada.

Shipping is the most efficient type of transportation and plays a significant role in global trade. However, it has some negative externalities and creates environmental pollution. With the growth of shipping, the potential for low-frequency noise increases along with its negative effects such as impacts on marine species and threat to sustainable shipping, e.g. its intensity has been doubling in the North Pacific Ocean every decade for the past 60 years and it is predicted to increase by 87–102% on average by 2030. In contrast to other environmental issues, the underwater noise is not visible, so to raise awareness and show its negative impacts, a scientific approach and data collection are required. While awareness of the society in respect of the other pollutions such as oil, dangerous goods, noxious liquids substances, sewage, and air has been raised and those issues are regulated properly, society has not been familiar with under-water noise pollution and it has not been regulated properly. As such, legal gaps exist this study is a holistic approach to UWN pollution. The main sources and the ways to mitigate UWN pollution and its effect on sustainable shipping will be reviewed. Meanwhile, with reference to the previous environmental issues and present information and data collection, the general trends for the future of UWN pollution will be suggested. Moreover, in the case study (the Trans Mountain Project (TMP)), mitigation measures to reduce the negative impacts of the growth of shipping in the Haro Strait will be suggested. Furthermore, by creating four scenarios and modelling, simulations, utilizing the MCDM (MADM) algorithms, and TOPSIS techniques the trade-off between the environmental (noise and Co2 emission) and economical (fuel cost) aspects of the project will be conducted to enhance the Decision Support System (DSS). This will help the decision makers to have a multi-dimensional thinking instead of the single dimensional thinking in addressing and tackling the negative externalities of the TMP in the area. Moreover, at the end of each scenario, a sensitivity analysis will be conducted to provide a clean environment for decision makers