2020
Authors
Vilaca, P; Saraiva, JT; Fidalgo, JN;
Publication
International Conference on the European Energy Market, EEM
Abstract
This paper reports the main results that were obtained in the scope of a consultancy study that was developed for EDP Distribuição, the main Portuguese distribution company, to evaluate the impact of a number of changes to be introduced in the Tariff System. These changes were proposed by ERSE, the Portuguese Regulatory Agency for the Energy Services, and included the redesign of the tariff periods and the possible introduction of a geographic differentiation on the Access Tariff to reflect different daily and yearly demand and flow patterns along the country. This work involved the development of a Cost Benefit Analysis, CBA, as well as a Pilot Project that included 82 MV and HV consumers to evaluate several Key Performance Indices, KPI, used to characterize the proposed changes on the tariff system. © 2020 IEEE.
2020
Authors
Sun, SL; Li, TT; Ma, H; Li, RYM; Gouliamos, K; Zheng, JM; Han, Y; Manta, O; Comite, U; Barros, T; Duarte, N; Yue, XG;
Publication
SUSTAINABILITY
Abstract
This paper investigated the impact of employee quality on corporate social responsibility (CSR). Based on data from China A-share-listed companies for the years 2012-2016 and using ordinary least squares, our empirical results show that the educational level of the workforce, as a proxy for employee quality, is positively associated with CSR, which suggests that higher education can promote CSR implementation. Additional analyses found that this positive relationship is more pronounced in non-state-owned enterprises, enterprises in regions with lower marketisation processes, and firms with lower proportions of independent directors. This study extends the literature on human capital at the level of firms' entire workforce and CSR by elaborating the positive effect of employee quality on CSR in the context of an emerging economy (China). The results suggest that it is necessary to consider the educational level of employees when analysing CSR, which is of strategic significance for corporate sustainable development.
2020
Authors
Shtul, A; Baquero, C; Almeida, PS;
Publication
CoRR
Abstract
2020
Authors
Muhammad, SH; Brazdil, P; Jorge, A;
Publication
ECIR (2)
Abstract
Sentiment lexicon plays a vital role in lexicon-based sentiment analysis. The lexicon-based method is often preferred because it leads to more explainable answers in comparison with many machine learning-based methods. But, semantic orientation of a word depends on its domain. Hence, a general-purpose sentiment lexicon may gives sub-optimal performance compare with a domain-specific lexicon. However, it is challenging to manually generate a domain-specific sentiment lexicon for each domain. Still, it is impractical to generate complete sentiment lexicon for a domain from a single corpus. To this end, we propose an approach to automatically generate a domain-specific sentiment lexicon using a vector model enriched by weights. Importantly, we propose an incremental approach for updating an existing lexicon to either the same domain or different domain (domain-adaptation). Finally, we discuss how to incorporate sentiment lexicons information in neural models (word embedding) for better performance.
2020
Authors
Oliveira, BMPM; Ozturk, ME; Poinhos, R; Afonso, C; Ayhan, NY; de Almeida, MDV;
Publication
PROCEEDINGS OF THE NUTRITION SOCIETY
Abstract
2020
Authors
Are, M; Santos, E; Oliveira, BMPM; Correia, F; Poínhos, R;
Publication
PROCEEDINGS OF THE NUTRITION SOCIETY
Abstract
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