Abstract

Abstract
Aim There has been an increase in the development of technologies that can deliver personalised dietary advice. Devising healthy, sustainable dietary plans will mean taking into consideration extrinsic factors such as individual social circumstances. The aim of this study was to identify societal groups more or less receptive to and likely to engage with personalised nutrition initiatives. Sample and methods Volunteers were recruited via a social research agency from within the UK. The resultant sample (N = 1061) was 49% female, aged 18-65 years. Results MANOVA (Tukey HSD applied) indicated that females and younger people (aged 18-29 years) had more favourable attitudes and were more likely to intend to adopt personalised nutrition. There were no differences in attitude toward or intention to adopt personalised nutrition between different education levels, income brackets or occupational groups. Conclusion These results imply that females and younger people may be most likely to adopt personalised nutrition in the future. Initiatives to promote healthy eating should target males and older people.

Abstract
In the current competition environment, transportation costs continue to rise, causing a reduction in the profit margins of companies. There are several tools in the literature to support the planning of logistics activities, but individualised solutions are not yet effective. In this study, a linear programming model is proposed to jointly plan the demand fulfilment of two competing companies by encouraging the search for synergies that enhance collaboration in the use of existing resources. To demonstrate the validity of the proposed mode, a case study is carried out and the results obtained with the initiation of the collaboration are evaluated. In conclusion, the proposed model reduces the logistics costs by up to 13%, as well as decreases the carbon footprint by 37%. By focusing on optimising economic and environmental aspects, this approach serves as a guide for companies to promote collaborations and to facilitate decision making at a managerial level.

Abstract
This study aims to conduct a bibliometric analysis inherent to assessing competitive intelligence and business intelligence concepts using the Scopus database and the Bibliometrix R software. The study's articles were found using precise criteria in the Scopus database. The 42 publications were then examined with Bibliometrix software, which included extensive parameterization for each component under evaluation. The results of this study consisted of establishing the number of existing publications on the topic under analysis between 2017 and 2021 - in this sense, it was possible to identify that the publications are experiencing an annual decrease rate of 22.69%; the trends in terms of publications and collaborations between countries; the most relevant journals in the area; and the interconnections between authors, keywords, and publications. This study has as an added value the possibility to evaluate the relevance attributed by academics to ascertain the most important contributions in terms of authors, articles, and journals. One major limitation in this study could be addressed in future research. The study focused on a limited study field in the context of business, management, and accounting, so it would be very pertinent to understand how this topic has evolved, particularly in the area of computer science.

Abstract

Abstract
Electric springs (ESs) have proven effective for integrating renewable generation into power systems. An ES connected in series with a non-critical load forms a smart load whose consumption can be dynamically controlled for voltage regulation and demand side management. In most existing applications, smart loads have been devoted to providing services to the grid without accounting for their own interests. The novelty of this paper is to propose a price-based strategy to coordinate the operation of multiple ESs in microgrids. Smart loads consisting of ESs connected to electric water heaters are modeled as rational agents that locally optimize their own objectives by adjusting their consumption schedules in response to price/control signals. Such signals are determined at the microgrid central controller (MGCC) when solving the microgrid operation scheduling problem formulated to minimize the microgrid operation cost taking into account the smart loads' consumption schedules. An iterative optimization algorithm determines the equilibrium between the microgrid and smart loads' objectives requiring only the exchange of price/control signals and power schedules between the local controllers and the MGCC. Case studies show the effectiveness of the proposed strategy to economically benefit both the microgrid and smart loads when scheduling their operation.