Abstract
Demand aggregators are new market players that represent a group of consumers in the electricity market. This paper proposes an aggregator model responsible for gathering residential and commercial consumers, which has the role of managing their flexible consumption in the day-ahead electricity market. A methodology to optimize the aggregator's bids is also presented. It optimizes the scheduling of the flexible loads taking simultaneously into account the consumers' preferences and temporal trajectories of forecasted outdoor temperatures and electricity prices. The proposed methodology was tested using a case study with 200 residential and commercial consumers from the Iberian market.

Abstract
Histogram-valued variables are a particular kind of variables studied in Symbolic Data Analysis where to each entity under analysis corresponds a distribution that may be represented by a histogram or by a quantile function. Linear regression models for this type of data are necessarily more complex than a simple generalization of the classical model: the parameters cannot be negative; still the linear relation between the variables must be allowed to be either direct or inverse. In this work, we propose a new linear regression model for histogram-valued variables that solves this problem, named Distribution and Symmetric Distribution Regression Model. To determine the parameters of this model, it is necessary to solve a quadratic optimization problem, subject to non-negativity constraints on the unknowns; the error measure between the predicted and observed distributions uses the Mallows distance. As in classical analysis, the model is associated with a goodness-of-fit measure whose values range between 0 and 1. Using the proposed model, applications with real and simulated data are presented.

Abstract
Purpose - The study aims to define a research agenda for creating resource-efficient supply chains (RESCs) by identifying and analysing their key characteristics as well as future research opportunities. Design/methodology/approach - We follow a systematic review method to analyse the literature and to understand RESC, taking a substantive theory approach. Our approach is grounded in a specific domain, the agri-food sector, because it is an intensive user of an extensive range of resources. Findings - The review shows that works of literature has looked at the use of resources primarily from the environmental impact perspective. There is a need to explore whether or not and how logistics/supply chain decisions will affect the overall configuration of future food supply chains in an era of resource scarcity and depletion and what the trade-offs will be. Research limitations/implications - The paper proposes an agenda for future research in the area of RESC. The framework proposed along with the key characteristics identified for RESC can be applied to other sectors. Practical implications - Our research should facilitate further understanding of the implications and trade-offs of supply chain decisions taken on the use of resources by supply chain managers. Originality/value - The paper explores the interaction between supply chains and natural resources and defines the key characteristics of RESC.

Abstract
A methodology based on Fourier transform infrared spectroscopy with attenuated total reflectance sampling technique, combined with multivariate analysis, was developed to monitor adulteration of extra virgin olive oil (EVOO) with peanut oil (PEO). Principal components regression (PCR), partial least squares regression (PLS-R), and linear discriminant analysis (LDA) allowed quantification of percentage of adulteration based on spectral data of 192 samples. Wavenumbers associated with the biochemical differences among several types of edible oils were investigated by principal component analysis. Two sets of frequencies were selected in order to establish a robust regression model. Set A consisted on the frequency regions from 600 to 1,800 cm-1 and from 2,750 to 3,050 cm-1. Set B comprised 17 discrete peak absorbance frequencies for which the communality value was higher than 0.6. Analysis of an external set of 25 samples allowed the validation and evaluation of the predictability of the models. When using a specific set of discrete peak absorbance frequencies, the R 2 coefficients for the prediction were 0.960 and 0.977, and the root mean square error (RMSE) were 1.49 and 1.05% V/V when using the PCR or PLS-R models, respectively. LDA was successful in the binary classification presence/absence of PEO in adulterated EVOO (with 5% V/V of less of PEO). LDA provided 92.3% correct classification for the calibration set and 88.3% correct classification when cross-validated. The lowest detectable concentration of PEO in EVOO was the lowest adulteration level studied, 0.5% V/V.

Abstract
This paper presents a management model developed to promote the sustainability of artisanal fisheries, considering socioeconomic and environmental dimensions. The management of artisanal fisheries faces many challenges, including the lack of appropriate tools to support policy makers and stakeholders in the decision-making process. The model presented in this study is based on system dynamics and allows the simulation of the behavior of the artisanal dredge fishery on the south coast of Portugal, including four main species and two fleets. Two scenarios were simulated to assess the impact of regulatory measures on the system sustainability: scenario 1 simulated a permanent reduction of fishing effort, returning an improvement of biologic and economic sustainability; scenario 2 simulated the closure of one species for a period of one year to allow its recovery. It was found that although the revenue of fisheries decreases in that year, the system is able to return to the average levels of revenue three years after the closure. The study aimed to guide decision makers in the selection of the most appropriate measures toward the preservation of marine ecosystems and socioeconomic stability of fishing communities. The developed system dynamics model, combining available data with knowledge of fisheries experts, proved to be a useful scientific tool to identify management policies and organizational structures leading to greater success. This technique can be applied to the study of other fishery systems worldwide.

Abstract
The objective of this study is to explore the effectiveness of three digital shopping platforms (Plain Interactive, Marker-based Augmented Reality and Markerless Augmented Reality), on the impressions and purchase intentions of consumers. The study is mainly interested in analysing whether intelligent shopping platforms with AR elements provide any added advantage to an advertised product in the form of favourable attitude or a stronger purchase impulse. During the tests with the three shopping platforms, quantitative data was collected via computerised questionnaire. High and Low class users were statistically extracted, corresponding to the high or low probability to buy or recommend the advertised brand. The results show that Markerless AR system clearly outperforms the Marker-based AR and the Plain Interactive in terms of positive attitude from the users. The second better performing system is the Marker-based AR, which closely follows the Markerless AR, while the Plain Interactive system obtains least approval.