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Encouraged by the Clean Energy for all Europeans package, small-scale distributed electricity generation (both individual prosumership projects and local energy communities) is expected to increase in the coming years, impacting on the planning and operation of smart grids. This work aims to assess how the operation of a residential local energy community (LEC) is affected when it receives different signals from the power grid, encouraging or penalizing the injection of self-generated electricity surpluses. Optimization processes are integrated into a multiagent framework to model the LEC and agents' individual and collective objectives are exploited. The results show how individual electricity costs and collective self-consumption rates change across different incentive/penalty scenarios.

The emergence of energy communities is expected to reshape the design of the electricity system at the same time as peer-to-peer (P2P) trading is transforming the traditional utilities business models. In this setting, this work proposes a multiagent framework endowed with optimization tools to model P2P electricity trading within an energy community. Emphasis is given to the inclusion of vulnerable consumers and the economic outcomes of P2P transactions for community members, while fairness concerns are considered in the distribution of the existing energy resources.

Energy communities are gaining momentum as they may impact demand and reshape the design of future energy systems. This paper exploits the contribution that residential demand-side flexibility can provide to an energy community by combining multi-agent systems (MAS) modeling and genetic algorithms (GA) to minimize both electricity costs and end-users' dissatisfaction. The overall community self-sufficiency in different generation scenarios is quantified by assessing how much energy demand can be locally supplied. The results reveal that important economic benefits can be obtained through the management of local energy resources, confirming that community members benefit of being involved in a communal environment.

Power systems have undergone major changes, mainly due to the increasing penetration of renewable distributed generation and levels of automation. Prosumers have the capability to become (almost) independent of the grid and can exchange energy with other community members. In this setting, it is relevant to exploit generation/demand flexibility from an energy community perspective, including multiple agents that exchange and manage the available energy resources in an integrated manner. Agent-based modeling (ABM) tools have been increasingly used to model complex energy systems, since they are especially suited to model systems influenced by social interactions between agents that are flexible, autonomous and proactive. This paper aims to present the initial development stage of a modelling approach to simulate energy trading between two energy community members capable of exchanging information and energy between them. Particular attention is paid to the influence of end-users’ behaviors on demand-side flexibility.

Helicobacter pylori infection represents a serious health problem, given its association with serious gastric diseases as gastric ulcers, cancer and MALT lymphoma. Currently no vaccine exists and antibiotic-based eradication therapy is already failing in more than 20% of cases. To increase the knowledge on the infection process diverse gastric cell lines, e.g. the adenocarcinona gastric (AGS) cell line, are routinely used has in vitro models of gastric epithelia. In the present work the molecular fingerprint of infected and non-infected AGS cell lines, by diverse H. pylori strains, was acquired using vibrational infrared spectroscopy. These molecular fingerprints enabled to discriminate infected from non-infected AGS cells, and infection due to different strains, by performing Principal Component Analysis. It was also possible to estimate, from the AGS cells molecular fingerprint, the effect of the infection on diverse biochemical and metabolic cellular status. In resume infra-red spectroscopy enabled the acquisition of infected AGS cells molecular fingerprint with minimal sample preparation, in a rapid, high-throughput, economic process yielding highly sensitive and informative data, most useful for promoting critical knowledge on the H. pylori infection process.

Methods for fast and accurate identification and quantification of the composition of pharmaceutical mixtures are important in many scientific and industrial applications. When this goal is approached via hyperspectral data analysis, the problem becomes one of hyperspectral unmixing, where the goal is to identify the pure materials (also called endmembers) present in a mixture, as well as their relative abundances. In this paper, we assess the performance of a state-of-the-art method (named SISAL-simplex identification via split augmented Lagrangian) recently proposed for remote sensing applications, in the context of hyperspectral unmixing of NIR and Raman spectra for pharmaceutical applications. The NIR and Raman data are complementary in nature and have different spatial resolutions. The central goal of this study is to show that these two different spectroscopic techniques allow the identification of different endmembers, and also that the higher spatial resolution of Raman data allows more accurate predictions of the endmembers spectral signatures.

Counterfeit pharmaceutical products pose a serious public health problem. It is thus important not only to detect them, but also to identify their composition and assess the risk for the patient. Identifying the spectral signatures of the pure compounds present in a (maybe counterfeit) tablet of unknown origin is clearly a hyperspectral unmixing problem. In fact, under a linear mixing model, the hyperspectral vectors belong to a simplex whose vertices are the spectra of the pure compounds in the mixture. Minimum volume simplex analysis (MVSA) and minimum-volume enclosing simplex (MVES) are recently proposed algorithms, exploiting the idea of finding a simplex of minimum volume fitting the observed data. This work gives evidence of the usefulness of MVES and MVSA for unmixing near infrared (NIR) hyperspectral data of tablets of unknown composition. Experiments reported in this paper show that MVES and MVSA strongly outperform the state-of-the-art method in analytical chemistry for spectral unmixing: multivariate curve resolution - alternating least squares (MCR-ALS). These experiments are based on synthetic data (studying the effect of noise and of the presence/absence of pure pixels) and on a real dataset composed of NIR hyperspectral images of counterfeit tablets.