Abstract
This work exploits the nexus of agricultural activities, water, and electrical and thermal energies to propose a framework to develop efficient circular renewable energy communities for the agricultural sector, by analyzing and optimizing the resources and the energy flows among them, profiting from the energy sources available. In this framework, local industries and agricultural facilities can invest in solar PV plants, livestock residues digestors to produce biogas, and cogeneration plants to supply the thermal and electrical energy needs. A simplified case study is presented, based on using biomass residues from livestock processed in an anaerobic digestor to produce biogas for a cogeneration plant. Their optimal capacities are computed considering the optimal supply of thermal and electrical energy needs and the supply from the public electricity and gas grids.

Abstract
This scientific paper delves into the effects of water stress on grapevines, specifically focusing on gene expression and polyphenol production. We conducted a controlled greenhouse experiment with three hydric conditions and analyzed the expression of genes related to polyphenol biosynthesis. Our results revealed significant differences in the expression of ABCC1, a gene linked to anthocyanin metabolism, under different irrigation treatments. These findings highlight the importance of anthocyanins in grapevine responses to abiotic stresses. By integrating genomics, metabolomics, and systems biology, this study contributes to our understanding of grapevine physiology under water stress conditions and offers insights into developing sensor technologies for real-world applications in viticulture. © 2023 by the authors.

Abstract
Multiple Sclerosis is one of the most common diseases of the central nervous system that affects millions of people worldwide. The prediction of this disease is considered a challenge since the symptoms are highly variable as the disease worsens and, as such, it has emerged as a topic that artificial intelligence scientists have tried to challenge. With the goal of providing a brief review that may serve as a starting point for future researchers on such a deep field, this paper puts forward a summary of artificial intelligence applications for Multiple Sclerosis evaluation and diagnosis. It includes a detailed recap of what Multiple Sclerosis is, the connections between artificial intelligence and the human brain, and a description of the main proposals in this field. It also concludes what the most reliable methods are at the present time, discussing approaches that achieve accuracy values up to 98.8%. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Humanity faces serious problems related to water supply, which will be aggravated by population growth. The water used in human activities must be treated to make it available again without posing risks to human health and the environment. In this context, Wastewater Treatment Plants (WWTPs) have gained importance. The treatment process in WWTPs is complex, consisting of several stages, which consume considerable amounts of resources, mainly electrical energy. Minimizing such energy consumption while satisfying quality and environmental requirements is essential, but it is a challenging task due to the complexity of the processes carried out in WWTPs. One form of evaluating the performance of WWTPs is through the well-known Key Performance Indicators (KPIs). The KPIs are numerical indicators of process performance, being a simple and common way to assess the efficiency and eco-efficiency of a process. By applying KPIs to WWTPs, techniques for monitoring, predicting, controlling, and optimizing the efficiency and eco-efficiency of WWTPs can be created or improved. However, the use of computational methodologies that use KPIs (KPIs-based methodologies) is still limited. This paper provides a literature review of the current state-of-the-art of KPI-based methodologies to monitor, control and optimize energy efficiency and eco-efficiency in WWTPs. In this paper, studies presented on 21 papers are identified, assessed and synthesized, 12 being related to monitoring and predicting problems, and 9 related to control and optimization problems. Future research directions relating to unresolved problems are also identified and discussed.

Abstract
Coupled multi-scale finite element analyses have gained traction over the last years due to the increasing available computational resources. Nevertheless, in the pursuit of accurate results within a reasonable time frame, replacing these high-fidelity micromechanical simulations with reduced-order data-driven models has been explored recently by the modelling community. In this work, two classes of machine learning models are trained for a porous hyperelastic microstructure to predict (i) whether the microscopic equilibrium problem is likely to fail and (ii) the stress-strain response. The former may be used to identify critical macroscopic points where one may fall back to the high-fidelity analysis and possibly apply convergence bowl-widening techniques. For the latter, both a linear regression with polynomial features and artificial Neural Networks have been used, and the required stress-strain derivatives for solving the equilibrium problem have been derived analytically. A weight regularisation is introduced to stabilise the tangent operator and several strategies are discussed for imposing null stresses in undeformed configurations for both regression models. The regression techniques, here analysed exclusively in the context of porous hyperelastic materials, evidence very promising prospects to accelerate multi-scale analyses of solids under large deformation.

Abstract
This paper presents a new hybridization approach to improve CEVESA, a multi-zonal hydro-thermal equilibrium model for the joint dispatch of energy and secondary reserve capacity for the Iberian Electricity Market (MIBEL). Like similar fundamental models, CEVESA provides market prices that typically show an average systematic bias compared to real market prices. This is because these models do not always capture the true variable production costs of the generation units or the additional markups that generation companies may include in their pricing strategy. Based on real market outcomes, this paper proposes a new methodology built on a previous hybridization approach that estimated a constant monthly markup per thermal offering unit [1]. This new methodology is based on a functional estimation of the offering unit cost (or bidding price), using as input the initial CEVESA production costs based on the fuel and emissions commodities' prices, correcting the power plants' markup.