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This study evaluates machine learning models for stock market prediction in the European stock market EU50, with emphasis on the integration of key technical indicators. Advanced techniques, such as ANNs, CNNs and LSTMs, are applied to analyze a large EU50 dataset. Key indicators, such as the simple moving average (SMA), exponential moving average (EMA), moving average convergence/divergence (MACD), stochastic oscillator, relative strength index (RSI) and accumulation/distribution (A/D), were employed to improve the model's responsiveness to market trends and momentum shifts. The results show that CNN models can effectively capture localized price patterns, while LSTM models excel in identifying long-term dependencies, which is beneficial for understanding market volatility. ANN models provide reliable benchmark predictions. Among the models, CNN with RSI obtained the best results, with an RMSE of 0.0263, an MAE of 0.0186 and an R2 of 0.9825, demonstrating high accuracy in price prediction. The integration of indicators such as SMA and EMA improves trend detection, while MACD and RSI increase the sensitivity to momentum, which is essential for identifying buy and sell signals. This research demonstrates the potential of machine learning models for refined stock prediction and informs data-driven investment strategies, with CNN and LSTM models being particularly well suited for dynamic price prediction. [ABSTRACT FROM AUTHOR]

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Sustainable development and its significant challenges motivate various international organisations in a way that has never been seen before. With Europe at the forefront, countries such as the United States want to be included in the progress and what a clear and determined commitment to sustainability means for future generations. Our study aimed to go deeper into the follow-up and monitoring of the development of reliable indicators that make the continuous improvement process in sustainability robust. To this end, and using the fuzzy logic methodology, we applied it to one of the indices that have been developed to date, the "Sustainable Development Report" (in its 2022 edition), working on the specific application of SDG 11. Our results show favourable positions for countries such as Brunei Darussalam, Tonga, Tuvalu, Andorra, and the Netherlands and provide robustness when there is a lack of data quality and improvements in the implementation of the process when experts intervene. [ABSTRACT FROM AUTHOR]

The deployment of distributed and affordable renewable energy has led to the development of the prosumer concept in the field of energy. To better understand its relevance and to analyse the main trends and research developments, a systematic literature review was performed. This work gathered 1673 articles related to this topic that were analysed following the PRISMA methodology with the help of VOSviewer 1.6.18 bibliometric software. These papers are classified into four clusters: smart grids, microgrids, peer to peer networks, and prosumers. The first two clusters show a certain degree of maturity, while the latter maintain a growing interest. The analysis of the articles provides a broad view of the prosumer's role in energy and its potential, which is not limited to simple energy exchanges. Furthermore, this systematic review highlights the challenges, not only technical but also in terms of electricity market design and social aspects. The latter require further research, as society is undergoing a paradigm shift in the way in which energy is produced and used. How this shift occurred will determine whether it can lead to true prosumer empowerment and a fairer energy transition. [ABSTRACT FROM AUTHOR]

In recent years it has been demonstrated that the use of the traditional property registry models involves the risk of corruption along with long waiting times. This paper points out the main problems associated with conventional models and makes a survey of the new ones that are based on blockchain technology. This type of model is already being developed as a proof of concept by different countries. With the use of this technology in land registry systems, it is possible to improve the transparency of the processes as well as optimize costs and execution time. To show the theoretical results of this study, the Spanish land registry has been taken as an example of a use case scenario. [ABSTRACT FROM AUTHOR]

Currently, the concern for achieving and fulfilling the Sustainable Development Goals (SDGs) is a constant in advanced societies. The scientific community and various organisations are working on obtaining an information system that will make it possible to offer the necessary value to this type of sustainability information. The article aims to incorporate criteria on the technology used in the reporting system, specifically in collecting the different types of data and generating other interfaces. The methods described here are carried out on a specific case study, a Smart City, showing the different types of data that exist and the possible interfaces that allow objective monitoring of the achievement of the SDGs. It is, therefore, a descriptive study of a process whose results are the establishment of criteria concerning the different data sources as well as the generation of a set of interfaces that motivate the monitoring that can be carried out in a specific city to observe its compliance and deviations from critical values, for example, environmental. The main conclusions of this research establish the importance of incorporating and sizing the technology needed to develop the criteria for monitoring the SDGs. There is a need for convergence between the correct, objective and universal provision of this type of sustainability information and the technology used for the collection and presentation of the data. [ABSTRACT FROM AUTHOR]

Featured Application: This work is intended to be used as a reference for designing photovoltaic installations in buildings which do not have architectural surfaces with optimal orientations or when it is required the use of several surfaces for maximizing total energy production. As solar photovoltaics in buildings reaches maturity, grid integration and economic yield are topics of greater interest. The traditional design of photovoltaic installations has considered the optimal orientation of photovoltaic modules to be that which yields the maximum annual energy production. The influence of the consumption patterns and the hourly variable electricity prices implies that this traditional optimal design might not be the most profitable. Using a full-year dataset for a residential installation, alternative installations using canopies and modules attached to the façades are simulated. By evaluating the energy balances for different annual consumptions, it is found that the canopy and façade installations offer better self-consumption of the PV produced energy, reflected in a 9% higher self-consumption degree using modules on façades and a 5% higher self-consumption degree using canopies. The economic evaluation under the new electricity tariffs in Spain shows a better profit for PV self-consumption, reducing the time of return on investment by more than 2 years. The analysis of different alternatives for an industrial PV allowed us to identify several benefits for these orientations which are confirmed after a full year of operation, such as an increase of up to 59% in annual energy production over the optimal-producing orientation. [ABSTRACT FROM AUTHOR]

This article describes the development of a recommender system to obtain buy/sell signals from the results of technical analyses and of forecasts performed for companies operating in the Spanish continuous market. It has a modular design to facilitate the scalability of the model and the improvement of functionalities. The modules are: analysis and data mining, the forecasting system, the technical analysis module, the recommender system, and the visualization platform. The specification of each module is presented, as well as the dependencies and communication between them. Moreover, the proposal includes a visualization platform for high-level interaction between the user and the recommender system. This platform presents the conclusions that were abstracted from the resulting values. [ABSTRACT FROM AUTHOR]

This paper aims to analyse the behaviours related to the decoupling of the disclosed information on Corporate Social Responsibility (CSR) and corporate sustainability, deepening these practices' knowledge within family businesses. For this purpose, we defined decoupling as a gap between social responsibility performance (internal actions) and disclosures (external actions). For a sample of 33,809 observations for the period 2011–2019, corresponding to 5029 companies, 19% being family firms, our empirical evidence supports that family firms present a less wide gap between performance and disclosure, confirming the prevalence of socioemotional wealth dimensions in the decision-making of these companies. In firms without controlled shareholders, the quality of nonfinancial reporting could be understood as ambiguous, understanding that the most useful CSR information is found in the reports of family-owned companies. [ABSTRACT FROM AUTHOR]

Globalization has led to a new paradigm where the traditional industries, such as agriculture, employ vanguard technologies to broaden its possibilities into what is known as smart farming and the agri-food industry 4.0. This industry needs to adapt to the current market through an efficient use of resources while being environmentally friendly. The most commonly used approaches for analyzing efficiency and sustainability on farms are production efficiency based analyses, such as Data Envelopment Analysis and Stochastic Frontier Analysis, since they allow to see how efficient the outputs are generated regardless of the units of measurement of the inputs. This work presents a real scenario for making farms more profitable and sustainable through the analysis of the Data Envelopment Analysis and the application of the Internet of Things and Edge Computing. What makes this model interesting is that it allows monitoring the ambient conditions with real-time data from the different sensors that have been installed on the farm, minimizing costs and gaining robustness in the transmission of the data to the cloud with Edge Computing, and then to have a complete overview in terms of monthly resource efficiency through the Data Envelopment Analysis. The results show that including the costs of edge and non-edge data transfer have an impact on the efficiency. This small-scale study set the basis for a future test with many farms simultaneously. [ABSTRACT FROM AUTHOR]

Demand charges are widely used for commercial and industrial consumers. These costs are often not well known, let alone the effects that PV can have on them. This work proposes a methodology to assess the effect of PV on reducing these charges and to optimise the power to be contracted, using techniques taken from exploratory data analysis. This methodology is applied to five case studies of industrial consumers from different sectors in Spain, finding savings between 5 % and 11 % of demand charges in industries with continuous operation and up to 28 % in cases of discontinuous operation. These savings can be even greater if the maximum power that can be contracted is lower than the optimum. The demand charges in Spain consist of a fixed part proportional to the contracted power and a variable part depending on the power peaks exceeding it. Since for the variable part the coincident and non-coincident models coexist, a comparison is made between the two models, finding that in the general case PV users can achieve higher savings with the coincident model., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris-Vélib' Métropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.