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Urbanisation and population growth continue to accelerate waste generation, posing serious environmental and logistical challenges for the management of Municipal Solid Waste (MSW) management. The present study proposes a predictive framework for forecasting the behaviour of individual Discharge Points (DPs), with the view to enhancing decision-making in urban waste management. The necessity for localised predictions that extend beyond the scope of aggregated waste indicators is identified by research. Furthermore, it addresses the requirement for finer predictive granularity, which is capable of capturing the dynamic variations observed across DPs. The findings underscore the potential of data-driven approaches to facilitate more efficient, scalable, and intelligent waste collection planning in urban contexts by the incorporation of contextual and temporal information. By enabling accurate short-term forecasts, the proposed approach facilitates the transition from reactive to proactive collection planning, reducing operational cost and environmental footprints. Overall, the research contributes to advancing data-driven strategies for sustainable MSW management and demonstrates the potential of AI-based predictive model to support intelligent and scalable urban waste collection systems.

This research investigates the behaviour of the Discharge Points in a Municipal Solid Waste Management System to evaluate the feasibility of making individual predictions of every Discharge Point. Such predictions could enhance system management through optimisation, improving their ecological and economic impact. The current approaches consider installations as a whole, but individual predictions may yield better results. This paper follows a methodology that includes analysing data from 200 different Discharge Points over a period of four years and applying twelve forecast algorithms found as more commonly used for these predictions in the literature, including Random Forest, Support Vector Machines, and Decision Tree, to identify predictive patterns. The results are compared and evaluated to determine the accuracy of individual predictions and their potential improvements. As the results show that the algorithms do not capture the individual Discharge Points behaviour, alternative approaches are suggested for further development.

This paper describes the design and methodology for the development and validation of an intelligent model in the healthcare domain. The generated model relies on artificial intelligence techniques, aiming to predict the length of stay and survival rate of patients admitted to a critical care hospitalization unit with better results than predictive systems using scoring. The proposed methodology is based on the following stages: preliminary data analysis, analysis of the architecture and systems integration model, the big data model approach, information structure and process development, and the application of machine learning techniques. This investigation substantiates that automated machine learning models significantly surpass traditional prediction techniques for patient outcomes within critical care settings. Specifically, the machine learning-based model attained an F1 score of 0.351 for mortality forecast and 0.615 for length of stay, in contrast to the traditional scoring model’s F1 scores of 0.112 for mortality and 0.412 for length of stay. These results strongly support the advantages of integrating advanced computational techniques in critical healthcare environments. It is also shown that the use of integration architectures allows for improving the quality of the information by providing a data repository large enough to generate intelligent models. From a clinical point of view, obtaining more accurate results in the estimation of the ICU stay and survival offers the possibility of expanding the uses of the model to the identification and prioritization of patients who are candidates for admission to the ICU, as well as the management of patients with specific conditions.

In recent years, smartphones have become the main computing tool for most of the population, making them an ideal tool in many areas. Most of these smartphones are equipped with cutting-edge hardware on their digital cameras, sensors and processors. For this reason, this paper discusses the possibility of using smartphones as an accessible and accurate tool, focusing on the measurement of light, which is an element that has a high impact on human behavior, which promotes conformance and safety, or alters human physiology when it is inappropriate. To carry out this study, three different ways to measure light through smartphones have been checked: the ambient light sensor, the digital camera and an external Bluetooth luxmeter connected with the smartphone. As a result, the accuracy of these methods has been compared to check if they can be used as accurate measurement tools.

Street lighting installations are an essential service for modern life due to their capability of creating a welcoming feeling at nighttime. Nevertheless, several studies have highlighted that it is possible to improve the quality of the light significantly improving the uniformity of the illuminance. The main difficulty arises when trying to improve some of the installation’s characteristics based only on statistical analysis of the light distribution. This paper presents a new algorithm that is able to obtain the overall illuminance uniformity in order to improve this sort of installations. To develop this algorithm it was necessary to perform a detailed study of all the elements which are part of street lighting installations. Because classification is one of the most important tasks in the application areas of artificial neural networks, we compared the performances of six types of training algorithms in a feed forward neural network for analyzing the overall uniformity in outdoor lighting systems. We found that the best algorithm that minimizes the error is “Levenberg-Marquardt back-propagation”, which approximates the desired output of the training pattern. By means of this kind of algorithm, it is possible to help to lighting professionals optimize the quality of street lighting installations.

Street lighting has a big impact on the energy consumption of Spanish municipalities. To decrease this consumption, the Spanish government has developed two different regulations to improve energy savings and efficiency, and consequently, reduce greenhouse-effect gas emissions. However, after these efforts, they have not obtained the expected results. To improve the effectiveness of these regulations and therefore to optimize energy consumption, a study has been done to analyze the different devices which influence energy consumption with the intention of better understanding their behavior and performance. The devices analyzed were lamps, ballasts, street lamp globes, control systems and dimmable lighting systems. To improve their performance, they have been analyzed from three points of view: changes in technology, use patterns and standards. Thanks to this study, some aspects have been found that could be taken into account if we really wanted to use energy efficiently.

Clinical Decision Support Systems have the potential to reduce lack of communication and errors in diagnostic steps in primary health care. Literature reports have showed great advances in clinical decision support systems in the recent years, which have proven its usefulness in improving the quality of care. However, most of these systems are focused on specific areas of diseases. In this way, we propose a rule-based expert system, which supports clinicians in primary health care, providing a list of possible diseases regarding patient’s laboratory tests results in order to assist previous diagnosis. Our system also allows storing and retrieving patient’s data and the history of patient’s analyses, establishing a basis for coordination between the various health care levels. A validation step and speed performance tests were made to check the quality of the system. We conclude that our system could improve clinician accuracy and speed, resulting in more efficiency and better quality of service. Finally, we propose some recommendations for further research.

Energy consumption rose about 28% over the 2001 to 2011 period in the Spanish residential sector. In this environment, domestic hot water (DHW) represents the second highest energy demand. There are several methodologies to estimate DHW consumption, but each methodology uses different inputs and some of them are based on obsolete data. DHW energy consumption estimation is a key tool to plan modifications that could enhance this consumption and we decided to update the methodologies. We studied DHW consumption with data from 10 apartments in the same building during 18 months. As a result of the study, we updated one chosen methodology, adapting it to the current situation. One of the challenges to improve efficiency of DHW use is that most of people are not aware of how it is consumed in their homes. To help this information to reach consumers, we developed a website to allow users to estimate the final electrical energy needed for DHW. The site uses three estimation methodologies and chooses the best fit based on information given by the users. Finally, the application provides users with recommendations and tips to reduce their DHW consumption while still maintaining the desired comfort level.

The EU research project between industry and academia pDevOps is a collaborative research project formed by an international network of organizations including industry and academia that aims to tackle current challenges of microservice development operations. An important case study considered in this project is the Cyber Ranges application a cyber security training and capability development exercises using microservices for the design, delivery, and management of simulation-based, experiences in cyber security developed by Silensec as one of the partners. This work describes the results of analyzing the scenario usage dataset of the Cyber Ranges training platform. This includes the matrix of starts for scenario/user and the attributes of scenarios. The aims are to produce recommendations of scenarios for users based on previous activity and to predict the success of scenarios as measured by the number of starts.

The objective of this paper is to describe a developed model and its corresponding application, known as System to Evaluate the Water Consumption at Home (SEWAT). The aim is to create a new model to evaluate the efficiency of water consumption. Thanks to the input of the water bills by users, the model allows them to check if water consumption is efficient, in order to give them an opportunity to evaluate their water usage. To succeed in it, several researches were tracked in order to establish consumer trends and to identify the most efficient value for this magnitude. Furthermore, a survey was conducted to obtain updated values to validate information from previous studies. However, the main aim of this model is to use the resources efficiently, so it has to be useful accordingly. Therefore, after the evaluation, the application has a section with recommendations for the users to reduce their water consumption through a range of different indications. This section is divided into four: bathroom, kitchen, new appliance and reusing water. Each section shows the expected benefits if the users follow the recommended options. The main result is a unique application in Spain, which includes a system of evaluation, comparison and a section of recommendations for the users. Eventually, the model will have a promising outcome, because it surely will change the awareness of citizens about this subject.