With climate change driving an increasingly stronger influence over governments and municipalities, sustainable development, and renewable energy are gaining traction across the globe. This is reflected within the EU 2030 agenda, that envisions a future where there is universal access to affordable, reliable and sustainable energy. One of the challenges to achieve this vision lies on the low reliability of certain renewable sources. While both particulars and public entities try to reach self-sufficiency through sustainable energy generation, it is unclear how much investment is needed to mitigate the unreliability introduced by natural factors such as varying wind speed and daylight across the year. In this sense, a tool that aids predicting the energy output of sustainable sources across the year for a particular location can aid greatly in making sustainable energy investments more efficient. In this paper, we make use of Open Data sources, Internet of Things (IoT) sensors and installations distributed across Europe to create such tool through the application of Artificial Neural Networks. We analyze how the different factors affect the prediction of energy production and how Open Data can be used to predict the expected output of sustainable sources. As a result, we facilitate users the necessary information to decide how much they wish to invest according to the desired energy output for their particular location. Compared to state-of-the-art proposals, our solution provides an abstraction layer focused on energy production, rather that radiation data, and can be trained and tailored for different locations using Open Data. Finally, our tests show that our proposal improves the accuracy of the forecasting, obtaining a lower mean squared error (MSE) of 0.040 compared to an MSE 0.055 from other proposals in the literature.
Industrial machinery maintenance constitutes an important part of the manufacturing company’s budget. Fault Detection and Diagnosis (henceforth referenced as FDD) plays a key role on maintenance, since it allows for shorter maintenance times and, in the long run, to train predictive maintenance algorithms. The impact of proper maintenance is reflected on an especially costly type of industrial machine: gas turbines. These devices are complex, large pieces of machinery that cause considerable service disruption when downtime occurs. In an effort to shorten these service disruptions and establish the basis for the development of predictive maintenance, we present in this paper an approach to FDD of industrial machinery, such as gas turbines. Our approach exploits the data generated by industrial machinery to train a machine-learning based architecture, combining several algorithms with autoencoders and sliding windows. Our proposed solution helps to achieve early malfunctioning detection and has been tested using real data from real working environments. In order to build our solution, first, we analyze the behavior of the gas turbine from a mathematical point of view. Then, we develop an architecture that is capable of detecting when the gas turbine presents an abnormal behavior. The great advantage of our proposal is that (i) does not require existing disruption data, which can be difficult to obtain, (ii) is not limited to processes with specific time windows, and (iii) provides crucial information in real time to the monitoring staff, generating valuable data for further predictive maintenance. It is worth highlighting that although we exemplify our approach using gas turbines, our approach can be tailored to other FDD problems in complex industrial processes with variable duration that could benefit from the aforementioned advantages.
The international maritime traffic of people and goods has often contributed to the spread of pathogens affecting public health. The Maritime Declaration of Health (MDH), according to the International Health Regulations (IHR) (2005), is a document containing data related to the state of health on board a ship during passage and on arrival at port. It is a useful tool for early detection of public health risks. The main objective of our study was to evaluate compliance with the model provided in the IHR, focusing on the format and degree of completion of MDH forms received at Spanish ports. We reviewed the content of 802 MDH forms submitted to nine Spanish ports between October 2014 and March 2015. Study results show that 22% of MDH forms presented did not comply with the recommended model and 39% were incomplete. The proportion of cargo ships with correct and complete MDH forms was lower than passenger ships; thus, the nine health questions were answered less frequently by cargo ships than passenger ships (63% vs 90%, p value < 0.001). The appropriate demand and usage of MDH forms by competent authorities should improve the quality of the document as a tool and improve risk assessment.
Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients’ data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient’s evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.
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