Renewable energy forecasting is crucial for pollution prevention, management, and long‐term sustainability. In response to the challenges associated with energy forecasting, the simultaneous deployment of several data‐processing approaches has been used in a variety of studies in order to improve the energy–time‐series analysis, finding that, when combined with the wavelet analysis, deep learning techniques can achieve high accuracy in energy forecasting applications. Consequently, we investigate the implementation of various wavelets within the structure of a long short‐term memory neural network (LSTM), resulting in the new LSTM wavelet (LSTMW) neural network. In addition, and as an improvement phase, we modeled the uncertainty and incorporated it into the forecast so that systemic biases and deviations could be accounted for (LSTMW with luster: LSTMWL). The models were evaluated using data from six renewable power generation plants in Chile. When compared to other approaches, experimental results show that our method provides a prediction error within an acceptable range, achieving a coefficient of determination ( R 2 ) between 0.73 and 0.98 across different test scenarios, and a consistent alignment between forecasted and observed values, particularly during the first 3 prediction steps.

Even after early repair of aortic coarctation (AoCo), life expectancy is reduced due to complications such as hypertension. Invasive diagnostic catheterization is used to evaluate peak-to-peak pressure gradients (PGpp) across the CoAo. Clinically significant PGppare those greater than 20 mmHg under resting conditions, in which case the patient is referred for a second intervention to repair the CoAo. In this study, we demonstrate the feasibility of using Physics-Informed Neural Networks (PINNs) to predict PGppin patients with AoCo non-invasively, based on images obtained from cardiac magnetic resonance imaging. We analyzed a group of 3 patients with CoAo under resting and pharmacological stress conditions. We were able to obtain PGppvalues very close to the actual values obtained by diagnostic catheterization, with an absolute error and average percentage error of 0.57 mmHg and 8.29% for the resting condition, and 4.13 mmHg and 8.63% for the pharmacological stress condition. Our method also successfully identified the only patient who presented a clinically significant PGppunder resting conditions, with differences of less than 1 mmHg.

Cyberattacks are increasing every day, demanding that security incident response teams proactively determine potential threats early. Although social networks such as Twitter are a rich and up-to-date source of information where users use to tweet about different topics, it is complex to efficiently and effectively obtain results that support decision-making on a specific subject, such as cyberattacks. Therefore, in this work, we propose to use an offline mining process based on the clique percolation method over a corpus of tweets in order to generate an indexed knowledge base about cyberattacks. Results are promising to observe threats under evolution. Then, to show results properly, we generate an observatory prototype to allow cybersecurity researchers to explore threats over time and space.

Functional magnetic resonance imaging (fMRI) is a key neuroimaging technique. The classic fMRI analysis pipeline is based on the assumption that the hemodynamic response (HR) is the same across brain regions, time, and subjects. Although convenient, there is ample evidence that this assumption does not hold, and that these differences result in inaccuracies in brain activity detection. This article presents a new fMRI processing pipeline that captures the intrinsic intra- and intersubject variability of the HR. At the core of this new pipeline is the definition of a fuzzy hemodynamic response function (HRF). The proposed pipeline includes a new fuzzy general linear model (GLM) able to handle the fuzzy HRF, including a practical realization based on the LR representation of fuzzy numbers. This article also describes how to obtain activation maps from the fuzzy GLM, and a methodology to compute the statistical power of the analysis. The method is evaluated in synthetic and real fMRI data and compared with other state-of-the-art techniques. The experiments based on synthetic data show that the fuzzy GLM approach is more robust under uncertainty regarding the true specific shape of the HR. The experiments based on the real data show an increased volume of the activated brain areas, suggesting that the proposed method is able to prevent false negative errors in the boundaries of target brain regions in which HR should be negligible.

Hesitant fuzzy sets have been proposed as an extension of fuzzy sets to address situations in which decision makers exhibit variations in their alternatives' assessment values. However, in real-world problems, the decision-making process has to be accomplished under situations where these assessment values may also drastically change over time. In this paper, we propose a prioritized aggregation operator to combine a time sequence of hesitant fuzzy information, where the time-based hesitancy due to changing environment is mitigated. The proposed method is applied to the service selection problem in service-based systems, where software architects must select as a group the service that has the best combination of features based on their historical assessments. We claim that the time-based hesitant fuzzy information aggregation method addresses the hesitancy at intra- and interexpert levels obtaining more robust decisions.