This article proposes an informative exploratory tool, the functional boxplot, for visualizing data, as well its generalization, enhanced boxplot. Based on center outward ordering induced by band depth descriptive statistics of a boxplot are: envelope 50% central region, median curve, and maximum non-outlying envelope. In addition, outliers can be detected in 1.5 times region empirical rule, analogous to rule classical boxplots. The construction is illustrated series sea surface temperatures...

Accurately detecting Parkinson's disease (PD) at an early stage is certainly indispensable for slowing down its progress and providing patients the possibility of accessing to disease-modifying therapy. Towards this end, premotor in PD should be carefully monitored. An innovative deep-learning technique introduced uncover whether individual affected with or not based on features. Specifically, stage, several indicators have been considered study, including Rapid Eye Movement olfactory loss,...

Photocatalytic hydrogen production is crucial for solar-to-chemical conversion process, wherein high-efficiency photocatalysts lie in the heart of this area. A photocatalyst hierarchically mesoporous titanium phosphonate based metal-organic frameworks, featuring well-structured spheres, a periodic mesostructure, and large secondary mesoporosity, are rationally designed with complex polyelectrolyte cathodic surfactant serving as template. The hierarchical porosity homogeneously incorporated...

Wind power represents a promising source of renewable energies. Precise forecasting wind generation is crucial to mitigate the challenges balancing supply and demand in smart grid. Nevertheless, major difficulty its high fluctuation intermittent nature, making it challenging forecast. This study aims develop efficient data-driven models accurately forecast generation. Crucially, main contributions this work are listed following elements. Firstly, we investigate performance enhanced machine...

Accurately predicting key features in WWTPs is essential for optimizing plant performance and minimizing operational costs. This study assesses the potential of various machine learning models inflow to anoxic sludge reactors. Firstly, it conducts a comprehensive evaluation diverse models, including k-Nearest Neighbors (kNN), Random Forest (RF), XGBoost, CatBoost, LightGBM, Decision Tree Regression (DTR), flow into Anoxic section under weather conditions (dry, rainy, stormy). Secondly,...

Precise traffic flow prediction is a central component of advancing intelligent transportation systems, providing essential insights for optimizing management, reducing travel times, and alleviating congestion. This study introduces an efficient deep learning approach that synergistically integrates the benefits wavelet-based denoising Recurrent Neural Networks (RNNs). integrated methodology introduced to effectively capture inherent nonlinearity temporal dependencies in time series data....

Abstract We introduce a valid parametric family of cross-covariance functions for multivariate spatial random fields where each component has covariance function from well-celebrated Matérn class. Unlike previous attempts, our model indeed allows various smoothnesses and rates correlation decay any number vector components. present the conditions on parameter space that result in models with varying degrees complexity. discuss practical implementations, including reparameterizations to...

This paper presents a model-based anomaly detection method for supervising the direct current (dc) side of photovoltaic (PV) systems. Toward this end, framework combining benefits k-nearest neighbors (kNN) with univariate monitoring approaches has been proposed. Specifically, kNN-based Shewhart and exponentially weighted moving average (EWMA) schemes parametric nonparametric thresholds have introduced to suitably detect faults in PV The choice kNN separate normal abnormal features is...

Recognition of human movements is very useful for several applications, such as smart rooms, interactive virtual reality systems, detection and environment modeling. The objective this work focuses on the classification falls based variations in silhouette shape, a key challenge computer vision. Falls are major health concern, specifically elderly. In study, achieved with multivariate exponentially weighted moving average (MEWMA) monitoring scheme, which effective detecting because it...

The accurate modeling and forecasting of the power output photovoltaic (PV) systems are critical to efficiently managing their integration in smart grids, delivery, storage. This paper intends provide efficient short-term solar production using Variational AutoEncoder (VAE) model. Adopting VAE-driven deep learning model is expected improve accuracy because its suitable performance time-series flexible nonlinear approximation. Both single- multi-step-ahead forecasts investigated this work....

Engineering intrinsic selenium vacancies (Se-vacancies) was achieved in mechanically exfoliated WSe2 monolayer nanosheets (WSe2 MLNSs) via an annealing treatment. Our theoretical and experimental results reveal that these Se-vacancies can efficiently activate optimize the basal planes of MLNSs. As expected, optimized catalyst exhibits efficient electrocatalytic hydrogen evolution.

The accurate forecast of wastewater treatment plant (WWTP) key features can comprehend and predict the behavior to support process design controls, improve system reliability, reduce operational costs, endorse optimization overall performances. Deep learning technologies as proven data-driven soft-sensors should be developed for WWTP applications tackle non-linearity dynamic nature environmental data. This study adopts deep learning-based models features, such influent flow, temperature,...