Laser-Induced Vertical Graphene Nanosheets for Electrocatalytic Hydrogen Evolution.

Efficient and affordable electrocatalysts are fundamental for the sustainable production of hydrogen from water electrolysis. Here, an approach for the rapid production of laser-induced vertical graphene nanosheets (LIVGNs) through the exfoliation of the graphite foil under laser irradiation is presented. The density of the formed LIVGNs is ∼3 per 100 μm.

Deep Learning-Based Automated Cell Detection-Facilitated Meat Quality Evaluation.

Meat consumption is increasing globally. The safety and quality of meat are considered important issues for human health. During evaluations of meat quality and freshness, microbiological parameters are often analyzed.

Vertical graphene nanowalls supported hybrid WC/WO composite material as an efficient non-noble metal electrocatalyst for hydrogen evolution.

Research for the development of noble metal-free electrodes for hydrogen evolution has blossomed in recent years. Transition metal carbides compounds, such as WC, have been considered as a promising alternative to replace Pt-family metals as electrocatalysts towards hydrogen evolution reaction (HER). Moreover, hybridization of TMCs with graphene nanostructures has emerged as a reliable strategy for the preparation of compounds with high surface to volume ratio and abundant active sites.

Turbulence hierarchy in foreign exchange markets.

We present a multiscale stochastic analysis of foreign exchange rates using the H-theory formalism, which provides a hierarchical intermittency model for the information cascade in the currency market. We examine the distributions of returns and volatilities for the three most traded currency pairs: euro-U.S.

Machine learning classification based on k-Nearest Neighbors for PolSAR data.

In this work, we focus on obtaining insights of the performances of some well-known machine learning image classification techniques (k-NN, Support Vector Machine, randomized decision tree and one based on stochastic distances) for PolSAR (Polarimetric Synthetic Aperture Radar) imagery. We test the classifiers methods on a set of actual PolSAR data and provide some conclusions. The aim of this work is to show that suitable adapted standard machine learning methods offer excellent performances vs.

On the Use of Machine Learning Techniques and Non-Invasive Indicators for Classifying and Predicting Cardiac Disorders.

This research aims to enhance the classification and prediction of ischemic heart diseases using machine learning techniques, with a focus on resource efficiency and clinical applicability. Specifically, we introduce novel non-invasive indicators known as Campello de Souza features, which require only a tensiometer and a clock for data collection. These features were evaluated using a comprehensive dataset of heart disease cases from a machine learning data repository.

Advancements in Plasma-Enhanced Chemical Vapor Deposition for Producing Vertical Graphene Nanowalls.

In recent years, vertical graphene nanowalls (VGNWs) have gained significant attention due to their exceptional properties, including their high specific surface area, excellent electrical conductivity, scalability, and compatibility with transition metal compounds. These attributes position VGNWs as a compelling choice for various applications, such as energy storage, catalysis, and sensing, driving interest in their integration into next-generation commercial graphene-based devices. Among the diverse graphene synthesis methods, plasma-enhanced chemical vapor deposition (PECVD) stands out for its ability to create large-scale graphene films and VGNWs on diverse substrates.

Similarity-Based Predictive Models: Sensitivity Analysis and a Biological Application with Multi-Attributes.

Predictive models based on empirical similarity are instrumental in biology and data science, where the premise is to measure the likeness of one observation with others in the same dataset. Biological datasets often encompass data that can be categorized. When using empirical similarity-based predictive models, two strategies for handling categorical covariates exist.

Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics.

The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables.

Hybrid Nanostructured Compounds of MoC on Vertical Graphene Nanoflakes for a Highly Efficient Hydrogen Evolution Reaction.

Organizing a post-fossil fuel economy requires the development of sustainable energy carriers. Hydrogen is expected to play a significant role as an alternative fuel as it is among the most efficient energy carriers. Therefore, nowadays, the demand for hydrogen production is increasing.

Weibull Regression and Machine Learning Survival Models: Methodology, Comparison, and Application to Biomedical Data Related to Cardiac Surgery.

In this article, we propose a comparative study between two models that can be used by researchers for the analysis of survival data: (i) the Weibull regression model and (ii) the random survival forest (RSF) model. The models are compared considering the error rate, the performance of the model through the Harrell C-index, and the identification of the relevant variables for survival prediction. A statistical analysis of a data set from the Heart Institute of the University of São Paulo, Brazil, has been carried out.

ModInterv COVID-19: An online platform to monitor the evolution of epidemic curves.

We present the software ModInterv as an informatics tool to monitor, in an automated and user-friendly manner, the evolution and trend of COVID-19 epidemic curves, both for cases and deaths. The ModInterv software uses parametric generalized growth models, together with LOWESS regression analysis, to fit epidemic curves with multiple waves of infections for countries around the world as well as for states and cities in Brazil and the USA. The software automatically accesses publicly available COVID-19 databases maintained by the Johns Hopkins University (for countries as well as states and cities in the USA) and the Federal University of Viçosa (for states and cities in Brazil).

Multiple waves of COVID-19: a pathway model approach.

A generalized pathway model, with time-dependent parameters, is applied to describe the mortality curves of the COVID-19 disease for several countries that exhibit multiple waves of infections. The pathway approach adopted here is formulated explicitly in time, in the sense that the model's growth rate for the number of deaths or infections is written as an explicit function of time, rather than in terms of the cumulative quantity itself. This allows for a direct fit of the model to daily data (new deaths or new cases) without the need of any integration.

Neotropical stingless bees display a strong response in cold tolerance with changes in elevation.

Tropical pollinators are expected to experience substantial effects due to climate change, but aspects of their thermal biology remain largely unknown. We investigated the thermal tolerance of stingless honey-making bees, the most ecologically, economically and culturally important group of tropical pollinators. We assessed changes in the lower (CT) and upper (CT) critical thermal limits of 17 species (12 genera) at two elevations (200 and 1500 m) in the Colombian Andes.

High thermal tolerance in high-elevation species and laboratory-reared colonies of tropical bumble bees.

Bumble bees are key pollinators with some species reared in captivity at a commercial scale, but with significant evidence of population declines and with alarming predictions of substantial impacts under climate change scenarios. While studies on the thermal biology of temperate bumble bees are still limited, they are entirely absent from the tropics where the effects of climate change are expected to be greater. Herein, we test whether bees' thermal tolerance decreases with elevation and whether the stable optimal conditions used in laboratory-reared colonies reduces their thermal tolerance.

Thermal limits of Africanized honey bees are influenced by temperature ramping rate but not by other experimental conditions.

Interest in assessing the critical thermal limits of bees is rapidly increasing, as these physiological traits are good predictors of bees' potential responses to extreme temperature changes, which is relevant in the context of global climate change. However, estimates of thermal limits may be influenced by several factors and published studies differ in experimental methods and conditions, such as the rate of temperature change (ramping rate) and feeding status, which might yield inaccurate predictions and limit comparisons across taxa and regions. Using Africanized honey bees as a model organism, we assessed the effect of ramping rate (0.

Ingredients for Responsible Machine Learning: A Commented Review of .

In , Biecek, Kozak, and Zawada (here BKZ) provide an illustrated and engaging step-by-step guide on how to perform a machine learning (ML) analysis such that the algorithms, the software, and the entire process is interpretable and transparent for both the data scientist and the end user. This review summarises BKZ's book and elaborates on three elements key to ML analyses: inductive inference, causality, and interpretability.

ModInterv: An automated online software for modeling epidemics.

The COVID-19 pandemic has proven the importance of mathematical tools to understand the evolution of epidemic outbreaks and provide reliable information to the general public and health authorities. In this perspective, we have developed ModInterv, an online software that applies growth models to monitor the evolution of the COVID-19 epidemic in locations chosen by the user among countries worldwide or states and cities in the USA or Brazil. This paper describes the software capabilities and its use both in recent research works and by technical committees assisting government authorities.

Infrarenal EVAR for Penetrating Aortic Ulcer: A Comparative Study with Abdominal Aortic Aneurysm.

Background: Endovascular aortic repair (EVAR), currently the preferred treatment for abdominal aortic aneurysm (AAA), has been described also for penetrating aortic ulcers (PAU) of the infrarenal aorta. However, data on its performance in this particular setting are still sparse in the literature. Aim of this study is to compare patient clinical characteristics, aorto-iliac features, and post-operative outcomes between infrarenal PAU and AAA treated by standard EVAR.

A New Wavelet-Based Privatization Mechanism for Probability Distributions.

In this paper, we propose a new privatization mechanism based on a naive theory of a perturbation on a probability using wavelets, such as a noise perturbs the signal of a digital image sensor. Wavelets are employed to extract information from a wide range of types of data, including audio signals and images often related to sensors, as unstructured data. Specifically, the cumulative wavelet integral function is defined to build the perturbation on a probability with the help of this function.

Carotid endarterectomy in asymptomatic octogenarians: Outcomes at 30 days and 5 years.

Background: The following study investigated the 30-day and 5-year relative survival rate and freedom from neurological events in asymptomatic carotid stenosis (ACS) octogenarians who had undergone elective carotid endarterectomy (CEA).

Methods: Between January 2008 and June 2014, a retrospective review was conducted on ACS patients who had undergone elective CEA. The patients' sample was divided into two groups: Group A (GA) included octogenarians and Group B (GB) included younger patients.

Adaptive kernel fuzzy clustering for missing data.

Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of strategies to deal with missing data. The first strategy, called Whole Data Strategy (WDS), performs clustering only on the complete part of the dataset, i.

Prevalence ratio estimation via logistic regression: a tool in R.

The interpretation of odds ratios (OR) as prevalence ratios (PR) in cross-sectional studies have been criticized since this equivalence is not true unless under specific circumstances. The logistic regression model is a very well known statistical tool for analysis of binary outcomes and frequently used to obtain adjusted OR. Here, we introduce the prLogistic for the R statistical computing environment which can be obtained from The Comprehensive R Archive Network, https://cran.

The Utility of Intraoperative Contrast-enhanced Ultrasound for Immediate Treatment of Type Ia Endoleak during EVAR: Initial Experience.

Objectives Type Ia endoleak (EL) after endovascular abdominal aortic repair (EVAR) may be misdiagnosed at completion angiography. Intraoperative contrast-enhanced ultrasound (CEUS) may play a role in early detection and immediate treatment of type Ia EL. Methods From January 2017 to April 2018, patients treated with EVAR underwent intraoperative CEUS.

[Development and validation of a diagnostic model of antimicrobial resistance by extended-spectrum beta-lactamases in community-acquired urinary tract infections. How to adjust the prediction in variable outcome prevalences? The usefulness of contracting predictors with LASSO regression].

Background: In the case of community-acquired urinary tract infection, the identification of Enterobacteriaceae with extended spectrum beta-lactamases (ESBL) can optimize treatment, control and follow-up strategies, however the effect of variable prevalences of this resistance pattern has affected the external validity of this type of models.

Aim: To develop a diagnostic predictive model that adjusts the prediction error in variable prevalences using the LASSO regression.

Methods: A diagnostic predictive model of community-acquired urinary tract infection by infection by ESBL producing Enterobacteriaceae was designed.