Axiomatic geometric formulation of electromagnetism with only one axiom: the field equation for the bivector field F with an explanation of the Trouton-Noble experiment

In this paper we present an axiomatic, geometric, formulation of electromagnetism with only one axiom: the field equation for the Faraday bivector field F. This formulation with F field is a self-contained, complete and consistent formulation that dispenses with either electric and magnetic fields or the electromagnetic potentials. All physical quantities are defined without reference frames, the absolute quantities, i.e., they are geometric four dimensional (4D) quantities or, when some basis is introduced, every quantity is represented as a 4D coordinate-based geometric quantity comprising both components and a basis. The new observer independent expressions for the stress-energy vector T(n)(1-vector), the energy density U (scalar), the Poynting vector S and the momentum density g (1-vectors), the angular momentum density M (bivector) and the Lorentz force K (1-vector) are directly derived from the field equation for F. The local conservation laws are also directly derived from that field equation. The 1-vector Lagrangian with the F field as a 4D absolute quantity is presented; the interaction term is written in terms of F and not, as usual, in terms of A. It is shown that this geometric formulation is in a full agreement with the Trouton-Noble experiment.Comment: 32 pages, LaTex, this changed version will be published in Found. Phys. Let

Avaliação do desempenho dos diferentes métodos de interpoladores para parâmetros do balanço hídrico climatológico

A geoestatística está associada a uma classe de técnicas utilizadas para analisar e inferir valores de uma variável distribuída no espaço ou no tempo, mediante o que se propôs, no presente trabalho avaliar, através de diferentes técnicas de interpolação, os seguintes parâmetros climáticos: precipitação, deficiência hídrica, excedente hídrico, evapotranspiração potencial, evapotranspiração real e disponibilidade hídrica, no estado do Espírito Santo. Para tanto, utilizaram-se dados meteorológicos de temperatura do ar e precipitação pluviométrica, compreendidos no período de 1977 a 2006, para o cálculo do balanço hídrico climatológico conforme método proposto por Thornthwaite & Mather (1955), adotando uma capacidade de armazenamento de 100 mm. Os resultados mostram que o método da krigagem é o mais eficiente para a espacialização dos parâmetros climáticos, baseado no menor valor da Raiz do Erro Médio Quadrático (REMQ) e outros parâmetros calculados que auxiliaram na escolha do melhor modelo.Geostatistics is associated with a class of techniques used to analyze and to infer values of a variable distributed in space or time. By means of this, the objective of this work was to evaluate different techniques of interpolation for the following climatic parameters: precipitation, water deficit, water surplus, potential evapotranspiration, actual evapotranspiration and water availability in the State of the Espirito Santo. Meteorological data of air temperature and precipitation were used in the climatic water balance determination, according to Thornthwaite & Mather (1955), adopting a storage capacity of 100 mm. The results show that the method of kriging was the most efficient for the spatialization of climatic parameters, based on the lower value of the Root of the Mean Quadratic Error (REMQ) and other calculated parameters that helped in choosing the best model

Classification of aortic stenosis using conventional machine learning and deep learning methods based on multi-dimensional cardio-mechanical signals

AbstractThis paper introduces a study on the classification of aortic stenosis (AS) based on cardio-mechanical signals collected using non-invasive wearable inertial sensors. Measurements were taken from 21 AS patients and 13 non-AS subjects. A feature analysis framework utilizing Elastic Net was implemented to reduce the features generated by continuous wavelet transform (CWT). Performance comparisons were conducted among several machine learning (ML) algorithms, including decision tree, random forest, multi-layer perceptron neural network, and extreme gradient boosting. In addition, a two-dimensional convolutional neural network (2D-CNN) was developed using the CWT coefficients as images. The 2D-CNN was made with a custom-built architecture and a CNN based on Mobile Net via transfer learning. After the reduction of features by 95.47%, the results obtained report 0.87 on accuracy by decision tree, 0.96 by random forest, 0.91 by simple neural network, and 0.95 by XGBoost. Via the 2D-CNN framework, the transfer learning of Mobile Net shows an accuracy of 0.91, while the custom-constructed classifier reveals an accuracy of 0.89. Our results validate the effectiveness of the feature selection and classification framework. They also show a promising potential for the implementation of deep learning tools on the classification of AS.</jats:p

Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters

AbstractRecent research has shown promising results for the detection of aortic stenosis (AS) using cardio-mechanical signals. However, they are limited by two main factors: lacking physical explanations for decision-making on the existence of AS, and the need for auxiliary signals. The main goal of this paper is to address these shortcomings through a wearable inertial measurement unit (IMU), where the physical causes of AS are determined from IMU readings. To this end, we develop a framework based on seismo-cardiogram (SCG) and gyro-cardiogram (GCG) morphologies, where highly-optimized algorithms are designed to extract features deemed potentially relevant to AS. Extracted features are then analyzed through machine learning techniques for AS diagnosis. It is demonstrated that AS could be detected with 95.49–100.00% confidence. Based on the ablation study on the feature space, the GCG time-domain feature space holds higher consistency, i.e., 95.19–100.00%, with the presence of AS than HRV parameters with a low contribution of 66.00–80.00%. Furthermore, the robustness of the proposed method is evaluated by conducting analyses on the classification of the AS severity level. These analyses are resulted in a high confidence of 92.29%, demonstrating the reliability of the proposed framework. Additionally, game theory-based approaches are employed to rank the top features, among which GCG time-domain features are found to be highly consistent with both the occurrence and severity level of AS. The proposed framework contributes to reliable, low-cost wearable cardiac monitoring due to accurate performance and usage of solitary inertial sensors.</jats:p