A generalization of the chargedC metric to the nonstationary case is given. The possibility of associating the energy-momentum tensor with the electromagne
We apply results on symmetries of equations of motion and equivalent Lagrangians to obtain a constant of motion for a particle travelling through a viscous medium and for the damped harmonic oscillator. PACS No.: 45.20Jj
The start-up process of water-distribution networks has been extensively investigated in recent years, particularly regarding the pressure surges that may occur during such transient events. In this context, researchers have concentrated on exploring physical formulations capable of describing the behaviour of the two interacting phases—water and air—typically resolved through numerical approaches. This paper presents an analytical solution to the nonlinear mathematical model governing the start-up of water pipelines containing a trapped air pocket. The model adopts the rigid water column approximation for the liquid phase and a polytropic gas law to account for the compressibility of the air. The resulting system can be formulated as a second-order nonlinear differential equation. The analytical approach consists of transforming the governing equation into a first-order linear ordinary differential equation, in which the square of the water front velocity is expressed as a function of the water column length. This transformation yields a closed-form solution expressed as a special integral series. The required integrals are evaluated using binomial expansions and incomplete gamma functions, enabling the derivation of a general solution valid within alternating intervals of monotonic motion. A practical application involving an 800 m pipeline is presented. Furthermore, the proposed solution is validated against experimental measurements, demonstrating the accuracy and effectiveness of the analytical approach in capturing the system’s transient behaviour.
This paper introduces a methodology aimed at validating anomalies identified through unsupervised techniques. Our approach is grounded in the assumption that machine learning models perform optimally when trained on data free of anomalies. Therefore, to assess the veracity of anomalies pinpointed by an unsupervised method, we undertake a two-fold process. Initially, anomalies are removed from the training dataset. Subsequently, we gauge the expected enhancement in the performance of classification models. To evaluate the effectiveness of our methodology, we employed three well-established unsupervised anomaly detection techniques: Local Outlier Factor (LOF), Isolation Forest (iForest), and Autoencoders. These methods were complemented by a voting system designed to identify anomalous data records. The reliability of these detected anomalies was rigorously tested using various classification models, including K-Nearest Neighbors, Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Classifier (SVC). This evaluation was conducted both before and after the removal of anomalies from the training dataset. Our methodology underwent rigorous testing across five distinct datasets: Breast Cancer, German Credit, Diabetes, Heart Failure Disease, and Titanic Survivor. The results provide evidence of its effectiveness, with notable improvements observed in the classification performance across four of the five datasets.
The main objective of this research is to analyze the effect of applying some image preprocessing techniques on the performance of convolutional neural networks for the classification of COVID19, Pneumonia, and Normal patients from chest X-ray images. The normal...
Tsamparlis et al. [3] have developed a systematic method for computing of the conformal algebra of 1+3 space-times. The proper CKV’s are
According to a scalar theory of diffraction, light propagation can be expressed by two-dimensional fractional order Fourier transforms. Since the fractional Fourier transform of a chirp function is a Dirac distribution, focusing a light beam is optically achieved by using a diffractive screen whose transmission function is a two-dimensional chirp function. This property is applied to designing Fresnel microlenses, and the orders of the involved Fourier fractional transforms depend on diffraction distances as well as on emitter and receiver radii of curvature. If the emitter is astigmatic (with two principal radii of curvature), the diffraction phenomenon involves two one-dimensional fractional Fourier transforms whose orders are different. This degree of freedom allows us to design microlenses that can focus astigmatic Gaussian beams, as produced by a line-shaped laser diode source.
El desarrollo de nuevas moléculas es un proceso que requiere de múltiples etapas y los ensayos clínicos para verificar su eficacia cuesta miles de millones de dólares cada año. El aprendizaje automático es una herramienta que está avanzando rápidamente en el reconocimiento de imágenes, voz y texto, y trabajar In silico aumentaría la capacidad de predecir y priorizar la función de un medicamento. En esta investigación nos preguntamos si la función de los medicamentos de uso terapéutico se puede predecir a partir de la configuración estereoquímica de la molécula. Nosotros usamos redes neuronales convolucionales para predecir el uso terapéutico de fármacos, entrenadas tanto con información bidimensional como con información tridimensional de su estructura química. El modelo entrenado solamente con seis vistas de la información 3D de la estructura molecular mejoró la exactitud en un 10 respecto al modelo entrenado con la información 2D.
Recently obtained results linking several constants of motion to one (non-Noetherian) symmetry to the problem of geodesic motion in Riemannian space-times are applied. The construction of conserved quantities in geodesic motion as well as the deduction of geometrical statements about Riemannian space-times are achieved.
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