Classification of Cancer Tissue With Machine Learning Algorithms Using Microwave Datasets.

The role of blocking dynamics and current on wave scattering due to finite floating elastic plate

Electrocardiogram analysis using a combination of statistical, geometric, and nonlinear heart rate variability features

P-wave reflection and transmission coefficients for non-welded interface play crucial roles in broad practical engineering productions, involving fracture properties prediction and seismic inversion. However, the existing reflection coefficient equations for non-welded interface in elasto-plastic media are seldom studied, although the elasto-plastic deformation is frequently encountered in the Earth’s subsurface due to artificial and tectonic activities. In this study, we proposed the accurate reflection and transmission coefficients equation for a non-welded interface embedded in an elasto-plastic deformed medium based on the elasto-plastic acoustoelastic and linear-slip theory. In detail, this paper uses elasto-plastic acoustoelastic theory to derive the reflection and transmission coefficients equation. The reflection and transmission coefficients matrix are solved using the linear-slip theory as the boundary condition. Moreover, we use the hardening parameter and plastic deformation to represent the plastic properties of the rock, which is a function of stress and plastic deformation. Through Numerical analysis, the deformation caused by static stress has significantly changed the amplitude and the slope of the reflection and transmission coefficients amplitude. As the stress increases, the rock’s velocity becomes higher, and all reflection and transmission coefficients (i.e., RPP, RPS, TPP, TPS) abruptly change at the critical angle. Furthermore, with the increase in plastic deformation, the critical angle of the incident P-wave and the hardening parameter becomes larger than the unstressed state. The non-welded interface exhibits a low-pass frequency filter for reflected SV-waves and a high-pass frequency filter for reflected P-waves and transmitted P and SV waves. In addition, we can observe that static vertical stress can weaken the anomalous reflections caused by non-welded formations, but the effect is insignificant. On the other hand, the effect of fracture normal compliance to reflection and transmission is detailly investigated. When N<2.5*10-10(MPa-1), The non-welded interface is close to the welded interface, while N>2.5*10-5(MPa-1), the non-welded interface is close to the solid-air interface.

Plane-wave reflection and transmission coefficients were calculated for a porous viscoelastic seabed with arbitrary pore-size distributions based on a previously developed theory [Yamamoto and Turgut, J. Acoust. Soc. Am. 83, 1744–1751 (1988)]. A sensitivity analysis indicated that, for a fixed permeability value, arbitrary pore-size distribution seems to broaden the frequency dependency of plane-wave reflection and transmission coefficients. Assuming a single pore-size Biot model, performance of global optimization inversion algorithms was investigated using the synthetic chirp signals reflected form a porous seabed with an arbitrary pore-size distribution. Inversion of porosity, shear modulus, and permeability was primarily attempted by assigning representative values to other Biot parameters which have secondary effects on the plane-wave reflection coefficient within their physical bounds. Perfect agreement was obtained between inverted and true values of porosity and shear modulus. However, estimated permeability values were always smaller than true permeabilty values due to the assumption of single pore size.

The results of measurement and monitoring of reflection and transmission properties of cement-based specimens (blocks of mortar, concrete) during long time of their service lives, including hydration process, and different curing conditions at microwave frequencies (X-band) are presented. A simple and inexpensive measurement system that utilizes the nondestructive and contactless free space method is used. Dependencies of the reflection and transmission coefficients on water-to-cement ratio, preparing and curing conditions of the specimens are demonstrated. It is shown that the reflection coefficient is approximately stable after hydration process while the transmission coefficient changes during long time of the specimen's service life. The complex dielectric permittivity of the cement-based materials is calculated by a new method using only the amplitudes of the reflection and transmission coefficients. The expected applications of the results are discussed.

The variations of reflection and transmission coefficients, as well as of the fluorescence response of the arterial wall are studied as a function of temperature. In a first experiment, reflection coefficient, transmission coefficient, and temperature, are measured during laser irradiation of the tissue. The fluorescence spectrum is recorded in a second experiment, together with temperature, during heating of the arterial wall in a vapor bath. The temperature dependence of the optical parameters and of the fluorescence response are then deduced. For the reflection and transmission coefficients, a direct correlation of their variations with temperature is measured. Extrema are observed in the temperature region at about 50°C. A global decrease of the fluorescence intensity is characteristic of a temperature increase. These variations can be used as thermal markers, which in turn could give a criterion to control heat deposition in the tissue during laser anastomose.

Air pollution in Macau has become a serious problem following the Pearl River Delta’s (PRD) rapid industrialization that began in the 1990s. With this in mind, Macau needs an air quality forecast system that accurately predicts pollutant concentration during the occurrence of pollution episodes to warn the public ahead of time. Five different state-of-the-art machine learning (ML) algorithms were applied to create predictive models to forecast PM2.5, PM10, and CO concentrations for the next 24 and 48 h, which included artificial neural networks (ANN), random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and multiple linear regression (MLR), to determine the best ML algorithms for the respective pollutants and time scale. The diurnal measurements of air quality data in Macau from 2016 to 2021 were obtained for this work. The 2020 and 2021 datasets were used for model testing, while the four-year data before 2020 and 2021 were used to build and train the ML models. Results show that the ANN, RF, XGBoost, SVM, and MLR models were able to provide good performance in building up a 24-h forecast with a higher coefficient of determination (R2) and lower root mean square error (RMSE), mean absolute error (MAE), and biases (BIAS). Meanwhile, all the ML models in the 48-h forecasting performance were satisfactory enough to be accepted as a two-day continuous forecast even if the R2 value was lower than the 24-h forecast. The 48-h forecasting model could be further improved by proper feature selection based on the 24-h dataset, using the Shapley Additive Explanations (SHAP) value test and the adjusted R2 value of the 48-h forecasting model. In conclusion, the above five ML algorithms were able to successfully forecast the 24 and 48 h of pollutant concentration in Macau, with the RF and SVM models performing the best in the prediction of PM2.5 and PM10, and CO in both 24 and 48-h forecasts.

Efficient and accurate mathematical codes for the prediction of underwater sound propagation are a critical component of SONAR system development and operation. The goal of the research presented herein is to develop, implement, and verify an efficient and rigorous coupled-mode solution for acoustic wave propagation in shallow water range-dependent environments. A theoretical framework involving a range-expanded inner product for capturing the coupling between modes as they propagate through a horizontally-variable medium is presented. This framework includes a novel discretization of the range-dependent acoustic medium. A difference equations approach, which implements the inner product to account for non-adiabatic energy transfer between modes, is used to recursively compute reflection and transmission coefficients throughout the discretized environment. Increased efficiency is gained in the method due to the ability to compute coupling via closed-form algebraic expressions and in the application of asymptotic analysis to simplify the transmission and reflection coefficients. Results will be shown for a sample of waveguides with horizontally-variable bottom depth, along with comparable results from R. Evans’ COUPLE model and an FEM solution produced by ARL-UT. [This work was funded by the Naval Undersea Warfare Center, Newport Rhode Island.]

Comparison of passive microwave brightness temperature prediction sensitivities over snow-covered land in North America using machine learning algorithms and the Advanced Microwave Scanning Radiometer

This paper presents the theoretical development of the covariance between reflection and transmission coefficients caused by system loads in electric grids. This result allows the calculation of the expected value and covariance of the channel response; these parameters can be used as design criteria in telecommunication networks over power lines in in-home environments. Additionally, the product of these variables is encountered commonly in the channel transfer function, whereby it can be determined if the calculation of these parameters can be simplified when this product is on a given path. For this purpose, the random behavior of the channel response caused by human activity is represented by a multipath model with a stochastic addition, where the reflection coefficient is treated as a random complex variable, providing more information compared to the real field.

The tunneling effect is a phenomenon of the behavior of particles with small energy having a probability to penetrate the barrier potential. The transmission coefficient is the ratio of breakthrough particles to incoming particles. The reflection coefficient is the ratio of reflected particles to incoming particles. The tunneling effect is analyzed using the Schrodinger equation with the matrix propagation method. The purpose of this study is to analyze the relationship between the distance barriers with the transmission coefficient and the reflection coefficient. The barriers used are GaAs and GaAs with potential energy of 1,424 eV with a bandgap of 0.565 nm. The researcher used 1 eV with a distance between barriers 0.25-2 nm with a change in the distance between barriers is 0.25 nm. Changes in the distance between barriers GaAs and GaAs affect the value of the transmission coefficient and the reflection coefficient. Changes in the distance between the barrier produce a transmission coefficient and a reflection coefficient that is periodic because the distance between the barriers has the form of complex exponential functions. This research application is used to design and develop new structures in improving the quality of semiconductor devices such as diodes, transistors, and IC.

Diffraction of water waves by a small cylindrical elevation of the bottom of a laterally unbounded ocean covered by an ice sheet is investigated by the perturbation analysis. The ice sheet is modelled as a thin elastic plate. The reflection and transmission coefficients are evaluated up to the first order in terms of integrals involving the shape function representing the bottom elevation. Three particular forms of the shape function are considered for which explicit expressions for these coefficients are obtained. For the particular case of a patch of sinusoidal undulations at the bottom, the reflection coefficient up to first order is found to be an oscillatory function of the ratio of the wavelength of the bottom undulations and that of the incident wave train. When this ratio approaches 0.5, the reflection coefficient becomes a multiple of the number of undulations and high reflection of the incident wave energy occurs if this number is large. Reflection coefficient is depicted graphically to visualize the effect of the presence of ice-cover and the number of undulations.

Recent advances in machine learning techniques have significantly influenced the development of early diabetes detection systems. In this paper, different machine learning (ML) algorithms are employed for diabetes detection based on hand movement. The spatial and temporal information for horizontal and vertical hand movement deviations for both diabetic and non-diabetic subjects are obtained using a data acquisition system. A dataset is constructed based on the obtained hand movement information along with other related attributes such as age, gender, weight and vision health. Binary classification of diabetic and non-diabetic subjects is achieved using several machine learning algorithms. These algorithms include Naïve Bayes (NB), Logistic Regression (LR), Linear Discriminate Analysis (LDA), Classification and Regression Tree (CART), k-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-layer Perceptron (MLP). The considered ML algorithms are evaluated and ranked according to a set of key performance metrics that include accuracy, precision, recall and F-1 score. Algorithm ranking results demonstrate that LR, CART, KNN and MLP algorithms have achieved higher ranking positions as compared to other algorithms. The best ranked ML algorithms are then validated and thoroughly investigated to prove their ability to discriminate between diabetic and non-diabetic subjects. Validation results show that the best-ranked ML algorithms have achieved high classification accuracy, precision, recall and F-1 scores. Therefore, the considered machine learning algorithms can serve as efficient tools for diabetes detection based on the constructed dataset.

Accurate classifications of land use/land cover (LULC) in arid regions are vital for analyzing changes in climate. We propose an ensemble learning approach for improving LULC classification accuracy in Xinjiang, northwest China. First, multisource geographical datasets were applied, and the study area was divided into Northern Xinjiang, Tianshan, and Southern Xinjiang. Second, five machine learning algorithms—k-nearest neighbor, support vector machine (SVM), random forest (RF), artificial neural network (ANN), and C4.5—were chosen to develop different ensemble learning strategies according to the climatic and topographic characteristics of each subregion. Third, stratified random sampling was used to obtain training samples and optimal parameters for each machine learning algorithm. Lastly, each derived approach was applied across Xinjiang, and subregion performance was evaluated. The results showed that the LULC classification accuracy achieved across Xinjiang via the proposed ensemble learning approach was improved by ≥6.85% compared with individual machine learning algorithms. By specific subregion, the accuracies for Northern Xinjiang, Tianshan, and Southern Xinjiang increased by ≥6.70%, 5.87%, and 6.86%, respectively. Moreover, the ensemble learning strategy combining four machine learning algorithms (i.e., SVM, RF, ANN, and C4.5) was superior across Xinjiang and Tianshan; whereas, the three-algorithm (i.e., SVM, RF, and ANN) strategy worked best for the Northern and Southern Xinjiang. The innovation of this study is to develop a novel ensemble learning approach to divide Xinjiang into different subregions, accurately classify land cover, and generate a new land cover product for simulating climate change in Xinjiang.

Gravity wave interaction with an articulated submerged plate resting on a Winkler foundation