Cosine Transform Research Articles

Efficient evaluation of near-singular integrals of the eXtended Isogeometric Boundary Element Method for three-dimensional fracture mechanics

The Boundary Element Method (BEM) has proven to be a suitable method for the numerical assessment of numerous engineering problems in both two-dimensional and three-dimensional forms. In particular, BEM robustly handles discontinuities in the mechanical response present in fracture mechanics applications. Additionally, its boundary-only nature simplifies the re-meshing process during crack growth modelling. Furthermore, coupling BEM with isogeometric analysis (IgA) is straightforward, as both strategies rely on boundary representation, enabling the direct use of Computer-Aided Design (CAD) geometry as the mesh for the method. IgA basis functions can accurately represent complex surfaces such as spheres and toroids. In this context, the Isogeometric Boundary Element Method (IGABEM) emerges as a reliable tool for solving engineering problems, combining the advantages of BEM with the improved geometric accuracy provided by IgA. In addition, a relevant approach in fracture mechanics is using enrichment functions that incorporate known solution fields, addressing response aspects that standard isogeometric basis functions cannot capture. Combining the enrichment strategy with IGABEM leads to the eXtended IGABEM (XIGABEM) method. Specifically, enriching functions that account for the square-root inverse behaviour on crack surfaces promotes several improvements, such as improved convergence rate and removal of a non-physical jump displacement in the crack front. Another advantage is the direct extraction of Stress Intensity Factors (SIF) as system variables, eliminating the need for costly extraction techniques in three-dimensional problems. However, near-singular integrals appear after the Singularity Subtraction Technique (SST) of the enriching terms in 3D XIGABEM. This study proposes using the hyperbolic sine transformation for the near singular surface integrals that arise from the SST to evaluate them efficiently. Numerical applications demonstrate a reduction in the required integration points for these integrals compared to traditional methods. Therefore, this advancement in the XIGABEM framework reduces the computational cost of this strategy while maintaining accuracy.

Optimized Fault Classification in Electric Vehicle Drive Motors Using Advanced Machine Learning and Data Transformation Techniques

The increasing use of electric vehicles has made fault diagnosis in electric drive motors, particularly in variable speed drives (VSDs) using three-phase induction motors, a critical area of research. This article presents a fault classification model based on machine learning (ML) algorithms to identify various faults under six operating conditions: normal operating mode (NOM), phase-to-phase fault (PTPF), phase-to-ground fault (PTGF), overloading fault (OLF), over-voltage fault (OVF), and under-voltage fault (UVF). A dataset simulating real-world operating conditions, consisting of 39,034 instances and nine key motor features, was analyzed. Comprehensive data preprocessing steps, including missing value removal, duplicate detection, and data transformation, were applied to enhance the dataset’s suitability for ML models. Yeo–Johnson and Hyperbolic Sine transformations were used to reduce skewness and improve the normality of the features. Multiple ML algorithms, including CatBoost, Random Forest (RF) Classifier, AdaBoost, and quadratic discriminant analysis (QDA), were trained and evaluated using Bayesian optimization with cross-validation. The CatBoost model achieved the best performance, with an accuracy of 94.1%, making it the most suitable model for fault classification in electric vehicle drive motors.

An omics-driven computational model for angiogenic protein prediction: Advancing therapeutic strategies with Ens-deep-AGP

Angiogenic proteins (AGPs) play a critical role in both pathological and physiological activities, making them key therapeutic targets in diseases like cancer, heart disease, and stroke. Traditional methods for identifying AGPs are labor-intensive and time-consuming, creating a need for more efficient approaches. This study addresses this challenge by developing a novel computational model, Ens-Deep-AGP, designed to enhance AGP prediction. The model introduces innovative feature engineering techniques, including Position Specific Scoring Matrix-Decomposition-Discrete Cosine Transform (PSSM-DC-DCT) and Position Specific Scoring Matrix-Auto-Cross-Discrete Wavelet Transform (PSSM-ACC-DWT), which capture comprehensive protein sequence information. The ensemble feature set of these approaches are then fed into Multi-headed Ensemble Residual Convolutional Neural Network (MERCNN), a robust deep learning architecture. Ens-Deep-AGP achieved remarkable accuracy rates of 99.79 % on training dataset and 92.97 % on testing dataset, surpassing Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Bidirectional Long Short-Term Memory Networks (BiLSTM). The successful prediction of AGPs is crucial for accelerating drug development, discovering novel therapeutic targets and deepen our understanding of AGPs' complex roles in healthcare.

Sine Transform Based Preconditioning for an Inverse Source Problem of Time-Space Fractional Diffusion Equations

Sine Transform Based Preconditioning for an Inverse Source Problem of Time-Space Fractional Diffusion Equations

Short-term effects of PM2.5 components on the respiratory infectious disease: a global perspective.

Although previous research has reached agreement on the significant impact of particulate matter (PM2.5) on respiratory infectious diseases, PM2.5 acts as an aggregation of miscellaneous pollutants and the individual effect of each component has not been examined. Here, we investigate the effects of PM2.5 components, including black carbon (BC), organic carbon (OC), sulfate ion (SO4), dust, and sea salt (SS), on the morbidity and mortality of the recent respiratory disease, i.e. COVID-19. The daily data of 236 countries and provinces/states (e.g., in the United States and China) worldwide during 2020-2022 are utilized. To derive the pollutant-specific causal effects, optimal instrumental variables for each pollutant are selected from a large set of atmospheric variables. We find that one µg/m3 increase in OC increases the number of cases and death by about 3% to 6% from the mean worldwide during a lag of one day up to three days. Our findings remain consistent and robust when we change control variables such as the flight index and weather proxies, and also when applying a sine transformation to the positivity and death rate. When analyzing health effects among different areas, we find stronger impact in China, for its higher local OC concentration, as opposed to the impact in the United States. Health benefits from PM2.5 pollution reduction are comparatively high for developed regions, yet decreases in cases and deaths number are rather overt in less developing regions. Our research provides inspiration and reference for dealing with other respiratory diseases in the post-pandemic era.

Application of the QDST algorithm for the Schrödinger particle simulation in the infinite potential well

This paper examines whether a quantum computer can efficiently simulate the time evolution of the Schrödinger particle in a one-dimensional infinite potential well. In order to solve the Schrödinger equation in the quantum register, an algorithm based on the Quantum Discrete Sine Transform (QDST) is applied. The paper compares the results obtained in this way with the results given by the previous method (based on the QFT algorithm).

Enhancing speaker identification through reverberation modeling and cancelable techniques using ANNs.

This paper introduces a method aiming at enhancing the efficacy of speaker identification systems within challenging acoustic environments characterized by noise and reverberation. The methodology encompasses the utilization of diverse feature extraction techniques, including Mel-Frequency Cepstral Coefficients (MFCCs) and discrete transforms, such as Discrete Cosine Transform (DCT), Discrete Sine Transform (DST), and Discrete Wavelet Transform (DWT). Additionally, an Artificial Neural Network (ANN) serves as the classifier for this method. Reverberation is modeled using varying-length comb filters, and its impact on pitch frequency estimation is explored via the Auto Correlation Function (ACF). This paper also contributes to the field of cancelable speaker identification in both open and reverberation environments. The proposed method depends on comb filtering at the feature level, deliberately distorting MFCCs. This distortion, incorporated within a cancelable framework, serves to obscure speaker identities, rendering the system resilient to potential intruders. Three systems are presented in this work; a reverberation-affected speaker identification system, a system depending on cancelable features through comb filtering, and a novel cancelable speaker identification system within reverbration environments. The findings revealed that, in both scenarios with and without reverberation effects, the DWT-based features exhibited superior performance within the speaker identification system. Conversely, within the cancelable speaker identification system, the DCT-based features represent the top-performing choice.

Licensing Scheme in the Green Industry Sector: The Case of Georgia Tree Care Service Providers

AbstractTree care license requirements are expected to improve service quality and provider competencies. The study elicits the licensing fee Georgia firms would pay using survey data from 153 tree care firms. An empirical relationship identifies firm characteristics that influence the fee size using the inverse hyperbolic sine transformation function. Results show that respondents who propose higher annual licensing fees are on average younger, more educated, male, and likely to agree that mandatory licensing is necessary to establish professionalism in the tree care industry. Also, those often engaged in tree trimming services and firms with higher annual revenues contemplate paying higher licensing fees.

Imposing different boundary conditions for thermal computational homogenization problems with FFT‐ and tensor‐train‐based Green's operator methods

AbstractTo compute the effective properties of random heterogeneous materials, a number of different boundary conditions are used to define the apparent properties on cells of finite size. Typically, depending on the specific boundary condition, different numerical methods are used. The article at hand provides a unified framework for Lippmann–Schwinger solvers in thermal conductivity and Dirichlet (prescribed temperature), Neumann (prescribed normal heat flux) as well as periodic boundary conditions. We focus on the explicit jump finite‐difference discretization and discuss different techniques for computing the discrete Green's operator. These include Fourier‐type methods, that is, based on discrete sine, cosine and Fourier transformations, as well as low‐rank tensor methods, that is, the tensor‐train approximation, whose computational prowess was demonstrated recently. We use the developed computational technology to investigate a number of interesting random materials and assess different microstructure‐generation techniques in terms of their compatibility with the prescribed boundary conditions.

Probabilistic Optimal Power Flow Computation Using Stratified Quadrature Rule

In this paper, the probabilistic optimal power flow (POPF) problem is modeled as a black box uncertainty quantification problem. If distribution functions of POPF inputs are unknown, only samples are available, the multivariate Gaussian mixture model is suggested to recover the joint cumulative distribution function of POPF inputs, and a probability selection method is introduced to relate POPF inputs to an independent standard normal vector. Then, moment matching equations are proposed to characterize the uncertainty effects of POPF inputs on outputs, whereby some properties of the POPF problem are revealed, and a stratified quadrature rule (SQR) is proposed for POPF computation by combining a Hadamard matrix based quadrature rule and a discrete sine transformation matrix based quadrature rule. This newly developed SQR has a computational burden linear with the number of POPF inputs, and performs well in estimating means and standard deviations POPF outputs. Finally, case studies are conducted on a modified IEEE 118-bus system to illustrate the accuracy and efficiency of the proposed method.

68Ga-PSMA-11 PET and mpMRI in the diagnosis of initial lymph node staging of prostate cancer: a head-to-head comparative meta-analysis.

This meta-analysis evaluates the comparative diagnostic efficacy of 68Ga-prostate-specific membrane antigen-11 PET (68Ga-PSMA-11 PET) and multiparametric MRI (mpMRI) for the initial lymph node staging of prostate cancer. We searched PubMed and Embase databases through October 2023 for studies that provide a head-to-head comparison of 68Ga-PSMA-11 PET and mpMRI, using pelvic lymph node dissection as the gold standard. We assessed sensitivity and specificity using the DerSimonian and Laird method, with variance stabilization via the Freeman-Tukey double inverse sine transformation. The quality of included studies was evaluated using the Quality Assessment of Diagnostic Performance Studies (QUADAS-2) tool. The meta-analysis incorporated 13 articles, involving a total of 1,527 patients. 68Ga-PSMA-11 PET demonstrated an overall sensitivity of 0.73 (95% CI: 0.51-0.91) and a specificity of 0.94 (95% CI: 0.88-0.99). In comparison, mpMRI showed a sensitivity of 0.49 (95% CI: 0.30-0.68) and a specificity of 0.94 (95% CI: 0.88-0.99). Although 68Ga-PSMA-11 PET appeared to be more sensitive than mpMRI, the differences in sensitivity (p = 0.11) and specificity (p = 0.47) were not statistically significant. Our findings indicated that 68Ga-PSMA-11 PET and mpMRI exhibit similar sensitivity and specificity in the diagnosis of initial lymph node staging of prostate cancer. However, given that most included studies were retrospective, further prospective studies with larger sample sizes are essential to validate these results. PROSPERO code is CRD42023495266.

The performance of DST-Wavelet feature extraction for guitar chord recognition

Small systems can be designed to be more energy-efficient compared to larger systems. On small systems, the need for data processing with small data sizes becomes a necessity. In the context of small systems for guitar chord recognition, there are indications that further efforts can be made to reduce the size of feature extraction data. This paper introduces DST (Discrete Sine Transform)-Wavelet feature extraction to achieve this reduction. Basically, this work evaluated the frame blocking length, the number of DST cutting factors, and the type of wavelet filters (Daubechies and biorthogonal families) to obtain the optimal number of feature extraction data. Based on the evaluation, the optimal result obtained was a number of four feature extraction data. This optimal result was obtained by using a frame blocking length of 512 points, a DST cutting factor of 0.5, and a biorthogonal 3.3 wavelet filter. Testing with 140 test chords using these four feature extraction data could give an accuracy of up to 92.86%.

Why Transform Y? The Pitfalls of Transformed Regressions with a Mass at Zero*

AbstractApplied economists often transform a dependent variable that is non‐negative and skewed with the natural log transformation, the inverse hyperbolic sine transformation, or power function. We show that these transformations separate the zeros from the positives such that the estimated parameters are related to those from a scaled linear probability model. The retransformed marginal effects and elasticities are sensitive to changes in a shape parameter, ranging in magnitude between those of an untransformed least squares regression and those of a scaled linear probability model. Instead of transforming the dependent variable with non‐negative outcomes that includes zeros, we recommend using a non‐transformed dependent variable, such as a two‐part model, untransformed linear regression, or Poisson.

Estimating COVID-19 under-reporting through stochastic frontier analysis and official statistics: A case study of São Paulo State, Brazil

During outbreaks, natural disasters, and any unexpected event, it is usual that public authorities face issues tracking the outcomes, and the available reports of casualties tend to be lower than actual numbers. Using weekly data at the municipality level in São Paulo State (the most densely populated region in Brazil), we employ stochastic frontier methods to fit the dynamics of the COVID-19 outbreak spanning from March 2020 to December 2021. The empirical model incorporates the inverse hyperbolic sine transformation to address the issue of zero reporting of COVID-19 deaths. Furthermore, we utilize a flexible frontier method to capture the S-shaped epidemic curve in two distinct waves of infections/deaths. Our results reveal that the actual death toll resulting from COVID-19 is, on average, 1.24 times higher than the officially reported figures. Consequently, these findings hold significant implications for public authorities in identifying regions characterized by substantial under-reporting of COVID-19 fatalities. Moreover, this study presents an empirical framework that can be used for other municipalities, states, or countries confronting outbreak scenarios.

The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout

BackgroundDual energy computed tomography (DECT) allows direct visualization of monosodium urate crystal deposition in gout. However, DECT urate volume data are often highly skewed (mostly small volumes with the remainder considerably larger), making statistical analyses challenging in longitudinal research. The aim of this study was to explore the ability of various analysis methods to normalise DECT urate volume data and determine change in DECT urate volumes over time. MethodsSimulated datasets containing baseline and year 1 DECT urate volumes for 100 people with gout were created from two randomised controlled trials. Five methods were used to transform the DECT urate volume data prior to analysis: log-transformation, Box-Cox transformation, log(X-(min(X)-1)) transformation; inverse hyperbolic sine transformation, and rank order. Linear regression analyses were undertaken to determine the change in DECT urate volume between baseline and year 1. Cohen's d were calculated as a measure of effect size for each data treatment method. These analyses were then tested in a validation clinical trial dataset containing baseline and year 1 DECT urate volumes from 91 people with gout. ResultsNo data treatment method successfully normalised the distribution of DECT urate volumes. For both simulated and validation data sets, significant reductions in DECT urate volumes were observed between baseline and Year 1 across all data treatment methods and there were no significant differences in Cohen's d effect sizes. ConclusionsNormalising highly skewed DECT urate volume data is challenging. Adopting commonly used transformation techniques may not significantly improve the ability to determine differences in measures of central tendency when comparing the change in DECT urate volumes over time.

Income inequality and its association with COVID-19 cases and deaths: a cross-country analysis in the Eastern Mediterranean region

IntroductionThere is limited evidence on the associations between economic and social disparities in the Eastern Mediterranean region (EMR) with COVID-19 infections and deaths. This study aims to investigate the relationship...

Applications of the Lipschitz Summation Formula and a Generalization of Raabe’s Cosine Transform

Applications of the Lipschitz Summation Formula and a Generalization of Raabe’s Cosine Transform

The Schur decomposition of discrete Sine and Cosine transformations of type IV

Eigendecomposition of discrete sine and cosine transformations of type IV is the main objective of this study. A closed form formula for eigenvectors is derived using the property that a square of the transform matrix is proportional to the identity matrix. Odd and even transform sizes are considered separately. In both cases, it has been demonstrated that the eigenvectors can be obtained by a straightforward modification of the columns of the transformation matrix. Comparing this approach with the related works, it provides a simple explanation of the eigendecomposition with a reliable numerical stability. Additionally, these eigenvectors can be used to obtain the eigendecomposition of the counterpart offset discrete Fourier transform.

The thermal radiation effect on Casson nanofluids EG-GO and EG-CNTs models using Caputo fractional order derivative with shape effects: Via analytical investigations

This research examines the analytical investigations for radiating Casson nanofluid models with unsteady convecting flow, which are valued by the order of Caputo fractional derivative (CFD). Ethylene glycol (EG) is used as a base fluid, and graphene oxide (GO) and carbon nanotubes (CNTs) are used as nanoparticles. The problem’s leading PDEs are nondimensionalized by applying the proper nondimensional variables. The solutions to the dimensionless governing equations are found by using the Fourier sine and Laplace transformation techniques together. For an enormous study of the problem, graphical illustrations and tables are developed by using MATLAB software programming with the help of Euler inversion. We examine the impact on the fractional heat and momentum equation of the [Formula: see text], Gr, Pr, [Formula: see text], R, [Formula: see text], oscillations. Using the properties of the fluid, important discoveries were made that indicated a number of elements for a number of flow parameters as well as fractional parameters. The thermal profiles are increased for [Formula: see text] decreased for [Formula: see text] at [Formula: see text] and [Formula: see text]. The velocity profiles are increased for R and Gr decreased for [Formula: see text] and [Formula: see text] at [Formula: see text] and 1.4. Different shapes of nanoparticles are performed for ordinary fractional parameters, which are increased for temperature as well as velocity.

Medical video encryption using novel 2D Cosine-Sine map and dynamic DNA coding.

Modern healthcare systems contain a large amount of sensitive information related to a patient in textual and visual form. Surgical videos and diagnosis data such as ultrasound, Computed Tomography (CT) scan, and Magnetic Resonance Imaging (MRI) are examples of healthcare video data. The secure storage and transmission of medical data have become an important issue in medical applications. To handle this challenge, chaos based cryptosystems are widely used these days. The work in this paper proposes a novel 2D Cosine-Sine map that exploits the existing Sine map and cosine transformation in its mathematical computation. The performance assessment of the suggested map indicates that it possesses a broader chaotic range, more dynamic and hyperchaotic nature when compared to existing chaotic maps. The proposed work, also, combines this novel 2D Cosine-Sine map with dynamic DNA encoding to propose a new scheme to encrypt medical videos. The approach consists primarily of four distinct phases. In the initial stage, the video is divided into frames, and for each frame, a chaotic sequence is generated using a 2D Cosine-sine map. During the second stage, we proceed to pixel permutation for each frame by utilizing the chaotic sequence. The third step is diffusion, in this, we integrate the 2D Cosine-Sine map with dynamic DNA encoding and apply double DNA operations to substitute the pixel values. The last step is DNA decoding to get the frames back in binary format. DNA encoding/decoding rules and operands of DNA operations are not fixed. They are selected dynamically using a random key for each frame. The dynamic selection of encoding/decoding rules and operands is a unique feature that enhances the scheme's security. Simulation results and security analysis proves that the proposed medical video encryption scheme can resist different types of attacks.