Sinusoidal Function Research Articles

Impact of oxidized low-density lipoprotein on rat liver sinusoidal endothelial cell morphology and function

Atherogenesis is associated with elevated plasma levels of oxidized low-density lipoproteins (oxLDL). In vivo, oxLDL causes liver endothelial swelling, and disrupts liver sinusoidal endothelial cell (LSECs) fenestrations. We mapped the nanoscale kinetics of these changes in vitro in isolated rat LSECs challenged with oxLDL and monitored viability with endocytosis and cytotoxicity assays. OxLDL disrupted LSEC ultrastructure – increasing oxLDL concentrations and oxidation levels caused sieve plate loss, fenestration fusion, and gap formation. Importantly, these effects were not uniform across all LSECs. LSECs retained the ability to endocytose ligands irrespective of the presence of oxLDL. However, increasing oxidation levels and concentrations of oxLDL inhibited LSEC mediated degradation of endocytosed ligands. Viability was unaffected by any oxLDL challenge. In conclusion, oxLDL disrupts LSEC ultrastructural morphology in vitro but LSECs remain viable and mostly maintain the scavenging function during oxLDL challenge.

Hybrid modeling approach for precise estimation of energy production and consumption based on temperature variations

This study introduces an advanced mathematical methodology for predicting energy generation and consumption based on temperature variations in regions with diverse climatic conditions and increasing energy demands. Using a comprehensive dataset of monthly energy production, consumption, and temperature readings spanning ten years (2010–2020), we applied polynomial, sinusoidal, and hybrid modeling techniques to capture the non-linear and cyclical relationships between temperature and energy metrics. The hybrid model, which combines sinusoidal and polynomial functions, achieved an accuracy of 79.15% in estimating energy consumption using temperature as a predictor variable. This model effectively captures the seasonal and non-linear consumption patterns, demonstrating a significant improvement over conventional models. In contrast, the polynomial model for energy production, while yielding partial accuracy (R² = 0.65), highlights the need for more advanced techniques to fully capture the temperature-dependent nature of energy production. The results indicate that temperature variations significantly affect energy consumption, with higher temperatures driving increased energy demand for cooling, while lower temperatures affect production efficiency, particularly in systems like hydropower. These findings underscore the necessity for integrating sophisticated models into energy planning to ensure resilience in energy systems amidst climate variability. The study offers critical insights for policymakers to optimize energy generation and distribution in response to changing climatic conditions.

Phase-Sensitive Fluorescence Image Correlation Spectroscopy.

Fluorescence lifetime imaging microscopy is sensitive to molecular interactions and environments. In homo-dyne frequency-domain fluorescence lifetime imaging microscopy, images of fluorescence objects are acquired at different phase settings of the detector. The detected intensity as a function of detector phase is a sinusoidal function that is sensitive to the lifetime of the fluorescent species. In this paper, the theory of phase-sensitive fluorescence image correlation spectroscopy is described. In this version of lifetime imaging, image correlation spectroscopy analysis (i.e., spatial autocorrelation) is applied to successive fluorescence images acquired at different phase settings of the detector. Simulations of different types of lifetime distributions reveal that the phase-dependent density of fluorescent objects is dependent on the heterogeneity of lifetimes present in the objects. We provide an example of this analysis workflow to a cervical cancer cell stained with a fluorescent membrane probe.

Periodic morphing of a NACA6409 aerofoil in ground effect, its wake mechanisms and thrust generation

Abstract The camber morphing of an aerofoil in ground effect was investigated using the FishBAC method and Detached Eddy Simulations with the k-omega SST turbulence model at a Reynolds number of 320,000. The aerofoil was periodically morphed at a start location of 25% chord from the leading edge with a trailing edge deflection range of 0.1% to 3% and morphing frequencies between a Strouhal number of 0.45 to 4 at a constant ground clearance of 10%. Periodically morphing the aerofoil using a sinusoidal function showed that lift and drag increased on the downstroke and decreased on the upstroke in the cycle, resulting in periodic values of lift and drag throughout the cycle. The amplitude of lift and drag increased as the morphing frequency and/or trailing edge deflection increased. It was found that the wake characteristics varied as a function of trailing edge deflection and morphing frequency. For small trailing edge deflections below 0.4% and frequencies below a 2.2 Strouhal number, Kelvin Helmholtz shedding was observed, and above this the wake became chaotic. Large trailing edge deflections showed Von-Karman shedding, where the interaction between the lower counter-clockwise vortex and the ground plane resulted in a jet-like flow that caused forward thrust. For the maximum deflection and morphing frequency tested in this study, reversed Von-Karman shedding was observed, which caused forward thrust from the interaction of the two-shedding counter-rotating vortices. Von-Karman or reversed Von-Karman shedding shows positive thrust generation, however, chaotic shedding should be avoided due to large drag gains. Varying the Reynolds number caused the Strouhal number to change as they depend on the same variables. It was found that the Strouhal number variation had a large effect on the wake, however, the Reynolds number had a minimal effect.

Advanced ENF Region Classification Using UniTS-SinSpec: A Novel Approach Integrating Sinusoidal Activation Function and Spectral Attention

The electric network frequency (ENF), often referred to as the industrial heartbeat, plays a crucial role in the power system. In recent years, it has found applications in multimedia evidence identification for court proceedings and audio–visual temporal source identification. This paper introduces an ENF region classification model named UniTS-SinSpec within the UniTS framework. The model integrates the sinusoidal activation function and spectral attention mechanism while also redesigning the model framework. Training is conducted using a public dataset on the open science framework (OSF) platform, with final experimental results demonstrating that, after parameter optimization, the UniTS-SinSpec model achieves an average validation accuracy of 97.47%, surpassing current state-of-the-art and baseline models. Accurate classification can significantly aid in ENF temporal source identification. Future research will focus on expanding dataset coverage and diversity to verify the model’s generality and robustness across different regions, time spans, and data sources. Additionally, it aims to explore the extensive application potential of ENF region classification in preventing crimes such as telecommunications fraud, terrorism, and child pornography.

Modeling of equivalent strain in 2D cross-sections of open die forged components using neural networks

Open die forging is one of the oldest manufacturing methods known to remove defects in the ingot resulting from the casting process. The improved properties of the final component are highly dependent on the strain distribution. Although sinusoidal equations and empirical formulations have been already used to estimate the strain, they have been applied only to the core of the workpiece. In this work, a novel approach is presented to model the equivalent strain distribution in 2D cross-sections, in the direction of the press, of open die forged components using neural networks. The proposed method efficiently combines a parametric sinusoidal function with a neural network to learn the complex relationships between the process parameters and the resulting local strain. The neural network is trained on a dataset of finite element (FE) simulations of rectangular geometries that cover a wide range of aspect ratios, bite ratios, and height reductions. The presented methodology with near real-time prediction capabilities shows good agreement with FE results. Moreover, the parametric function captures the characteristic pattern of the strain distribution and reveals certain physical relationships affecting the deformation of the material. These patterns are later examined by analyzing the parameters identified in the parametric sinusoidal function.

Mesh-Free Solution of 2D Poisson Equation with High Frequency Charge Patterns Using Data-Free Physics Informed Neural Network

Abstract In this paper, we present a data-free physics-informed neural networks (PINNs) approach for solving two-dimensional (2D) Poisson equation, which is pivotal in fields such as electromagnetics, mechanical engginering, and thermodynamics. Traditional numerical method for solving this equation often require structured mesh generation such as Finite Element Method (FEM), which can be computationally expensive when dealing with high resolution Poisson Equation Solution. To address this challenge, we leverage the capabilities of PINNs capturing pattern of complex system by incorporating physical law and boundary condition as part of loss function on training model. While PINNs provide a robust framework for solving differential equations within boundary condition, they have struggle with capturing high-frequency pattern due to smooth nature of typical activation function used in neural networks. To evercome this issue, we enhance our model by incorporating Fourier Features Networks, which map inputs through a series of sinusoidal functions before feeding the input into the neural network. The result show that Fourier feature network can enhance convergence of training of PINNs model faster and obtained better result than PINNs without Fourier feature networks.

Crack Growth Patterns of Aluminum Tubular Specimens Subjected to Cyclic Tensile Loads

This study presents a detailed analysis of a fatigue test campaign in order to identify different crack patterns. It was conducted on 6061-T6 aluminum tubular specimens featuring an internal diameter of 10 mm and different thicknesses (2, 3 and 4 mm). These specimens were subjected to cyclic tensile loads with a load ratio of R = 0.1, utilizing a sinusoidal load function at a frequency of 3 Hz. The investigation examines the crack growth rates, the stress intensity factor, and the final and intermediate fracture zones by applying overloads in some cases. The differences with two-dimensional specimens and the importance of this study for the interpretation of results with biaxial loading states are highlighted. The different states of crack growth detected are analyzed using artificial vision techniques. The differences between the exterior and interior faces of the specimen are revealed, and a series of states prior to the formation of the radial crack front expected in these specimens are identified.

An analytic, efficient and optimal readout algorithm for compact interferometers based on deep frequency modulation

Compact laser interferometers with large dynamic range are one of the core emerging tools to improve low frequency performance in gravitational wave detectors by providing local displacement sensing with sub 1 pmHz-0.5\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ ext {pm Hz}^{-0.5}$$\\end{document} precision. Strong sinusoidal frequency modulations are used in such laser interferometers to create heterodyne-like photodetector signals from which the phase and other parameters, such as the absolute distance, can be extracted. The nested sinusoidal function in such signals is a challenge for the real-time parameter estimation in low-noise applications. In this article, we present an algorithm to calculate exact signal parameters in a non-iterative way from such interferometric signals. The algorithm makes use of a recurrence relation between Bessel functions to enable a direct extraction of modulation parameters from the signal. Additionally, the algorithm is capable of dealing with high phase dynamics where the Doppler-shift of the signal becomes relevant and can limit the range and precision of the parameter estimation, if not accounted for. Simulations show that the algorithm is computationally efficient, can be well parallelised and the phase estimation is close to optimal precision given by the Cramer–Rao lower bound of the signal parameters.

Flat limit of massless scalar scattering in AdS2

We explore the flat limit of massless scalar scattering in AdS2. We derive the 1→1S-matrix from the CFT 2-point function. We show a key property of the 2→2S-matrix in 2d, where the contact interaction in the flat limit gives momentum conserving delta function. We show the factorization of the n→nS-matrix for integrable models in the flat limit, focusing on contact interactions. We calculate the S-matrix by linking the CFT operator on the AdS boundary to the scattering state in flat-space. We use bulk operator reconstruction to study massless scalar scattering in the flat limit and solve the Klein-Gordon equation in global AdS2 for the massless scalar field. The solution is simple, involving a pure phase in global time and a sinusoidal function in the radial coordinate. This simplicity also extends to the smearing function, allowing us to map the scattering state to the CFT operator while taking AdS corrections into account.

Mobile Robot Motion Behavior Based on Hopfield Neural Network Dynamics Under Electromagnetic Radiation

The nonlinear characteristic of activation function is a critical aspect that affects the performance of artificial neural networks (ANNs). Despite its importance, the impact of neuronal nonlinearity variations on the dynamics of ANNs has not been thoroughly studied in previous research. This paper aims to fill this gap by exploring the influence of nonlinearity changes in activation function due to memristive electromagnetic induction on the dynamical characteristics of ANNs. We first propose a memristor model with sinusoidal conductance function. Then, the effects on the biological neuron under the electromagnetic induction are simulated by replacing the gradient of hyperbolic tangent activation function with the conductance of the proposed memristor. Furthermore, a Hopfield neural network (HNN) is constructed by taking the nonlinearity changes of activation function into consideration and its dynamical behaviors are analyzed through Lyapunov exponent spectra, bifurcation diagram, biparameter-based dynamic maps and phase portraits. The numerical simulation results show complex dynamical behaviors such as single-scroll chaotic spiral attractor, double-scroll chaotic attractors and initial-value-dependent offset boosted attractors, demonstrating that nonlinearity changes of activation function induced by the electromagnetic induction have a significant impact on the dynamics of HNN. To further verify our findings, we implement the proposed HNN in hardware and find that the results are in line with our numerical simulations. Finally, we apply the chaotic HNN in the mobile robot path planning task, which achieves higher coverage rate than the random number-based random walk method.

Development and Test of Low-Cost Multi-Channel Multi-Frequency Lock-In Amplifier for Health and Environment Sensing.

Optical-based sensing techniques and instruments, such as fluorometric systems, absorbance-based sensors, and photoacoustic spectrometers, are important tools for detecting food fraud, adulteration, and contamination for health and environmental purposes. All the aforementioned optical equipments generally require one or more low-frequency Lock-In Amplifiers (LIAs) to extract the signal of interest from background noise. In the cited applications, the required LIA frequency is quite low (up to 1 kHz), and this leads to a simplification of the hardware with consequent good results in portability, reduced size, weight, and low-cost characteristics. The present system, called ENEA DSP Box Due, is based on a very inexpensive microcontroller proto-board and can replace four commercial LIAs, resulting in significant savings in both cost and space. Furthermore, it incorporates a dual-channel oscilloscope and a sinusoidal function generator. This article outlines the architecture of the ENEA DSP Box Due, its electrical characterization, and its applications within a project concerning laser techniques for food and water safety.

Ethno-Science, Technology, Engineering, and Mathematics (EthnoSTEM) Ideas in the Sama Mat-Weaving

Mat weaving has been culturally and economically significant among the Sama ethnolinguistic group since time immemorial. This study sought to find concepts and processes of ethnoscience, ethnotechnology, ethnoengineering, and ethnomathematics (ethnoSTEM) present in weaving tepo, a hand-woven mat of the Sama, made from indigenously processed leaves of pandan or screw pine (Pandanus tectorius). To determine ethnoSTEM ideas, concepts, and processes in tepo weaving, an ethnography was conducted involving five female mat weavers in a coastal village in Tawi-Tawi’s major producer of tepo, the Municipality of Tandubas. Data were gathered primarily through observations during a monthlong community immersion. Results revealed that Ethnotechnology tools were in the form of bolo, pandan-presser, pandan slitter, traditional stove, bamboo scalp scratcher, and other local cooking tools, with each tool exhibiting unique characteristics and functions needed for weaving. Ethnoengineering was evident in preparing pandan strips to create, bleach, and dye pandan strips for weaving and fastening the tepo. Ethnomathematics comprised primitive length measurement, arithmetic calculations, ratio and proportion, linear and quadratic equations, sinusoidal functions, basic geometric concepts, circles, symmetries, and isometries. Ethnoscience was observed in the processes determining the dyeability of pandan strips, as well as in the procedures employed in its softening and bleaching. It is concluded that the concepts and ideas of ethnoSTEM found in Sama weaving of tepo are loaded with scientific affluence that should be preserved to preclude them from fading to oblivion.

Dynamics analysis and FPGA implementation of discrete memristive cellular neural network with heterogeneous activation functions

The activation function, as an important component of artificial neural networks, endows neural networks with rich dynamical phenomena by virtue of its nonlinear properties. However, especially for cellular neural networks (CNNs), the exploration of neural networks consisting of heterogeneous activation functions has not been sufficient. In this article, we introduce heterogeneous activation functions in discrete cellular neural network (DCNN) for the first time. In order to enhance the dynamics of the original DCNN, we propose our discrete memristive cellular neural network (DMCNN) based on a memristor with a sinusoidal function. Using various methods of dynamical characterization, such as Lyapunov exponential analysis, bifurcation diagrams and spectral entropy, the rich dynamical behaviour of the proposed model is comprehensively investigated, including hyperchaos, transient chaos, high spectral entropy and multiple types of coexisting attractors in the proposed model. Last but not least, the hardware platform of the proposed model is implemented by field programmable gate array (FPGA), and the dynamics of the proposed model is verified by a combination of software simulation and hardware experiments. The new exploration of DMCNN with heterogeneous activation functions in this article lays the foundation for further research into neural networks with complex dynamical behaviour.

Parametric controllable planar multi-scroll chaotic attractors in a 3-D memristive tabu learning single neuron model

In this paper, a three-dimensional (3D) autonomous tabu learning single neuron model is proposed, which is achieved by using a sinusoidal activation function and introducing a memristor synapse. This model exhibits the remarkable capability to produce a series of planar multi-scroll chaotic attractors, and its unique feature lies in the ability to control the number of scrolls. The investigation of the planar multi-scroll chaotic attractors and its dynamical behaviors is conducted through the analysis of phase plane portraits, bifurcation diagrams, and spectral entropies. The numerical simulations unveil a compelling relationship between the number of chaotic scrolls and specific control parameters governing the model. To further validate the findings, a 3D autonomous tabu learning single neuron model is implemented on a digital hardware platform. In an effort to extend the practical significance of this research, the multi-scroll chaotic phenomenon generated by the proposed model is deployed for image encryption. The fusion of mathematical modeling, digital hardware implementation, and practical application underscores the universality and significance of the proposed single neuron model in the fields of chaotic systems and engineering applications.

Rheological analysis of pressure-driven Jeffrey fluid flow between corrugated porous curved walls with slip constraints

Pressure-driven movement is a fundamental concept with numerous applications in various industries, scientific disciplines, and fields of engineering. Its proper execution is vital for promoting revolutionary innovations and providing solutions in numerous sectors. Therefore, this article scrutinizes the pressure-driven flow of a magnetized Jeffrey fluid between two curved corrugated walls. The geometry of the channel is represented mathematically in an orthogonal curvilinear coordinate system. The corrugation grooves are described by sinusoidal functions with phase differences between the corrugated curved walls. The boundary perturbation method is used to find the analytical solution for the velocity and temperature taking the corrugation amplitude as the perturbation parameter. Furthermore, the volumetric flow rate, skin friction coefficient, and local Nusselt numbers are precisely calculated numerically for a variety of parameters, with the results presented comprehensively in tabular form. The impact of dissimilar parameters, such as the curvature parameter, wave number, magnetic parameter, Darcy number, thermal radiation, heat source/sink parameter, Jeffery fluid parameter, and amplitude parameter, on the flow fields is analyzed through graphical and tabular forms and discussed in detail. The results show that the velocity profile increases due to the curvature parameter and the Jeffrey fluid parameter. However, it decreases due to the magnetic parameter. The temperature distribution rises with the thermal slip and heat source/sink parameters. Meanwhile, it declined for the radiation parameter and the curvature parameter. The model can be used to simulate blood flow in arteries with varying geometries and magnetic fields, aiding in the study of cardiovascular diseases and the design of medical devices such as stents.

Rheological study of water-based Cu nanofluid between two corrugated curved walls under constant pressure gradient

Nanofluids have captured the attention of scientists due to their superior thermophysical properties compared to ordinary liquids. Consequently, nanofluids serve as suitable cooling agents applicable to systems requiring swift response to thermal changes, such as vehicle engines. Therefore, the current article scrutinizes the characteristics of heat transfer on the pressure-driven flow of magnetized nanofluid between two curved corrugated surfaces in the presence of heat generation/absorption. Copper oxide nanoparticles (Cu) have been combined with pure water to form a nanofluid called Cu/H2O. The geometry of the channel is represented mathematically in an orthogonal curvilinear coordinate system. The corrugation grooves are described by sinusoidal functions with phase differences between the corrugated curved walls. The boundary perturbation method is used to find the analytical solution for the velocity field, temperature field, and volumetric flow rate taking the corrugation amplitude as the perturbation parameter. The impact of dissimilar parameters such as the curvature parameter (0.5≤k≤10), wave number (1≤α≤3), magnetic parameter (1≤M≤5), and wave amplitude (0.1≤ε≤0.8) on the flow fields are analyzed through graphs and discussed in detail. The results show that the peak of the velocity increases with the radius of curvature and the width of the channel for a constant pressure gradient. If we increase the magnetic parameter from 1 to 4, the velocity profile at the specified point decreases by 30 %. If we increase the heat source/sink parameter from 2 to 5, the temperature profile at the specified point increases by approximately 17 %. The flow rate is increased by the corrugations for any phase difference between the corrugated curved walls depending on the corrugation wavenumber and the channel radius of curvature.

Synchronization of high-dimensional Kuramoto-oscillator networks with variable-gain impulsive coupling on the unit sphere

Kuramoto models (KMs) in scalar or high-dimensional form can describe the synchronization phenomenon for large populations of coupled oscillators in networks of dynamical systems such as power grids, satellite mobile sensing networks, etc. However, these models are developed based on continuous-time coupling among oscillators, which is not applicable to networks where the coupling between oscillators occurs only at impulsive instants. Herein, we propose for the first time a generalized high-dimensional Kuramoto oscillator network (HDKON) with variable-gain impulsive coupling on the unit sphere. The proposed HDKON can be reduced to a scalar form comprising a sinusoidal function, thereby generalizing the scalar KM in both temporal and spatial domains. Furthermore, we provide some variation coefficients of the synchronization errors for the oscillator pairs at impulsive instants, and derive a sufficient condition for the exponential synchronization of the HDKON with identical natural frequency. Moreover, we consider an HDKON with a central oscillator and demonstrate that peripheral oscillators almost globally exponentially synchronize to the central oscillator under a sufficient condition. Finally, numerical simulations are performed to verify the main theoretical results.

Data-Centric Benchmarking of Neural Network Architectures for the Univariate Time Series Forecasting Task

Time series forecasting has witnessed a rapid proliferation of novel neural network approaches in recent times. However, performances in terms of benchmarking results are generally not consistent, and it is complicated to determine in which cases one approach fits better than another. Therefore, we propose adopting a data-centric perspective for benchmarking neural network architectures on time series forecasting by generating ad hoc synthetic datasets. In particular, we combine sinusoidal functions to synthesize univariate time series data for multi-input-multi-output prediction tasks. We compare the most popular architectures for time series, namely long short-term memory (LSTM) networks, convolutional neural networks (CNNs), and transformers, and directly connect their performance with different controlled data characteristics, such as the sequence length, noise and frequency, and delay length. Our findings suggest that transformers are the best architecture for dealing with different delay lengths. In contrast, for different noise and frequency levels and different sequence lengths, LSTM is the best-performing architecture by a significant amount. Based on our insights, we derive recommendations which allow machine learning (ML) practitioners to decide which architecture to apply, given the dataset’s characteristics.

Deep neural network homogenization of multiphysics behavior for periodic piezoelectric composites

We present a physically informed deep neural network homogenization theory for identifying homogenized moduli and local electromechanical fields of periodic piezoelectric composites. The theory employs a multi-network model to represent solutions to stress equilibrium and electrostatic partial differential equations in the inclusion and the matrix phases, leading to a more accurate depiction of field variables. Satisfaction of periodicity conditions for hexagonal and cubic symmetries are efficiently tackled using a series of sinusoidal functions with known periods and adjustable parameters. Additionally, the theory applies fully trainable weights to the collocation points. These trainable weights, trained concurrently with the neural network weights, compel the neural networks to enhance their performance when faced with large local stress/deformation gradients. The predictive capability of the proposed theory is illustrated by comparison with finite-element solutions for composites reinforced with unidirectional fiber, spherical particle, or weakened by an ellipsoidal cavity over a wide range of volume fractions.