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S-N behavior has been a backbone of material fatigue life studies since the 19th century. Numerous S-N curve models have been produced but they have been arbitrarily chosen in numerous research works dominantly for composite materials. In this paper, they were critically reviewed and evaluated for capability using the following criteria: data fitting capability, efficiency of curve fitting, applicability to data sets at various stress ratios (−0.43, −1, −3, 0.1, and 10), representability of fatigue damage at failure, and satisfaction of the initial boundary condition. The S-N curve models were found to be in two categories—one for fatigue data characterization independent of stress ratio, and the other for those designed for predicting the effect of stress ratio. The models proposed by Weibull, Sendeckyj, and Kim and Zhang for fatigue data characterization appeared to have the best capabilities for experimental data obtained from Weibull for R = −1, from Sendeckyj for R = 0.1, and from Kawai and Itoh (for R = −0.43, −3, and 10). The Kim and Zhang model was found to have an advantage over the Weibull and the Sendeckyj models for representing the fatigue damage at failure. The Kohout and Vechet model was also found to have a good fitting capability but with an inherent limitation for shaping the S-N curve at some stress ratios (e.g., R = −0.43). The S-N curve models developed for predicting the effect of stress ratio were found to be relatively inferior in data fitting capability to those developed directly for fatigue data characterization.

Land surface phenology (LSP) is an integrative indicator of vegetation dynamics under a changing environment. Increasing amounts of remote sensing measurements and CO2flux observations offer unprecedented opportunities to quantify LSP phases at landscape scale. LSP start of season (SOS) and end of season (EOS) estimates are often based on the use of a single‐purpose vegetation index derived from optical satellite data, characterized by poor performances in decoupling soil and snow cover dynamics from LSP cycles, as well as contrasting responses of the needleleaf and broadleaf forests in boreal ecosystems. We propose a new remote‐sensing‐based phenology index (PI) which combines the merits of normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) by taking the difference of squared greenness and wetness to remove the soil and snow cover dynamics from key vegetation LSP cycles. We have cross‐validated the remote‐sensing‐based LSP dates with those of CO2 flux observations from 11 selected tower sites across Canada and the United States consisting of needleleaf forests, broadleaf forests, and croplands. The results indicate that PI estimates the SOS and EOS dates better than NDVI when compared to the LSP dates from CO2flux measurements (reduced RMSE, bias and dispersions, and higher correlation). PI‐based SOS and EOS estimates are in good agreement with those derived from CO2flux measurements with mean bias comparable to the temporal resolution of the high‐quality, 8‐day composite satellite measurements. Finally, PI also shows a smoother time series compared to NDVI and NDII. Key Points A new phenology index (PI) is developed from red, NIR and SWIR spectral bands PI removes the effect of soil and snow from key seasonal vegetation cycles Remote sensing can be used to accurately estimate the end of growing season

To optimize the tradeoff between imperceptibility and robustness properties, this paper proposes a robust and invisible blind image watermarking scheme based on a new combination of discrete cosine transform (DCT) and singular value decomposition (SVD) in discrete wavelet transform (DWT) domain using least-square curve fitting and logistic chaotic map. Firstly cover image is decomposed into four subbands using DWT and the low frequency subband LL is partitioned into non-overlapping blocks. Then DCT is applied to each block and several particular middle frequency DCT coefficients are extracted to form a modulation matrix, which is used to embed watermark signal by modifying its largest singular values in SVD domain. Optimal embedding strength for a specific cover image is obtained from an estimation based on least-square curve fitting and provides a good compromise between transparency and robustness of watermarking scheme. The security of the watermarking scheme is ensured by logistic chaotic map. Experimental results demonstrate the better effectiveness of the proposed watermarking scheme in the perceptual quality and the ability of resisting to conventional signal processing and geometric attacks, in comparison with the related existing methods.

Solar photovoltaic (PV) energy is one of the most prominent topics that have attracted the attention of researchers in recent years. The use of solar energy is increasing rapidly in the world. Although using PV energy has various advantages, it has some disadvantages. Among these disadvantages, power factor (PF) and total harmonic distortion (THD) issues are discussed in this article. When solar PV systems are integrated into the grid, various power quality problems arise. In addition, due to low power quality and high harmonics, power system components overheat and start operating in undesirable regions; causes great damage. The magnitude of PF and THD is dependent on solar irradiation values. In order to determine how the power quality in the grid-connected solar system is affected by changes in solar irradiation ( G ), results for various irradiation situations are presented and analyzed. In addition, at low irradiance values, the amplitude of harmonic components and reactive power increases, whereas the power factor of the PV system decreases. Low power factor and high amplitude of harmonics cause the efficiency of the solar system to decrease. In this study, PF and THD I values were measured on a particular cloudy day for analysis. An analysis of the solar PV system was conducted using Matlab/simulation program to model the grid-connected PV system. Thus, the analytical expression of the PF and THD I , which are dependent on irradiation, was found with a new method by using the Statistical Package for the Social Sciences (SPSS) program and the curve fitting method. Obtaining the analytical expressions for both solar irradiation ( G ) and power factor (PF) used the SPSS program and also solar irradiation ( G ) and total harmonic distortion (THD I ) used the MATLAB curve fitting method which contributed to the science comparing to the existing literature. It can be prevented the low power quality by using such these expressions at low solar irradiation cases.

Topological indices are obtained from molecular graphs and are real numbers that can forecast the biological and physicochemical properties of several anti-cancer treatments, including skin cancer, breast cancer, and blood cancer. This article focuses on the application of topological indices in predicting the effectiveness of several drugs used to cure blood cancer, such as Pamidronic acid, Alpelisib, Prednisone, Olaparib, Ribociclib, Tucatinib, dexamethasone, docetaxel, Midostaurin, paclitaxel, toremifene, and venetoclax. The article investigates the mathematical relationships between physical and chemical qualities and data encoded in chemical structures under characteristics such as molecular weight, molar volume, and complexity. Several topological indices are used in this context to forecast the physicochemical characteristics of the drugs.

This paper presents a novel computational-geometry-based approach to generating random packing of complex-shaped 3D particles with quantitatively controlled sizes and shapes for discrete modeling of granular materials. The proposed method consists of the following five essential steps: (1) partitioning of the packing domain into a prescribed number of random polyhedrons with desired sizes and form-scale shapes using the constrained Voronoi tessellation; (2) extraction of key points from the edges and facets of each polyhedron; (3) construction of a freeform curve network in each polyhedron based on Bézier curve fitting; (4) generation of solid particles with smooth, convex surfaces using the biharmonic-based surface interpolation of the constructed network; and (5) creation of concavity by superimposing spherical harmonic-based random noise. To ensure that the obtained shape descriptors (e.g., the elongation, flatness, roundness and convexity ratio) match the hypothesized values, an inverse Monte Carlo algorithm is employed to iteratively fine-tune the control parameters during particle generation. The ability of the proposed approach to generate granular particles with the desired geometric properties and packing is demonstrated through several examples. This study paves a viable pathway for realistic modeling of granular media pertaining to various engineering and industrial processes.

Accurate phenological information is crucial for evaluating ecosystem dynamics and the carbon budget. As one of China’s largest terrestrial ecosystem carbon pools, Southwest China plays a significant role in achieving the “dual carbon” goals of carbon peaking and carbon neutrality. However, evergreen forests are widely distributed in this region, and phenology extraction based on vegetation indices has certain limitations, while SIF-based phenology extraction offers a viable alternative. This study first evaluated phenological results derived from three solar-induced chlorophyll fluorescence (SIF) datasets, six curve-fitting methods, and five phenological extraction thresholds at flux sites to determine the optimal threshold and SIF data for phenological indicator extraction. Secondly, uncertainties in phenological indicators obtained from the six fitting methods were quantified at the regional scale. Finally, based on the optimal phenological results, the spatiotemporal variations in phenology in Southwest China were systematically analyzed. Results show: (1) Optimal thresholds are 20% for the start of growing season (SOS) and 30% for the end of growing season (EOS), with GOSIF best for SOS and EOS, and CSIF for the peak of growing season (POS). (2) Cubic Smoothing Spline (CS) has the lowest uncertainty for SOS, while Savitzky–Golay Filter (SG) has the lowest for EOS and POS. (3) Phenology exhibits significant spatial heterogeneity, with SOS and POS generally showing an advancing trend, and EOS and length of growing season (LOS) showing a delaying (extending) trend. This study provides a reference for phenology extraction in regions with frequent cloud cover and widespread evergreen vegetation, supporting effective assessment of regional ecosystem dynamics and carbon balance.

The interaction between groundwater and surface water plays a crucial role in determining water quality and ecological health, highlighting the need for a comprehensive understanding to ensure effective water resource management. In this study, multiple methods—seepage meters, piezometers, and the type-curve fitting method using temperature profile data—were employed to estimate fluxes at the groundwater-surface water interface of a small pond (Osong Pond) in South Korea. Measurements were conducted and compared during the wet season of 2020 (July–August 2020). Additionally, temperature data were collected during the dry seasons of 2021 and 2022 (November 2021–March 2022) to assess the applicability of the type-curve fitting method for long-term monitoring. The average vertical velocity measured by seepage meters was the highest (2.67 × 10⁻ 8  m/s), while the type-curve fitting method estimated the lowest average velocity (2.58 × 10⁻ 10  m/s). During the dry seasons of 2021–2022, the type-curve fitting method yielded an average flow velocity of 7.11 × 10⁻ 10  m/s, comparable to the dry season values of 2020. Although the lakebed temperature-based method underestimated vertical velocities in this study area, it can be effective for long-term monitoring. We recommend combining multiple measurement techniques tailored to the geological characteristics (e.g., topography and sediment composition) and climatic conditions of study sites. This integrated approach facilitates a more accurate evaluation of groundwater-surface water interactions and enhances understanding of the broader flow system.

Vehicle suspension system plays a crucial role in ensuring ride comfort, stability and safety of vehicle and passengers. Damping, a critical component of suspension system, attenuates vehicle vibrations and impacts onto the vehicle structure and passengers, thereby enhancing the overall vehicle performance, namely ride comfort and road holding. Traditionally, linear damping model has been employed, however the dynamic nature of real-world driving conditions often requires a more detailed understanding of damping behaviour, particularly in non-linear regime. This paper focuses on the determination and characterization of the optimum non-linear damping profile in vehicle suspension considering vehicle ride performance criteria using a 2 degree-of-freedom quarter vehicle model and then comparing them between linear and non-linear damping regimes. A comprehensive computational mathematical model is developed in MATLAB ® environment to simulate the complex interactions between the vehicle, suspension components and road input. Multi-objective genetic algorithm optimization technique is then implemented to determine the optimum non-linear damping in the form of Pareto front using MATLAB ® Global Optimization Toolbox by minimizing the two conflicting objectives which are vehicle body acceleration and dynamic tire load, which represents the ride comfort and road holding ability. Through computational simulation studies, the effects of non-linear damping on ride comfort and road holding ability can be evaluated and compared with linear damping model. In non-linear damping, the damping force does not increase linearly with the velocity of the suspension movement as in linear damping. Instead, the damping force changes at different rates depending on the velocity or displacement change. However, its non-linear profile remains unknown and need to be determined. Hence, the characteristic of non-linear damping is then determined through curve-fitting method, selecting a mathematical representation which best-fits the non-linear profile. As a result, the non-linear damping profile obtained is discovered to have low damping at the early velocity range, proceeding with an overall increasing trend, and then saturated towards the end of the velocity range, is a 5 th order polynomial damping model.

The minimum zone fitting and error evaluation for the arc-modified convex contour of a bearing roller have important applications for consistency detection and quantificational research of the elastohydrodynamic lubrication of a bearing roller. Based on the definition of the shape error and the geometric characteristics of the arc corrected roller convexity line of the bearing, a new fitting and error evaluation method for the total convexity contour of a bearing roller is presented. First, the reference cutoff points of the arc segment and straight line are determined based on the curvature difference of each measurement point. Then, the measuring points on both sides of the two reference cutoff points are selected as auxiliary cutoff points for arc fitting. The fitting error is obtained based on the minimum area method. Finally, a series of tangent equations are obtained based on the tangent principle between a line and two arcs, and the straightness error is determined by calculating the distances between the measuring points and the tangents. The example results show that an arc-modified convex contour can be fitted, and its global error can be evaluated effectively and precisely using the presented method. This study also provides a new idea for the minimum zone fitting of multi-segment curves along a plane.