Time Dependence Research Articles

The James Webb Space Telescope Absolute Flux Calibration. II. Mid-infrared Instrument Imaging and Coronagraphy

The absolute flux calibration of the Mid-Infrared Instrument imaging and coronagraphy is based on observations of multiple stars taken during the first 2.5 yr of James Webb Space Telescope operations. The observations were designed to ensure that the flux calibration is valid for a range of flux densities, different subarrays, and different types of stars. The flux calibration was measured by combining observed aperture photometry corrected to infinite aperture with predictions based on previous observations and models of stellar atmospheres. A subset of these observations was combined with model point-spread functions to measure the corrections to infinite aperture. Variations in the calibration factor with time, flux density, background level, type of star, subarray, integration time, rate, and well depth were investigated, and the only significant variations were with time and subarray. Observations of the same star taken approximately every month revealed a modest time-dependent response loss seen mainly at the longest wavelengths. This loss is well characterized by a decaying exponential with a time constant of ∼200 days. After correcting for the response loss, the band-dependent scatter around the corrected average (i.e., repeatability) was found to range from 0.1% to 1.2%. Signals in observations taken with different subarrays can be lower by up to 3.4% compared to FULL frame. After correcting for the time and subarray dependencies, the scatter in the calibration factors measured for individual stars ranges from 1% to 4% depending on the band. The formal uncertainties on the flux calibration averaged for all observations are 0.3%–1.0%, with longer-wavelength bands generally having larger uncertainties.

Marine propulsion shafting-oriented status detection algorithm for uncertainty time-varying

The marine propulsion shafting is the lifeline of navigation. The marine propulsion shafting vibration signals tend to contain rich connotations that suggest the shafting status. In this paper, aiming at the difficulty in the capture of time dependence in uncertainty time-varying signals of marine propulsion shafting, the concept of Degree of Membership (DM) in fuzzy mathematics is introduced into Recurrent Neural Network (RNN), called Degree of Membership Recurrent Neural Network (DM-RNN). A marine propulsion shafting status detection algorithm based on DM-RNN is proposed. Specifically, the mathematical expressions for forward propagation and back propagation of DM-RNN are theoretically derived. The self-propulsion test scheme based on a full-scale boat is designed. Actual vibration signals under multiple working conditions are collected. The progressiveness of the present algorithm under shafting vibration signals of the full-scale boat is verified by the experiment. The results of the two comparison experiments indicate that the present algorithm significantly improved the detection accuracy compared with the classical ANNs, up to 93.25% ± 0.71%. Furthermore, a DM function with a simple form and fewer parameters was more conducive to the high detection accuracy of DM-RNN.

A Novel Analytical Solution of Radial Diffusion Equation with Constant Terminal Rate of Slightly Compressible Fluid

In reservoir engineering, the flow and type of fluids crossing porous media, are associated with pressure drops leading to variations on hydrodynamic parameters and the filtration stages which are of interest to be assessed as they impact the production efficiency of oil and gas industry. These parameters are the key to the practical solutions of numerous subjects faced during oil field exploitation. Dealing with the problem of sound field development initiatives requires a rigorous examination of unsteady filtration of slightly compressible fluid in the reserves layer. The state of the art of the proposed scientific work is to present an innovative mathematical approach that gives unique results, in the mathematical connection and combination of the equations used versus existing diffusion equation in the case of the constant terminal rate solution. This model helps the designers in the field of oil and gas to better and faster evaluate the diffusivity of the pay zones, the different hydrodynamic parameters, and the different variables that take part in the development of fluid filtration processes in the porous medium expressed as in the dependence of time, distance, and other variables, all of which together impact the well testing and long-term projections.

Mechanical properties and constitutive model of deep marble under alternating creep-fatigue action

Given the time dependence and susceptibility to disturbance of deep rocks, this paper analyzes mechanical properties, acoustic emission (AE) signals, and pore radius distribution characteristics of deep marble under alternating creep-fatigue (ACF) action at different confining pressures by experiment and theoretical analysis. The results show that: (1) the alternation of creep and fatigue accelerates the rock damage, and the number of alternations increases with the increasing confining pressure. (2) Strain increments exhibit a pattern of first decreasing and then increasing, and they undergo a transformation from “V”-type to “U”-type with the increase of confining pressure. (3) The AE ringing count is mainly generated in the fatigue segment. The radius of large pores inside the rock is constrained by the confining pressure. (4) The ACF constitutive model is established, where model parameters show a nonlinear decreasing trend of the power exponential function with the strain increment.

Static and Dynamic Model Calibration for Upper Thermosphere Determination

AbstractThermospheric density, a crucial factor in spacecraft operations, poses a significant challenge in accurate determination due to the intricate coupling of the thermosphere‐ionosphere system. Despite capturing long‐term trends, the widely used empirical models, which are used for low Earth orbit spacecraft operations, often fail to reproduce small‐scale or short‐term variations in the thermosphere. This work presents a novel approach for model improvement. The background model employed is our recently developed empirical model, which blends numerical simulations and satellite measurements. The model residuals are compiled into longitude and latitude bins, and the corresponding basic modes of describing variabilities are subsequently derived via the PCA method. The attendant amplitudes exhibit significant local time and seasonal dependencies, motivating optimized parameterization, that is, static calibration. Moreover, the present study reveals that the most dominant mode correlates to land‐sea contrasts and manifests global synchronicity upon excluding local time and seasonal dependences. This establishes the real‐time model adjustment foundation by exploiting limited calibration observations, which is referred to as dynamic calibration. The results from the dynamic calibration model indicated considerably improved thermospheric representation, especially for small‐scale or short‐term variations, toward a better thermospheric prediction.

Synergic effects of time dependence and thermodynamic driving on metastable phase separation of liquid Fe50Cu50 alloy

Synergic effects of time dependence and thermodynamic driving on metastable phase separation of liquid Fe50Cu50 alloy

Capillary infiltration measurements by finite size drop method: wetting, spreading and infiltration of liquid silver in porous iron

The study proposes a direct method for studying capillary infiltration of porous media using high-speed measurements of the shape of an imbibited droplet of finite size. The method assumes isotropic infiltration, and the change in the droplet volume on the surface is equated to the volume of imbibited liquid. The shape of the imbibited volume in the case of a droplet of finite volume is assumed to be a flattened spheroid. In the mathematical model, the rate of change of the apparent volume obtained in the experiment is reduced to the rate of movement of the liquid front in the porous media.Measurements of capillary processes in the silver (melt)-iron (porous or dense solid) system were carried out at various temperatures in a vacuum of 10-3Pa to verify the model. Time dependencies of the wetting contact angle and the contact diameter of pure silver melt on the surface of dense solid iron, the imbibition rate of silver melt into porous iron, and the kinetics of sintering of iron powder were measured. All measurements were conducted using direct methods of high-speed video and thermal imaging. As a result, in addition to the application of the proposed imbibition model, kinetic dependencies of the mentioned processes at various temperatures in the range from 1000 to 1150 °C were obtained and their activation energies were determined.

Absorption and diffusion process at the CO[formula omitted]-decane interface considering density fluctuations near the critical CO[formula omitted] point

The injection of CO2 into geological formations for storage and for efficiency enhancement of oil production is considered a promising technology. Fundamental is an understanding of the absorption and diffusion process at the CO2-decane interface and its pressure and temperature dependence when approaching the critical CO2 point (31 °C, 7.38 MPa).Based on a new conceptual model, which considers CO2 density fluctuations in the critical range, we were able to consistently explain the time dependence of the volume increase for the respective thermodynamic state. The experimental results confirm the new conceptual model that volume swelling in the non-critical pressure range (< 6 MPa) is a surface effect with limited penetration depth. In contrast, the volume increase in the critical pressure range (6 - 8 MPa) is caused by mixing of liquid CO2droplets and the oil phase, i.e. to a level increase of the liquid mixed phase. Our experimental contact angles confirm this. The measured minimum miscible pressures (MMP) are 5.6 and 6.5 MPa at 20 °C and 30 °C, respectively.The CO2 absorption process at the CO2-decane interface is studied independently with pressure decay experiments. Our model results show excellent agreement (relative error ≅ 1 %) with the experimental results for both the non-equilibrium and equilibrium model, and the early-time diffusion coefficient is 4.7±1.9×10−7m2/s, confirming a fast CO2 mass transfer process.

Unveiling the apparent density of swollen PDMS in hydrocarbons – A distinction between dimensional-based and volume-additivity methods

This article reports the swelling-induced effect on the density, mass, area, and thickness of the polydimethylsiloxane (PDMS) membrane in C6–C12 linear alkanes. Their time dependence (up to 500 min) was determined at 20 °C and atmospheric pressure. From the combination of areal and thickness data, the experimental PDMS volumes of the swollen polymer were calculated, and the time dependence of the apparent density of the swollen membrane (i.e. its mass divided by its real volume) was revealed. The equilibrium apparent density of the swollen PDMS increased with the increasing number of C-atoms of liquid alkanes and liquid density. For higher alkanes (C number > 10), the calculated equilibrium apparent density of the swollen polymer was thus higher than that of the dry polymer. The dimensional-based PDMS volumes of the swollen polymer were compared with those calculated by the volume-additivity rules. For each C6–C12 linear alkane, the volume-additivity method overestimated the swollen polymer volumes compared to the experimentally determined ones. Such a finding indicates the non-ideal PDMS swelling character, which is not solely additive due to the force interactions between the polymer chains and solvent molecules and moving of polymer chains apart (free volume increases).

Approaches to the method of granulation of fly ash of power plants

Relevance. The research is aimed at reducing the impact of deficiencies (dusting) when involving in circulation fly ash, which prevents the wider use of valuable secondary raw materials in the production of demanded products. The direction of work corresponds to the Comprehensive Waste Disposal Plan for coal-fired power plants and boiler houses, approved by the Decree of the Government of the Russian Federation dated 06/15/2022 No. 1557-R. Aim. Development of an optimal technology for the production of granules from fly ash of Kemerovo GRES using two types of substances of a similar nature as a binder, depending on the concentration of the binder in an aqueous solution and the delay time of strength gain and abrasion index. Object. A granulate or pellet made of fly ash based on a binder of experimental aluminosilicate glue and liquid sodium glass "Diola-53". Methods. Strength tests were carried out on a hydraulic press according to SS 8929-2020 and the abrasion index of a large filler in a shelf drum was determined out according to SS 9758-2012. Two methods of granulation of fly ash were used: 1) two-stage – preparation (cooking) of aluminosilicate glue from fly ash and granulation; 2) one-stage – granulation using market liquid glass. Liquid aluminosilicate glue was produced experimentally in a container using a flask heater and a mixing device. Results and conclusions. The authors have established the differences in the delay time of strength gain and the abrasion index of granules obtained by two methods based on fly ash from Kemerovo Urban District Power Plant using aluminosilicate glue and liquid sodium glass "Diola-53" as a binder. They found the dependences of the delay time of strength gain and the abrasion index of granules on the concentration (fraction) of an aqueous solution of the binder. The specific costs for the production of granules for each binder and its proportion in an aqueous solution are determined. Granulate from fly ash based on a binder: experimental aluminosilicate glue and liquid sodium glass "Diola-53", can be transported without dusting by any type of transport and serve as a potential raw material for cement production and a component in concrete production.

Quasi-steady transitions in confined convection

We study the effect of geometrical confinement on thermal convection by laboratory experiments and direct numerical simulations using Hele-Shaw geometries (typically the gap-to-height aspect ratio $0.12$ ) for the Prandtl number $Pr \geq 40$ and the Rayleigh number $Ra \leq 6 \times 10^7$ . Under such strong unidirectional confinement, the convective flows are forced to squeeze within the narrow gap and exhibit unique spatiotemporal signatures, which contrast those in unconfined systems. With the increase of $Ra$ , we identify that the system experiences five convective regimes that can be classified from two aspects, time dependency and flow dimensionality: (I) quasi-two-dimensional (quasi-2-D) steady flow; (II) quasi-2-D flow with oscillatory corner rolls; (III) three-dimensional (3-D) flow with oscillatory corner rolls; (IV) 3-D steady flow; and (V) 3-D time-dependent motion of plumes around sidewalls. Notably, unsteadiness does not emerge globally, but is localised near the sidewalls as oscillatory corner rolls, resulting in the regime transitions happening in a quasi-steady manner. We confirm that these regime transitions show less dependence on both $Pr$ and the other (wider) horizontal scale of the geometry. Moreover, we find that a recently proposed criterion ‘degree of confinement’ (Noto et al., Proc. Natl Acad. Sci. USA, vol. 121, issue 28, 2024, e2403699121) successfully explains the emergence of 3-D structures, expanding its applicable range to smaller $Ra$ . This study deepens the comprehension of the thermal convection emerging in tight geometries, impacting across disciplines, such as Earth and planetary science, and thermal engineering.

A Varying Precision Beta Prime Autoregressive Moving Average Model With Application to Water Flow Data

ABSTRACTWe introduce a dynamic model tailored for positively valued time series. It accommodates both autoregressive and moving average dynamics and allows for explanatory variables. The underlying assumption is that each random variable follows, conditional on the set of previous information, the beta prime distribution. A novel feature of the proposed model is that both the conditional mean and conditional precision evolve over time. The model thus comprises two dynamic submodels, one for each parameter. The proposed model for the conditional precision parameter is parsimonious, incorporating first‐order time dependence. Changes over time in the shape of the density are determined by the time evolution of two parameters, and not just of the conditional mean. We present simple closed‐form expressions for the model's conditional log‐likelihood function, score vector, and Fisher's information matrix. Monte Carlo simulation results are presented. Finally, we use the proposed approach to model and forecast two seasonal water flow time series. Specifically, we model the inflow and outflow rates of the reservoirs of two hydroelectric power plants. Overall, the forecasts obtained using the proposed model are more accurate than those yielded by alternative models.

Elucidation of Electronic Structures of Mixed-Valence States Induced by dσ-π Charge Delocalization in Linear-Chain and Discrete Rhodium-Dioxolene Tetrameric Complexes.

We have successfully synthesized unique linear-chain and discrete mixed-valence tetrameric complexes, {[Rh(3,6-DBDiox-4,5-S2CO)(CO)2]4·hexane}∞ (4) and [Rh(3,6-DBDiox-4,5-S2CO)(CO)2]4 (5), by carefully choosing the solvent. X-ray photoelectron spectra (XPS) confirm that 4 and 5 are in the Rh(I,II) mixed-valence state. Analyses of the metrical oxidation state (MOS) of dioxolene ligands reveal that in 4 and 5, the electron density corresponding to one electron per tetramer is transferred from Rh(I) ions to semiquinonato ligands, and the transferred charge is delocalized throughout the four dioxolene ligands. Due to their mixed-valence state, 4 and 5 are semiconductors with relatively high electrical conductivity at room temperature. Density functional theory (DFT) calculations of tetrameric complex demonstrated for the first time that the dσ* orbitals of the Rh atoms and the π* orbitals of the semiquinonato ligands, which are originally highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), respectively, strongly hybridize with each other, leading to the Rh(I,II)-semiquinonato/catecholato mixed-valence state. Furthermore, time-dependent (TD)-DFT calculations have also revealed that the low energy absorption band observed centered at 5700 cm-1 is attributed to a charge transfer from [dσ*(Rh)] (HOMO) or [π*(SQ)-dσ*(Rh)] (HOMO-1) to [π*(SQ)-dσ*(Rh)] (LUMO/LUMO+1). Although 4 and 5 are tetramers with nearly identical structures, their magnetic interactions are found to differ significantly depending on their crystal structures.

Thermodynamics of black holes featuring primary scalar hair

In this work, we embark on the thermodynamic investigation concerning a family of primary charged black holes within the context of shift and parity symmetric Beyond Horndeski gravity. Employing the Euclidean approach, we derive the functional expression for the free energy and derive the first thermodynamic law, offering a methodology to address the challenge of extracting the thermal quantities in shift-symmetric scalar tensor theories characterized by linear time dependence in the scalar field. Following the formal analysis, we provide some illustrative examples focusing on the thermal evaporation of these objects. Published by the American Physical Society 2024

Characterization of New Experimental Materials for Hemodialysis Membranes and Simulation of Urea Dialysis Process with Their Use

The acute shortage of hemodialysis cartridges in Russia, caused by restrictions imposed by the European Union on the supply of high-tech equipment, has led to the nessesity for the production of domestic inexpensive and effective membranes for hemodialysis. In this work, experimental membranes based on polysulfone were obtained and their characterization was carried out. The influence of the blowing agent (polyethylene glycol and polyvinylpyrrolidone) on the structure and transport properties of the obtained membranes was compared. A non-steady state one-dimensional mathematical model of urea dialysis is proposed. A special feature of the model is the accounting the membrane microheterogeneous structure. A comparison of the modeling results with experimental data on the urea concentration time dependences in the dialysate compartment of the dialysis system allows us to conclude that the model adequately describes the system under study. A theoretical assessment of the obtained membrane material efficiency under conditions corresponding to the hemodialysis process, as well as a comparison of urea removal performance with Nephral ST hemodialysis cartridges from Baxter (a company widely represented on the world market) was carried out. It was shown that a polysulfone-based membrane obtained using polyvinylpyrrolidone demonstrates results slightly inferior to those of commercially produced cartridges, which indicates its promise for the production of hollow fiber membranes for hemodialysis cartridges.

Rain detection for rain-contaminated ground-based microwave radiometer data using physics-informed machine learning method

Because the radiation signal is strongly influenced by emission and scattering from rain, microwave radiometer data suffer from rain contamination. The traditional method of using rain gauges to detect rain for microwave radiometers has limitations. For example, it can only detect rain that reaches the ground and is ineffective for raindrops suspended in the atmosphere that can still contaminate remote sensing data. This article presents a rain detection method for microwave radiometer measurements, based on Gradient Boosted Decision Trees (GBDT). First, the characteristic that the increase in microwave radiometer brightness temperature when raindrops are present in the atmosphere, along with the seasonal dependency of rainfall patterns, is combined with meteorological variables to form feature vectors. Then, the GBDT is employed to classify data into rain-free and rain-contaminated categories. Microwave radiometer (MWR) measurements and simultaneous Micro Rain Radar (MRR) target classification collected from the Swiss Plateau in 2008 are utilized to train the model, which is subsequently tested using two testing schemes: ten-fold cross-validation technique and time series test sets. Compared with the detection accuracy of the integrated liquid water (ILW) threshold method (73.6% and 68.3%) in both testing schemes, our GBDT-based method achieved superior accuracy, recording approximately 100% and 98.4%, respectively. The proposed method exhibits strong generalization capabilities, allowing it to directly detect rain contamination in time series data and effectively overcome the time dependence of rainfall occurrence. In addition, compared with the ILW threshold method, the GBDT-based method considers various rainfall patterns contained in various seasons. Features selected for this method enable its direct application to other tropospheric microwave radiometer systems.

Combined first-principles and Boltzmann transport theory methodology for studying magnetotransport in magnetic materials

Unusual magnetotransport behaviors, such as temperature-dependent negative magnetoresistance (MR) and bowtie-shaped MR, have puzzled us for a long time. Although several mechanisms have been proposed to explain these phenomena, the absence of comprehensive quantitative calculations has made these explanations less convincing. In our work, we introduce a methodology to study magnetotransport behaviors in magnetic materials. This approach integrates the anomalous Hall conductivity induced by the Berry curvature, with a multiband ordinary conductivity tensor, employing a combination of first-principles calculations and semiclassical Boltzmann transport theory. Our method also incorporates the temperature dependence of the relaxation time and the anomalous Hall conductivity, as well as the field dependence of the anomalous Hall conductivity. We initially test this approach on models, obtaining distinct behaviors of magnetoresistance and Hall resistivity across several types of magnetic materials, and then apply it to a Weyl semimetal Co3Sn2S2. The results, which align well with experimental observations in terms of magnetic field and temperature dependencies, demonstrate the efficacy of our approach. This methodology provides a comprehensive and efficient means to understand the underlying mechanisms of the unusual complex behaviors observed in magnetotransport measurements in magnetic materials. Published by the American Physical Society 2024

Fast mass transfer processes of interfering trapped CO2-clusters at reservoir conditions: Experiment and theory

The gas-to-oil ratio (GOR) of a reservoir fluid plays a critical role in optimizing oil recovery and oil production strategies, improving oil recovery efficiency, and predicting reservoir behavior. Understanding the complicated kinetics of multicomponent mass transfer at the pore scale after gas injection into petroleum reservoirs is of great importance for estimating GOR and enhanced oil recovery (EOR). However, to date, there is a significant gap in the fundamental process understanding of the complex mass transfer process at reservoir conditions. Using micro-CT technology, we investigate the time dependence of CO2 mass transfer and cluster growth after high pressure CO2 injection into sedimented porous media (sintered 0.2 mm glass beads).Surprisingly, the CO2 partitioning equilibrium is already reached after 2 h. To the best of our knowledge, such a fast CO2 transport through water-saturated porous media, which cannot be explained by linear diffusion models (time scale 100 days for a diffusion length of 10 cm), has not been reported in the literature before. We proposed a conceptual model that assumes CO2 inflation of interfering gas clusters drives cascading CO2 transport. We verified this conceptual model through a time series of experiments at different initial gas saturation, analyzing in each case the spatial cluster distribution, cluster size distribution, and pore occupancy frequency.Whether CO2 inflation of the gas clusters occurs depends on the critical initial gas saturation, which is about 10%. Our main conclusion is that CO2 migration should be considered as gas phase diffusion cascading along a quasi-percolating cluster, since CO2 inflation leads to a high gas saturation of about 26%, which is close to the percolation threshold. Our experimental results support this physical hypothesis. We show for the first time that CO2 clusters can expand over large pore spaces and thus are close to the critical percolation threshold (mobility threshold). The cluster size distribution can be described by a power distribution with the critical exponent 2.19, i.e., it shows universal scaling.We model the interfering mass transfer processes of neighboring gas clusters with a multi-sphere model. Exploring different scenarios for the dissolution kinetics of an ensemble of interfering gas clusters allow a deeper understanding of the complicated mass transfer kinetics and agree well with experimental cluster analyses at specific times.

Deep reinforcement learning-based spatio-temporal graph neural network for solving job shop scheduling problem

The job shop scheduling problem (JSSP) is a well-known NP-hard combinatorial optimization problem that focuses on assigning tasks to limited resources while adhering to certain constraints. Currently, deep reinforcement learning (DRL)-based solutions are being widely used to solve the JSSP by defining the problem structure on disjunctive graphs. Some of the proposed approaches attempt to leverage the structural information of the JSSP to capture the dynamics of the environment without considering the time dependency within the JSSP. However, learning graph representations only from the structural relationship of nodes results in a weak and incomplete representation of these graphs which does not provide an expressive representation of the dynamics in the environment. In this study, unlike existing frameworks, we defined the JSSP as a dynamic graph to explicitly consider the time-varying aspect of the JSSP environment. To this end, we propose a novel DRL framework that captures both the spatial and temporal attributes of the JSSP to construct rich and complete graph representations. Our DRL framework introduces a novel attentive graph isomorphism network (Attentive-GIN)-based spatial block to learn the structural relationship and a temporal block to capture the time dependency. Additionally, we designed a gated fusion block that selectively combines the learned representations from the two blocks. We trained the model using the proximal policy optimization algorithm of reinforcement learning. Experimental results show that our trained model exhibits significant performance enhancement compared to heuristic dispatching rules and learning-based solutions for both randomly generated datasets and public benchmarks.

In vitro evaluation of the antitumor and antioxidant effects of purified and characterized polysaccharides from Ganoderma applanatum

Background In this study, the chemical properties of Ganoderma applanatum polysaccharides (GAP) were systematically investigated, followed by a comprehensive analysis of their antitumor and antioxidant capabilities. Methods Ultrasonic circulation technology was employed for the extraction of GAP, facilitating the procurement of crude polysaccharides through the Sevag method, dialysis, and sequential alcohol precipitation. The chemical constituents of these polysaccharides were subsequently analyzed utilizing Fourier-transform infrared spectroscopy and gas chromatography-mass spectrometry. The antitumor and antioxidant properties of the polysaccharide components were assessed utilizing 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay and free radical scavenging methods, respectively. Results Gradient ethanol precipitation yielded three principal polysaccharide fractions: GAP-40, GAP-60 and GAP-80. Analysis revealed glucose as the predominant component in these fractions, with average molecular weights determined to be 77.75, 9.25 and 1.03 kDa, respectively. The antitumor activity of GAP-40, GAP-60 and GAP-80 against MCF-7 cells demonstrated both time and concentration dependence. Significantly, GAP-40, at a concentration of 1000 μg/mL over 48 h, presented a notable inhibition rate of 56.77%, outperforming GAP-60 and GAP-80. Furthermore, the antioxidant capacities of GAP-40, GAP-60 and GAP-80 were comparably significant to that of vitamin C, with detailed analysis revealing marked differences in antioxidant activity among the GAP variants. Specifically, GAP-40 exhibited superior efficacy in scavenging 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS+) radicals relative to GAP-60 and GAP-80. In contrast, GAP-80 was distinguished by its exceptional hydroxyl radical scavenging capacity, surpassing that of both GAP-40 and GAP-60. Conclusion These results substantiate the potential of GAPs as viable and effective antitumor agents, additionally suggesting their utility as functional foods endowed with antioxidant attributes.