Minute Changes Research Articles

Soliton wave profiles and dynamical analysis of fractional Ivancevic option pricing model

This study dynamically investigates the mathematical Ivancevic option pricing governing system in terms of conformable fractional derivative, which illustrates a confined Brownian motion identified with a non-linear Schrödinger type equation. This model describes the controlled Brownian motion that comes with a non-linear Schrödinger type equation. The solution to comprehend the market price fluctuations for the suggested model is developed through the application of a mathematical strategy. The modified Kudryashov analytical method is applied to find the fractional analytical exact soliton solution. The restrictions on the parameters required for these solutions to exist were also the result of this approach. The dynamical insights are examined and significant aspects of the phenomenon under study are discussed through the use of the bifurcation analysis. In the related dynamical system, the phase portraits of market price fluctuations are displayed at equilibrium points and for different parameter values. Additionally, the chaos analysis was carried out to show the quasi-periodic and periodic chaotic patterns. In order to track changes in market price, the sensitivity analysis of the studied model is also looked at and presented at different initial conditions. It was discovered that the model experienced price fluctuations as a result of minute changes in initial conditions.

Sensitive fluorescent biosensor reveals differential subcellular regulation of PKC.

The protein kinase C (PKC) family of serine and threonine kinases, consisting of three distinctly regulated subfamilies, has been established as critical for various cellular functions. However, how PKC enzymes are regulated at different subcellular locations, particularly at emerging signaling hubs, is unclear. Here we present a sensitive excitation ratiometric C kinase activity reporter (ExRai-CKAR2) that enables the detection of minute changes (equivalent to 0.2% of maximum stimulation) in subcellular PKC activity. Using ExRai-CKAR2 with an enhanced diacylglycerol (DAG) biosensor, we uncover that G-protein-coupled receptor stimulation triggers sustained PKC activity at the endoplasmic reticulum and lysosomes, differentially mediated by Ca2+-sensitive conventional PKC and DAG-sensitive novel PKC, respectively. The high sensitivity of ExRai-CKAR2, targeted to either the cytosol or partitioning defective complexes, further enabled us to detect previously inaccessible endogenous atypical PKC activity in three-dimensional organoids. Taken together, ExRai-CKAR2 is a powerful tool for interrogating PKC regulation in response to physiological stimuli.

Automated EEG-based language detection using directed quantum pattern technique

Electroencephalogram (EEG) signals contain complex useful information about brain activities. These EEG signals are noisy, highly varying and nonstationary in nature. Hence, extracting meaningful information from these signals is challenging. The existing machine learning systems struggle to capture the minute changes from the signals and yield high performance.This study introduces a novel quantum-inspired feature extraction technique called Directed Quantum Pattern (DQP), designed to address these challenges by using a lattice structure to capture directional binary features. These directions (paths) are computed using a maximum function providing a dynamic and adaptive feature representation.This paper presents a novel DQP-LangNet developed using DQP for automated classification of two- languages using EEG signals. We have proposed a hybrid approach, combining DQP, statistical features, and multi-level discrete wavelet transform (MDWT) to extract salient features similar to the deep learning approach. The EEG dataset consisting of 14 channels, produces 7 feature vectors per channel, yielding 98 feature vectors. Neighborhood component analysis and Chi-square (Chi2) feature selection approaches generated 196 feature vectors.In addition to the innovative feature extraction a new classification structure called “t” is proposed k-nearest neighbor (tkNN) and support vector machine (tSVM) classifiers are employed. Using the proposed tkNN and tSVM classifiers, 392 (=196×2) classifier-based outcomes are obtained. To further improve classification performance, we applied the iterative majority voting (IMV) technique to automatically select the best result.Our DQP-based model achieved a classification accuracy of 95.68 %using EEG language dataset with leave-one-subject-out (LOSO) cross-validation strategy. Also, an explainable feature engineering (XFE) structure of DQP-LangNet is employed to obtain channel-specific explainable results. Our proposed DQP-LangNet model can be employed for other applications in neuroscience.

Review of Innovative Cavity Designs in Metal–Insulator-Metal Waveguide-Based Plasmonic Sensors

AbstractPlasmonic sensors utilizing metal–insulator-metal (MIM) waveguides represent a significant advancement in sensing technology due to their high sensitivity and versatility. These sensors leverage surface plasmon polaritons to detect minute changes in the surrounding environment, making them highly effective for a range of applications. For instance, they can precisely measure variations in the Refractive Index, which is crucial for monitoring chemical concentrations and biological interactions. Additionally, MIM waveguides can be adapted to sense temperature fluctuations, pressure changes, and the presence of specific gases, providing valuable insights in fields such as environmental surveillance, medical diagnostics, and industrial processes. In recent years, a variety of sensor cavity shapes have been proposed to enhance sensor performance. This review examines how these innovative geometries optimize sensor cavities to achieve unprecedented levels of resolution and sensitivity, underscoring their transformative potential across a broad spectrum of scientific and practical applications.

The potential of heavily doped n-type silicon in plasmonic sensors

In this paper, we introduce a CMOS-compatible H-shaped plasmonic sensor with a silicon-insulator-silicon (SIS) configuration, utilizing highly doped n-type silicon as an alternative to traditional plasmonic materials like silver and gold. By employing precise carrier concentrations, we tune both the real and imaginary parts of permittivity, enabling the negative real permittivity required for efficient surface plasmon polariton (SPP) propagation. Our findings reveal a significant correlation between silicon doping concentration and sensitivity, highlighting the optimization potential of n-type silicon for advanced plasmonic applications. Through Finite Element Method (FEM) optimization, the sensor achieved a peak sensitivity of 5841.43 nm/RIU and a FOM of 23.8426. The sensor’s capabilities extend to temperature sensing with PDMS and ethanol, blood electrolyte detection for sodium and potassium, chemical detection, and magnetic field sensing. By utilizing minute changes in resonant wavelength through SPPs and SIS structures, our approach underscores n-type silicon’s potential in advancing plasmonic sensor technology.

LSPR Sensitivity of Ag-X NRs Core-Shell (X=Au, Al, TiO2, ZnO, SiO2): A Boundary Element Method Simulation Study

Abstract Silver nanorods (Ag NRs) have garnered significant attention in sensor applications due to their exceptional sensitivity to localized surface plasmon resonance (LSPR), facilitating the detection of minute changes in the surrounding environment. However, the inherent instability of silver in various environmental conditions poses a considerable challenge to the long-term reliability and reproducibility of Ag NR-based sensors. Core-shell structure Ag NRs by coating with Au, Al, TiO2, ZnO, and SiO2 layers reduce instability. These coatings act as protective barriers, shielding the underlying Ag NRs from environmental factors while preserving their LSPR properties. In this study, we employ the Boundary Element Method (BEM) simulation to investigate the sensitivity of coating Ag NRs with different materials (Au, Al, TiO2, ZnO, and SiO2) in enhancing both their stability and sensitivity for LSPR-based sensing applications.

Turn on, Tune in, and Listen up: Maximizing Side-Channel Recovery in Cross-Platform Time-to-Digital Converters

Voltage fluctuation sensors measure minute changes in an FPGA power distribution network, allowing attackers to extract information from concurrently executing computations. Previous voltage fluctuation sensors make assumptions about the co-tenant computation and require the attacker have a priori access or system knowledge to tune the sensor parameters statically. Additionally, prior voltage fluctuation sensors make use of proprietary vendor intellectual property and do not provide guidance on sensor migration to other vendors. We present the open-source design of the Tunable Dual-Polarity Time-to-Digital Converter, which introduces three dynamically tunable parameters that optimize signal measurement, including the transition polarity, sample window, frequency, and phase. We show that a properly tuned sensor improves co-tenant classification accuracy by 2.5 \(\times\) over prior work and increases the ability to identify the co-tenant computation and its microarchitectural implementation. Across 13 varying applications, our techniques yield an 80% classification accuracy that generalizes beyond a single board. Our sensor improves the ability of a correlation power analysis attack to rank correct subkey values by 2 \(\times\) . As an extension to our prior work, we show that the voltage fluctuation sensor is portable to multiple FPGA vendors, and we demonstrate implementations on both Xilinx and Intel FPGA systems.

Testing home-based exercise strategies in underserved minority cancer patients undergoing chemotherapy (THRIVE) trial: a study protocol.

Higher rates of physical inactivity and comorbid conditions are reported in Hispanic/Latinx and Black cancer patients receiving chemotherapy compared to their White counterparts. Despite the beneficial effect of exercise training for cancer patients, rates of participation in exercise oncology clinical trials are low among disadvantaged and racial and ethnic minority groups. Here, we will examine the effect of an exercise intervention using a novel, accessible, and cost-effective home-based exercise approach among Hispanic/Latinx and Black cancer patients receiving chemotherapy on exercise participation and cardiovascular disease risk. The THRIVE trial is an 8-month prospective, three-arm study of 45 patients who are randomized in a 1:1:1 fashion to a supervised exercise intervention (SUP), unsupervised exercise (UNSUP), or an attention control (AC) group. Eligible patients include those with breast, colorectal, or prostate cancer, who are sedentary, overweight or obese, self-identify as Hispanic/Latinx or Black, and plan to receive chemotherapy. Patients randomized to the SUP group participate in a home-based 16-week periodized aerobic and resistance exercise program performed three days per week, supervised through video conference technology. Patients randomized to the UNSUP group participate in an unsupervised 16-week, telehealth-based, periodized aerobic and resistance exercise program performed three days per week using the same exercise prescription parameters as the SUP group. Patients randomized to the AC group receive a 16-week home-based stretching program. The primary outcome is changes in minutes of physical activity assessed by 7-day accelerometry at post-intervention. Secondary outcomes include cardiovascular risk factors, patient-reported outcomes, and physical function. Outcome measures are tested at baseline, post-intervention at month 4, and after a non-intervention follow-up period at month 8. The THRIVE trial is the first study to employ a novel and potentially achievable exercise intervention for a minority population receiving chemotherapy. In addition, this study utilizes an intervention approach to investigate the biological and behavioral mechanisms underlying exercise participation in these cancer patients. Results will guide and inform large randomized controlled trials to test the effect of home-based exercise on treatment outcomes and comorbid disease risk in minority patients with cancer undergoing chemotherapy. https://classic.clinicaltrials.gov/ct2/show/NCT05327452, identifier (NCT#05327452).

Development of microsurgical forceps equipped with haptic technology for in situ differentiation of brain tumors during microsurgery

The stiffness of human cancers may be correlated with their pathology, and can be used as a biomarker for diagnosis, malignancy prediction, molecular expression, and postoperative complications. Neurosurgeons perform tumor resection based on tactile sensations. However, it takes years of surgical experience to appropriately distinguish brain tumors from surrounding parenchymal tissue. Haptics is a technology related to the touch sensation. Haptic technology can amplify, transmit, record, and reproduce real sensations, and the physical properties (e.g., stiffness) of an object can be quantified. In the present study, glioblastoma (SF126-firefly luciferase-mCherry [FmC], U87-FmC, U251-FmC) and malignant meningioma (IOMM-Lee-FmC, HKBMM-FmC) cell lines were transplanted into nude mice, and the stiffness of tumors and normal brain tissues were measured using our newly developed surgical forceps equipped with haptic technology. We found that all five brain tumor tissues were stiffer than normal brain tissue (p < 0.001), and that brain tumor pathology (three types of glioblastomas, two types of malignant meningioma) was significantly stiffer than normal brain tissue (p < 0.001 for all). Our findings suggest that tissue stiffness may be a useful marker to distinguish brain tumors from surrounding parenchymal tissue during microsurgery, and that haptic forceps may help neurosurgeons to sense minute changes in tissue stiffness.

Ultra-sensitive Two-dimensional Photonic Crystal Biosensor for Oral Cancerous Cell Detection

Background &amp; Objectives: Over the past two decades, biophotonic sensors based on two-dimensional (2D) photonic crystals (PhCs) have garnered significant attention in cancer diagnosis. This technology has become a crucial tool in early cancer detection and treatment response monitoring due to its ability to detect minute changes in biomarker concentrations and molecular interactions. The development of these sensors through advanced nano and microfabrication techniques has significantly improved diagnostic accuracy and speed, promising substantial enhancements in therapeutic outcomes for cancer patients. Materials &amp; Methods: A biosensor based on a 2D PhC was designed and simulated for the detection of oral cancer cells in a sample. The biosensor, structured with silicon rods in air using the Finite-Difference Time-Domain tool, utilizes five rods as an analyte for detecting normal and cancerous cells, thereby evaluating the sensor’s performance. Results: The variation in the transmission spectrum was studied to detect the presence of malignant cells in the test sample. The sensor’s structural parameters were carefully adjusted to enhance its sensitivity, a crucial factor in the accurate detection of cancerous cells. Two critical parameters, Q-factor and sensitivity, were derived from the results. The sensor achieved a sensitivity of 1148 nm/RIU with a Q-factor of 193. Conclusion: The designed biosensor demonstrates superior accuracy and sensitivity in identifying both malignant and normal cells in the test sample, making it suitable for real- time deployment in point-of-care applications.

Unraveling magneto-elastoresistance in the Dirac nodal-line semi-metal ZrSiSe

Quantum materials are often characterized by a marked sensitivity to minute changes in their physical environment, a property that can lead to new functionalities and thereby, to novel applications. One such key property is the magneto-elastoresistance (MER), the change in magnetoresistance (MR) of a metal induced by uniaxial strain. Understanding and modeling this response can prove challenging, particularly in systems with complex Fermi surfaces. Here, we present a thorough analysis of the MER in the nearly compensated Dirac nodal-line semi-metal ZrSiSe. Small amounts of strain (0.27%) lead to large changes (7%) in the MR. Subsequent analysis reveals that the MER response is driven primarily by a change in transport mobility that varies linearly with the applied strain. This study showcases how the effect of strain tuning on the electrical properties can be both qualitatively and quantitatively understood. A complementary Shubnikov-de Haas oscillation study sheds light on the root of this change in quantum mobility. Moreover, we unambiguously show that the Fermi surface consists of distinct electron and hole pockets revealed in quantum oscillation measurements originating from magnetic breakdown.

Regulating intermolecular hydrogen bonding to construct solvation-induced emission shift quinoline derivatives for real-time monitoring of water content in organic solvents

Water constitutes the most prevalent impurity in organic solvents, exerting significant influence on chemical reactions and potentially leading to fires and explosions, even in minute quantities. Thus, the development of convenient, rapid, and cost effective fluorescent probes for real-time monitoring of water content in organic solvents is imperative. Although some fluorescent materials have been synthesized for this purpose, most suffer from laborious preparation processes and poor cycling performance, constraining their practical application. This study investigates the impact of hydrogen bonding on the aggregation-induced emission (AIE) properties of quinoline derivatives, leveraging quinoline as the foundational scaffold and its nitrogen atom as the hydrogen bond acceptor. Research findings elucidate that intermolecular hydrogen bonding of quinoline is the primary determinant of their AIE behaviors. By harnessing the phenomenon of water molecules forming intermolecular hydrogen bonds with quinoline nitrogen atoms, we devised a straightforward and rapid method to fabricate a fluorescent test paper for real-time monitoring of water content in organic solvents. Experimental results demonstrate that even minute changes in water content, down to concentrations as low as 0.5 % by volume in organic solvents, can induce fluorescence changes in the test paper, which also exhibits favorable cycling performance. This study not only explores the influence of hydrogen bonds on the AIE properties of quinoline derivatives but also pioneers the development of a cost-effective, rapid, and recyclable test paper for real-time monitoring of water content fluctuations in organic solvents.

Rapid phagosome isolation enables unbiased multiomic analysis of human microglial phagosomes

Microglia are the resident macrophages of the central nervous system (CNS). Their phagocytic activity is central during brain development and homeostasis-and in a plethora of brain pathologies. However, little is known about the composition, dynamics, and function of human microglial phagosomes under homeostatic and pathological conditions. Here, we developed a method for rapid isolation of pure and intact phagosomes from human pluripotent stem cell-derived microglia under various invitro conditions, and from human brain biopsies, for unbiased multiomic analysis. Phagosome profiling revealed that microglial phagosomes were equipped to sense minute changes in their environment and were highly dynamic. We detected proteins involved in synapse homeostasis, or implicated in brain pathologies, and identified the phagosome as the site where quinolinic acid was stored and metabolized for de novo nicotinamide adenine dinucleotide (NAD+) generation in the cytoplasm. Our findings highlight the central role of phagosomes in microglial functioning in the healthy and diseased brain.

Spectral Fingerprinting Coupled Machine Learning: An Optimized Tool for Cellular Phenotype Identification

Spectral fingerprinting has emerged as a powerful tool for unraveling intricate interactions between the intracellular environment and nanomaterials. This study leverages spectral fingerprinting to deepen our comprehension of the interplay between macrophages and nanotubes, utilizing their Near Infrared fluorescence to discern cellular characteristics. This study employs Raman microscopy to showcase significant differences in defect ratio and intracellular processing of nanotubes, among macrophage phenotypes. The near infrared features of DNA-SWCNTs serve as distinctive markers for identifying different macrophage phenotypes. Here we use a support vector machine learning model to accurately identify M1 and M2 macrophages, achieving an impressive accuracy of 95%. The implications of this research extend to the development of nanomaterial-based platforms for cellular identification, holding promise for potential applications in real time in-vivo cell differentiation monitoring.This study underlines the pivotal role of nanotubes in delineating the diverse micro-environments of distinct macrophage phenotypes for clinical research. It emphasizes the potential of short DNA sequences and machine learning in characterizing and classifying cell phenotypes, offering valuable insights into the intersection of nanotechnology and biomedical research.This innovative approach holds great promise in advancing our understanding of dynamic cellular processes, disease states, disease progression, and response to stimuli in-vivo. The platform’s sensitivity to minute changes in the Near Infrared spectrum positions it as a valuable tool for researchers and clinicians. It facilitates the real time monitoring of cellular behavior within living systems, offering insights that extend to both fundamental research and clinical applications. The implications of this research pave the way for future developments in nanotechnology and biomedicine, bridging the gap between spectral analysis and cellular dynamics for enhanced diagnostic and therapeutic interventions. Figure 1

Self-Reporting Therapeutic Protein Nanoparticles.

We present a modular strategy to synthesize nanoparticle sensors equipped with dithiomaleimide-based, fluorescent molecular reporters capable of discerning minute changes in interparticle chemical environments based on fluorescence lifetime analysis. Three types of nanoparticles were synthesized with the aid of tailor-made molecular reporters, and it was found that protein nanoparticles exhibited greater sensitivity to changes in the core environment than polymer nanogels and block copolymer micelles. Encapsulation of the hydrophobic small-molecule drug paclitaxel (PTX) in self-reporting protein nanoparticles induced characteristic changes in fluorescence lifetime profiles, detected via time-resolved fluorescence spectroscopy. Depending on the mode of drug encapsulation, self-reporting protein nanoparticles revealed pronounced differences in their fluorescence lifetime signatures, which correlated with burst- vs diffusion-controlled release profiles observed in previous reports. Self-reporting nanoparticles, such as the ones developed here, will be critical for unraveling nanoparticle stability and nanoparticle-drug interactions, informing the future development of rationally engineered nanoparticle-based drug carriers.

A novel and effective oxidation-resistant approach in plasmonic MIM biosensors for real-time detection of urea and glucose in urine for monitoring diabetic and kidney disease severity

In this research article, we introduce a novel oxidation-resistant approach in a Metal–Insulator–Metal (MIM) plasmonic biosensor, specifically designed for the detection and diagnosis of diabetes and kidney disease severity using urea and glucose biomarkers in urine. The proposed biosensor features a four-hand-shaped fan resonator with integrated nanodots and stubs within its waveguide. We optimized its performance through Finite Element Method (FEM) simulations, achieving a maximum sensitivity of 2470.48 nm/RIU and a Figure of Merit (FOM) of 50.15. To ensure chemical stability, we utilized an oxidation-resistant plasmonic material (Au) in our sensor, a critical factor for reliable biosensing applications. Additionally, we focus on Diabetic Kidney Disease (DKD), a significant global health challenge, emphasizing the need for early detection and point-of-care treatment. By exploiting minute changes in resonant wavelength resulting from light–matter interactions through the properties of Surface Plasmon Polaritons (SPPs) and MIM structures, our biosensor demonstrates promising capabilities for real-time and quantitative analysis of biomarkers critical for monitoring the stages of DKD.

On the Emergence of Ferromagnetism in LaCoO3 Ultrathin Films

AbstractIt is well known that the properties of a crystal evolve as it increases in size from a single atomic plane to that of the bulk. Such size‐dependent transitions can stem from many different origins and depend on minute changes to crystal bonding and composition. A model example is that of LaCoO3, which is non‐magnetic in the bulk but can display ferromagnetism at the nanoscale. Here, the evolution of structure‐property relationships is studied in the LaCoO3−δ/SrTiO3 (001) system as the thickness of LaCoO3−δ is increased from a single plane to 10 unit cells. In situ synchrotron X‐ray studies are performed during and post‐deposition to probe changes in the interactions between structure, stoichiometry, and magnetic behavior. Structural quantification indicates that the oxygen octahedral rotation pattern evolves with thickness, due to inherent differences in crystal symmetry between the film and substrate. The change in rotation modifies the required energy barrier for the spin state transition via the Co–O bond length and Co–O–Co bond angle, affecting the appearance of ferromagnetism. Our results highlight the contributions of high spin Co2+ and/or high spin Co3+ to respective weak and robust ferromagnetism and the evolution of properties with size in ultrathin LaCoO3−δ heterostructures.

Enhancing Precision in SQUID Sensors: Analyzing Washer Geometry Dependence at the Microscale

In the field of high-precision physical field detection, measurements based on magnetic signals are crucial due to their exceptional accuracy, sensitivity, and stability. Superconducting Quantum Interference Devices (SQUIDs), with their ultra-high sensitivity, have become key in detecting minute changes in magnetic flux. However, with increasing demands for higher precision, further enhancing device performance under quantum constraints remains a significant challenge. This study improved the precision of SQUID sensors by optimizing the geometry of microscale washers using the physical response of superconducting films under micromachining technologies and quantum constraints. Our research extensively examined the effects of square, octagonal, and circular washers on the magnetic field response, shielding currents, and inductance. Experimental results showed that, compared to traditional square washers, octagonal washers significantly enhanced sensor sensitivity and efficiency by minimizing edge magnetic flux and optimizing current distribution. Notably, all observed inductance values exceeded predictions based on traditional empirical formulas, with square washers showing the highest average relative error of 80.8%, while circular and octagonal washers had errors of 65.31% and 66.43%, respectively. This breakthrough not only lays a new theoretical foundation for the design of SQUID sensors but also provides robust evidence for enhancing magnetic field detection through microscale technological innovation.

A Review of Recent Advancements and Perspectives of Nanotechnology in the Application of Biomedical Imaging and Instrumentation

AbstractThe development of imaging, diagnosis, prognosis and early detection of diseases has been greatly impacted by nanotechnology by enhancing already existing clinically applicable technologies. With the help of their capacity to alter nanoparticles for molecular‐level specificity, tissue‐specific diagnosis is made possible gratitude to the unique biophysical features of the nanoparticles that enable contrast augmentation will boost biomedical imaging. The unique prospect of multiplexing is possible by the fact that minute changes in the nanoparticles’ size or composition can have significant effects on their optical, magnetic, or electrical capabilities. This article examines nanotechnology's function in biomedical imaging. In this article, the fundamentals and applications of biomedical imaging, pharmaceutical application of microbial surfactants, intelligent drug delivery systems, and green metallic nanoparticle manufacturing are all examined. The biomedical applications comprising organic (carbon nanotubes, metal oxide and liposomes) and inorganic (metal oxide, metal) nanoparticles, and materials with nanopatterns in diagnostics, biosensing, and bioimaging devices, along with drug delivery systems, are the main topics of discussion. studies conducted in vitro and in vivo have demonstrated that different nanoparticles can be utilized to detect seizures early and precisely along with to treat them successfully. To decrease the possible toxicity and enable improved target specificity, respectively, more development in the synthesis and functionalization of adaptable nanotechnologies is required.

Prediction of micro wear depth between engineering polymers

In the polymer industry, changes in the wear area that affect surface aesthetics have emerged as a significant issue in applications where surface appearance is crucial. Even minute changes in surface height can be visible to the naked eye, necessitating the need for micro-scale wear prediction of polymers. While the Archard model has been verified for decades for metal-metal or polymer-metal contact, predicting wear between polymer-polymer contacts remains challenging due to limitations in wear measurement and wear coefficient determination. In this study, we propose a method for determining the wear coefficient for polymer-polymer contact and validate the prediction model with a component wear test on polymer parts. Wear depth, an essential indicator of wear is measured after the sliding wear test of the polycarbonate (PC) pin and Acrylonitrile butadiene styrene (ABS) copolymer plate. The wear prediction model with the obtained wear coefficient exhibits 99.7 % accuracy in comparison to the test wear depth. A component wear test is performed on engineering polymer parts, and wear prediction under test conditions is made using the proposed model. The measured and predicted wear depth of the component demonstrates a difference smaller than 5 μm, which is within the range of no change in scratch visibility. This study ensures the accurate wear prediction of engineering polymer components with affordable computational efficiency. The ability to predict polymer-polymer wear, previously unattainable, will enable several applications in the polymer industry, such as component design, scratch visibility prediction, and quality assurance.