Method For Rapid Evaluation Research Articles

Cardiovascular autonomic neuropathy is associated with SLEDAI in patients with systemic lupus erythematosus.

As a prevalent and severe complication of systemic lupus erythematosus (SLE), cardiovascular autonomic neuropathy (CAN) has garnered increasing attention. Reports suggested that CAN may be related to the disease activity of SLE. This study aims to explore whether Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) is associated with CAN and to evaluate its diagnostic value for CAN. Altogether, 144 patients with SLE from the Rheumatology Department of Nanjing Drum Tower Hospital were included. Each patient underwent assessment with the SLEDAI and cardiovascular reflex tests (CARTs). Patients were classified into three groups: non-CAN, early-CAN and diagnosed-CAN based on the CARTs results. The relationship of CARTs and SLEDAI were analyzed using SPSS 26.0. After being divided into three groups, there were significant differences in SLEDAI among them. With increasing SLEDAI score (P < 0.05), both CARTs scores and four individual parameters score increased significantly, both before and after adjusting for influencing factors (P < 0.05). Besides, in Logistic regression analysis, it identified that SLEDAI as an independent risk factor for CAN (OR = 1.227, 95%CI = 1.143-1.316, P < 0.001). Finally, after accounting for the influence of glucocorticoids, a significant positive correlation between CARTs and SLEDAI remained (P < 0.05). As the gold standard in the diagnosing CAN, CARTs and four parameters are significantly correlated with SLEDAI. Furthermore, SLEDAI is also an independent risk factors for its development. In conclusion, this research demonstrated that SLEDAI is a dependable indicator for the onset and progression of CAN. Key Points • This study is the first to demonstrate a strong association between SLEDAI and cardiovascular autonomic neuropathy, identifying SLEDAI as a risk factor for CAN in SLE patients. • This study offers a convenient and rapid method for the clinical evaluation of CAN in SLE patients, providing significant value in assessing cardiovascular complications.

Comprehensive Quality Evaluation of Traditional Chinese Medicine Guanxinjing Capsule Based on Fingerprint and Multicomponent Simultaneous Quantitative Method.

A novel dual-wavelength ultrahigh performance liquid chromatography (UHPLC) fingerprint was established, 56 common peaks were confirmed and attributed to the source of the medicinal materials, and 13 chromatographic peaks of them were identified by UHPLC quadrupole time-of-flight (Q-TOF)-MS/MS and UHPLC-UV method. Furthermore, a simple and sensitive HPLC-quadrupole trap (Q-TRAP)-MS/MS was developed for the simultaneous determination of 16 active components with electrospray ionization (ESI) source switching between positive and negative modes in a single run. The above two methods were successfully applied for the quality evaluation of Guanxinjing capsule (GXJC). Finally, statistical analysis including principal component analysis (PCA), hierarchical cluster analysis (HCA), and orthogonal partial least squares discriminate analysis (OPLS-DA) were applied, and 13 variables with great contribution to different groups of samples were screened. This study not only provides a rapid, accurate, and comprehensive qualitative and quantitative method for quality evaluation of GXJC but it also provides a reference workflow for the other traditional Chinese medicines.

Geotechnical characterization of Geomaterial: case study aptian limestones of El Malabiod region Tebessa, NE Algeria

Prediction of sedimentary limestone's compressive strength (Rc) constitutes a new approach in the construction fields. However, there is evidence that current conventional testing programs could lead to erroneous results. Several approaches to estimate Rc have been proposed, yet no universally accepted method has been suggested. In this study, 18 limestone samples taken from the El Malabiod quarry in Tebessa (Algeria) were subjected to various laboratory tests. A statistical model capable of indirectly estimating Rc is provided. The model is based on a correlation of Rc with specific weight (SP), carbonate content (CaCO3), and compressive strength by Franklin (Rcf), and was checked by statistical method to examine multicollinearity. The proposed equation demonstrates a strong correlation coefficient (R2) of 0.98 compared to direct laboratory results. Excellent estimation capacity was obtained using this method compared to previous models developed. The equation proposed in this paper provides an alternative and rapid evaluation method for design engineers to estimate compressive strength in practice.

A Rapid Evaluation Method of Permafrost Bearing Capacity in the Tibetan Plateau Region Based on the Correlation Between CPT-CBR

Permafrost, as a soil type under unique environmental conditions, has bearing characteristics that are highly susceptible to thermo-hydraulic environments. Rapid evaluation of the bearing characteristics of in situ permafrost at various depths in perennially frozen regions is a critical scientific issue urgently needing resolution in the road design and construction sectors in China. To address this, this study investigates the correlation between CPT parameters and laboratory mechanical indices under the combined effects of multiple factors in frozen sandy soils. By conducting both CPT and CBR tests on frozen sandy soils, the study analyzes the trends in changes in cone tip resistance (qc) and CBR values under the influence of temperature (T), moisture content (ω), and compaction degree (K) and establishes a functional relationship between them. Based on the standard requirements for indoor CBR, an evaluation criterion using qc for assessing the bearing capacity of frozen sandy soil is proposed. The results indicate that both CBR values and qc increase initially and then stabilize as K increases. With decreasing temperature, both indices stabilize after an initial increase, with turning points at −3.1 °C for CBR values and −2.5 °C for qc. As ω increases, both indices first increase and then stabilize, with a turning point at 40%ω. There is a robust linear relationship between the CBR values and qc, with the ratio of predicted CBR values to actual values showing a histogram and log-normal distribution accounting for 81% and 51.3%, respectively, within a 20% accuracy level, indicating good predictive performance. Referring to the highway subgrade specifications for indoor CBR, a standard for evaluating the bearing capacity of frozen sandy soils using CPT technology is proposed. This study provides new insights for geological surveys in perennially frozen regions and a theoretical basis for the application of CPT technology in evaluating the bearing capacity of permafrost.

Quantitative analysis of performance-oriented design efficiency in early divergent parametric design of office building façade

Optimizing early-stage building design significantly impacts energy efficiency, carbon reduction, and indoor air quality. While parametric design coupled with Multi-Objective Optimization is a prevalent approach in energy-focused preliminary design, it's important to note that the emphasis has been on parameter adjustments rather than algorithm modifications. However, altering the design algorithm can have a more fundamental influence on early-stage design, yet this area remains relatively under-researched. To aid designers in producing better performance-oriented designs at an early stage, this study proposes a set of quantitative index and methods to swiftly evaluate and compare the performance potential of design algorithms. Office passive design case studies are utilized to assess the performance of two common divergent design algorithms. Through solution space sampling, we analyze their respective performance boundaries and distributions by conducting sampling analysis and performance optimization for each algorithm. Notably, energy consumption differences can reach up to 5.07 kWh/ (m2·a), while sUDI variations may amount to 25.2%, and these variances are contingent upon climate zones. Furthermore, the efficiency and convergence speed of these design algorithms during optimization processes were quantitatively compared. Results indicate that optimal energy consumption can vary by up to 2.13 kWh/ (m2·a), with a time cost ranging from three to four times for approximating an optimal solution. The key contribution lies in providing designers with a rapid evaluation methods for performance-oriented comparison for different early design algorithms.

Accurate acoustic classification research of visually similar monochrome porcelain fragments

AbstractExploring a non-destructive and rapid evaluation method for precious ancient ceramic relics is of significant importance. Currently, there are countless monochrome porcelain fragments awaiting measurement and categorization. Various instruments such as XRF, XRD, SEM, OM, and thermoluminescence dating have been extensively utilized by numerous researchers to study ancient ceramics. However, these techniques pose challenges in reliably identifying monochrome porcelain fragments from the same kiln with similar appearance, content, and microstructure due to their limitations. To address this issue, this study presents an acoustic measurement system that utilizes audible frequencies to non-destructively evaluate monochrome porcelain fragments. The proposed method enables the extraction of parameters related to time domain analysis (e.g., group delay), frequency domain analysis (e.g., resonance), and sound loss characteristics of these fragments. This non-destructive and efficient technology for detecting acoustic characteristics of monochrome porcelain fragments presented in this work clarifies the fundamental principles governing the interaction between sound waves and ancient ceramic fragments while providing a completely non-destructive and highly efficient method for classifying and restoring valuable solid cultural heritages like stone, jade, bronze etc. Moreover, this approach can also be applied for non-destructive testing of elastic modulus in advanced ceramic devices including detecting small cracks, deterioration effects due to aging as well as other defects.

A rapid evaluation method for pumpkin sensory quality based on near‐infrared spectroscopy

AbstractThe sensory quality (SQ) of pumpkin fruit is one of the most important quality indexes for consumers. In this study, a rapid pumpkin SQ evaluation method based on near‐infrared spectroscopy (NIRS) was introduced. The starch and moisture contents of pumpkins were first determined by NIRS, and then the SQ of pumpkin could be estimated indirectly by these components. Test was carried out on 97 samples of 34 pumpkin fruits. The pumpkin starch and water NIRS models based on variable dimension particle swarm optimization combined moving window (VDPSO‐CMW) and standard normal variate transformation (SNV) showed the best predictive performances, whose predictive determination coefficients (R2p) were 0.775 and 0.932, respectively. A multiple linear regression model (correlation coefficient = 0.788) was established based on the prediction results of the two models and the SQ values obtained from cooking experiments. The proposed method has the potential to provide effective support for pumpkin breeding and production.

Rapid screening and identification strategy of lactic acid bacteria and yeasts based on Ramanomes technology and its application in fermented food

There is an urgent need for quick, sensitive, thorough, and low-cost preliminary screening and rapid identification method for probiotic resource development. Here, we constructed a culture-free, accurate and sensitive “separation-enrichment-detection” rapid evaluation and identification method of probiotics in fermented food based on Ramanomes technology, and established a Ramanome reference of lactic acid bacteria and yeasts by AgNPs nanostructure array (AgNPs NSA) chips with uniform “hot spots” and high signal enhancement ability as SERS substrates. Systematic cluster analysis could clearly distinguish among different genera of lactic acid bacteria and yeast, and could indicate the affinity and difference between lactic acid bacteria of the same genus and yeast of the same genus. The recognition accuracy of convolutive neural networks was 100 %, and the recognition sensitivity was less than 10 CFU/mL. We constructed a probiotic isolation and enrichment method by velocity gradient centrifugation and density gradient differential centrifugation, and the average recoveries of lactobacilli and yeasts were greater than 98 % in the practical application, and the accuracy of the verified classification was 100 %. In conclusion, this study has established a preliminary screening strategy of “screening before cultivating” for lactic acid bacteria and yeasts, which breaks through the principle limitation of the current traditional paradigm of “cultivating before screening”. It can greatly improve the preliminary screening rate of probiotics in fermented foods, and provide a good technical support for the mining of probiotic resources from environmental or complex matrix samples.

Rapid and non-invasive estimation of total phenol content and species identification in dried wild edible bolete using FT-NIR spectroscopy

The market for dried wild edible mushrooms is characterized by quality discrepancies and species disorganization, which are a matter of concern. The feasibility of using fourier transform near-infrared (FT-NIR) spectroscopy with chemometrics to predict the total phenol content and identify species was investigated in dried bolete. For the determination of total phenolic content, FT-NIR spectral acquisition, and two-dimensional correlation spectroscopy (2DCOS) image acquisition, five common species of dried edible bolete (n = 144) were utilized. The results showed that partial least squares regression (PLSR) combined with Fourier transform near-infrared spectroscopy could predict the total phenol content of dried boletes, with the best prediction coefficient of determination (R2p) = 0.79. The partial least squares discriminant analysis (PLS-DA) model accurately identified Phlebopus portentosus, with Matthews correlation coefficient (MCC), Precision (PRC), Sensitivity (SEN), and Specificity (SPE) all being 1. The support vector machines (SVM) model is performed optimally to identify processing edible bolete (Lanmaoa asiatica) with an accuracy of 100 % in the test set. 2DCOS images combined with the residual convolutional neural networks (ResNet) model demonstrated the feasibility of FT-NIR full spectral bands (10,000–4,000 cm−1) and characteristic spectral bands (6,500–4,000 cm−1) for species identification of boletes. The method applies to the case of consistent or inconsistent sample sizes between groups, with an accuracy of 1.00 for both the training and test sets. The study serves as a rapid, non-invasive, and convenient method for real-time evaluation of the quality of dried edible bolete in the market.

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

Investigation of correlations between powder functionalities and powder surface properties for milk protein ingredients

Milk protein powders are widely used ingredient in processed food products, meanwhile, characterization of powder functionalities is of technical challenge to the manufacturers of ingredients and consumer foods. Rapid evaluation methods or indicators are needed to fill the mentioned technological gap. This study aimed to investigate correlations between a series of powder functionalities and powder surface properties, which may be characterized using a relative rapid method. Six types of milk protein powder with 2 different batches were used as samples in this study. The powder wettability was determined based on a modified International Dairy Federation method. Powder flowability was determined using the Warren Spring cohesion measured by rheometer. The dispersibility was indicated by the median particle size at a designated dispersing time point. Regarding the surface properties, the contact angle of powder samples was determined by the sessile drop method using both water and diiodomethane as reference liquids. Subsequently, surface free energy was computed through the Owens, Wendt, Rabel, and Kaelble model. Pearson correlation analysis revealed meaningful correlations between surface properties (e.g., surface free energy, cohesion work, and adhesion work) and powder functionality attributes (e.g., wettability and flowability). This study suggests that the wettability and flowability of milk protein powders can be indicated by powder surface energy-related parameter, such as work of cohesion.

Fast Green FCF Improves Depiction of Extracellular Matrix in Ex Vivo Fluorescence Confocal Microscopy.

Rapid microscopic analysis of tissue is an essential diagnostic tool in oncological surgery. The gold standard for intraoperative histological tissue evaluation is frozen sections. However, frozen sections are prone to a variety of artefacts and require skilled staff and specialized lab equipment. A potential method for rapid intraoperative tissue evaluation that does not require fixation, freezing, or sectioning of the tissue is ex vivo fluorescence confocal microscopy (FCM). The visualization of the structurally important extracellular matrix (ECM) in conventional ex vivo FCM lags behind the standards of conventional histology. The objective of this study was to find a stain that would improve the depiction of the ECM to resemble FFPE H&E sections as closely as possible. Eleven different tissue stains were tested on 122 tissue samples submitted to the Department of Pathology at the Medical University of Vienna. This study was conducted on the RS-G4 Upright (Caliber I.D. Rochester, NY, USA, distributed in Europe by MAVIG GmbH, Munich, Germany). Fast Green FCF (FGFCF) in combination with acridine orange as a nuclear stain improved the visibility of the structural details of the ECM. Morphological details in FCM were equivalent or even superior to frozen sections in most analyzed categories. The addition of FGFCF to the conventional staining protocol improves the assessment of the ECM and analysis of fibrosis. The rapid staining protocol is compatible with an application in intraoperative microscopy.

A rapid immersion measurement for the diffusion constant of chloride ions in the geopolymer concrete

Accurate measurement of chloride ion diffusion coefficient, without external interference, is essential for obtaining data that closely reflects practical conditions. The diffusion constants of chloride ions within fly ash-based geopolymer concrete (FGPC) with a one-dimension immersion method employed to record the time taken for diffusion to occur over a specific distance were investigated in this work. The results showed a clear relationship between the diffusion constant and the strength of FGPC. Within the same strength of FGPC, the diffusion distance (thickness) is proportional to the square root of diffusion time. The minimum diffusion constant is approximately 8.90 × 10−11 m2/s which is close to the result in an immersion test. The method employed in this study is demonstrated to be both feasible and applicable for assessing the diffusion constant of chloride ions in FGPC quickly. It promotes a rapid evaluation method to predict the diffusion process under practical conditions.

Comparison of Remote-Controlled Microscopy, Telecytology and In-Person Methods for Rapid On-Site Evaluation

Comparison of Remote-Controlled Microscopy, Telecytology and In-Person Methods for Rapid On-Site Evaluation

A Queuing Model Based Rapid Evaluation Method for Automotive Control Design

This paper evaluates the ergonomics performance of automotive driving systems through a new computational model, aiming to enhance vehicle control design more cost-effectively than traditional experimental human factors research in the automotive field. Parameters such as spatial coordinates and control dimensions were measured for different driver interaction controls (e.g., hazard light switches, steering wheel buttons) across three typical passenger vehicles. These parameters were integrated into the QN-MHP-U to simulate driver operational behaviors and predict task performance. A computational method was introduced to assess the ergonomic scores of automotive control designs based on the modeling results. The QN-MHP-U provides a systematic and universally applicable solution for evaluating and comparing vehicle control designs within automotive driving systems. This allows automotive designers to assess and improve vehicle control designs from an ergonomic perspective more efficiently in terms of time and economic costs.

Evaluation of the Strengthening Effects on Prestressed Carbon-Fiber-Reinforced-Polymer-Strengthened Steel Beam Bridges Using Macro-Strain Influence Lines

Effectively evaluating the effectiveness of bridge strengthening is a necessary means to ensure the normal operation of existing strengthened bridges, especially when evaluating the effectiveness of bridge strengthening without interrupting normal traffic. Based on a distributed long-gauge Fiber Bragg Grating (FBG) sensor, this paper derived the macro-strain influence line (MSIL) formula for a simply supported beam bridge under a moving vehicle load, studied the changes in the MSIL at the bottom of the beam under the vehicle load before and after the prestressed CFRP plate strengthening, and proposed a rapid evaluation method for the strengthening effect based on the amplitude of the MSIL as the evaluation index for the strengthening effect. Finally, the prestressed CFRP-strengthened steel beam was tested under the moving vehicle load. The theoretical analysis and the experimental results confirm that under the load of moving vehicles, the macro-strain–time history amplitude of the strengthened steel beams under different prestressed tensioning conditions is different. The amplitude of the macro-strain time history of the strengthened bridge is reduced compared to before strengthening, and the local strengthening effect of the bridge can be monitored by the amplitude change in a single sensor. The change in global stiffness can be evaluated by monitoring the MSIL obtained from multiple long-gauge strain sensors.

UV–vis spectroscopy as a rapid method for evaluation of total phenolic hydroxyl structures in lignin

Abstract Phenolic hydroxyl groups in lignin are crucial for understanding its structure, reactivity, and potential applications. Various methods have been developed for the determining phenolic groups in lignin. This study focuses on the comparison of a simple, cost-effective, and time-efficient UV–vis ionization difference technique with the highly accurate 31P NMR spectroscopy for analyzing lignin samples of different origins and isolation methods. The results were carefully evaluated, and the strengths and limitations of each method were discussed. Two eco-friendly UV–vis approaches were proposed for a rapid and comprehensive evaluation of the total phenolic-OH groups: one using a strong alkaline solution for analyzing common types of technical lignins, and another employing multipoint wavelength calculations, effective for analyzing softwood lignins regardless of the extraction method. Additionally, the research highlighted the importance of selecting appropriate model phenolic compounds to accurately assess the phenolic hydroxyl group content in lignins using the UV–vis method. Offering straightforward and rapid analysis, with results closely aligning with 31P NMR data, this method is a promising alternative for routine analysis.

Empirical analysis of the influence of vegetation photosynthetic productivity on negative air ions in forest ecosystems driven by solar radiation

Air pollution stands as the single largest environmental risk to human health globally, and negative air ions (NAIs) serve as essential indicators for measuring regional air quality. They play a crucial role in alleviating the pressure and risks posed by air pollution. NAIs are notably abundant in forests, where the photosynthesis of vegetation serves as a significant factor influencing their presence. However, a dearth of quantitative research exists regarding the relationship between vegetation photosynthesis and NAIs. Current literature only offers qualitative insights, suggesting that vegetation photosynthesis may foster NAIs production. The mechanisms through which the photosynthetic process influences NAIs and the potential contribution of forest vegetation to NAIs remain unclear, lacking quantitative investigation. In this study, Quercus variabilis, a typical tree species in the warm temperate zone, was chosen as the research subject. We examined the dynamic changes in NAIs and net ecosystem productivity (NEP) from 2019 to 2021. The results demonstrated a strong correlation between NAIs and NEP at the ecosystem scale during the primary vegetation growing season. NEP effectively tracked daily changes in NAIs and exhibited a good fit. Notably, the scatter plots of NAIs and NEP exhibited clear stratification under varying photosynthetically active radiation (PAR) intensities. The correlation between NAIs and NEP was significantly higher under high PAR conditions compared to low PAR conditions, with R2 values of 0.458 and 0.367, respectively. Consequently, NEP emerges as a superior indicator for tracking NAIs variation characteristics at the ecosystem scale compared to PAR. This finding offers a rapid and accurate evaluation method for discerning the spatial and temporal distribution patterns of NAIs within forest ecosystems. Such insights provide a theoretical foundation for further assessing the potential contribution of forest vegetation to NAIs.

Affinity-Resolved Size Exclusion Chromatography Coupled to Mass Spectrometry: A Novel Tool to Study the Attribute-and-Function Relationship in Therapeutic Monoclonal Antibodies.

Assessment of critical quality attributes (CQAs) is an important aspect during the development of therapeutic monoclonal antibodies (mAbs). Attributes that affect either the target binding or Fc receptor engagement may have direct impacts on the drug safety and efficacy and thus are considered as CQAs. Native size exclusion chromatography (SEC)-based competitive binding assay has recently been reported and demonstrated significant benefits compared to conventional approaches for CQA identification, owing to its faster turn-around and higher multiplexity. Expanding on the similar concept, we report the development of a novel affinity-resolved size exclusion chromatography-mass spectrometry (AR-SEC-MS) method for rapid CQA evaluation in therapeutic mAbs. This method features wide applicability, fast turn-around, high multiplexity, and easy implementation. Using the well-studied Fc gamma receptor III-A (FcγRIIIa) and Fc interaction as a model system, the effectiveness of this method in studying the attribute-and-function relationship was demonstrated. Further, two case studies were detailed to showcase the application of this method in assessing CQAs related to antibody target binding, which included unusual N-linked glycosylation in a bispecific antibody and Met oxidation in a monospecific antibody, both occurring within the complementarity-determining regions (CDRs).

Impact of Mid-to-Low-Ash, Low-Viscosity Lubricants on Aftertreatment Systems after 210,000-Kilometer Real-World Road Endurance Trials

Engine lubricants globally face the challenge of meeting the demands of new engine technologies while enhancing energy efficiency and reducing emissions. Lubricants must enhance their performance and sustainability, improve reliability in complex and harsh environments, and minimize environmental impact and health risks. This study explores the influence of two different formulations of low viscosity lubricants, tested through actual road endurance trials, on a hybrid vehicle’s aftertreatment system performance and overall emission levels. The study includes 120,000 km of endurance testing in four different challenging environments in China, as well as 90,000 km of endurance testing in a typical urban and highway driving cycle in a large city. Results indicate that emissions from the test vehicles during the 120,000 km and 210,000 km durable Worldwide harmonized Light vehicle Test Cycles (WLTCs) meet China’s Stage 6 light-duty vehicle emission standards, with the 210,000 km Real Driving Emission test (RDE) results also conforming to these standards. Relative to fresh TWC, the light-off temperature increased by a mere 60 °C, and the oxygen storage capacity declined by around 19% following endurance testing. Additionally, the GPF exhibited satisfactory performance after 210,000 km of endurance testing, showing lower backpressure values compared to the fresh-coated samples, with no notable ash buildup observed in the substrate. Drawing on the outcomes of actual road endurance testing, this study illustrates that employing low-to-mid-ash-content, low-viscosity lubricants is both compatible and reliable for aftertreatment systems in present or advanced hybrid technologies. Premium lubricants facilitate vehicles in sustaining compliant and stable emission performance, even amid harsh environments and complex operating conditions. Furthermore, the tested lubricants effectively inhibit excessive aging of the aftertreatment system over prolonged mileage. Moreover, this study discusses the feasibility of rapid aging evaluation methods for aftertreatment systems based on engine test benches, juxtaposed with actual road endurance testing. These findings and conclusions offer crucial references and guidance for enhancing lubricant performance and sustainability. Subsequent studies can delve deeper into the correlation between lubricant performance and environmental impact, alongside optimization strategies for lubricants across various vehicle models and usage scenarios.