Screening Mechanism Research Articles

DRAG: A Novel Method for Automatic Geological Boundary Recognition in Shale Strata Using Multi-Well Log Curves

Ascertaining the positions of geological boundaries serves as a cornerstone in the characterization of shale reservoirs. Existing methods heavily rely on labor-intensive manual well-to-well correlation, while automated techniques often suffer from limited efficiency and consistency due to their reliance on single well log data. To overcome these limitations, an innovative approach, termed DRAG, is introduced, which uses deep belief forest (DBF), principal component analysis (PCA), and an enhanced generative adversarial network (GAN) for automatic layering recognition in logging curves. The approach employed in this study involves the use of PCA for dimensionality reduction across multiple well log datasets, coupled with a sophisticated GAN to generate representative samples. The DBF algorithm is then applied for stratification, incorporating a confidence screening mechanism to improve computational efficiency. In order to improve both accuracy and stability, a coordinate system is introduced that adjusts for stratification variations among neighboring wells around the target well. Experimental comparisons demonstrate the superior performance of the proposed algorithm in reducing stratification fluctuations and improving precision.

A hybrid classification method via keywords screening and attention mechanisms in extreme short text

Short text classification has provoked a vast amount of attention and research in recent decades. However, most existing methods only focus on the short texts that contain dozens of words like Twitter and Microblog, while pay far less attention to the extreme short texts like news headline and search snippets. Meanwhile, contemporary short text classification methods that extend the features via external knowledge sources always introduce lots of useless concepts, which may be detrimental to classification performance. Moreover, unlike traditional short text classification methods, the classification results of extreme short texts are often determined by a few even one or two keywords. To address these problems, we propose a novel hybrid classification method via Keywords Screening and Attention Mechanisms in extreme short text, called KSAM. More specifically, firstly, the attention-based BiLSTM is introduced in our method to enhance the role of keywords. Secondly, we screen the keywords in the extreme short text for obtaining the true class label, and the concepts concerning the keywords are retrieved from external open knowledge sources like DBpedia. Thirdly, the attention mechanisms are introduced to acquire the weight of these retrieved concepts. Finally, conceptual information is utilized to assist the classification of the extreme short text. Extensive experiments have demonstrated the effectiveness of our method compared to other state-of-the-art methods.

Screening, probiotic properties, and inhibition mechanism of a Lactobacillus antagonistic to Listeria monocytogenes

Listeria monocytogenes is one of the most lethal foodborne pathogens, and there is a lack of microorganisms that can strongly inhibit its growth. Safe lactic acid bacteria with probiotic and antibacterial properties are ideal sources of antagonistic bacteria. This study isolated a strain of Lactobacillus plantarum 4–10 that completely killed L. monocytogenes from northeastern Chinese sauerkraut. Probiotic characterization revealed broad-spectrum bacterial inhibition, antagonizing 16 Gram-positive, Gram-negative, and fungal species. After tolerance to simulated intestinal and gastric fluids, the survival rate was >45 %. L. plantarum 4–10 was sensitive to chloramphenicol, doxycycline, erythromycin, and tetracycline, and exhibited good hydrophobicity, auto-aggregation, and co-aggregation. It could disrupt the cell structure when co-cultured with L. monocytogenes and act as a lethal agent within 15 h. Through transcriptomic analysis and validation experiments, we found that L. plantarum 4–10 could inhibit the expression of L. monocytogenes membrane transport-related genes by producing bacteriocins, thus disrupting the cell membrane structure and inhibiting the growth, metabolic viability, and biofilm formation of L. monocytogenes in a short time. In conclusion, L. plantarum 4–10 has good probiotic properties and antibacterial effects and shows excellent research and application prospects as a natural bacteriostat.

Interval time series forecasting: A systematic literature review

AbstractInterval time series forecasting can be used for forecasting special symbolic data comprising lower and upper bounds and plays an important role in handling the complexity, instability, and uncertainty of observed objects. The purpose of this research is to identify the most widely used definition of interval time series; classify existing research into mature research, current research focus, and research gaps within the defined framework; and recommend future directions for interval forecasting research. To achieve this goal, we have conducted a systematic literature review, comprising search strategy planning, screening mechanism determination, document analysis, and report generation. During the search strategy planning stage, eight literature search libraries are selected to obtain the most extensive studies (total of 525 targets). In the screening‐mechanism determination stage, through the inclusion and exclusion mechanism, the literature that is repetitive, of low‐relevance, and from other fields are discarded, and 125 studies are finally selected. In the document analysis stage, tag‐based methods and classification grids are selected to analyze the shortlisted studies. The results show that there are still numerous research gaps in interval time series forecasting, such as the establishment of hybrid models, application of multisource information, development and application of evaluation techniques, and expansion of application scenarios. In the report‐generation stage, the problems that have been solved and encountered in interval forecasting are summarized, and future research directions are proposed. Finally, the most significant contribution of this research is to provide an overview of interval time series forecasting for easy reference by researchers and to facilitate further research.

Pre-emptive depoliticisation: the European Commission and the EU foreign investment screening regulation

ABSTRACT In March 2019 the European Union adopted the first pan-European investment screening framework, thus joining the bandwagon of growing investment screening mechanisms around the world. This article explores why the European screening framework was adopted swiftly and with no politicisation, even as the negotiations unfolded against the background of heightened politicisation of trade and investment policy. The paper develops three expectations: (1) converging member state preferences; (2) securitisation of investment policy; and (3) Commission entrepreneurship. It then explores them empirically through process-tracing, drawing on extensive interviews with the actors involved. We argue that the European Commission played a pivotal role. To avoid falling into another strand of politicisation and to defuse political mines, the Commission engaged in a ‘pre-emptive depoliticization’ strategy that shortened the policy process, limited the number of actors involved, and justified the policy options in a legalistic framing.

Layer-stacked network hybrid aerogels for the adsorption of U(VI): Concentration screening, efficient recovery, and cycle mechanism

Uranium contamination is a key issue in the sustainable development of nuclear energy. In this study, a cellulose/sericite hybrid aerogel with a layer-stacked network structure (MCC/AS-P) was prepared for uranium-contaminated wastewater treatment. Systematic characterization and multiple-batch static adsorption experiments were conducted to analyze the aerogel’s preparation, adsorption, and desorption. The kinetics demonstrated a noticeable transition between mass transfer diffusion control and mass transfer control, approaching adsorption equilibrium within 8 min and 180 min, respectively, wherein polymer layers led to a more stable adsorption process. Adsorption isotherm and thermodynamic studies established that the theoretical adsorption capacity of MCC/AS-P for U(VI) at T = 298 K could reach 374.5 mg·g−1. The adsorption behavior was endothermic and spontaneous, and the DFT calculations demonstrated that the adsorption energy of MCC/AS for UO22+ was − 506.5 kcal/mol. Temperature, U(VI) concentration, and desorption can all lead to a transition of the dominant mechanism between chemisorption and physisorption. After six swelling–deswelling adsorption cycles, the adsorption efficiency remained above 80%, and the structure remained intact. Furthermore, the excellent performance in terms of interference resistance and chemical stability offers potential for practical application.

An in vitro model of meningioma constructed by 3D coaxial bioprinting

Meningioma, a type of brain tumor, has a high incidence rate and requires a comprehensive treatment approach due to its invasive nature and varying malignancy. In vitro models for studying the molecular mechanisms of malignant meningiomas and drug screening are urgently needed. However, two-dimensional (2D) culture of meningioma cells has limitations and challenges. Three-dimensional (3D) printing technology can provide a more realistic in vitro research platform for tumor research. In this study, 3D coaxial bioprinting was used to fabricate an in vitro 3D model of meningioma that faithfully recapitulates the biological characteristics and microenvironment of this malignancy. The bioprinted construct features a fibrous core-shell structure that allows cell clustering and fusion into cell fibers, ultimately forming a complex 3D structure resembling meningioma. Our findings suggest that the 3D model of meningioma generated by coaxial bioprinting closely resembles the in vivo morphology and growth pattern. Compared with 2D culture, 3D culture conditions better simulated the tumor microenvironment and exhibited higher invasiveness and tumorigenicity. Comparative analysis of biomarker expression further demonstrated that 3D culture provides a more accurate reflection of the biological characteristics of tumors. Our research affirms that microtissue models produced by 3D coaxial bioprinting can replicate the in vivo environment of cancer cells, producing survival conditions that are nearly realistic and more conducive to the restoration of cancer cell traits in vitro. 

The Diagnostic Accuracy of Artificial Intelligence in Radiological Markers of Normal-Pressure Hydrocephalus (NPH) on Non-Contrast CT Scans of the Brain.

Diagnosing normal-pressure hydrocephalus (NPH) via non-contrast computed tomography (CT) brain scans is presently a formidable task due to the lack of universally agreed-upon standards for radiographic parameter measurement. A variety of radiological parameters, such as Evans' index, narrow sulci at high parietal convexity, Sylvian fissures' dilation, focally enlarged sulci, and more, are currently measured by radiologists. This study aimed to enhance NPH diagnosis by comparing the accuracy, sensitivity, specificity, and predictive values of radiological parameters, as evaluated by radiologists and AI methods, utilizing cerebrospinal fluid volumetry. Results revealed a sensitivity of 77.14% for radiologists and 99.05% for AI, with specificities of 98.21% and 57.14%, respectively, in diagnosing NPH. Radiologists demonstrated NPV, PPV, and an accuracy of 82.09%, 97.59%, and 88.02%, while AI reported 98.46%, 68.42%, and 77.42%, respectively. ROC curves exhibited an area under the curve of 0.954 for radiologists and 0.784 for AI, signifying the diagnostic index for NPH. In conclusion, although radiologists exhibited superior sensitivity, specificity, and accuracy in diagnosing NPH, AI served as an effective initial screening mechanism for potential NPH cases, potentially easing the radiologists' burden. Given the ongoing AI advancements, it is plausible that AI could eventually match or exceed radiologists' diagnostic prowess in identifying hydrocephalus.

Dilaton-induced open quantum dynamics

In modern cosmology, scalar fields with screening mechanisms are often used as explanations for phenomena like dark energy or dark matter. Amongst a zoo of models, the environment dependent dilaton, screened by the Polyakov–Damour mechanism, is one of the least constrained ones. Using recently developed path integral tools for directly computing reduced density matrices, we study the open quantum dynamics of a probe, modelled by another real scalar field, induced by interactions with an environment comprising fluctuations of a dilaton. As the leading effect, we extract a correction to the probe’s unitary evolution, which can be observed as a frequency shift. Assuming the scalar probe to roughly approximate a cold atom in matter wave interferometry, we show that comparing the predicted frequency shifts in two experimentally distinct setups has the potential to exclude large parts of the dilaton parameter space.

The impact of social media on guideline-concordant cervical cancer–screening: insights from a national survey

ObjectivesCervical cancer is one of the leading causes of cancer mortality in women, yet routine screenings lead to early detection and sometimes even prevention. Screening is an effective way to prevent cervical cancer, and it has been implemented in many countries and regions worldwide, especially in developed countries. However, the incidence of cervical cancer remains a public health problem due to screening disparities in the population. Social media engagement and overloading of online health information may be the cause of this disparity. Study designCross-sectional study. MethodsData from the Health Information National Trends Survey (a national survey conducted by the National Cancer Institute) was used to characterise cervical cancer screening into two dimensions; namely, high-frequency screening and guideline-concordant screening. The differences between these two screening frequency behaviours were compared by applying ordered logistic regression and binary logistic regression, and the mechanisms of guideline-concordant screening were explored. ResultsThe factors influencing high-frequency screening and guideline-concordant screening were different. Only self-efficacy (odds ratio [OR] = 1.16; 95% confidence interval [CI] = 0.98, 1.37) had a significant positive association with the high-frequency screening behaviour. Social media engagement (OR = 0.57; 95% CI = 0.33, 0.96) was shown to have a significant negative impact on guideline-concordant screening. A theory-based mechanism of screening behaviour found that traditional health perception factors no longer influence guideline-concordant screening behaviour, whereas environmental factors (e.g., social media) significantly reduce guideline-concordant screening behaviour. ConclusionsThe results from this study indicate that while the internet has become the main channel through which women acquire health resources, and social media has become a main platform for people to obtain health information, online information cannot guide people to engage in appropriate healthy behaviours. Overloading of online health information and the digital divide may lead to excessive screening. Consequently, it is important to address the screening disparity caused by health behaviours as a result of environmental factors and the digital divide.

A habituation memristor model for lung cancer screening application

—Lung cancer screening is critical to the diagnosis and treatment of patients. Today, computed tomography (CT) scanning technology provides a promising approach for the screening of lung cancer. Nevertheless, the redundant information in CT images often limits the efficiency and accuracy of screening. The biological sensory nervous system has an important mechanism for screening out redundant information, namely habituation. Here, we designed a second-order memristor model with habituation characteristics. Some of the habituation behavior of the memristor model has been demonstrated with LTspice simulation. Furthermore, the habituation memristor model is incorporated in a volatile memristor based leaky integrate and fire (LIF) neuron circuit to construct a simple neural system. The simulation results indicate that the neural system exhibits reliable habituation behaviors. Finally, lung cancer screening tasks have been implemented based on the neural system with habituation behavior. The habituation memristor circuit serves as a data preprocessing layer that filters out relevant information from lung cancer images. The results indicate that the performance and accuracy of lung cancer screening performance are noticeably better than the neural system without habituation behavior. This work provides a new idea for lung cancer screening implementation.

Thinking Past Naivete: Investment Screening by the EU as a Problem of (Mis)trust in International Relations

The EU’s investment screening mechanism (ISM) has been a prime example of the unilateral turn in EU trade and investment policy and of a broader global trend of a rise in ISMs. Being a champion of market liberalization and open investment regimes, however, for the EU to introduce a regulatory barrier to cross-border capital flows is particularly remarkable. This contribution argues that conceptually, ISMs may be understood as exposing a problem of trust in international relations (IR) and that a greatly consequential, if not decisive, underlying factor affecting both the introduction and usage of the EU ISM has been the level of trust between the EU and the People’s Republic of China. The introduction of the EU ISM is a symptom of mistrust in EU-China relations in its legislative form, and a test of trust in its individual decisions at Member State (MS) level. Discussing the above, the article aims to fulfil two objectives. First, it demonstrates how the concept of trust can illustrate the genesis and later the functioning of the EU ISM. Secondly, it explores what legal tools may be adopted to either rebuild trust or to render trust inessential by curtailing the security vulnerabilities which require it. EU External Affairs, China, Foreign Investment, FDI, Investment Screening, National Security, Trust

Modeling and Testing Screening Mechanisms in the Laboratory and in Space

The non-linear dynamics of scalar fields coupled to matter and gravity can lead to remarkable density-dependent screening effects. In this short review, we present the main classes of screening mechanisms, and discuss their tests in laboratory and astrophysical systems. We particularly focused on reviewing numerical and technical aspects involved in modeling the non-linear dynamics of screening and on tests using laboratory experiments and astrophysical systems, such as stars, galaxies, and dark matter halos.

On validity of the quasi-static approximation in scalar-tensor theories

The discovery of cosmic acceleration motivated extensive studies of dynamical dark energy and modified gravity models. Of particular interest are the scalar-tensor theories, with a scalar field dark energy non-minimally coupled to matter. Cosmological constraints on these models often employ the quasi-static approximation (QSA), in which the dynamics of the scalar field perturbations is proportional to the perturbation in the matter density. Using the QSA simplifies the physical interpretation of the phenomenology of scalar-tensor theories, and results in substantial savings of computing time when deriving parameter constraints. Focusing on the symmetron model, which is a well-motivated scalar-tensor theory with a screening mechanism, we compare the exact solution of the linearly perturbed field equations to those obtained under the QSA and identify the range of the model parameters for which the QSA is valid. We find that the evolution of background scalar field is most important, namely, whether it is dominated by the Hubble friction or the scalar field potential. This helps us derive a criterion for the symmetron model, but same argument can be applied to other scalar-tensor theories of generalized Brans-Dicke type. We consider two scenarios, one where the scalar field is only coupled to dark matter and where it couples to all of the matter.

Development of a pumpless acoustofluidic device for rapid food pathogen detection

Mixing, homogenization, separation, and filtration are crucial processes in miniaturized analytical systems employed for in-vitro biological, environmental, and food analysis. However, in microfluidic systems achieving homogenization becomes more challenging due to the laminar flow conditions, which lack the turbulent flows typically used for mixing in traditional analytical systems. Here, we introduce an acoustofluidic platform that leverages an acoustic transducer to generate microvortex streaming, enabling effective homogenizing of food samples. To reduce reliance on external equipment, tubing, and pump, which is desirable for Point-of-Need testing, our pumpless platform employs a hydrophilic yarn capable of continuous wicking for sample perfusion. Following the homogenization process, the platform incorporates an array of micropillars for filtering out large particles from the samples. Additionally, the porous structure of the yarn provides a secondary screening mechanism. The resulting system is compact, and reliable, and was successfully applied to the detection of Escherichia coli (E. coli) in two different types of berries using quantitative polymerase chain reaction (qPCR). The platform demonstrated a detection limit of 5 CFU g−1, showcasing its effectiveness in rapid and sensitive pathogen detection.

Intelligent Path Planning of Underwater Robot Based on Reinforcement Learning

As one of the commonly used vehicles for underwater detection, underwater robots are facing a series of problems. The real underwater environment is large-scale, complex, real-time and dynamic, and many unknown obstacles may exist in the underwater environment. Under such complex conditions and lack of prior knowledge, the existing path planning methods are difficult to plan, therefore they cannot effectively meet the actual demands. In response to these problems, a three-dimensional marine environment including multiple obstacles is established with the real ocean current data in this paper, which is consistent with the actual application scenarios. Then, we propose an N-step Priority Double DQN (NPDDQN) path planning algorithm, which potently realizes obstacle avoidance in the complex environment. In addition, this study proposes an experience screening mechanism, which screens the explored positive experience and improves its reuse rate, thus efficiently improving the algorithm stability in the dynamic environment. This paper verifies the better performance of reinforcement learning compared with a variety of traditional methods in three-dimensional underwater path planning. Underwater robots based on the proposed method have good autonomy and stability, which provides a new method for path planning of underwater robots. <i>Note to Practitioners</i>&#x2014;The goal of this study is to provide a new solution for obstacle avoidance in path planning of underwater robots, which is consistent with the dynamic and real-time demands of the real environment. Existing underwater path planning researches lack a consistent environment with the actual application, and therefore we firstly construct a three-dimensional ocean environment with real ocean current data to provide support for the algorithms. Additionally, most of the algorithms are pre-planning methods or require long-time calculation, and there is little research on obstacle avoidance. In the face of obstacle changes, underwater robots with poor adaptability will cause performance decline and even economic losses. The proposed algorithm learns through interaction with the environment, and therefore it does not require any prior experience, and has good adaptability as well as fast inference speed. Especially, in the dynamic environment, algorithm performance is difficult to guarantee due to less positive experience in exploration. The proposed experience screening mechanism improves the stability of the algorithm, so that the underwater robot maintains stable performance in different dynamic environments.

Challenges and prospects of human resource management under the background of big data

With the development of information technology and Internet computing, big data has led individuals and enterprises to step into the era of automation in many ways. Big data technology usually starts with the classification and large-scale uptake of resource information, and then integrates, stores and utilizes the information displayed in the data after certain screening processing. The value of data sets is diverse, so it often requires more scientific and systematic classification analysis and computational processing design than traditional classification to make the resource set play an ideal role. Big data has many characteristics, the core of which is extensive information sources, rich classification, high gold content, fast and efficient analysis and processing process, and can simultaneously take into account a wide range of coverage and fine direction.&#x0D; No matter in the enterprise or the market, it is generally believed that human resources are the primary resources to help economic progress. Human resource management includes a large amount of personal information of enterprises and employees. On this basis, big data can also help enterprises to better develop human resource modules by virtue of its excellent information collection and processing ability. In order to transfer from the traditional human resources management and control means to a more efficient way, it is necessary to achieve reasonable evaluation while taking into account the breadth and depth of information sources, introduce and further cultivate high-end employees through screening mechanism, and down-to-earth analysis of all aspects of the integration of big data and human resources management. This paper analyzes the existing problems, application advantages, challenges and innovations of the connection between human resource management and big data, and summarizes the various foundations that human resource management should have under the background of big data technology.

Identification, Screening and Antibacterial Mechanism Analysis of Novel Antimicrobial Peptides from Sturgeon (Acipenser ruthenus) Spermary.

Fish is an important source of antimicrobial peptides. This study aimed to identify and screen antibacterial peptides with excellent antibacterial activity derived from sturgeon spermary peptides (SSPs) and to analyze their antibacterial activity and mechanism. Liquid chromatography-mass spectrometry/mass spectrometry methods were used to analyze and identify peptide sequences, computational prediction tool and molecular docking methods were used for virtual screening of antimicrobial peptides, and finally, candidate peptides were synthesized by solid-phase synthesis method. The results demonstrate that SSPs have excellent inhibitory activity against Escherichia coli with an inhibitory rate of 76.46%. Most parts of the SSPs were derived from the sturgeon (Acipenser ruthenus) histones, and the coverage of histone H2B was the highest (45%). Two novel peptides (NDEELNKLM and RSSKRRQ) were obtained by in silico prediction tools and molecular docking, which may interact with the DNA gyrase and dihydrofolate reductase of E. coli by forming salt bridges and hydrogen bonds. Compared to the individual peptides, the antibacterial effect was significantly improved by mixing the two peptides in equal proportions. Two novel peptides change the permeability of the E. coli cell membranes and may exert antimicrobial activity by inhibiting the metabolic process of the nucleic acids.

PbTiO3 Based Single-Domain Ferroelectric Photocatalysts for Water Splitting

ConspectusSolar-driven photocatalytic water splitting paves a way to produce green hydrogen for building a sustainable clean energy system, particularly within the framework of the carbon-neutral initiative. However, to date, the solar-to-hydrogen (STH) conversion efficiency of particulate photocatalysts falls far short of the demand of over 10% for industrial applications. Single-domain ferroelectric semiconductor materials with amazing unidirectional spontaneous polarization penetrating the bulk are promising candidates as photocatalysts for water splitting. Their existing inherent internal field set up by polarization can cause both two oppositely charged surfaces (namely, polar surfaces) and spatial separation of photogenerated charge carriers between them upon light excitation. These unique properties provide sufficient new room for flexibly engineering of bulk and surface/interface structures to enhance photocatalytic water splitting.In this Account, we offer a systematic overview of the exploration of PbTiO3 based single-domain ferroelectric photocatalysts for efficient solar water splitting. In detail, the controlled growth, surface modification, heterostructures, and Z-scheme system of single-domain PbTiO3 ferroelectrics are discussed by considering polar surfaces, spatial separation of the photoexcited charges, selective distribution of defects, and selective adsorption of reactive species. We start with the controlled synthesis of single-domain ferroelectric PbTiO3 nanoplates to realize the opposite directional transport of photoexcited charges driven by the polarized internal electric field, which can be verified by the fact that photochemical reduction/oxidation deposits occur on opposite polar surfaces. Then, we move to the selective modification of the specific polar surface to promote the transfer of photoexcited charges by applying the spatial separation effect of photoexcited charges and defects related internal screening mechanism. Furthermore, asymmetric heterostructures and Z-scheme systems are rationally designed by selective adsorption of reactive species on charged surfaces to promote charge separation and suppress redox mediator side reactions, respectively. Although significant advances have been achieved, many efforts remain needed toward the industrial production target. Based on single-domain ferroelectric photocatalysts, we present the following perspectives on the utilization of a wide solar spectrum for improving solar energy conversion efficiency. First, proper dopants can be incorporated homogeneously in the bulk to reduce the bandgap without obviously weakening ferroelectricity. Second, the epitaxial growth of narrow bandgap semiconductors on the ferroelectrics is feasible to induce additional polarization in the region close to their interface. These two aspects are anticipated to enable strong visible light absorption and charge separation efficiency. Third but not least, the assembly of Z-scheme systems with two narrow bandgap ferroelectrics is desirable to mimic natural photosynthetic systems with quantum yield approaching one unit.

Virtual screening, identification, and potential antioxidant mechanism of novel bioactive peptides during aging by a short-chain peptidomics, quantitative structure–activity relationship analysis, and molecular docking

Antioxidant peptides have received a great deal of attention. However, only a few studies have been conducted on the antioxidant peptides originating from Baijiu. A total of 1490 features deemed potential short-chain peptides (the amino acid number between 2 and 4, SCPs) were screened and analyzed by a customized short-chain peptidomics approach in Feng-flavor Baijiu (FFB) during 14 years of aging, with an obvious discrepancy between FFB aged for 3 years and 6 years being observed. Thirty-nine characteristic SCPs in total were identified and accurately quantified by high-throughput parallel reaction monitoring-based synthetic standards, with the contents ranging from 0.16 to 279.33 μg L−1. Combined with the absorption, distribution, metabolism, excretion, and toxicity analysis model, PGRW, WK, SC, and PAW, four novel antioxidant peptides with high ABTS radical scavenging capacity, were obtained using a customized quantitative structure–activity relationship (QSAR) model based on a two terminal position numbering method, with satisfied coefficients of determination (R2), internal cross-validated R2 (Q2), and external R2 (R2pre) of 0.925, 0.808, and 0.665, respectively. Furthermore, these 4 antioxidant peptides could block the Keap-Nrf2 interaction and promote the accumulation of Nrf2 by molecular docking analysis, and the interaction energy between peptide PGRW and Keap1 was higher than that between epigallocatechin gallate and Keap1 based on CHARMm forced field. Overall, this study facilitated the discovery of functional peptides in Baijiu and the understanding of aging mechanisms.