Deep Process Research Articles

Research Progress on Removal of Oxygenates in Fischer-Tropsch Oil by Adsorption

The oxygen-containing compounds in Fischer Tropsch synthetic oil greatly affect the downstream deep processing of hydrocarbons, and effective removal is required. Compared to traditional removal technologies such as hydrogenation deoxygenation, solvent extraction, and extraction distillation, adsorption deoxygenation technology has the advantages of low cost, mild operating conditions, easy removal and recovery, and minimal impact on oil quality. Therefore, adsorption deoxygenation technology has devel‐ oped rapidly in various removal processes and has become a research hotspot in the current Fischer Tropsch oil deoxygenation. Adsorbents are the core of adsorption deoxygenation technology. Therefore, this article briefly introduces the adsorption mechanism and summarizes the research progress of adsorbents widely used in recent years, such as silica gel, alumina, molecular sieves, and metal organic frameworks, in adsorbing oxygen-containing compounds in Fischer Tropsch synthetic oils. And provide reference suggestions for further adsorption and deoxygenation directions in the future.

AI Driven Applications for Precision Weed Management in Agricultural Crops

Agriculture plays a most important role in our Indian economy and therefore lowering the cost of production and improving the quality of agricultural products is highly demanded. A weed is a plant which grows in wrong place at the wrong time and doing mostly harm than crops. Weed competes with the crops for water, light, nutrients and space, and therefore it prevents crop yields. This paper proposes a new method in a contrary way, which combines deep learning and image processing technology to prevent these weeds. Machine learning technologies, are becoming crucial in agriculture to increase productivity, where advanced automation and control have been required. Based on large training datasets and pre-trained models, (Deep Learning) DL-based Convolutional Neural Networks (CNN) methods have proven to be more accurate than previous traditional techniques. Recently, Deep Learning (DL) has gained much attention due to its advantages in object detection, classification, and feature extraction. The system implementation of image processing technique for weed detection, a trained image is taken as a sample in order to demonstrate the difference between weed and the crop. Yolo frame work is used for annotate boundary boxes to the image with datasets. The effectiveness of the (You Only Live Once) YOLO-WEED system for real-time Unmanned Aerial Vehicle (UAV) weed detection, given its high speed and high accuracy in detection. After certain steps, we get desired output, where the weeds are separated from the crop that has been taken in the sample image. Key Words: DeepLearning,CNN, Unmanned Aerial Vehicle (UAV), Precision Weed Detection.

Quality detection and variety classification of pecan seeds using hyperspectral imaging technology combined with machine learning

Pecan (Carya illinoinensis K.), a well-known dried seed and woody oil tree, faces challenges in its industry due to complex quality assessment methods and confusing varieties. These challenges have seriously hampered the development of a large-scale pecan deep processing industry. This work aimed to apply hyperspectral imaging technology (HSI) combined with machine learning to evaluate the quality of pecan seeds and perform variety classification. The samples of this work were composed of 19 varieties of pecan seeds, with 30 seeds per variety. After spectral preprocessing, spectral features were extracted from the spectral profiles using feature extraction methods. Back-propagation neural network models and partial least squares models were established to predict the contents of crude fat and moisture in pecan seeds. Predictions of the best models gave good results with R2-score of 0.887 for the crude fat model and 0.950 for the moisture model. Additionally, support vector machine models were developed to identify pecan varieties. The model achieved good results in 19 pecan varieties identification with accuracy of 0.965. In conclusion, the combination of HSI and machine learning could be an effective tool in improving the pecan industry and providing sustainable and efficient methods in the production of pecan seeds.

Flexible and fine-grained simulation of speed in language processing

According to the embodied cognition theory, language comprehension is achieved through mental simulation. This account is supported by a number of studies reporting action simulations during language comprehension. However, which details of sensory-motor experience are included in these simulations is still controversial. Here, three experiments were carried out to examine the simulation of speed in action language comprehension. Experiment 1 adopted a lexical decision task and a semantic similarity judgment task on isolated fast and slow action verbs. It has been shown that fast action verbs were processed significantly faster than slow action verbs when deep semantic processing is required. Experiment 2 and Experiment 3 investigated the contextual influence on the simulation of speed, showing that the processing of verbs, either depicting fast actions or neutral actions, would be slowed down when embedded in the slow action sentences. These experiments together demonstrate that the fine-gained information, speed, is an important part of action representation and can be simulated but may not in an automatic way. Moreover, the speed simulation is flexible and can be modulated by the context.

Combining Artificial Intelligence and Simplified Image Processing for the Automatic Detection of Mycobacterium tuberculosis in Acid-fast Stain: A Cross-institute Training and Validation Study.

Tuberculosis (TB) poses a significant health threat in Taiwan, necessitating efficient detection methods. Traditional screening for acid-fast positive bacilli in acid-fast stain is time-consuming and prone to human error due to staining artifacts. To address this, we present an automated TB detection platform leveraging deep learning and image processing. Whole slide images from 2 hospitals were collected and processed on a high-performance system. The system utilizes an image processing technique to highlight red, rod-like regions and a modified EfficientNet model for binary classification of TB-positive regions. Our approach achieves a 97% accuracy in tile-based TB image classification, with minimal loss during the image processing step. By setting a 0.99 threshold, false positives are significantly reduced, resulting in a 94% detection rate when assisting pathologists, compared with 68% without artificial intelligence assistance. Notably, our system efficiently identifies artifacts and contaminants, addressing challenges in digital slide interpretation. Cross-hospital validation demonstrates the system's adaptability. The proposed artificial intelligence-assisted pipeline improves both detection rates and time efficiency, making it a promising tool for routine pathology work in TB detection.

Automated Pavement Condition Index Assessment with Deep Learning and Image Analysis: An End-to-End Approach.

The degradation of road pavements due to environmental factors is a pressing issue in infrastructure maintenance, necessitating precise identification of pavement distresses. The pavement condition index (PCI) serves as a critical metric for evaluating pavement conditions, essential for effective budget allocation and performance tracking. Traditional manual PCI assessment methods are limited by labor intensity, subjectivity, and susceptibility to human error. Addressing these challenges, this paper presents a novel, end-to-end automated method for PCI calculation, integrating deep learning and image processing technologies. The first stage employs a deep learning algorithm for accurate detection of pavement cracks, followed by the application of a segmentation-based skeleton algorithm in image processing to estimate crack width precisely. This integrated approach enhances the assessment process, providing a more comprehensive evaluation of pavement integrity. The validation results demonstrate a 95% accuracy in crack detection and 90% accuracy in crack width estimation. Leveraging these results, the automated PCI rating is achieved, aligned with standards, showcasing significant improvements in the efficiency and reliability of PCI evaluations. This method offers advancements in pavement maintenance strategies and potential applications in broader road infrastructure management.

Monkeypox Detection using CSA Based K-Means Clustering with Swin Transformer Model

Despite the global COVID-19 pandemic, public health professionals are also concerned about a possible new monkeypox epidemic. Similar to vaccinia, cowpox, and variola, the orthopoxvirus that causes monkeypox has two strands that are double-stranded. Many people have propagated the current pandemic through sexual means, particularly those who identify as bisexual or gay. The speed with which monkeypox was detected is the most important element here. In order to catch monkeypox before it infects more people, machine learning could be a huge help in making a quick and accurate diagnosis. Finding a solution is the driving force behind this project, which aims to develop a model for detecting monkeypox using deep learning and image processing. For optimal cluster selection during photo segmentation, the Chameleon Swarm Algorithm (CSA) employs K-means clustering. Examining the accuracy with which the Swin Transformer model identified instances of monkeypox was the driving force for this study. The proposed techniques are evaluated on two datasets: Kaggle Monkeypox Skin Lesion Dataset (MSLD) besides the Monkeypox Skin Image Dataset (MSID). We assessed the outcomes of various deep learning models using sensitivity, specificity, and balanced accuracy. Positive results from the projected process raise the possibility of its widespread application in monkeypox detection. This ingenious and cheap method can be put to good use in economically deprived communities that may not have access to proper laboratory facilities.

Allanite U–Pb dating places new constraints on the high‐pressure to high‐temperature evolution of the deep Himalayan crust

AbstractDuring continental collision, crustal rocks are buried, deformed, transformed and exhumed. The rates, timescales and tectonic implications of these processes are constrained through the sequence and conditions of metamorphic reactions in major and accessory phases. Petrographic, isotopic and elemental data from metabasite samples in NW Bhutan, eastern Himalaya, suggest initial equilibration under high‐pressure (plagioclase‐absent and rutile‐present) conditions, followed by decompression to lower pressure conditions at high‐temperatures that stabilized plagioclase, orthopyroxene and ilmenite. Field observations and chemical indicators suggest equilibration under the lower pressure conditions is likely linked to the infiltration of melt from the host metasedimentary rocks. The metabasites preserve two metamorphic growth stages of chemically‐and petrographically distinct allanite that temporally overlap two stages of zircon growth. Allanite cores and zircon mantles grew at c. 19 ± 2 and 17–15.5 Ma respectively, linked texturally and chemically to the high‐pressure evolution. Symplectitic rims on embayed allanite cores, wholly symplectized Aln–Ilm and Aln–Cpx grains, and high U zircon rims grew at c. 15.5–14.5 Ma, linked chemically to the presence of melt and lower pressure, high‐temperature conditions. A single garnet Lu–Hf date is interpreted as geologically meaningless, with the bulk rock composition modified by melt infiltration after garnet formation. The open system evolution of these rocks precludes precise determination of the reactive bulk composition during metamorphic evolution and thus absolute conditions, especially during the early high‐pressure evolution. Despite these limitations, we show that combined geochemical and petrographic datasets are still able to provide insights into the rates and timescales of deep orogenic processes. The data suggest a younger and shallower evolution for the NW Bhutan metabasites compared to similar rocks in the central and eastern Himalayas.

Deep Learning and Natural Language Processing Technology Based Display and Analysis of Modern Artwork

The modern artwork analysis display system, empowered by natural language processing (NLP) technology, revolutionizes the way audiences interact with and understand art. By integrating NLP algorithms, this system offers a dynamic and user-friendly platform for analyzing and displaying artwork. Utilizing NLP, visitors can engage in interactive conversations with the system, asking questions or making inquiries about the artwork on display. The system processes these inquiries, extracting relevant information from curated databases and scholarly sources to provide insightful and context-rich responses. Additionally, NLP algorithms can analyze textual descriptions, artist statements, and critical reviews to offer nuanced interpretations and historical context for each artwork. This paper presents the design and implementation of an innovative modern artwork analysis and display system, leveraging deep learning and natural language processing (NLP) technology, integrated with Multi-Feature Extraction Fuzzy Classification (MFEFC). The system offers a comprehensive platform for analyzing and presenting modern artworks, enhancing user engagement and understanding. Deep learning algorithms are employed to extract high-level features from visual artworks, allowing for automatic recognition of artistic styles, genres, and themes. Concurrently, NLP techniques process textual descriptions, artist biographies, and critical reviews to provide contextual information and interpretative insights. The integration of MFEFC enables precise classification of artworks based on multiple features extracted from both visual and textual sources, facilitating accurate analysis and categorization. Simulation of the NLP techniques demonstrated an average precision of 90% in extracting relevant contextual information from textual descriptions and artist biographies. Furthermore, MFEFC achieved a classification accuracy of 88% in categorizing artworks based on combined visual and textual features.

Seismic structure of the 2015 Mw7.8 Gorkha earthquake revealed by ambient seismic noise and teleseismic surface wave tomography

The destructive 2015 Mw7.8 Gorkha earthquake occurred in the Main Himalayan Thrust due to the collision of the Indian and Asian plates, which provides a unique opportunity to understand the deep dynamic processes and seismogenic mechanisms of strong earthquakes. We construct a regional-scale shear-wave velocity model of the crust and uppermost mantle using ambient seismic noise and teleseismic surface wave at periods of 5–100 s around the Gorkha earthquake region. The new shear-wave velocity model exhibits prominently lateral heterogeneities in the Gorkha earthquake areas. We observe a high-velocity (high-V) zone around the Gorkha main shock in the Main Himalayan Thrust, indicating the existence of a high-strength asperity that sustains the stress accumulating. The aftershocks are primarily located in the low-velocity (low-V) anomalies and enclosed by two high-V anomalies, which appear to act as structural barriers that influence the spread of the aftershocks. Prominent low-Vanomalies from the lower crust to the mantle lithosphere are observed along the north–south trending rifts, suggesting the hot materials upwelling due to the tearing of the northward subducting Indian lithosphere. These observations may indicate that seismic velocity heterogeneity could play an essential role in earthquake initiation and the rupture process.

Recent advances in petrochronology: from dates to ages and rates of deep orogenic processes

Recent advances in petrochronology: from dates to ages and rates of deep orogenic processes

Enhanced Driver Drowsiness Detection System using Deep Learning and Image Processing Techniques

Driver drowsiness remains a critical factor contributing to road accidents worldwide. To address this issue, we present an enhanced driver drowsiness detection system leveraging advanced deep learning and image processing techniques. Our research focuses on developing a robust and accurate model capable of detecting drowsiness indicators in real-time from driver facial images. We conducted a comprehensive review of existing literature on driver drowsiness detection systems and identified key challenges and opportunities in the field. Leveraging this knowledge, we propose a novel methodology that integrates convolutional neural networks (CNNs) for feature extraction and classification, coupled with sophisticated image processing algorithms for facial recognition and eye state analysis. We describe the experimental setup, data collection process, and model training procedures, followed by a detailed presentation of results and performance evaluation metrics. Our findings demonstrate significant improvements in drowsiness detection accuracy, with the proposed system achieving promising results in both laboratory and real-world driving scenarios. The implications of our research extend to the development of more effective driver assistance systems and the enhancement of road safety measures. We conclude by discussing future research directions and potential applications for advancing driver drowsiness detection technology. Keywords— Driver Drowsiness Detection, Deep Learning, Image Processing, Convolutional Neural Networks (CNNs), Facial Recognition, Road Safety, Real-Time Monitoring, Driver Assistance Systems, Accident Prevention

Truculent Post Analysis for Hindi Text

INTRODUCTION: With the rise of social media platforms, the prevalence of truculent posts has become a major concern. These posts, which exhibit anger, aggression, or rudeness, not only foster a hostile environment but also have the potential to stir up harm and violence.
 OBJECTIVES: It is essential to create efficient algorithms for detecting virulent posts so that they can recognise and delete such content from social media sites automatically. In order to improve accuracy and efficiency, this study evaluates the state-of-the-art in truculent post detection techniques and suggests a unique method that combines deep learning and natural language processing. The major goal of the proposed methodology is to successfully regulate hostile social media posts by keeping an eye on them.
 METHODS: In order to effectively identify the class labels and create a deep-learning method, we concentrated on comprehending the negation words, sarcasm, and irony using the LSTM model. We used multilingual BERT to produce precise word embedding and deliver semantic data. The phrases were also thoroughly tokenized, taking into consideration the Hindi language, thanks to the assistance of the Indic NLP library.
 RESULTS:  The F1 scores for the various classes are given in the "Proposed approach” as follows: 84.22 for non-hostile, 49.26 for hostile, 68.69 for hatred, 49.81 for fake, and 39.92 for offensive
 CONCLUSION: We focused on understanding the negation words, sarcasm and irony using the LSTM model, to classify the class labels accurately and build a deep-learning strategy.

Synthesis of MOF-5/polyethersulfone (PES) mixed matrix membranes for enhancing membrane filtration performance in polyphenol purification

The various apple products industries produce a large amount of apple residue, which is easily fermented, causes environmental pollution, and its disposal cost is high, but is rich in nutrients, such as polyphenols. Polyphenols can be purified to realize high-value deep processing of apple pomace and to promote energy reuse of food waste. In this study, the highly selective purification of polyphenols was achieved by membrane filtration using prepared Metal-organic framework (MOF)-5/PES mixed matrix membranes with apple peels as raw material. The polyethersulfone mixed matrix membrane was loaded with MOF-5 by the phase inversion method, and their structural and physicochemical properties were characterized by scanning electron microscopy (SEM), and X-ray diffraction (XRD). Zeta potential and specific surface area of MOF-5 particles were measured, as well as the water contact angle and anti-fouling properties of the mixed matrix membrane were analyzed. It was confirmed that the membrane loaded with MOF-5 showed better hydrophilicity and mechanical properties compared with the pristine polyether sulfone membrane. Under practical conditions, the increased hydrophilicity could enhance the anti-fouling properties of membranes, which would improve the flux recovery ratio of membranes. In addition, the prepared MOF-5/PES mixed matrix membrane was applied to the purification of polyphenols, showing excellent purification performance of polyphenols. In particular, the purity of polyphenol after membrane filtration could reach 70.45% when the additional amount of MOF-5 was 10%. This research provides a method to prepare MOF-5/PES mixed matrix membranes, which effectively solves the problem of unstable and unsatisfactory purification effect of commercially available membranes, promotes the development of new materials in membrane science, and realizes high-value deep processing and comprehensive resource development of food waste using membrane filtration.

Effects of Sheep Bone Peptide-Chelated Calcium on Calcium Absorption and Bone Deposition in Rats Fed a Low-Calcium Diet

Sheep bones, as the main byproduct after sheep slaughter, are rich in proteins, but their utilization in deep processing is relatively low. Currently, calcium deficiency is a common health issue that can lead to metabolic bone diseases. Peptide calcium chelate is considered a superior substance, having a significant effect on improving the absorption and utilization efficiency of calcium in the human body. This study used sheep bone byproducts as the raw material to prepare sheep bone peptide-chelated calcium (SBP-Ca) and its structure was characterized and analyzed in depth. SBP-Ca was supplemented in a low-calcium diet to assess its effects on calcium absorption and bone formation in a rat model experiencing calcium deficiency. The results showed that under optimal preparation conditions for SBP-Ca (peptide-calcium ratio set at 1 : 1, pH maintained at 7, temperature set to 45°C, and duration of 60 min), calcium could chelate with sheep bone peptide (SBP) at the sites C=O, -NH2, and -COOH sites, forming a structurally dense SBP-Ca with a chelation rate of up to 89.24%. In rats fed a low-calcium diet, SBP-Ca significantly improved the rat femoral diameter, dry weight, trabecular bone structure, bone density, and bone volume fraction (P<0.05). A decline in serum alkaline phosphatase levels (P<0.05), elevation in bone calcium content, and a rise in serum osteocalcin levels (P<0.05) were observed. Furthermore, morphological studies on bone tissue suggest that SBP-Ca has the ability to restore trabecular bone structure. In conclusion, SBP-Ca has been successfully prepared as a novel calcium absorption promoter. By improving the bioavailability of calcium, bone formation was facilitated, demonstrating a significant application value in the prevention of calcium deficiency disorders.

Research on Multi-Physical Field Characteristics of Deep Coal Seam Mining Based on the Rock-Coal-Rock Model

In order to disclose the multi-physical field characteristics of the deep coal seam mining process and their dynamic evolution legislation, based on the “rock-coal-rock” model, during the mining process, the stress field, displacement field, energy field, and plastic zone evolution process are all simulated using FLAC3D6.0. The findings show that stress in the original rock is redistributed as a result of coal seam mining, creating a pressure relief zone in the middle of the goaf and advanced support pressure in the front part of the working face. The roof falls following the termination of coal seam mining. The collapsed blocks fill the middle of the goaf, playing a supporting role. The floor bulges as a new supporting pressure zone forms and builds up high elasticity. The stress reduction zone shifts from a rectangular to an inner circular distribution and an outer square as the working face’s mining distance increases and the range of the fracture field expands accordingly. In addition, a complete model was constructed to verify the correctness of the “rock-coal-rock” model. The stress, displacement, and energy curves of the overlying strata at a distance of 12 m from the bottom of the coal seam in the middle of the goaf obtained by the two methods were basically consistent. Ultimately, the findings of the numerical simulation were compared with the advanced support pressure data that were acquired on-site and they were good. This work can provide a reference for the safe mining of deep coal seams.

Pressure-driven membranes for advanced treatment of textile wastewater: Dye/salt fractionation, concentration, and cleaning strategies

Textile wastewater commonly contains various organic compounds and salt mixtures, potentially raising serious environmental and health concerns. Traditional treatment approaches are hindered by high salinity; consequently, membrane separation technologies have attracted increasing attention over the past few decades. Herein, four commercial pressure-driven membranes with molecular weight cut-off (MWCO) gradients were employed for the advanced treatment of different types of textile wastewater. Specifically, these membranes were initially subjected to a fractionation test toward the dye/salt mixtures, followed by deep concentration of the dye molecules. Using Congo red (CR), methyl blue (MB), NaCl, and Na2SO4 as representative dyes and salts, the results indicated that all membranes could effectively separate dye/salt mixtures and further conduct a deep concentration process toward the separated dye solutions. However, their efficiency and effectiveness largely depend on the membrane pore size and solution characteristics. For instance, membranes with lower MWCOs experience less fouling and shorter operation durations when treating CR-related solutions. All membranes showed improved fractionation efficiency toward MB/salt mixtures, requiring only approximately 10.2–42.9 % of the operation duration compared to CR-related solutions. The loose nanofiltration membrane proved superior to tight ultrafiltration membranes in both fractionation and concentration processes. Further research on cleaning methodologies demonstrated that both alkali and ethanol cleaning were influential in restoring the membrane performance. An appropriate ethanol/water concentration can effectively regenerate severely fouled membranes, achieving a high flux recovery ratio of ∼95 %. This study evaluated state-of-the-art pressure-driven membranes for textile wastewater treatment and guided cleaning strategies for optimal performance recovery.

Optimization of the fermentation process and antioxidant activity of mixed lactic acid bacteria for honeysuckle beverage

The aim of the research was to obtain a high healthcare honeysuckle beverage with strong antioxidant activity. Honeysuckle (Lonicera japonica Thunb) was used as the raw material in this experiment. The effects of fermentation temperature, fermentation time, lactic acid bacteria inoculation amount, and sugar addition amount on the sensory quality of honeysuckle beverage were investigated by single factor test and orthogonal test, and the best process was obtained. The physicochemical indexes and antioxidant activity of honeysuckle beverages fermented with lactic acid bacteria were studied. The results showed that the fermentation temperature of the beverage was 37 °C, the fermentation time was 24 h, the inoculation amount of Lactiplantibacillus plantarum and Lactobacillus acidophilus mixed starter (1:1) was 3%, and 8% white granulated sugar was added. The highest sensory score was 87.30 ± 0.17, which was the optimal process. The honeysuckle liquid mixed inoculation with Lactiplantibacillus plantarum and Lactobacillus acidophilus was fermented for 24 h. The number of viable bacteria reached 9.84 ± 0.02 lg cfu/mL, the pH value was 3.10 ± 0.01, and the total polyphenol content was 7.53 ± 0.03 mg GAE/g. The number of lactic acid bacteria, pH, total polyphenol content, and free radical scavenging rate were significantly increased (p < 0.05) compared with the non-inoculated and single-inoculated lactic acid bacteria. To sum up, it was concluded that a better quality beverage could be obtained by fermenting a solution of honeysuckle with Lactiplantibacillus plantarum and Lactobacillus acidophilus mixed fermentation agent, providing a new approach and new ideas for the development of deep processing and fermented beverages using honeysuckle.

That means something to me: How linguistic and emotional experience affect the acquisition, representation, and processing of novel abstract concepts.

We used a novel linguistic training paradigm to investigate the experience-dependent acquisition, representation, and processing of novel emotional and neutral abstract concepts. Participants engaged in mental imagery (n = 32) or lexico-semantic rephrasing (n = 34) of linguistic material during five training sessions and successfully learned the novel abstract concepts. Feature production after training showed that specifically emotion features enriched the emotional concepts' representations. Unexpectedly, for participants engaging in vivid mental imagery during training a higher semantic richness of the acquired emotional concepts slowed down lexical decisions. Rephrasing, in turn, promoted a better learning and processing performance than imagery, probably due to stronger established lexical associations. Our results confirm the importance of emotional and linguistic experience and additional deep lexico-semantic processing for the acquisition, representation, and processing of abstract concepts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Effects of pre-strain deep cryogenic aging on the mechanical and corrosion properties of 2A97 aluminum–lithium alloy

In order to improve the mechanical properties and corrosion resistance of 2A97 aluminum-lithium alloy, this research develops a novel strain heat-treatment technology. Surprisingly, 2A97 alloy treated by 4% pre-strain and 48 h deep cryogenic aging exhibits excellent mechanical properties, whose yield strength, tensile strength, and elongation are 573.7 MPa, 593.6 MPa, and 14.25%, respectively. Furthermore, the corrosion resistance of samples treated by the above pre-strain deep cryogenic aging process is two times as much as traditional T6 aged. This study deeply investigates the influence of various precipitates in 2A97 alloy on its performance through microstructure analysis. The results show that the introduction of pre-strain inhibits the precipitates of the T1 and δ′ phases at the grain boundaries, and prevents the formation and widening of precipitate-free zones. At the same time, it reduces the potential difference between grain boundaries and matrix, and enhances the corrosion resistance of 2A97 alloy. Deep cryogenic can inhibit recovery and maintain deformation dislocation, so that a large number of defects produced by pre-strain promote the nucleation and diffusion of precipitates in the matrix, resulting in tiny and uniformly distributed precipitates. Therefore, the novel pre-strain deep cryogenic aging not only endues 2A97 alloy excellent performance, but also offers a new strategy for controlling precipitation.