Important Application Value Research Articles

Terahertz Spectroscopy in the Field of Food Safety: A Review

with the continuous improvement of people's living standards, food quality and safety problem has caused the wide attention of the whole society, the traditional food quality detection method has certain limitations, terahertz spectral technology (THZ) is a new spectral detection technology, its strong perspective, efficient spectral resolution ability, special fingerprint and nondestructive, in the field of food quality and safety detection has important application value. Due to the high efficiency of food quality and the need for accurate nondestructive testing, automated nondestructive testing for food quality and safety has aroused great interest from the food sector. Faster, accurate, reliable and simplified techniques are needed to classify good quality. With their unique low-energy, penetrating, rapid and non-destructive techniques are widely used as a non-destructive and cost-effective method in the food sector. This paper summarizes the principles and instruments of terahertz spectrum, and introduces the research progress of terahertz spectral detection in food, analyzes the problems in the field of food quality and safety detection, and discusses the future development trend of terahertz spectral technology.

Origami-Inspired Vacuum-Actuated Foldable Actuator Enabled Biomimetic Worm-like Soft Crawling Robot.

The development of a soft crawling robot (SCR) capable of quick folding and recovery has important application value in the field of biomimetic engineering. This article proposes an origami-inspired vacuum-actuated foldable soft crawling robot (OVFSCR), which is composed of entirely soft foldable mirrored origami actuators with a Kresling crease pattern, and possesses capabilities of realizing multimodal locomotion incorporating crawling, climbing, and turning movements. The OVFSCR is characterized by producing periodically foldable and restorable body deformation, and its asymmetric structural design of low front and high rear hexahedral feet creates a friction difference between the two feet and contact surface to enable unidirectional movement. Combining an actuation control sequence with an asymmetrical structural design, the body deformation and feet in contact with ground can be coordinated to realize quick continuous forward crawling locomotion. Furthermore, an efficient dynamic model is developed to characterize the OVFSCR's motion capability. The robot demonstrates multifunctional characteristics, including crawling on a flat surface at an average speed of 11.9 mm/s, climbing a slope of 3°, carrying a certain payload, navigating inside straight and curved round tubes, removing obstacles, and traversing different media. It is revealed that the OVFSCR can imitate contractile deformation and crawling mode exhibited by soft biological worms. Our study contributes to paving avenues for practical applications in adaptive navigation, exploration, and inspection of soft robots in some uncharted territory.

The antihyperuricemic and nephroprotective effect of puerarin by reducing uric acid level and exerting anti‐inflammatory activity

AbstractHyperuricemia (HUA) is a common metabolic disease and kidney injury is one of its main complications. As a typical natural flavonoid, puerarin (PEA) has a range of pharmacological activity but the antihyperuricemic mechanism of PEA has not been reported. Herein, the inhibitory activity of PEA against xanthine oxidase (XOD) was evaluated by in vitro enzymatic reaction, and kinetic analysis, the antihyperuricemic activity, and nephroprotective effect of PEA were studied in HUA mice. The enzymatic reaction showed that the inhibitory effect of PEA (half maximal inhibitory concentration [IC50] = 4.39 µmol/L) on XOD was at the same level as allopurinol (IC50 = 4.05 µmol/L), and kinetic analysis indicated that PEA was a mixed competitive inhibitor. In vivo studies demonstrated that PEA exhibited excellent antihyperuricemic activity by inhibiting XOD and urate transporter 1 (URAT1), to reduce uric acid level. At the same time, PEA reduced the level of inflammatory cytokines and exerted significant nephroprotective effect through anti‐inflammatory activity. Molecular docking indicated that PEA closely bind to both XOD and URAT1, which had the potential to become an inhibitor of XOD and URAT1. In summary, PEA has important application value in developing novel functional food and medicine for the treatment of HUA and its complications.

Design and experimental research of orbital angular momentum multiplexing holography based on optical diffraction neural network

The orbital angular momentum (OAM) multiplexing holography has the advantages of large information capacity and high security, and has important application value in holographic storage, optical encryption, and optical computing. However, as the number of multiplexing channels increases, this technology suffers from deterioration in image quality, which limits its application scope. This article proposes an innovative design that introduces an optical diffractive neural network (ODNN) into OAM multiplexing holography, establishes a scientific image quality evaluation function, applies an end-to-end optimization method, and designs OAM multiplexing holograms in parallel, significantly improving the image quality of OAM holography. The design results show that compared to classical methods, the ODNN method proposed in this paper has improved diffraction efficiency and signal-to-noise ratio by 29% and 19%, respectively, and reduced mean square error and variance by 10% and 43%, respectively. Moreover, high-quality multi-channel OAM multiplexing holography has been achieved through experiments. The design method proposed in this article provides an efficient and practical way for future OAM multiplexing holographic technology to further enhance information capacity and improve security.

Research of the calculation formula for working clearance leakage flow of the hydrogen circulation pump

The hydrogen circulation pump (HCP), a device for recovering unconsumed hydrogen gas in fuel cell systems to improve efficiency, is an important equipment in fuel cell systems. Efficient calculation of the output flow rate of the HCP is crucial for accelerating the product development process, but there is a lack of an effective calculation formula for the working clearance leakage. In this paper, a series of HCP models with different working clearances are established and calculated using an overlapping grid simulation method, verifying that the traditional Roots blower leakage flow formula is feasible for HCP, although the maximum calculation error reaches 10.71%. Further study the pressure distribution law inside the HCP chamber, and revise the traditional calculation formula accordingly, so that the average calculation error of the series models is reduced from 5.75% to 3.82%, and the maximum error is also reduced to 7.73%. Compared with the prototype test data, though the flow values obtained from the two calculation formulas are slightly higher, the calculation error of the correction formula is relatively smaller. The research results indicate that the correction formula can more accurately predict the flow rate of HCP and has important application value.

Development of a System for Predicting Hospitalization Time for Patients With Traumatic Brain Injury Based on Machine Learning Algorithms: User-Centered Design Case Study.

Currently, the treatment and care of patients with traumatic brain injury (TBI) are intractable health problems worldwide and greatly increase the medical burden in society. However, machine learning-based algorithms and the use of a large amount of data accumulated in the clinic in the past can predict the hospitalization time of patients with brain injury in advance, so as to design a reasonable arrangement of resources and effectively reduce the medical burden of society. Especially in China, where medical resources are so tight, this method has important application value. We aimed to develop a system based on a machine learning model for predicting the length of hospitalization of patients with TBI, which is available to patients, nurses, and physicians. We collected information on 1128 patients who received treatment at the Neurosurgery Center of the Second Affiliated Hospital of Anhui Medical University from May 2017 to May 2022, and we trained and tested the machine learning model using 5 cross-validations to avoid overfitting; 28 types of independent variables were used as input variables in the machine learning model, and the length of hospitalization was used as the output variables. Once the models were trained, we obtained the error and goodness of fit (R2) of each machine learning model from the 5 rounds of cross-validation and compared them to select the best predictive model to be encapsulated in the developed system. In addition, we externally tested the models using clinical data related to patients treated at the First Affiliated Hospital of Anhui Medical University from June 2021 to February 2022. Six machine learning models were built, including support vector regression machine, convolutional neural network, back propagation neural network, random forest, logistic regression, and multilayer perceptron. Among them, the support vector regression has the smallest error of 10.22% on the test set, the highest goodness of fit of 90.4%, and all performances are the best among the 6 models. In addition, we used external datasets to verify the experimental results of these 6 models in order to avoid experimental chance, and the support vector regression machine eventually performed the best in the external datasets. Therefore, we chose to encapsulate the support vector regression machine into our system for predicting the length of stay of patients with traumatic brain trauma. Finally, we made the developed system available to patients, nurses, and physicians, and the satisfaction questionnaire showed that patients, nurses, and physicians agreed that the system was effective in providing clinical decisions to help patients, nurses, and physicians. This study shows that the support vector regression machine model developed using machine learning methods can accurately predict the length of hospitalization of patients with TBI, and the developed prediction system has strong clinical use.

Crystal field optimization, luminescence enhancement and thermal stability of novel K2NaAl0.4Ga0.6F6:Mn4+ solid solution red phosphor for high-performance warm WLED

Mn4+-doped fluoride phosphors have important application value in the background light and WLED lighting fields. Therefore, the synthesis of efficient and stable Mn4+-doped red phosphor is of great application value. In this study, a series of red-emitting K2NaAl1-yGayF6:xMn4+ (y = 0, 0.2, 0.4, 0.6, 0.8 and 1) (x = 0.01, 0.03, 0.05, 0.07 and 0.09) solid solution phosphors are successfully synthesized by simple coprecipitation method. The synthesis strategy of simple crystal structure optimization is proposed to realize simultaneously the crystal field optimization, luminescence enhancement and thermal stability of red phosphor. The calculation and theoretical analysis of crystal field strength are used to prove that solid solution matrix K2NaAl0.4Ga0.6F6 is the most ideal matrix material. X-ray powder diffraction, energy-dispersive X-ray spectrometer and scanning electron microscope are employed to determine the crystal structure, composition and morphology of all samples. Optical properties are characterized using excitation spectra, emission spectra and fluorescence lifetime curves. Moreover, the CIE coordinates of red-light show that the phosphor has low correlated color temperature and excellent color purity. Intriguingly, the as-prepared K2NaAl0.4Ga0.6F6:Mn4+ phosphor possesses admirable thermal quenching behavior and color stability at high temperature. At a temperature of 423 K, the photoluminescence intensity can be maintained at 52 % of the original intensity (298 K). The activation energy, the chromaticity shift and chromaticity coordinate variation are also systematically calculated. More importantly, A high-performance warm WLED with low correlated color temperature (CCT = 3982 K) and high color rendering index (Ra = 93.7) is achieved by using K2NaAl0.4Ga0.6F6:Mn4+ as a red-emitting material. These results demonstrate that K2NaAl0.4Ga0.6F6:Mn4+ red phosphor provides a novel choice for the warm WLED required for indoor lighting.

Hopf bifurcation control for the traffic flow model considering the tail light effect

The research on traffic congestion control has seen rapid development in recent years. Investigating the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can offer new methods for alleviating traffic congestion. This paper focuses on studying the bifurcation characteristics and nonlinear control of traffic flow based on the continuous model and the taillight effect. Firstly, the traffic flow model is transformed into a stability model suitable for branching analysis through the use of the traveling wave transform. This transformation facilitates the analysis of stability that reflects unstable traffic characteristics such as congestion. Based on this stability model, the existence condition of Hopf bifurcation is proved and some bifurcation points of the traffic system are identified. Secondly, the congestion and stability mutation behaviors near equilibrium and branching points are studied to understand the formation mechanism of traffic congestion. Finally, control schemes are designed using Chebyshev polynomial approximation and stochastic feedback control to delay or eliminate unstable bifurcation points and relieve traffic congestion. This improved traffic flow model helps explain changes in system stability through bifurcation analysis and identify unstable bifurcation points. It can also effectively manage these points by designing a feedback controller. It is beneficial for controlling sudden changes in traffic system stability behavior and mitigating traffic congestion, with important theoretical significance and practical application value.

Exploitation and Application of a New Genetic Sex Marker Based on Intron Insertion Variation of erc2 Gene in Oplegnathus punctatus.

Spotted knifejaw (Oplegnathus punctatus), one of the most valuable mariculture species, grows with significant sexual dimorphism, with males growing significantly faster than females. O. punctatus not only has excellent growth characteristics and high food value, but also shows high economic value in aquaculture, which has become a hotspot in the field of aquaculture. The current insufficiency of sex marker identification in O. punctatus restricts the process of its unisexual breeding. Rapid identification of sex will help to study the mechanisms of sex determination and accelerate the development of sex-controlled breeding. With the completion of the sequencing of the male and female genomes of O. punctatus, the efficient and precise development of genetic sex markers has been made possible. In this study, we used genome-wide information combined with molecular biology techniques from marker sequences to further establish a rapid method for DNA insertion variant detection in the intron of O. punctatus erc2 gene, which can be used to rapidly, accurately, and efficiently identify whether DNA insertion occurs in the intron of O. punctatus erc2 gene to be detected, and to identify the sex of O. punctatus to be detected. It could also be distinguished by agarose gel electrophoresis, which would shorten the time for accurate identification and improves the detection efficiency. Homozygous comparison of male and female individuals showed that the length of the DNA fragment of the erc2 gene was 239bp on chromosome X1 and 1173bp on chromosome Y. It can therefore be inferred that a 934bp insertion fragment exists on the Y chromosome. The PCR amplification results showed that two DNA fragments of 1173bp and 239bp could be amplified in male O. punctatus, and the 1173bp fragment was a marker fragment specific to the variant intron erc2 gene, while only a single DNA fragment of 239bp was amplified in female O. punctatus. It has important significance and application value in the study of neurotransmitter transmission and environmental adaptability of female and male fish based on erc2 gene, as well as the identification of male and female sex, the preparation of high male fry, and family breeding.

A virtual reconstruction method for corridor gable buildings based on the knowledge of structural dynamics: taking Leiyin Cave as an example

Virtual reconstruction of ancient buildings often has incomplete records of the original design and construction details, and can only be reconstructed based on limited data, drawings and photography, which is different from the actual conditions. The unique overhanging structure of the corridor gable building makes it vulnerable to damage in extreme weather conditions. In order to ensure that the virtual reconstruction results can not only reproduce the original appearance of history, but also ensure that the reconstructed model maintains structural stability in the long term. This paper proposes a reconstruction method of the original appearance of the corridor gable building remains based on structural dynamics analysis. This method comprehensively uses three-dimensional reconstruction, structural engineering, dynamic analysis, and computer simulation technology to ensure the structural accuracy and historical authenticity of the virtually reconstructed corridor gable building. First, through data collection and analysis, combined with ancient architectural construction techniques, a preliminary three-dimensional model was created, which included all structural elements and details. Several groups of reconstruction schemes are determined based on material properties. Then, using finite element analysis software, perform dynamic analysis on the three-dimensional model. Evaluate the stability of the reconstructed structure and optimize the material selection plan to ensure the feasibility and accuracy of the virtual reconstruction. Taking the virtual reconstruction of the eaves in front of Leiyin Cave as an example, it shows that this method is effective and feasible to achieve the virtual reconstruction of corridor gable buildings. It provides new ideas for virtual reconstruction of ancient buildings and has important practical application value.

Enhanced peroxymonosulfate activation for emerging contaminant degradation via defect-engineered interfacial electric field in FeNC

Peroxymonosulfate (PMS) activation technology has important application value in treating emerging contaminant (ECs), but it still faces challenges in achieving efficient electron transfer and metal valence cycling. In this study, the interfacial electric field characteristics of FeNC catalysts were adjusted by introducing NC defects to affect the electron transfer process, thereby enhancing the catalytic performance of PMS. It is found that in the FeNC structure, the shift of the charge generates an interfacial electric field, which can promote the directional transfer of electrons. Through quantitative structure–activity relationship (QSAR) analysis, it was confirmed that the defect played a decisive role in regulating the interfacial electric field and improving the catalytic reaction efficiency. The interfacial electric field-mediated superexchange interaction realizes the electron donor effect of organic pollutants and the effective electron transfer between the Fe site, accelerates the electron cycling of the Fe site, and realizes the rapid and stable catalysis of PMS. The increase of the occupancy state distribution of d orbitals near the Fermi level provides favorable conditions for electron transitions and catalytic activation of PMS. ECs can be converted into environmentally friendly, non-toxic and harmless substances through. This defect-controlled interface electric field strategy realizes rapid electron directional transfer, which provides a new solution for improving the catalytic efficiency of PMS and the safe treatment of ECs in water.

A facial expression recognition network based on attention double branch enhanced fusion.

The facial expression reflects a person's emotion, cognition, and even physiological or mental state to a large extent. It has important application value in medical treatment, business, criminal investigation, education, and human-computer interaction. Automatic facial expression recognition technology has become an important research topic in computer vision. To solve the problems of insufficient feature extraction, loss of local key information, and low accuracy in facial expression recognition, this article proposes a facial expression recognition network based on attention double branch enhanced fusion. Two parallel branches are used to capture global enhancement features and local attention semantics respectively, and the fusion and complementarity of global and local information is realized through decision-level fusion. The experimental results show that the features extracted by the network are made more complete by fusing and enhancing the global and local features. The proposed method achieves 89.41% and 88.84% expression recognition accuracy on the natural scene face expression datasets RAF-DB and FERPlus, respectively, which is an excellent performance compared with many current methods and demonstrates the effectiveness and superiority of the proposed network model.

Carbon Dots-Mediated Photodynamic Treatment Reduces Postharvest Senescence and Decay of Grapes by Regulating the Antioxidant System.

Grapes are susceptible to mold and decay during postharvest storage, and developing new technologies to extend their storage period has important application value. Photodynamic technology (PDT) in concurrence with carbon dots (CDs) proposes an innovative and eco-friendly preservation strategy. We examined the effects of carbon dots combined with photodynamic treatment on postharvest senescence and antioxidant system of table grape. The compounding of photodynamic technology with a 0.06 g L-1 CDs solution could possibly extend the postharvest storage period of grape berries. Through this strategy, we achieved a decreased rate of fruit rotting and weight loss alongside the delayed deterioration of fruit firmness, soluble solids, and titratable acid. As paired with photodynamic technology, CDs considerably decreased the postharvest storage loss of phenols, flavonoids, and reducing sugars as compared to the control group. Concurrently, it remarkably postponed the build-up of hydrogen peroxide (H2O2), superoxide anion (O2∙-), and malondialdehyde (MDA); elevated the levels of reduced ascorbic acid (AsA) and reduced glutathione (GSH); lowered the levels of dehydroascorbic acid (DHA) and oxidized glutathione (GSSG); raised the ratios of AsA/DHA and GSSH/GSSG; encouraged the activities of superoxide dismutase (SOD) and phenylalanine ammonia-lyase (PAL); and inhibited the activities of polyphenol oxidase (PPO) and lipoxygenase (LOX). Furthermore, it enhanced the iron reduction antioxidant capacity (FRAP) and DPPH radical scavenging capacity of grape berries. CDs combined with photodynamic treatment could efficiently lessen postharvest senescence and decay of grape berry while extending the storage time.

Structural Design and Research of Offshore Wind Turbine Towers

The structural design and research of offshore wind turbine tower need to involve knowledge and technology in many fields, such as mechanical engineering, material science, fluid mechanics and so on. Through the research and optimization of tower structure, it can promote the development of related industries and improve the technical level and competitiveness of the whole industrial chain. The structural design and research of offshore wind turbine tower has important practical significance and application value. Through in-depth research and optimization of design, the performance and reliability of offshore wind turbines can be improved, and the rapid development of offshore wind power technology can be promoted.

Integration of endoscopic professional database development and image annotation platform: Radiation control plan for gastric examination

Gastric examination is a common endoscopic procedure that can be used to assess stomach diseases and lesions. However, because the stomach examination requires the use of radiation imaging technology, patients are exposed to radiation risks. Therefore, the aim of this study is to develop a specialized database of endoscopy and integrate it with an image annotation platform to achieve effective control of gastric examination radiation. An image labeling platform was developed to label gastric lesions and structures, annotate and label endoscopic images, and describe the details of gastric lesions more comprehensively and accurately. A complete radiation imaging control system for gastric examination was formed by integrating professional database and image labeling platform. Through this integrated system, doctors can quickly access the endoscopy images in the database when performing stomach exams, and label lesions on the image labeling platform to more accurately evaluate the lesions and structure of the stomach, while reducing the number of times patients undergo radiation examinations. The results show that the radiation imaging of gastric examination can be effectively controlled through the integration of endoscopy database and image annotation platform. This integrated system helps to improve the accuracy and safety of the examination, and has important clinical application value for both doctors and patients.

A 1D-inception-ResNet based global detection model for thin-skinned multifruit spectral quantitative analysis

Near-infrared spectroscopy (NIRS) analysis technology has important research and application value in the inspection of agricultural product quality due to its nondestructive characteristics. Deep learning methods represented by convolutional neural networks (CNNs) reduce the reliance on expert experience and prior knowledge in spectral analysis. They can automatically extract effective feature information from the original spectral data to improve the prediction accuracy of the model. However, the diverse characteristics of fruit varieties present a challenge to the robustness of the model. This study proposes1D-Inception-ResNet, an end-to-end CNN model for multifruit spectral quantitative analysis. The effectiveness of the model is validated on two thin-skinned fruit datasets with similar physicochemical properties. The root mean square error of prediction (RMSEP) for soluble solids content (SSC) and dry matter (DM) were 0.48 °Brix and 0.60 %, and the coefficients of determination of prediction (Rp2) were 0.95 and 0.89, respectively. Compared with traditional partial least squares regression (Rp2 of 0.86 and 0.80, RMSEP of 0.84 °Brix and 0.81 %, respectively) and extreme learning machine (Rp2 of 0.82 and 0.77, RMSEP of 0.95 °Brix and 0.86 %, respectively), our proposed method resulted in a lower RMSEP (46.1% and 28.1% lower, respectively) and higher RP2 (13.1% and 13.4% higher, respectively) for SSC and DM prediction. The results show that compared to other linear and nonlinear algorithms, 1D-Inception-ResNet significantly enhances the modeling effectiveness. Furthermore, external validation using a new sample set showed RMSEP values of 0.62 °Brix and 1.45 % for the SSC and DM models of Inception-ResNet, respectively. In conclusion, using an appropriate CNN network structure to establish a global model for diverse fruits expands the application scope of the model and improves the prediction stability across different species, which is significant in the future application of agricultural products and foods.

Intelligent road extraction from high resolution remote sensing images based on optimized SVM

Road recognition and extraction based on high-resolution remote sensing images is currently a hot issue at the forefront of image processing and other disciplines, and its results not only help to enrich geographic information, but also have important application value in national defence construction and other aspects. For the intelligent extraction of roads in high-resolution remote sensing images, the study firstly preprocesses these images, and then extracts the roads by using variational function and support vector machine. As the extraction results are not satisfactory enough, the 3D wavelet transform technique is used to extract the spectral features of roads in the frequency domain, combined with the texture in the time domain, and optimized the support vector machine in order to extract the road images more accurately. The outcomes denote that in contrast with other methods, the optimized method can have optimal correctness and quality while guaranteeing higher integrity. The satellite road image of Chengdu A industrial park is rich in extracted information, having the highest completeness of 94.23%, correctness of 60.73%, and quality of 59.42%, and the satellite road image of Chengdu B industrial park is similarly rich in extracted information, having the highest completeness of 83.47%, correctness of 85.31%, and quality of 72.85%. The highest value of CPU usage for road extraction was 2000 kb at 37 s, and the average value of CPU usage for the whole 60 s was 500 kb. It shows that the research method has good results in high resolution remote sensing image road extraction.

Piezoelectric Hinged Beam with Arc Mass Stopper for Vibration and Human Motion Energy Harvesting.

Compact reliable structure and strong electromechanical coupling are hot pursuits in piezoelectric vibration energy harvester (PVEH) design. PVEH with a static arc stopper makes piezoelectric stress uniformly distributed and widens the frequency band by collision but wastes space. This Article proposes a hinged PVEH with two arc mass stoppers (AS-H-PVEH). Two arc stoppers as movable masses increase the vibration energy and the effective electromechanical coupling coefficient to achieve strong electromechanical coupling. AS-H-PVEH generates a 4.1 mW power output at 11.6-12.0 Hz and 0.2 g. AS-H-PVEH sustains 4 g acceleration vibration for 10 min without attenuation. To offset the resonance frequency increase caused by arc contact, we discuss the magnetic coupling, and axial force effects are discussed. The design of the arc stopper radius, nonlinear electromechanical coupling model, and system parameter identification method are presented. The displacement varied mechanical quality factor and effective electromechanical coupling coefficient are considered in the modified model for the first time. The model obtained good agreement under experiments. The power generation and driven wireless sensor performance of AS-H-PVEH was verified. This research has important theoretical and application value for the performance optimization of PVEH with an arc stopper.

Research on Emergency Logistics Optimization Strategy Based on Blockchain Technology

Emergency logistics is an important support for responding to emergencies, ensuring social stability, and ensuring the safety of people s lives and property. However, in reality, emergency logistics often faces challenges such as information asymmetry, non-standard management, low process transparency, and poor accountability. Therefore, utilizing emerging technologies such as blockchain to optimize and upgrade emergency logistics has important practical significance and application value. This article briefly analyzes the relationship between blockchain technology and emergency logistics, and conducts in-depth exploration of the significance of optimizing emergency logistics based on blockchain technology, then proposes an emergency logistics optimization strategy based on blockchain technology for reference.

A deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning

As a class of biologically active molecules with significant immunomodulatory and anti-inflammatory effects, anti-inflammatory peptides have important application value in the medical and biotechnology fields due to their unique biological functions. Research on the identification of anti-inflammatory peptides provides important theoretical foundations and practical value for a deeper understanding of the biological mechanisms of inflammation and immune regulation, as well as for the development of new drugs and biotechnological applications. Therefore, it is necessary to develop more advanced computational models for identifying anti-inflammatory peptides. In this study, we propose a deep learning model named DAC-AIPs based on variational autoencoder and contrastive learning for accurate identification of anti-inflammatory peptides. In the sequence encoding part, the incorporation of multi-hot encoding helps capture richer sequence information. The autoencoder, composed of convolutional layers and linear layers, can learn latent features and reconstruct features, with variational inference enhancing the representation capability of latent features. Additionally, the introduction of contrastive learning aims to improve the model's classification ability. Through cross-validation and independent dataset testing experiments, DAC-AIPs achieves superior performance compared to existing state-of-the-art models. In cross-validation, the classification accuracy of DAC-AIPs reached around 88%, which is 7% higher than previous models. Furthermore, various ablation experiments and interpretability experiments validate the effectiveness of DAC-AIPs. Finally, a user-friendly online predictor is designed to enhance the practicality of the model, and the server is freely accessible at http://dac-aips.online.