Heart Failure-Like Reaction Is Likely Involved in the Feeding Behaviour of Blood-Sucking Leeches

Medicinal leeches have been utilized in therapy for thousands of years. However, the adaptation physiology between leeches and hosts is not fully understand. To disclose the molecular mechanisms of adaptation between leech and host, the body transcriptomes of hunger and fed blood-sucking Poecilobdella javanica, Haemadipsa cavatuses, and Hirudo nipponia leeches were obtained by RNA sequencing, after comparison, a stratified unigenes group was obtained, which closely correlated to body distension. In the group, Rfamide receptor decreased significantly (P < 0.05) while serotonin receptor increased significantly (P < 0.05). Moreover, four KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including cardiac muscle contraction, complement and coagulation cascades, renin-angiotensin system, and hypertrophic cardiomyopathy were significantly enriched. The unigenes annotation, neuroregulators correlation analysis and induced function of the KEGG pathways, were consistently supported the same result as: vasoconstriction and systole reaction enhance in hunger leeches and vice versa vasodilation and diastole increase in fed leeches, meanwhile, Interspecific comparison and correlative analyses of physiological function showed that the strongest reaction of induced heart failure from four KEGG occur in strongest reaction of systole in hungry P. javanica and in strongest reaction of diastole in fed H. nipponia. Overall, heart failure is likely a physiological function involved in feeding behaviour.

Adsorption of Phenanthrene in Soil to Biochar Modified by β-Cyclodextrin

In this study, the adsorption effect of β-cyclodextrin modified biochar (BC) on phenanthrene (PHE) in contaminated soil was investigated, aiming to provide an efficient and environmentally friendly remediation strategy for Polycyclic Aromatic Hydrocarbons (PAHs) contaminated soil. Through kinetic and isotherm analysis, β-CDBC-CA showed excellent phenanthrene adsorption performance, and the adsorption effect increased with the increase of time and was affected by temperature. The results show that β-CDBC-CA can not only effectively adsorb phenanthrene in soil, but also serve as a surfactant to help desorption phenanthrene adsorbed by soil organic matter and improve the efficiency of microbial degradation. The experimental data showed that the Elovich model could describe the adsorption behavior of β-CDBC-CA on phenanthrene well, while Langmuir and Freundlich models performed better in fitting parameters, revealing the adsorption mechanism of phenanthrene in contaminated soil by β-cyclodextrin-modified biochar. In addition, temperature has a significant effect on the adsorption capacity of β-CDBC-CA, and its application in soil remediation can be optimized by adjusting temperature. This study not only provides new materials and technical means for soil remediation but also provides important data support for an in-depth understanding of the environmental behavior of PAHs. By citing relevant research results, this study further improves the control and understanding of environmental risks of PAHs, which is of great significance for the protection of ecological environment and human health.

Synthesis and Characterization of β-Cyclodextrin Modified Biochar Environmental Remediation Materials

In this paper, biochar (BC) was used as raw material, activated by deionizing aqueous solution, NaCl solution, CA solution and HCl solution respectively. Epichlorohydrin (EPI) was used as crosslinking agent, and β-cyclodextrin (β-CD) was used to modify biochar (BC). The prepared modified biochar materials were labeled with β-CDBC, β-CDBC-Na, β-CDBC-CA and β-CDBC-H, respectively. The infrared spectrum, X-ray diffractometer, scanning electron microscope and specific surface area of the four modified materials were tested. The results showed that the C-O stretching vibration peak at 1020 cm−1 of the modified materials was slightly offset compared with that of biochar. The characteristic absorption peaks of XRD pattern decrease obviously at 2θ = 26.7˚ and 29.5˚. It can be obviously observed on the electron microscope image that the surface is loaded or formed clathrates, and BET data and graphs also show that the specific surface area of the modified biochar is larger. Therefore, β-cyclodextrin successfully modified biochar and formed clathrates on the surface of biochar or was loaded in the pore structure of biochar, especially β-CDBC-CA achieved better modification effect. Because biochar and β-cyclodextrin raw materials are cheap, easy to prepare and green, and less prone to secondary pollution, it has a good advantage in environmental governance.

稀土高熵氧化物用于能源和环境催化

随着工业技术的快速发展,能源短缺与环境污染问题日益凸显,对人类生活和社会经济发展造成了严重影响.在这一背景下,加速能源系统转型、积极开发与利用可再生能源变得至关重要,成为当前研究领域的热点.同时,消除环境中的有毒有害物质也具有较高的研究价值,对维护生态平衡和人类健康具有重要意义.催化技术因在去除污染物和能源转换方面具有巨大的应用潜力而备受关注.其中,高熵氧化物因其具有多样可调的晶体结构和丰富的表面活性位点,展现出较好的催化性能,从而备受研究者关注.此外,稀土元素因以其相近的离子半径、独特的电子轨道和可变的氧化态,常被用作高熵氧化物的组成元素.通过引入稀土元素,可以有效改善高熵氧化物的结构和性能.近年来,稀土高熵氧化物在催化应用中取得较多的研究成果,然而却缺少对该方面工作系统全面的综述.本文旨在系统总结稀土高熵氧化物的结构特点、合成方法及其在催化领域的应用,以期为该领域的进一步发展提供有益的参考.本文从结构、合成及应用等方面,对稀土高熵氧化物在催化领域的研究进展进行了总结.首先,详细介绍了钙钛矿型、萤石型和烧绿石型三种类型稀土高熵氧化物的结构特征,并探讨了结构对催化反应过程的重要影响.研究表明,多组分的高熵效应保证了单相固溶体的形成和高温下的催化稳定性,而晶格畸变效应则促进了氧空位等活性位点的形成,进而提升了催化活性.复杂多变的晶体结构导致了离子扩散延迟,这也使得高熵材料在催化过程中具有优异稳定性.此外,揭示了多种元素间的协同作用对催化性能的提升作用.值得一提的是,非等摩尔金属成分也可形成高熵氧化物,为稀土高熵氧化物研究开辟了新的方向.其次,讨论了稀土高熵氧化物的合成方法,主要包括固相反应合成法、喷雾热解法、化学共沉淀法和溶液燃烧法,合成方法的选择和优化对于建立高熵系统至关重要.目前,众多简单快速的合成方法已取代耗时耗力的合成过程,为稀土高熵氧化物的制备提供了便利.最后,重点概述了稀土高熵氧化物在电催化、热催化和光催化中的应用进展,并详细介绍了多元素可调材料对反应活性的影响.同时,指出了稀土高熵氧化物未来发展所面临的挑战,并提出了相应的应对策略,以期为高熵系统的理论研究提供有力支撑.综上,稀土高熵氧化物因其复杂结构而展现出丰富的潜在催化反应性能,具有巨大的应用潜力.未来研究可以加强开发设计、精准调控结构以及深入分析反应机理,从而提升高熵氧化物的催化性能.希望本文能够为稀土高熵氧化物催化体系研究提供有益的参考.

无模板合成空心微球CdS/ZnO异质结光催化剂促进光催化产氢

以无水醋酸锌为锌源,通过乙二醇与丁四醇介导,无模板合成了具有良好分散性的锌基复合圆球,随后煅烧得到ZnO中空微球.通过化学沉淀法在ZnO微球上负载不同质量比的CdS纳米颗粒,获得系列CdS/ZnO复合光催化剂.实验结果表明,CdS的引入成功拓展了ZnO微球的光吸收范围,使得体系吸收更多光能用于后续反应.结合测试及计算得到的带隙、能带电位和功函数值,发现CdS与ZnO在接触界面处能带发生弯曲,其间构建的电场协助光生载流子的迁移.CdS与ZnO间形成紧密的Z型异质结,提升光生载流子的分离效率,为光催化还原析氢提供良好的保障,使2CZ复合样品可见光照射下的制氢效率为1558.85μmol/g/h.

Computational Experiments for Complex Social Systems:Integrated Design of Experiment System

Powered by advanced information industry and intelligent technology,more and more complex systems are exhibiting characteristics of the cyber-physical-social systems(CPSS).And human factors have become crucial in the operations of complex social systems.Traditional mechanical analysis and social simulations alone are powerless for analyzing complex social systems.Against this backdrop,computational experiments have emerged as a new method for quantitative analysis of complex social systems by combining social simulation(e.g.,ABM),complexity science,and domain knowledge.However,in the process of applying computational experiments,the construction of experiment system not only considers a large number of artificial society models,but also involves a large amount of data and knowledge.As a result,how to integrate various data,model and knowledge to achieve a running experiment system has become a key challenge.This paper proposes an integrated design framework of computational experiment system,which is composed of four parts:generation of digital subject,generation of digital object,design of operation engine,and construction of experiment system.Finally,this paper outlines a typical case study of coal mine emergency management to verify the validity of the proposed framework.

Genetically modified pigs:Emerging animal models for hereditary hearing loss

Hereditary hearing loss(HHL),a genetic disorder that impairs auditory function,significantly affects quality of life and incurs substantial economic losses for society.To investigate the underlying causes of HHL and evaluate therapeutic outcomes,appropriate animal models are necessary.Pigs have been extensively used as valuable large animal models in biomedical research.In this review,we highlight the advantages of pig models in terms of ear anatomy,inner ear morphology,and electrophysiological characteristics,as well as recent advancements in the development of distinct genetically modified porcine models of hearing loss.Additionally,we discuss the prospects,challenges,and recommendations regarding the use pig models in HHL research.Overall,this review provides insights and perspectives for future studies on HHL using porcine models.

Identification of different degrees of processed ginger using GC-IMS combined with machine learning

Ginger,the rhizomes of Zingiber officinale Roscoe,was a wellknown edible plant species commonly used in China,which has pungent flavor[1].Ginger has numerous chemical compounds,such as phenolic constituents,volatile compounds(VOCs),and polysaccharides[2].Among them,VOCs are considered one of the effective compounds in ginger due to their functional properties,including anti-inflammatory,antioxidant,and analgesic[3].

Micro defects formation and dynamic response analysis of steel plate of quasi-cracking area subjected to explosive load

As the protective component,steel plate had attracted extensive attention because of frequently threats of explosive loads.In this paper,the evolution of microstructure and the mechanism of damage in the quasi-cracking area of steel plate subjected to explosive load were discussed and the relationships between micro defects and dynamic mechanical response were revealed.After the explosion experiment,five observation points were selected equidistant from the quasi-cracking area of the section of the steel plate along the thickness direction,and the characteristics of micro defects at the observation points were analyzed by optical microscope(OM),scanning electron microscope(SEM) and electron backscattered diffraction(EBSD).The observation result shows that many slip bands(SBs) appeared,and the grain orientation changed obviously in the steel plate,the two were the main damage types of micro defects.In addition,cracks,peeling pits,grooves and other lager micro defects were appeared in the lower area of the plate.The stress parameters of the observation points were obtained through an effective numerical model.The mechanism of damage generation and crack propagation in the quasicracking area were clarified by comparing the specific impulse of each observation point with the corresponding micro defects.The result shows that the generation and expansion of micro defects are related to the stress area(i.e.the upper compression area,the neutral plane area,and the lower tension area).The micro defects gather and expand at the grain boundary,and will become macroscopic damage under the continuous action of tensile stress.Besides,the micro defects at the midpoint of the section of the steel plate in the direction away from the explosion center(i.e.the horizontal direction) were also studied.It was found that the specific impulse at these positions were much smaller than that in the thickness direction,the micro defects were only SBs and a few micro cracks,and the those decreased with the increase of the distance from the explosion center.

Microscopic defects formation and dynamic mechanical response analysis of Q345 steel plate subjected to explosive load

As the basic protective element, steel plate had attracted world-wide attention because of frequent threats of explosive loads. This paper reports the relationships between microscopic defects of Q345 steel plate under the explosive load and its macroscopic dynamics simulation. Firstly, the defect characteristics of the steel plate were investigated by stereoscopic microscope(SM) and scanning electron microscope(SEM). At the macroscopic level, the defect was the formation of cave which was concentrated in the range of 0-3.0 cm from the explosion center, while at the microscopic level, the cavity and void formation were the typical damage characteristics. It also explains that the difference in defect morphology at different positions was the combining results of high temperature and high pressure. Secondly, the variation rules of mechanical properties of steel plate under explosive load were studied. The Arbitrary Lagrange-Euler(ALE) algorithm and multi-material fluid-structure coupling method were used to simulate the explosion process of steel plate. The accuracy of the method was verified by comparing the deformation of the simulation results with the experimental results, the pressure and stress at different positions on the surface of the steel plate were obtained. The simulation results indicated that the critical pressure causing the plate defects may be approximately 2.01 GPa. On this basis, it was found that the variation rules of surface pressure and microscopic defect area of the Q345 steel plate were strikingly similar, and the corresponding mathematical relationship between them was established. Compared with Monomolecular growth fitting models(MGFM) and Logistic fitting models(LFM), the relationship can be better expressed by cubic polynomial fitting model(CPFM). This paper illustrated that the explosive defect characteristics of metal plate at the microscopic level can be explored by analyzing its macroscopic dynamic mechanical response.

Effect of land use on soil nematode community composition and co-occurrence network relationship

Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8and 15.1%higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50%of forest land genera were omnivores-predators and 36%of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8%lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH_(4)^(+)-N and NO_(3)^(–)-N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.

High-strength and thermally stable TiB_(2)-modified Al-Mn-Mg-Er-Zr alloy fabricated via selective laser melting

To increase the processability and plasticity of the selective laser melting(SLM)fabricated Al-Mn-Mg-Er-Zr alloys,a novel TiB_(2)-modified Al-Mn-Mg-Er-Zr alloy with a mixture of Al-Mn-Mg-Er-Zr and nano-TiB_(2) powders was fabricated by SLM.The pro-cessability,microstructure,and mechanical properties of the alloy were systematically investigated by density measurement,microstruc-ture characterization,and mechanical properties testing.The alloys fabricated at 250 W displayed higher relative densities due to a uni-formly smooth top surface and appropriate laser energy input.The maximum relative density value of the alloy reached(99.7±0.1)%,demonstrating good processability.The alloy exhibited a duplex grain microstructure consisting of columnar regions primarily and equiaxed regions with TiB_(2),Al6Mn,and Al3Er phases distributed along the grain boundaries.After directly aging treatment at a high tem-perature of 400℃,the strength of the SLM-fabricated TiB_(2)/Al-Mn-Mg-Er-Zr alloy increased due to the precipitation of the secondary Al6Mn phases.The maximum yield strength and ultimate tensile strength of the aging alloy were measured to be(374±1)and(512±13)MPa,respectively.The SLM-fabricated TiB_(2)/Al-Mn-Mg-Er-Zr alloy demonstrates exceptional strength and thermal stability due to the synergistic effects of the inhibition of grain growth,the incorporation of TiB_(2) nanoparticles,and the precipitation of secondary Al6Mn nanoparticles.

Integrated spatial metabolomics and transcriptomics decipher the hepatoprotection mechanisms of wedelolactone and demethylwedelolactone on non-alcoholic fatty liver disease

Eclipta prostrata L.has been used in traditional medicine and known for its liver-protective properties for centuries.Wedelolactone(WEL)and demethylwedelolactone(DWEL)are the major coumarins found in E.prostrata L.However,the comprehensive characterization of these two compounds on non-alcoholic fatty liver disease(NAFLD)still remains to be explored.Utilizing a well-established zebrafish model of thioacetamide(TAA)-induced liver injury,the present study sought to investigate the impacts and mechanisms of WEL and DWEL on NAFLD through integrative spatial metabolomics with liver-specific transcriptomics analysis.Our results showed that WEL and DWEL significantly improved liver function and reduced the accumulation of fat in the liver.The biodistributions and metabolism of these two compounds in whole-body zebrafish were successfully mapped,and the discriminatory endogenous metabolites reversely regulated by WEL and DWEL treatments were also characterized.Based on spatial metabolomics and transcriptomics,we identified that steroid biosynthesis and fatty acid metabolism are mainly involved in the hepatoprotective effects of WEL instead of DWEL.Our study unveils the distinct mechanism of WEL and DWEL in ameliorating NAFLD,and presents a“multi-omics”platform of spatial metabolomics and liver-specific transcriptomics to develop highly effective compounds for further improved therapy.

Industry-Oriented Detection Method of PCBA Defects Using Semantic Segmentation Models

Automated optical inspection(AOI)is a significant process in printed circuit board assembly(PCBA)production lines which aims to detect tiny defects in PCBAs.Existing AOI equipment has several deficiencies including low throughput,large computation cost,high latency,and poor flexibility,which limits the efficiency of online PCBA inspection.In this paper,a novel PCBA defect detection method based on a lightweight deep convolution neural network is proposed.In this method,the semantic segmentation model is combined with a rule-based defect recognition algorithm to build up a defect detection frame-work.To improve the performance of the model,extensive real PCBA images are collected from production lines as datasets.Some optimization methods have been applied in the model according to production demand and enable integration in lightweight computing devices.Experiment results show that the production line using our method realizes a throughput more than three times higher than traditional methods.Our method can be integrated into a lightweight inference system and pro-mote the flexibility of AOI.The proposed method builds up a general paradigm and excellent example for model design and optimization oriented towards industrial requirements.

A Review of the Application of Artificial Intelligence in Orthopedic Diseases

In recent years,Artificial Intelligence(AI)has revolutionized people’s lives.AI has long made breakthrough progress in the field of surgery.However,the research on the application of AI in orthopedics is still in the exploratory stage.The paper first introduces the background of AI and orthopedic diseases,addresses the shortcomings of traditional methods in the detection of fractures and orthopedic diseases,draws out the advantages of deep learning and machine learning in image detection,and reviews the latest results of deep learning and machine learning applied to orthopedic image detection in recent years,describing the contributions,strengths and weaknesses,and the direction of the future improvements that can be made in each study.Next,the paper also introduces the difficulties of traditional orthopedic surgery and the roles played by AI in preoperative,intraoperative,and postoperative orthopedic surgery,scientifically discussing the advantages and prospects of AI in orthopedic surgery.Finally,the article discusses the limitations of current research and technology in clinical applications,proposes solutions to the problems,and summarizes and outlines possible future research directions.The main objective of this review is to inform future research and development of AI in orthopedics.

Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.

Fine-mapping of a candidate gene for web blotch resistance in Arachis hypogaea L.

Peanut(Arachis hypogaea L.)is a globally important oil crop.Web blotch is one of the most important foliar diseases affecting peanut,which results in serious yield losses worldwide.Breeding web blotch-resistant peanut varieties is the most effective and economically viable method for minimizing yield losses due to web blotch.In the current study,a bulked segregant analysis with next-generation sequencing was used to analyze an F2:3 segregating population and identify candidate loci related to web blotch resistance.Based on the fine-mapping of the candidate genomic interval using kompetitive allele-specific PCR(KASP)markers,we identified a novel web blotch resistance-related locus spanning approximately 169 kb on chromosome 16.This region included four annotated genes,of which only Arahy.35VVQ3 had a non-synonymous single nucleotide polymorphism in the coding region between the two parents.Two markers(Chr.16.12872635 and Chr.16.12966357)linked to this gene were shown to be co-segregated with the resistance of peanut web blotch by 72 randomly selected recombinant inbred lines(RIL),which could be used in marker-assisted breeding of resistant peanut varieties.

Structure,ferroelectric,and enhanced fatigue properties of sol–gel-processed new Bi-based perovskite thin films of Bi(Cu_(1/2)Ti_(1/2))O_(3)–PbTiO_(3)

Bi-based perovskite ferroelectric thin films have wide applications in electronic devices due to their excellent ferroelectric properties.New Bi-based perovskite thin films Bi(Cu_(1/2)Ti_(1/2))O_(3)–PbTiO_(3)(BCT–PT) are deposited on Pt(111)/Ti/SiO_(2)/Si substrates in the present study by the traditional sol–gel method.Their structures and related ferroelectric and fatigue characteristics are studied in-depth.The BCT–PT thin films exhibit good crystallization within the phase-pure perovskite structure,besides,they have a predominant(100) orientation together with a dense and homogeneous microstructure.The remnant polarization(2P_(r)) values at 30 μC/cm^(2) and 16 μC/cm^(2) are observed in 0.1BCT–0.9PT and 0.2BCT–0.8PT thin films,respectively.More intriguingly,although the polarization values are not so high,0.2BCT–0.8PT thin films show outstanding polarization fatigue properties,with a high switchable polarization of 93.6% of the starting values after 10^(8) cycles,indicating promising applications in ferroelectric memories.

MXene Sediment-Based Poly(vinyl alcohol)/Sodium Alginate Aerogel Evaporator with Vertically Aligned Channels for Highly Efficient Solar Steam Generation

Solar-driven interfacial evaporation from seawater is considered an effective way to alleviate the emerging freshwater crisis because of its green and environmentally friendly characteristics.However,developing an evaporator with high efficiency,stability,and salt resistance remains a key challenge.MXene,with an internal photothermal conversion efficiency of 100%,has received tremendous research interest as a photothermal material.However,the process to prepare the MXene with monolayer is inefficient and generates a large amount of“waste”MXene sediments(MS).Here,MXene sediments is selected as the photothermal material,and a three-dimensional MXene sediments/poly(vinyl alcohol)/sodium alginate aerogel evaporator with vertically aligned pores by directional freezing method is innovatively designed.The vertical porous structure enables the evaporator to improve water transport,light capture,and high evaporation rate.Cotton swabs and polypropylene are used as the water channel and support,respectively,thus fabricating a self-floating evaporator.The evaporator exhibits an evaporation rate of 3.6 kg m^(-2)h^(-1)under one-sun illumination,and 18.37 kg m^(-2)of freshwater is collected in the condensation collection device after 7 h of outdoor sun irradiation.The evaporator also displays excellent oil and salt resistance.This research fully utilizes“waste”MS,enabling a self-floating evaporation device for freshwater collection.

A Task Offloading Strategy Based on Multi-Agent Deep Reinforcement Learning for Offshore Wind Farm Scenarios

This research is the first application of Unmanned Aerial Vehicles(UAVs)equipped with Multi-access Edge Computing(MEC)servers to offshore wind farms,providing a new task offloading solution to address the challenge of scarce edge servers in offshore wind farms.The proposed strategy is to offload the computational tasks in this scenario to other MEC servers and compute them proportionally,which effectively reduces the computational pressure on local MEC servers when wind turbine data are abnormal.Finally,the task offloading problem is modeled as a multi-intelligent deep reinforcement learning problem,and a task offloading model based on MultiAgent Deep Reinforcement Learning(MADRL)is established.The Adaptive Genetic Algorithm(AGA)is used to explore the action space of the Deep Deterministic Policy Gradient(DDPG),which effectively solves the problem of slow convergence of the DDPG algorithm in the high-dimensional action space.The simulation results show that the proposed algorithm,AGA-DDPG,saves approximately 61.8%,55%,21%,and 33%of the overall overhead compared to local MEC,random offloading,TD3,and DDPG,respectively.The proposed strategy is potentially important for improving real-time monitoring,big data analysis,and predictive maintenance of offshore wind farm operation and maintenance systems.