Insights into microbiota community dynamics and flavor development mechanism during golden pomfret(Trachinotus ovatus)fermentation based on single-molecule real-time sequencing and molecular networking analysis

Popular fermented golden pomfret(Trachinotus ovatus)is prepared via spontaneous fermentation;however,the mechanisms underlying the regulation of its flavor development remain unclear.This study shows the roles of the complex microbiota and the dynamic changes in microbial community and flavor compounds during fish fermentation.Single-molecule real-time sequencing and molecular networking analysis revealed the correlations among different microbial genera and the relationships between microbial taxa and volatile compounds.Mechanisms underlying flavor development were also elucidated via KEGG based functional annotations.Clostridium,Shewanella,and Staphylococcus were the dominant microbial genera.Forty-nine volatile compounds were detected in the fermented fish samples,with thirteen identified as characteristic volatile compounds(ROAV>1).Volatile profiles resulted from the interactions among the microorganisms and derived enzymes,with the main metabolic pathways being amino acid biosynthesis/metabolism,carbon metabolism,and glycolysis/gluconeogenesis.This study demonstrated the approaches for distinguishing key microbiota associated with volatile compounds and monitoring the industrial production of high-quality fermented fish products.

Biodiversity metrics on ecological networks: Demonstrated with animal gastrointestinal microbiomes

Biodiversity has become a terminology familiar to virtually every citizen in modern societies.It is said that ecology studies the economy of nature,and economy studies the ecology of humans;then measuring biodiversity should be similar with measuring national wealth.Indeed,there have been many parallels between ecology and economics,actually beyond analogies.For example,arguably the second most widely used biodiversity metric,Simpson(1949)’s diversity index,is a function of familiar Gini-index in economics.One of the biggest challenges has been the high“diversity”of diversity indexes due to their excessive“speciation”-there are so many indexes,similar to each country’s sovereign currency-leaving confused diversity practitioners in dilemma.In 1973,Hill introduced the concept of“numbers equivalent”,which is based on Renyi entropy and originated in economics,but possibly due to his abstruse interpretation of the concept,his message was not widely received by ecologists until nearly four decades later.What Hill suggested was similar to link the US dollar to gold at the rate of$35 per ounce under the Bretton Woods system.The Hill numbers now are considered most appropriate biodiversity metrics system,unifying Shannon,Simpson and other diversity indexes.Here,we approach to another paradigmatic shift-measuring biodiversity on ecological networks-demonstrated with animal gastrointestinal microbiomes representing four major invertebrate classes and all six vertebrate classes.The network diversity can reveal the diversity of species interactions,which is a necessary step for understanding the spatial and temporal structures and dynamics of biodiversity across environmental gradients.

Predicting microseismic,acoustic emission and electromagnetic radiation data using neural networks

Microseism,acoustic emission and electromagnetic radiation(M-A-E)data are usually used for predicting rockburst hazards.However,it is a great challenge to realize the prediction of M-A-E data.In this study,with the aid of a deep learning algorithm,a new method for the prediction of M-A-E data is proposed.In this method,an M-A-E data prediction model is built based on a variety of neural networks after analyzing numerous M-A-E data,and then the M-A-E data can be predicted.The predicted results are highly correlated with the real data collected in the field.Through field verification,the deep learning-based prediction method of M-A-E data provides quantitative prediction data for rockburst monitoring.

A generalized deep neural network approach for improving resolution of fluorescence microscopy images

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed,imaging resolution,and imagingflux.This paper proposes a deep neural network based on a generative adversarial network(GAN).The generator employs a U-Net-based network,which integrates Dense Net for the downsampling component.The proposed method has excellent properties,for example,the network model is trained with several different datasets of biological structures;the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging;and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets.In addition,experimental results showed that the method improved the resolution of caveolin-coated pits(CCPs)structures from 264 nm to 138 nm,a 1.91-fold increase,and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

Assessing environmental impact:Micro-energy network optimization in a Chinese industrial park

Micro-energy systems contribute significantly to environmental improvement by reducing dependence on power grids through the utilization of multiple renewable energy sources.This study quantified the environmental impact of a micro-energy network system in an industrial park through a life cycle assessment using the operation of the micro-energy network over a year as the functional unit and“cradle-to-gate”as the system boundary.Based on the baseline scenario,a natural gas generator set was added to replace central heating,and the light pipes were expanded to constitute the optimized scenario.The results showed that the key impact categories for both scenarios were global warming,fine particulate matter formation,human carcinogenic toxicity,and human non-carcinogenic toxicity.The overall environmental impact of the optimized scenario was reduced by 68%compared to the baseline scenario.A sensitivity analysis of the key factors showed that electricity from the power grid was the key impact factor in both scenarios,followed by central heating and natural gas.Therefore,to reduce the environmental impact of network systems,it is necessary to further optimize the grid power structure.The research approach can be used to optimize micro-energy networks and evaluate the environmental impact of different energy systems.

Femtosecond laser fabrication of 3D vertically aligned micro-pore network on thick-film Li_(4)Ti_(5)O_(12)electrode for high-performance lithium storage

The development of energy storage devices with high energy density relies heavily on thick film electrodes,but it is challenging due to the limited ion transport kinetics inherent in thick electrodes.Here,we report on the preparation of a directional vertical array of micro-porous transport networks on LTO electrodes using a femtosecond laser processing strategy,enabling directional ion rapid transport and achieving good electrochemical performance in thick film electrodes.Various three-dimensional(3D)vertically aligned micro-pore networks are innovatively designed,and the structure,kinetics characteristics,and electrochemical performance of the prepared ion transport channels are analyzed and discussed by multiple characterization and testing methods.Furthermore,the rational mechanisms of electrode performance improvement are studied experimentally and simulated from two aspects of structural mechanics and transmission kinetics.The ion diffusion coefficient,rate performance at 60 C,and electrode interface area of the laser-optimized 60-15%micro-porous transport network electrodes increase by 25.2 times,2.2 times,and 2.15 times,respectively than those of untreated electrodes.Therefore,the preparation of 3D micro-porous transport networks by femtosecond laser on ultra-thick electrodes is a feasible way to develop high-energy batteries.In addition,the unique micro-porous transport network structure can be widely extended to design and explore other high-performance energy materials.

Printed Circuit Board (PCB) Surface Micro Defect Detection Model Based on Residual Network with Novel Attention Mechanism

Printed Circuit Board(PCB)surface tiny defect detection is a difficult task in the integrated circuit industry,especially since the detection of tiny defects on PCB boards with large-size complex circuits has become one of the bottlenecks.To improve the performance of PCB surface tiny defects detection,a PCB tiny defects detection model based on an improved attention residual network(YOLOX-AttResNet)is proposed.First,the unsupervised clustering performance of the K-means algorithm is exploited to optimize the channel weights for subsequent operations by feeding the feature mapping into the SENet(Squeeze and Excitation Network)attention network;then the improved K-means-SENet network is fused with the directly mapped edges of the traditional ResNet network to form an augmented residual network(AttResNet);and finally,the AttResNet module is substituted for the traditional ResNet structure in the backbone feature extraction network of mainstream excellent detection models,thus improving the ability to extract small features from the backbone of the target detection network.The results of ablation experiments on a PCB surface defect dataset show that AttResNet is a reliable and efficient module.In Torify the performance of AttResNet for detecting small defects in large-size complex circuit images,a series of comparison experiments are further performed.The results show that the AttResNet module combines well with the five best existing target detection frameworks(YOLOv3,YOLOX,Faster R-CNN,TDD-Net,Cascade R-CNN),and all the combined new models have improved detection accuracy compared to the original model,which suggests that the AttResNet module proposed in this paper can help the detection model to extract target features.Among them,the YOLOX-AttResNet model proposed in this paper performs the best,with the highest accuracy of 98.45% and the detection speed of 36 FPS(Frames Per Second),which meets the accuracy and real-time requirements for the detection of tiny defects on PCB surfaces.This study can provide some new ideas for other real-time online detection tasks of tiny targets with high-resolution images.

Sugarcane/soybean intercropping with reduced nitrogen addition enhances residue-derived labile soil organic carbon and microbial network complexity in the soil during straw decomposition

Sugarcane/soybean intercropping with reduced nitrogen addition is an important sustainable agricultural pattern that can alter soil ecological functions,thereby affecting straw decomposition in the soil.However,the mechanisms underlying changes in soil organic carbon(SOC)composition and microbial communities during straw decomposition under long-term intercropping with reduced nitrogen addition remain unclear.In this study,we conducted an in-situ microplot incubation experiment with^(13)C-labeled soybean straw residue addition in a two-factor(cropping pattern:sugarcane monoculture(MS)and sugarcane/soybean intercropping(SB);nitrogen addition levels:reduced nitrogen addition(N1)and conventional nitrogen addition(N2))long-term experimental field plot.The results showed that the SBN1 treatment significantly increased the residual particulate organic carbon(POC)and residual microbial biomass carbon(MBC)contents during straw decomposition,and the straw carbon in soil was mainly conserved as POC.Straw addition changed the structure and reduced the diversity of the soil microbial community,but microbial diversity gradually recovered with decomposition time.During straw decomposition,the intercropping pattern significantly increased the relative abundances of Firmicutes and Ascomycota.In addition,straw addition reduced microbial network complexity in the sugarcane/soybean intercropping pattern but increased it in the sugarcane monoculture pattern.Nevertheless,microbial network complexity remained higher in the SBN1 treatment than in the MSN1 treatment.In general,the SBN1 treatment significantly increased the diversity of microbial communities and the relative abundance of microorganisms associated with organic matter decomposition,and the changes in microbial communities were mainly driven by the residual labile SOC fractions.These findings suggest that more straw carbon can be sequestered in the soil under sugarcane/soybean intercropping with reduced nitrogen addition to maintain microbial diversity and contribute to the development of sustainable agriculture.

Application of virtual reality technology improves the functionality of brain networks in individuals experiencing pain

Medical procedures are inherently invasive and carry the risk of inducing pain to the mind and body.Recently,efforts have been made to alleviate the discomfort associated with invasive medical procedures through the use of virtual reality(VR)technology.VR has been demonstrated to be an effective treatment for pain associated with medical procedures,as well as for chronic pain conditions for which no effective treatment has been established.The precise mechanism by which the diversion from reality facilitated by VR contributes to the diminution of pain and anxiety has yet to be elucidated.However,the provision of positive images through VR-based visual stimulation may enhance the functionality of brain networks.The salience network is diminished,while the default mode network is enhanced.Additionally,the medial prefrontal cortex may establish a stronger connection with the default mode network,which could result in a reduction of pain and anxiety.Further research into the potential of VR technology to alleviate pain could lead to a reduction in the number of individuals who overdose on painkillers and contribute to positive change in the medical field.

Preoperative prediction of hepatocellular carcinoma microvascular invasion based on magnetic resonance imaging feature extraction artificial neural network

BACKGROUND Hepatocellular carcinoma(HCC)recurrence is highly correlated with increased mortality.Microvascular invasion(MVI)is indicative of aggressive tumor biology in HCC.AIM To construct an artificial neural network(ANN)capable of accurately predicting MVI presence in HCC using magnetic resonance imaging.METHODS This study included 255 patients with HCC with tumors<3 cm.Radiologists annotated the tumors on the T1-weighted plain MR images.Subsequently,a three-layer ANN was constructed using image features as inputs to predict MVI status in patients with HCC.Postoperative pathological examination is considered the gold standard for determining MVI.Receiver operating characteristic analysis was used to evaluate the effectiveness of the algorithm.RESULTS Using the bagging strategy to vote for 50 classifier classification results,a prediction model yielded an area under the curve(AUC)of 0.79.Moreover,correlation analysis revealed that alpha-fetoprotein values and tumor volume were not significantly correlated with the occurrence of MVI,whereas tumor sphericity was significantly correlated with MVI(P<0.01).CONCLUSION Analysis of variable correlations regarding MVI in tumors with diameters<3 cm should prioritize tumor sphericity.The ANN model demonstrated strong predictive MVI for patients with HCC(AUC=0.79).

Unlocking the future:Mitochondrial genes and neural networks in predicting ovarian cancer prognosis and immunotherapy response

BACKGROUND Mitochondrial genes are involved in tumor metabolism in ovarian cancer(OC)and affect immune cell infiltration and treatment responses.AIM To predict prognosis and immunotherapy response in patients diagnosed with OC using mitochondrial genes and neural networks.METHODS Prognosis,immunotherapy efficacy,and next-generation sequencing data of patients with OC were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus.Mitochondrial genes were sourced from the MitoCarta3.0 database.The discovery cohort for model construction was created from 70% of the patients,whereas the remaining 30% constituted the validation cohort.Using the expression of mitochondrial genes as the predictor variable and based on neural network algorithm,the overall survival time and immunotherapy efficacy(complete or partial response)of patients were predicted.RESULTS In total,375 patients with OC were included to construct the prognostic model,and 26 patients were included to construct the immune efficacy model.The average area under the receiver operating characteristic curve of the prognostic model was 0.7268[95% confidence interval(CI):0.7258-0.7278]in the discovery cohort and 0.6475(95%CI:0.6466-0.6484)in the validation cohort.The average area under the receiver operating characteristic curve of the immunotherapy efficacy model was 0.9444(95%CI:0.8333-1.0000)in the discovery cohort and 0.9167(95%CI:0.6667-1.0000)in the validation cohort.CONCLUSION The application of mitochondrial genes and neural networks has the potential to predict prognosis and immunotherapy response in patients with OC,providing valuable insights into personalized treatment strategies.

Resting-state brain network remodeling after different nerve reconstruction surgeries:a functional magnetic resonance imaging study in brachial plexus injury rats

Distinct brain remodeling has been found after different nerve reconstruction strategies,including motor representation of the affected limb.However,differences among reconstruction strategies at the brain network level have not been elucidated.This study aimed to explore intranetwork changes related to altered peripheral neural pathways after different nerve reconstruction surgeries,including nerve repair,endto-end nerve transfer,and end-to-side nerve transfer.Sprague–Dawley rats underwent complete left brachial plexus transection and were divided into four equal groups of eight:no nerve repair,grafted nerve repair,phrenic nerve end-to-end transfer,and end-to-side transfer with a graft sutured to the anterior upper trunk.Resting-state brain functional magnetic resonance imaging was obtained 7 months after surgery.The independent component analysis algorithm was utilized to identify group-level network components of interest and extract resting-state functional connectivity values of each voxel within the component.Alterations in intra-network resting-state functional connectivity were compared among the groups.Target muscle reinnervation was assessed by behavioral observation(elbow flexion)and electromyography.The results showed that alterations in the sensorimotor and interoception networks were mostly related to changes in the peripheral neural pathway.Nerve repair was related to enhanced connectivity within the sensorimotor network,while end-to-side nerve transfer might be more beneficial for restoring control over the affected limb by the original motor representation.The thalamic-cortical pathway was enhanced within the interoception network after nerve repair and end-to-end nerve transfer.Brain areas related to cognition and emotion were enhanced after end-to-side nerve transfer.Our study revealed important brain networks related to different nerve reconstructions.These networks may be potential targets for enhancing motor recovery.

Ultrastructural Observation on the Intra-and Intercellular Microtrabecular Network of the Pollen Mother Cells in Onion ( Allium cepa )

Using DGD embedment_free electron microscopy, ultrastructural observation on the intra_ and intercellular microtrabecular network (MN) of the pollen mother cells (PMC) of the whole meiotic prophase Ⅰ in onion ( Allium cepa L.) was performed. Complex nuclear MN was observed in the nucleus of PMCs, spreading throughout the nuclear region. The nucleolus and chromosomes were connected with the MN filament network. The uniformity of nuclear MN changed with the development of the PMCs. A lamina_like structure surrounded the nucleus and joined the MN in nucleus and in cytoplasm, but it disassembled at the end of prophase Ⅰ. There was also a complex cytoplasmic MN in PMCs, without obvious variation during the prophase Ⅰ. Furthermore, MN in cytoplasmic connections (plasmodesmata and cytoplasmic channels) was noticed to link the frameworks in two neighboring PMCs into one entity. Cytomixis was observed at synizesis of prophase Ⅰ in this experiment, and MN in cytoplasm and in nucleus was noticed to distribute in these granules which migrated from one PMC into its neighboring cell. At this time the nucleus moved aside from center of the PMC, but the rest of the cell was still fulfilled with MN filaments. The relationships of nuclear MN with nucleolus and chromosomes, lamina with nucleus, as well as intra_ and intercellular MN with cytomixis are discussed in this paper.

Application of novel physical picture based on artificial neural networks to predict microstructure evolution of Al-Zn-Mg-Cu alloy during solid solution process

The effects of the solid solution conditions on the microstructure and tensile properties of Al?Zn?Mg?Cu aluminum alloy were investigated by in-situ resistivity measurement, optical microscopy (OM), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and tensile test. A radial basis function artificial neural network (RBF-ANN) model was developed for the analysis and prediction of the electrical resistivity of the tested alloy during the solid solution process. The results show that the model is capable of predicting the electrical resistivity with remarkable success. The correlation coefficient between the predicted results and experimental data is 0.9958 and the relative error is 0.33%. The predicted data were adopted to construct a novel physical picture which was defined as “solution resistivity map”. As revealed by the map, the optimum domain for the solid solution of the tested alloy is in the temperature range of 465?475 °C and solution time range of 50?60 min. In this domain, the solution of second particles and the recrystallization phenomenon will reach equilibrium.

Temperature compensation method of silicon microgyroscope based on BP neural network

The temperature characteristics of a silicon microgyroscope are studied, and the temperature compensation method of the silicon microgyroscope is proposed. First, an open-loop circuit is adopted to test the entire microgyroscope＇s resonant frequency and quality factor variations over temperature, and the zero bias changing trend over temperature is measured via a closed-loop circuit. Then, in order to alleviate the temperature effects on the performance of the microgyroscope, a kind of temperature compensated method based on the error back propagation（BP）neural network is proposed. By the Matlab simulation, the optimal temperature compensation model based on the BP neural network is well trained after four steps, and the objective error of the microgyroscope＇s zero bias can achieve 0.001 in full temperature range. By the experiment, the real time operation results of the compensation method demonstrate that the maximum zero bias of the microgyroscope can be decreased from 12.43 to 0.75（°）/s after compensation when the ambient temperature varies from -40 to 80℃, which greatly improves the zero bias stability performance of the microgyroscope.

不同方法对根管内氢氧化钙去除效果的micro-CT评价

目的:应用显微CT(micro-computed tomography,micro-CT)建立磨牙根管模型,评价不同方法对氢氧化钙的清除效率。方法:收集2023年10月—2024年2月口腔综合科门诊拔除的11颗磨牙(24个根管),按照标准根管治疗流程,镍钛器械预备完成后,在根管内置入氢氧化钙,1周后,根据不同冲洗方法将样本随机分为侧方开口注射器组、超声荡洗组(EMS组)和声波震荡组(EDDY组)等3组(n=8)。冲洗前后使用micro-CT重建根管系统并使用不同颜色标记独立根管,计算各个根管氢氧化钙体积,比较各组氢氧化钙清除率。采用SPSS 19.0软件包对数据进行统计学分析。结果:3种方法均无法完全去净根管内氢氧化钙。使用次氯酸钠作为冲洗液时,超声荡洗和声波震荡清除效果优于侧方开口冲洗器(P<0.05);超声荡洗和声波震荡去除氢氧化钙效率相当,差异无统计学意义(P>0.05);氢氧化钙残留部位集中于根尖区。结论:Micro-CT能较为高效地重建磨牙根管模型,可用于评价氢氧化钙去除效果。超声荡洗组和声波震荡组清除氢氧化钙效率相当,优于侧方开口注射器组。

Characterizing the influence of stress-induced microcracks on the laboratory strength and fracture development in brittle rocks using a finite-discrete element method-micro discrete fracture network FDEM-μDFN approach

Heterogeneity is an inherent component of rock and may be present in different forms including mineralheterogeneity, geometrical heterogeneity, weak grain boundaries and micro-defects. Microcracks areusually observed in crystalline rocks in two forms： natural and stress-induced; the amount of stressinducedmicrocracking increases with depth and in-situ stress. Laboratory results indicate that thephysical properties of rocks such as strength, deformability, P-wave velocity and permeability areinfluenced by increase in microcrack intensity. In this study, the finite-discrete element method （FDEM）is used to model microcrack heterogeneity by introducing into a model sample sets of microcracks usingthe proposed micro discrete fracture network （mDFN） approach. The characteristics of the microcracksrequired to create mDFN models are obtained through image analyses of thin sections of Lac du Bonnetgranite adopted from published literature. A suite of two-dimensional laboratory tests including uniaxial,triaxial compression and Brazilian tests is simulated and the results are compared with laboratory data.The FDEM-mDFN models indicate that micro-heterogeneity has a profound influence on both the mechanicalbehavior and resultant fracture pattern. An increase in the microcrack intensity leads to areduction in the strength of the sample and changes the character of the rock strength envelope. Spallingand axial splitting dominate the failure mode at low confinement while shear failure is the dominantfailure mode at high confinement. Numerical results from simulated compression tests show thatmicrocracking reduces the cohesive component of strength alone, and the frictional strength componentremains unaffected. Results from simulated Brazilian tests show that the tensile strength is influenced bythe presence of microcracks, with a reduction in tensile strength as microcrack intensity increases. Theimportance of microcrack heterogeneity in reproducing a bi-linear or S-shape failure envelope and itseffects on the mechanisms leading to spalling damage near an underground opening are also discussed.

Micro LED车灯投影光学系统设计与优化

本文提出了一种基于Micro LED阵列的车灯投影方案,设计了以像素尺寸为80μm×80μm的200×150白光Micro LED阵列作为显示光源,视场角为16°×34°的车灯投影光学系统,并对物面倾斜角度和光学系统结构进行了优化。此外,分别采用反向畸变处理方法和像素灰度调制方法用以解决车灯投影图像的梯形畸变和照度均匀性问题,并搭建了投影实验平台,对图像校正方法进行了验证。实验结果表明:校正后图像梯形畸变系数p1,p2分别从0.0932和0.3680下降至0.0835和0.0373,像面照度均匀性从83.2%提高到93.2%。本文通过对基于Micro LED的倾斜投影车灯光学系统进行优化设计及采用图像校正方法,实现了高光效、低畸变的车灯投影。

Planning and design of a micro energy network for seawater desalination and regional energy interconnection

With the rapid developme nt of the economy, the continu ously in creasing populati on, and ongoing climate change, the shortage of freshwater resources has become an increasingly important global problem. Seawater desalination tech no logy can effectively alleviate the pressure on freshwater supplies and has bee n in vestigated in many countries. However, the majority of existing projects focus on the research and development of desalination equipment and the use of new tech no logies and pay less atte ntion to the operation optimizati on of the desalinati on process. The micro energy n etwork (MEN) designed in this study is an efficient distributed energy supply system that can be used to simultaneously supply electricity, cooling, heating, and freshwater as photovoltaic power, wind power, combined heat and power (CHP), electric cooling and heating, and a seawater desalinati on device are in teg rated into the MEN. In this study, a model for operati on optimization of a MEN for seawater desalination was developed and the influences of the electric cooling and heating ratios and the operation optimization of the seawater desalination device were studied with the aim of minimizing the life cycle cost. Based on the results of this study, MENs can reduce the operation cost of desalination devices and improve the efficiency of renewable energy sources.

Active Vibration Isolation of Micro-Manufacturing Platform Based on Neural Network

The micromation and precision of the Micro-Electromechanical System demand that its manufacturing, measuring and assembling must work in a micro-manufacturing platform with good ability to isolate vibrations. This paper develops a vibration isolation system of micro-manufacturing platform. The brains of many kinds of birds can isolate vibrations well, such as woodpecker’s brain. When a woodpecker pecks the wood at the speed as 1.6 times as the velocity of sound, its brain will tolerate the wallop 1 500 times of the weight of itself without any damage. The isolation mechanics and organic texture of woodpecker’s brain that has good isolation characteristics were studied. A structure model of vibration isolation system for the micro-manufacturing platform is established based on the bionics of the bird’s brain vibration isolation mechanism. In order to isolate effectively the high frequency vibrations from the ground, a rubber layer is used to isolate vibrations passively between the micro-manufacturing platform’s pedestal and the ground. This layer corresponds to the cartilage and muscles in the outer meninges of the bird’s brain. The active vibration isolation technique is adopted to isolate vibrations between the micro-manufacturing platform and the pedestal. Air springs are used as elastic components, which correspond to the interspaces between the outer meninges and the encephala of the bird’s brain. Actuators are made of giant magnetostrictive material, and it corresponds to the nerves and neural muscles linking the meninges and the encephala. The actuators and air springs are arranged vertically in parallel to make use of the giant magnetostrictive actuators effectively. The air springs support almost all weight of the micro-manufacturing platform and the giant magnetostrictive actuators support almost no weight. In order to realize high performance to isolate complex micro-vibration, the control method using a three-layer neural network is presented. This vibration control system takes into account the floor disturbance and the direct disturbance acting on the micro-manufacturing platform. The absolute acceleration of the micro-manufacturing platform is used as the performance index of vibration control. The performance of the control system is tested by numerical simulation. Simulation results show that the active vibration isolation system has good isolation performance against the floor disturbance and the direct disturbance acting on the micro-manufacturing platform in all the frequency range.