近红外无创血糖浓度的Label Sensitivity算法和支持向量机回归

近红外光谱分析技术在生物医学工程领域具有广阔应用前景。无创且持续性地测量能实时监控人体血糖水平,给糖尿病患者带来极大便利性、提高生存质量、降低糖尿病并发症发生率具有很大的社会效益。无创血糖监测的想法提出较早,但仍然存在预测精度低、预测值与标签值相关性不高等难点,至今没有达到临床要求。近年来,光谱检测技术发展迅猛且机器学习技术在智能信息处理方面具有明显优势,两者结合可以有效提高人体无创血糖医学监测模型的精度和普适性。提出了一种标签敏感度算法(LS),并结合支持向量机方法建立了人体血糖含量预测模型。使用近红外光谱仪采集了4名志愿者食指处动态血液光谱数据(每名志愿者28组数据),并使用多元散射矫正(MSC)方法消除了部分光散射的影响。考虑血糖对不同波长光的吸收有差异,提出了基于血糖浓度标签差的特征波长挑选方法,并构建了标签敏感度支持向量机(LSSVR)预测模型。设计实验,对比该模型与偏最小二乘回归(PLSR)和区分度支持向量机(FSSVR)算法。结果表明,LS算法的最佳特征波长数为32,经特征波长选择后的LSSVR表现最佳,其均方误差降低至0.02 mmol·L^(-1),明显优于全谱段PLSR模型,血糖浓度的预测值与标签值的相关系数提升至99.8%,预测值全部位于可容许误差的克拉克网格A区内。LSSVR模型的优异表现为早日实现血糖的无创监测提供了新思路。

Identification, pathogenicity, and fungicide sensitivity of Eutiarosporella dactylidis associated with leaf blight on maize in China

Maize(Zea mays L.) is an economically vital grain crop that is cultivated worldwide. In 2011, a maize foliar disease was detected in Lingtai and Lintao counties in Gansu Province, China. The characteristic signs and symptoms of this disease include irregular chlorotic lesions on the tips and edges of infected leaves and black punctate fruiting bodies in dead leaf tissues. Given favourable environmental conditions, this disease spread to areas surrounding Gansu. In this study, infected leaves were collected from Gansu and Ningxia Hui Autonomous Region between 2018and 2020 to identify the disease-causing pathogen. Based on morphological features, pathogenicity tests, and multilocus phylogenetic analysis involving internal transcribed spacer(ITS), 18S small subunit rDNA(SSU), 28S large subunit rDNA(LSU), translation elongation factor 1-alpha(TEF), and β-tubulin(TUB) sequences, Eutiarosporella dactylidis was identified as the causative pathogen of this newly discovered leaf blight. Furthermore, an in vitro bioassay was conducted on representative strains using six fungicides, and both fludioxonil and carbendazim were found to significantly inhibit the mycelial growth of E. dactylidis. The results of this study provide a reference for the detection and management of Eutiarosporella leaf blight.

Future changes in precipitation and water availability over the Tibetan Plateau projected by CMIP6 models constrained by climate sensitivity

Precipitation projections over the Tibetan Plateau(TP)show diversity among existing studies,partly due to model uncertainty.How to develop a reliable projection remains inconclusive.Here,based on the IPCC AR6–assessed likely range of equilibrium climate sensitivity(ECS)and the climatological precipitation performance,the authors constrain the CMIP6(phase 6 of the Coupled Model Intercomparison Project)model projection of summer precipitation and water availability over the TP.The best estimates of precipitation changes are 0.24,0.25,and 0.45 mm d^(−1)(5.9%,6.1%,and 11.2%)under the Shared Socioeconomic Pathway(SSP)scenarios of SSP1–2.6,SSP2–4.5,and SSP5–8.5 from 2050–2099 relative to 1965–2014,respectively.The corresponding constrained projections of water availability measured by precipitation minus evaporation(P–E)are 0.10,0.09,and 0.22 mm d^(−1)(5.7%,4.9%,and 13.2%),respectively.The increase of precipitation and P–E projected by the high-ECS models,whose ECS values are higher than the upper limit of the likely range,are about 1.7 times larger than those estimated by constrained projections.Spatially,there is a larger increase in precipitation and P–E over the eastern TP,while the western part shows a relatively weak difference in precipitation and a drier trend in P–E.The wetter TP projected by the high-ECS models resulted from both an approximately 1.2–1.4 times stronger hydrological sensitivity and additional warming of 0.6℃–1.2℃ under all three scenarios during 2050–2099.This study emphasizes that selecting climate models with climate sensitivity within the likely range is crucial to reducing the uncertainty in the projection of TP precipitation and water availability changes.

Efficient slope reliability and sensitivity analysis using quantile-based first-order second-moment method

This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.

MEAN SENSITIVITY AND BANACH MEAN SENSITIVITY FOR LINEAR OPERATORS

Let(X,T)be a linear dynamical system,where X is a Banach space and T:X→X is a bounded linear operator.This paper obtains that(X,T)is sensitive(Li-Yorke sensitive,mean sensitive,syndetically mean sensitive,respectively)if and only if(X,T)is Banach mean sensitive(Banach mean Li-Yorke sensitive,thickly multi-mean sensitive,thickly syndetically mean sensitive,respectively).Several examples are provided to distinguish between different notions of mean sensitivity,syndetic mean sensitivi`ty and mean Li-Yorke sensitivity.

Multivariate Time Series Anomaly Detection Based on Spatial-Temporal Network and Transformer in Industrial Internet of Things

In the Industrial Internet of Things(IIoT),sensors generate time series data to reflect the working state.When the systems are attacked,timely identification of outliers in time series is critical to ensure security.Although many anomaly detection methods have been proposed,the temporal correlation of the time series over the same sensor and the state(spatial)correlation between different sensors are rarely considered simultaneously in these methods.Owing to the superior capability of Transformer in learning time series features.This paper proposes a time series anomaly detection method based on a spatial-temporal network and an improved Transformer.Additionally,the methods based on graph neural networks typically include a graph structure learning module and an anomaly detection module,which are interdependent.However,in the initial phase of training,since neither of the modules has reached an optimal state,their performance may influence each other.This scenario makes the end-to-end training approach hard to effectively direct the learning trajectory of each module.This interdependence between the modules,coupled with the initial instability,may cause the model to find it hard to find the optimal solution during the training process,resulting in unsatisfactory results.We introduce an adaptive graph structure learning method to obtain the optimal model parameters and graph structure.Experiments on two publicly available datasets demonstrate that the proposed method attains higher anomaly detection results than other methods.

Characterizing permeability-porosity relationships of porous rocks using a stress sensitivity model in consideration of elastic-structural deformation and tortuosity sensitivity

Clarifying the relationship between stress sensitivities of permeability and porosity is of great significance in guiding underground resource mining.More and more studies focus on how to construct stress sensitivity models to describe the relationship and obtain a comprehensive stress sensitivity of porous rock.However,the limitations of elastic deformation calculation and incompleteness of considered tortuosity sensitivity lead to the fact that the existing stress sensitivity models are still unsatisfactory in terms of accuracy and generalization.Therefore,a more accurate and generic stress sensitivity model considering elastic-structural deformation of capillary cross-section and tortuosity sensitivity is proposed in this paper.The elastic deformation is derived from the fractal scaling model and Hooke's law.Considering the effects of elastic-structural deformation on tortuosity sensitivity,an empirical formula is proposed,and the conditions for its applicability are clarified.The predictive performance of the proposed model for the permeability-porosity relationships is validated in several sets of publicly available experimental data.These experimental data are from different rocks under different pressure cycles.The mean and standard deviation of relative errors of predicted stress sensitivity with respect to experimental data are 2.63%and 1.91%.Compared with other models,the proposed model has higher accuracy and better predictive generalization performance.It is also found that the porosity sensitivity exponent a,which can describe permeability-porosity relationships,is 2 when only elastic deformation is considered.a decreases from 2 when structural deformation is also considered.In addition,a may be greater than 3 due to the increase in tortuosity sensitivity when tortuosity sensitivity is considered even if the rock is not fractured.

Comprehensive evaluation and spatial-temporal evolution characteristics of urban resilience in Chengdu-Chongqing Economic Circle

To clarify the connotations and extensions of urban resilience,this study focuses on the Chengdu-Chongqing Economic Circle with 16 cities as research subjects.A comprehensive evaluation index system was constructed to measure the resilience of each city from 2003 to 2020.The spatial-temporal evolution characteristics were analyzed using Kernel density estimation,standard deviation ellipse,and spatial Markov chain analysis,and the spatial Tobit model was introduced to discover the influencing factors.The results indicate the following:①Urban resilience in the Chengdu-Chongqing Economic Circle displays an upward trend,with the center of gravity moving to the southwest,and the polarization phenomenon intensifying.②The urban resilience level in a region has certain spatial and geographical dependence,while the probability of urban resilience transfer differs in adjacent cities with different resilience levels.③Urban centrality,economic scale,openness level,and financial development promote urban resilience,whereas government scale significantly inhibits it.Finally,this paper proposes countermeasures and suggestions to improve the urban resilience of the Chengdu-Chongqing Economic Circle.

AFSTGCN:Prediction for multivariate time series using an adaptive fused spatial-temporal graph convolutional network

The prediction for Multivariate Time Series(MTS)explores the interrelationships among variables at historical moments,extracts their relevant characteristics,and is widely used in finance,weather,complex industries and other fields.Furthermore,it is important to construct a digital twin system.However,existing methods do not take full advantage of the potential properties of variables,which results in poor predicted accuracy.In this paper,we propose the Adaptive Fused Spatial-Temporal Graph Convolutional Network(AFSTGCN).First,to address the problem of the unknown spatial-temporal structure,we construct the Adaptive Fused Spatial-Temporal Graph(AFSTG)layer.Specifically,we fuse the spatial-temporal graph based on the interrelationship of spatial graphs.Simultaneously,we construct the adaptive adjacency matrix of the spatial-temporal graph using node embedding methods.Subsequently,to overcome the insufficient extraction of disordered correlation features,we construct the Adaptive Fused Spatial-Temporal Graph Convolutional(AFSTGC)module.The module forces the reordering of disordered temporal,spatial and spatial-temporal dependencies into rule-like data.AFSTGCN dynamically and synchronously acquires potential temporal,spatial and spatial-temporal correlations,thereby fully extracting rich hierarchical feature information to enhance the predicted accuracy.Experiments on different types of MTS datasets demonstrate that the model achieves state-of-the-art single-step and multi-step performance compared with eight other deep learning models.

High-Precision Flow Numerical Simulation and Productivity Evaluation of Shale Oil Considering Stress Sensitivity

Continental shale oil reservoirs,characterized by numerous bedding planes and micro-nano scale pores,feature significantly higher stress sensitivity compared to other types of reservoirs.However,research on suitable stress sensitivity characterization models is still limited.In this study,three commonly used stress sensitivity models for shale oil reservoirs were considered,and experiments on representative core samples were conducted.By fitting and comparing the data,the“exponential model”was identified as a characterization model that accurately represents stress sensitivity in continental shale oil reservoirs.To validate the accuracy of the model,a two-phase seepage mathematical model for shale oil reservoirs coupled with the exponential model was introduced.The model was discretely solved using the finite volume method,and its accuracy was verified through the commercial simulator CMG.The study evaluated the productivity of a typical horizontal well under different engineering,geological,and fracture conditions.The results indicate that considering stress sensitivity leads to a 13.57%reduction in production for the same matrix permeability.Additionally,as the fracture half-length and the number of fractures increase,and the bottomhole flowing pressure decreases,the reservoir stress sensitivity becomes higher.

Sensitivity and resistance risk analysis of Didymella bryoniae populations to fluopyram

Fluopyram is an succinate dehydrogenase inhibitors(SDHI)fungicide that has been registered in China to control gummy stem blight(GSB)in watermelons for many years.However,whether the field pathogens of GSB are still sensitive to fluopyram or not is unknown.Therefore,we collected 69 Didymella bryoniae isolates from the fields that usually use fluopyram to control GSB to determine the sensitivity change.The EC_(50)(50%inhibition effect)values of fluopyram against D.bryoniae ranged from 0.0691 to 0.3503μg mL^(–1) and the variation factor was 5.07.The mean EC_(50) value was(0.1579±0.0669)μg mL^(–1) and the curve of sensitivity was unimodal.No resistant strains were found in the isolates,which means that the pathogens were still sensitive to fluopyram.The minimal inhibition concentration(MIC)of fluopyram against D.bryoniae was 3μg mL^(–1).Four low-resistant mutants and two medium-resistant mutants were obtained using fungicide taming and the resistance of mutants could be inherited stably.The growth rate of mutants decreased significantly compared with that of wild-type strains while the biomass of most mutants was similar to that of wild-type strains.The sensitivity of most resistant mutants to various stresses was increased compared with that of wild-type strains.The virulence of mutants receded except for low-resistant mutant XN51FR-1,which had the same lesion area as XN51 on the watermelon leaves.The results indicated that the fitness of resistant mutants was decreased compared with that of wild-type strains.The cross-resistance assay indicated that fluopyram-resistant mutants were positive cross-resistant to all six SDHI fungicides in this test but were still sensitive to fluazinam and tebuconazole.So the resistance risk of D.bryoniae to fluopyram was moderate.In addition,we found that the SdhB gene of low-resistant mutant XN30FR-1 had three new point mutations at positions K258N,A259P,and H277N.Medium-resistant mutant XN52FR-1 showed a mutation at position H277N and other mutants did not have any point mutation.

Effect of astigmatism and spherical equivalent correction on contrast sensitivity

AIM:To investigate the effect of astigmatism and spherical equivalent(SE)correction on contrast sensitivity(CS).METHODS:In this cross-sectional study,103 visually normal subjects aged 18 to 36y with bilateral regular astigmatism in range of 1.00 diopter cylinder(DC)to 4.00 DC and normal best-corrected visual acuity(20/20)were recruited.Binocular CS was assessed by linear sine-wave gratings at 1.5,3,6,12,and 18 cycles per degree(cpd),before correction of astigmatism,after full correction of astigmatism by cylindrical spectacle lenses,and after SE of refractive error.The repeated measures ANOVA and Bonferroni test were used to compare the effects of astigmatism correction on logCS.RESULTS:Totally 39 patients were male and 64 patients were female with the mean age of 28.25±5.38y.The average degree of astigmatism in right and left eye was 2.03±0.83 and 2.10±0.78,respectively.Increases in uncorrected astigmatic power correlated with decreases in the logCS,especially at high spatial frequencies.A statistically significant difference in logCS was found between these three cases:before correction of astigmatism,after SE of refractive error,and after full correction of astigmatism by cylindrical spectacle lenses at all frequencies(P<0.001),except at 18 cpd.At 18 cpd,there was no statistically significant difference between logCS before and after SE of refractive error(P=1.0).Also,there was no statistically significant difference in mean CS between with-the-rule(WTR)and against-the-rule(ATR)astigmatism,before correction of astigmatism,after correction of astigmatism with cylindrical lenses,and after SE of refractive error.CONCLUSION:Binocular astigmatism defocus decreases CS depending on the degree of astigmatism power;correction of this will improve patent’s quality of vision.Although high astigmatism refractive error(more than 2.00 DC)that is fully corrected by cylindrical spectacle lenses doesn’t increase the CS to the maximum value,especially at higher spatial frequencies(12 and 18).Also SE refractive error effects on improving CS in low astigmatism power(less than 2.00 DC),especially at lower spatial frequencies.

An efficient data-driven global sensitivity analysis method of shale gas production through convolutional neural network

The shale gas development process is complex in terms of its flow mechanisms and the accuracy of the production forecasting is influenced by geological parameters and engineering parameters.Therefore,to quantitatively evaluate the relative importance of model parameters on the production forecasting performance,sensitivity analysis of parameters is required.The parameters are ranked according to the sensitivity coefficients for the subsequent optimization scheme design.A data-driven global sensitivity analysis(GSA)method using convolutional neural networks(CNN)is proposed to identify the influencing parameters in shale gas production.The CNN is trained on a large dataset,validated against numerical simulations,and utilized as a surrogate model for efficient sensitivity analysis.Our approach integrates CNN with the Sobol'global sensitivity analysis method,presenting three key scenarios for sensitivity analysis:analysis of the production stage as a whole,analysis by fixed time intervals,and analysis by declining rate.The findings underscore the predominant influence of reservoir thickness and well length on shale gas production.Furthermore,the temporal sensitivity analysis reveals the dynamic shifts in parameter importance across the distinct production stages.

Assessment of desertification sensitivity using an improved MEDALUS model in Northern China

Assessment of land sensitivity to desertification is an important step to support desertification monitoring and control.Based on the Mediterranean Desertification and Land Use(MEDALUS)model,we defined four quality indicators(soil,climate,vegetation and management)to evaluate the sensitivity of land in northern China to desertification.We improved MEDALUS via excluding cities from the areas at risk of desertification by means of defining a threshold value for population density.The framework,validated in northern China,further optimizes the model to link priority areas and land restoration programmed to support desertification control.We found that the four indicators influenced and restricted each other,which jointly affected the distribution of desertification sensitivity in northern China.The spatial distribution of sensitivity in northern China showed large regional differences,with clear boundaries and concentrated distributions of regions with high and low sensitivity;the overall sensitivity decreased,with some areas rated as having moderate,severe,and extremely severe sensitivity changing to slight sensitivity;and the influence weight was much higher for the management quality index than for the climate,vegetation,and soil indexes.This suggests that management was the main factor that affected desertification sensitivity in northern China,and that climate factors exacerbated sensitivity,but the factors that are driving the spatial heterogeneity of the influencing factors need further study。

On an isotropic porous solid cylinder:the analytical solution and sensitivity analysis of the pressure

Within this work,we perform a sensitivity analysis to determine the influence of the material input parameters on the pressure in an isotropic porous solid cylinder.We provide a step-by-step guide to obtain the analytical solution for a porous isotropic elastic cylinder in terms of the pressure,stresses,and elastic displacement.We obtain the solution by performing a Laplace transform on the governing equations,which are those of Biot's poroelasticity in cylindrical polar coordinates.We enforce radial boundary conditions and obtain the solution in the Laplace transformed domain before reverting back to the time domain.The sensitivity analysis is then carried out,considering only the derived pressure solution.This analysis finds that the time t,Biot's modulus M,and Poisson's ratio ν have the highest influence on the pressure whereas the initial value of pressure P_(0) plays a very little role.

An impact sensitivity assessment method of spacecraft based on virtual exterior wall

The impact sensitivity assessment of spacecraft is to obtain the probability of spacecraft encountering the OD/M(orbital debris or meteoroid),which is a prerequisite for survivability assessment of on-orbit spacecraft.An impact sensitivity assessment method of spacecraft based on virtual exterior wall was proposed to improve the computational efficiency.This method eliminates determination of the outermost surface elements of the spacecraft before generating the debris rays,which are assumed to originate from a non-concave virtual wall that completely wraps the spacecraft.The Dist Mesh method was adopted for the generating of the virtual wall to ensure its mesh quality.The influences of the sizes,mesh densities,shapes of the virtual wall on the efficiency and accuracy were considered to obtain the best combination of the size and mesh density of the wall and spacecraft.The results of this method were compared with those of S3DE(Survivability of Spacecraft in Space Debris Environment),BUMPER,MDPANTO,ESABASE2/Debris to verify the feasibility of the method.The PCHIP(Piecewise Cubic Hermite Interpolating Polynomial)was used to fit the size vs.flux relationship of the space debris to acquire the impact probability of OD/M with arbitrary size on the spacecraft.

Phase sensitivity with a coherent beam and twin beams via intensity difference detection

We focus on the Mach–Zehnder interferometer(MZI) with the input of a coherent beam and one of the bright entangled twin beams with an external power reference beam employed for measurement. The results show that the phase sensitivity can reach sub-Heisenberg limit and approach quantum Cramer–Rao bound by changing the squeezing parameters and the photon number of the coherent beam, under the phase-matching condition. The absence of the external power reference beam will degrade the performance of the phase sensitivity. Meanwhile, this scheme shows good robustness against the losses of the photon detectors. We present a detailed discussion about the phase sensitivities when the inputs are two coherent beams, or a coherent beam plus a single-mode squeezed vacuum beam based on the MZI. This scenario can be applied in the field of phase precision measurements and other optical sensors.

Activation Distance and Capacity Analysis for Ambient Backscatter Communications with Sensitivity Constraint and Beamforming

Circuit sensitivity of sensors or tags without battery is one practical constraint for ambient backscatter communication systems.This letter considers using beamforming to reduce the sensitivity constraint and evaluates the corresponding performance in terms of the tag activation distance and the system capacity.Specifically,we derive the activation probabilities of the tag in the case of single-antenna and multi-antenna transmitters.Besides,we obtain the capacity expressions for the ambient backscatter communication system with beamforming and illustrate the power allocation that maximizes the system capacity when the tag is activated.Finally,simulation results are provided to corroborate our proposed studies.

Analysis of sensitivity to hydrate blockage risk in natural gas gathering pipeline

During the operational process of natural gas gathering and transmission pipelines,the formation of hydrates is highly probable,leading to uncontrolled movement and aggregation of hydrates.The continuous migration and accumulation of hydrates further contribute to the obstruction of natural gas pipelines,resulting in production reduction,shutdowns,and pressure build-ups.Consequently,a cascade of risks is prone to occur.To address this issue,this study focuses on the operational process of natural gas gathering and transmission pipelines,where a comprehensive framework is established.This framework includes theoretical models for pipeline temperature distribution,pipeline pressure distribution,multiphase flow within the pipeline,hydrate blockage,and numerical solution methods.By analyzing the influence of inlet temperature,inlet pressure,and terminal pressure on hydrate formation within the pipeline,the sensitivity patterns of hydrate blockage risks are derived.The research indicates that reducing inlet pressure and terminal pressure could lead to a decreased maximum hydrate formation rate,potentially mitigating pipeline blockage during natural gas transportation.Furthermore,an increase in inlet temperature and terminal pressure,and a decrease in inlet pressure,results in a displacement of the most probable location for hydrate blockage towards the terminal station.However,it is crucial to note that operating under low-pressure conditions significantly elevates energy consumption within the gathering system,contradicting the operational goal of energy efficiency and reduction of energy consumption.Consequently,for high-pressure gathering pipelines,measures such as raising the inlet temperature or employing inhibitors,electrical heat tracing,and thermal insulation should be adopted to prevent hydrate formation during natural gas transportation.Moreover,considering abnormal conditions such as gas well production and pipeline network shutdowns,which could potentially trigger hydrate formation,the installation of methanol injection connectors remains necessary to ensure production safety.

Distribution of pathogenic bacteria and antimicrobial sensitivity of eye infections in Suzhou

AIM:To investigate the types of bacteria in patients with eye infections in Suzhou and their drug resistance to commonly used antibacterial drugs.METHODS:The clinical data of 155 patients were retrospectively collected in this study,and the pathogenic bacteria species and drug resistance of each pathogenic bacteria were analyzed.RESULTS:Among the 155 patients(age from 12 to 87 years old,with an average age of 57,99 males and 56 females)with eye infections(160 eyes:74 in the left eye,76 in the right eye and 5 in both eyes,all of which were exogenous),71(45.81%)strains were gram-positive bacteria,23(14.84%)strains were gram-negative bacteria and 61(39.35%)strains were fungi.Gram-positive bacteria were highly resistant to penicillin and erythromycin(78.87%and 46.48%respectively),but least resistant to vancomycin at 0.Gram-negative bacteria were highly resistant to cefoxitin and compound sulfamethoxazole(100%and 95.65%respectively),but least resistant to meropenem at 0.Comparison of the resistance of gram-positive and gram-negative bacteria to some drugs revealed statistically significant differences(P<0.05)in the resistance of both to cefoxitin,cotrimoxazole,levofloxacin,cefuroxime,ceftriaxone and ceftazidime,and both had higher rates of resistance to gram-negative bacteria than to gram-positive bacteria.The distribution of bacterial infection strains showed that Staphylococcus epidermidis was the most common strain in the conjunctiva,cornea,aqueous humor or vitreous body and other eye parts.Besides,Fusarium and Pseudomonas aeruginosa were also among the most common strains of conjunctival and corneal infections.CONCLUSION:Gram-positive bacteria are the dominant bacteria in eye infections,followed by gram-negative bacteria and fungi.Considering the resistance of gramnegative bacteria to multiple drugs,monitoring of bacteria should be strengthened in eye bacterial infections for effective prevention and control to reduce complications caused by eye infections.