Attention-relation network for mobile phone screen defect classification via a few samples

How to use a few defect samples to complete the defect classification is a key challenge in the production of mobile phone screens.An attention-relation network for the mobile phone screen defect classification is proposed in this paper.The architecture of the attention-relation network contains two modules:a feature extract module and a feature metric module.Different from other few-shot models,an attention mechanism is applied to metric learning in our model to measure the distance between features,so as to pay attention to the correlation between features and suppress unwanted information.Besides,we combine dilated convolution and skip connection to extract more feature information for follow-up processing.We validate attention-relation network on the mobile phone screen defect dataset.The experimental results show that the classification accuracy of the attentionrelation network is 0.9486 under the 5-way 1-shot training strategy and 0.9039 under the 5-way 5-shot setting.It achieves the excellent effect of classification for mobile phone screen defects and outperforms with dominant advantages.

Effectiveness of Histopathological Examination of Ultrasound-guided Puncture Biopsy Samples for Diagnosis of Extrapulmonary Tuberculosis

Objective To evaluate the diagnostic value of histopathological examination of ultrasound-guided puncture biopsy samples in extrapulmonary tuberculosis(EPTB).Methods This study was conducted at the Shanghai Public Health Clinical Center.A total of 115patients underwent ultrasound-guided puncture biopsy,followed by MGIT 960 culture(culture),smear,Gene Xpert MTB/RIF(Xpert),and histopathological examination.These assays were performed to evaluate their effectiveness in diagnosing EPTB in comparison to two different diagnostic criteria:liquid culture and composite reference standard(CRS).Results When CRS was used as the reference standard,the sensitivity and specificity of culture,smear,Xpert,and histopathological examination were(44.83%,89.29%),(51.72%,89.29%),(70.11%,96.43%),and(85.06%,82.14%),respectively.Based on liquid culture tests,the sensitivity and specificity of smear,Xpert,and pathological examination were(66.67%,72.60%),(83.33%,63.01%),and(92.86%,45.21%),respectively.Histopathological examination showed the highest sensitivity but lowest specificity.Further,we found that the combination of Xpert and histopathological examination showed a sensitivity of 90.80%and a specificity of 89.29%.Conclusion Ultrasound-guided puncture sampling is safe and effective for the diagnosis of EPTB.Compared with culture,smear,and Xpert,histopathological examination showed higher sensitivity but lower specificity.The combination of histopathology with Xpert showed the best performance characteristics.

Research on the Encapsulation Device for Lunar Samples

The encapsulation of lunar samples is a core research area in the third phase of the Chinese Lunar Exploration Program.The seal assembly,opening and closing mechanism(OCM),and locking mechanism are the core components of the encapsulation device of the lunar samples,and the requirements of a tight seal,lightweight,and low power make the design of these core components difficult.In this study,a combined sealing assembly,OCM,and locking mechanism were investigated for the device.The sealing architecture consists of rubber and an Ag-In alloy,and a theory was built to analyze the seal.Experiments of the electroplate Au coating on the knife-edge revealed that the hermetic seal can be significantly improved.The driving principle for coaxial double-helical pairs was investigated and used to design the OCM.Moreover,a locking mechanism was created using an electric initiating explosive device with orifice damping.By optimizing the design,the output parameters were adjusted to meet the requirements of the lunar explorer.The experimental results showed that the helium leak rate of the test pieces were not more than 5×10^(-11) Pa·m^(3)·s^(-1),the minimum power of the OCM was 0.3 W,and the total weight of the principle prototype was 2.9 kg.The explosive driven locking mechanism has low impact.This investigation solved the difficulties in achieving tight seal,light weight,and low power for the lunar explorer,and the results can also be used to explore other extraterrestrial objects in the future.

Exploring device physics of perovskite solar cell via machine learning with limited samples

Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.

Mechanical behavior of 2G NPR bolt anchored rock samples under static disturbance loading

The deep mining of coal resources is accompanied by severe environmental challenges and various potential engineering hazards.The implementation of NPR(negative Poisson's ratio)bolts are capable of controlling large deformations in the surrounding rock effectively.This paper focuses on studying the mechanical properties of the NPR bolt under static disturbance load.The deep nonlinear mechanical experimental system was used to study the mechanical behavior of rock samples with different anchored types(unanchored/PR anchored/2G NPR anchored)under static disturbance load.The whole process of rock samples was taken by high-speed camera to obtain the real-time failure characteristics under static disturbance load.At the same time,the acoustic emission signal was collected to obtain the key characteristic parameters of acoustic emission such as acoustic emission count,energy,and frequency.The deformation at the failure of the samples was calculated and analyzed by digital speckle software.The findings indicate that the failure mode of rock is influenced by different types of anchoring.The peak failure strength of 2G NPR bolt anchored rock samples exhibits an increase of 6.5%when compared to the unanchored rock samples.The cumulative count and cumulative energy of acoustic emission exhibit a decrease of 62.16%and 62.90%,respectively.The maximum deformation of bearing capacity exhibits an increase of 59.27%,while the failure time demonstrates a delay of 42.86%.The peak failure strength of the 2G NPR bolt anchored ones under static disturbance load exhibits an increase of 5.94%when compared to the rock anchored by PR(Poisson's ratio)bolt.The cumulative count and cumulative energy of acoustic emission exhibit a decrease of 47.16%and 43.86%,respectively.The maximum deformation of the bearing capacity exhibits an increase of 50.43%,and the failure time demonstrates a delay of 32%.After anchoring by 2G NPR bolt,anchoring support effectively reduces the risk of damage caused by static disturbance load.These results demonstrate that the support effect of 2G NPR bolt materials surpasses that of PR bolt.

A Fault Detection Method for Electric Vehicle Battery System Based on Bayesian Optimization SVDD Considering a Few Faulty Samples

Accurate and reliable fault detection is essential for the safe operation of electric vehicles.Support vector data description(SVDD)has been widely used in the field of fault detection.However,constructing the hypersphere boundary only describes the distribution of unlabeled samples,while the distribution of faulty samples cannot be effectively described and easilymisses detecting faulty data due to the imbalance of sample distribution.Meanwhile,selecting parameters is critical to the detection performance,and empirical parameterization is generally timeconsuming and laborious and may not result in finding the optimal parameters.Therefore,this paper proposes a semi-supervised data-driven method based on which the SVDD algorithm is improved and achieves excellent fault detection performance.By incorporating faulty samples into the underlying SVDD model,training deals better with the problem of missing detection of faulty samples caused by the imbalance in the distribution of abnormal samples,and the hypersphere boundary ismodified to classify the samplesmore accurately.The Bayesian Optimization NSVDD(BO-NSVDD)model was constructed to quickly and accurately optimize hyperparameter combinations.In the experiments,electric vehicle operation data with four common fault types are used to evaluate the performance with other five models,and the results show that the BO-NSVDD model presents superior detection performance for each type of fault data,especially in the imperceptible early and minor faults,which has seen very obvious advantages.Finally,the strong robustness of the proposed method is verified by adding different intensities of noise in the dataset.

Yarn Quality Prediction for Small Samples Based on AdaBoost Algorithm

In order to solve the problems of weak prediction stability and generalization ability of a neural network algorithm model in the yarn quality prediction research for small samples,a prediction model based on an AdaBoost algorithm(AdaBoost model) was established.A prediction model based on a linear regression algorithm(LR model) and a prediction model based on a multi-layer perceptron neural network algorithm(MLP model) were established for comparison.The prediction experiments of the yarn evenness and the yarn strength were implemented.Determination coefficients and prediction errors were used to evaluate the prediction accuracy of these models,and the K-fold cross validation was used to evaluate the generalization ability of these models.In the prediction experiments,the determination coefficient of the yarn evenness prediction result of the AdaBoost model is 76% and 87% higher than that of the LR model and the MLP model,respectively.The determination coefficient of the yarn strength prediction result of the AdaBoost model is slightly higher than that of the other two models.Considering that the yarn evenness dataset has a weaker linear relationship with the cotton dataset than that of the yarn strength dataset in this paper,the AdaBoost model has the best adaptability for the nonlinear dataset among the three models.In addition,the AdaBoost model shows generally better results in the cross-validation experiments and the series of prediction experiments at eight different training set sample sizes.It is proved that the AdaBoost model not only has good prediction accuracy but also has good prediction stability and generalization ability for small samples.

Metal Corrosion Rate Prediction of Small Samples Using an Ensemble Technique

Accurate prediction of the internal corrosion rates of oil and gas pipelines could be an effective way to prevent pipeline leaks.In this study,a proposed framework for predicting corrosion rates under a small sample of metal corrosion data in the laboratory was developed to provide a new perspective on how to solve the problem of pipeline corrosion under the condition of insufficient real samples.This approach employed the bagging algorithm to construct a strong learner by integrating several KNN learners.A total of 99 data were collected and split into training and test set with a 9:1 ratio.The training set was used to obtain the best hyperparameters by 10-fold cross-validation and grid search,and the test set was used to determine the performance of the model.The results showed that theMean Absolute Error(MAE)of this framework is 28.06%of the traditional model and outperforms other ensemblemethods.Therefore,the proposed framework is suitable formetal corrosion prediction under small sample conditions.

Development of a loop‑mediated isothermal amplification assay for detection of Austropeplea tomentosa from environmental water samples

Lymnaeid snails are key intermediate hosts for the development and survival of Fasciola spp.,the causative agent of Fascioliasis which are economically important parasites infecting humans and livestock globally.The current control method for treating Fascioliasis is heavily reliant on anthelmintic drugs,particularly Triclabendazole(TCBZ)which has resulted in drug-resistant parasites and poses significant risk as there are no long-term efficacious alternatives available.Sustainable control measures at the farm level could include both parasite and snail control will play an important role in Fasciola spp.control and reduce the reliance on anthelmintic drugs.Implementation of such sustainable control measures requires effective identification of snails on the property however Lymnaeid snails are small and difficult to physically locate.Snail identification using an environmental DNA approach is a recent approach in which physically locating snails are not required.Austropeplea tomentosa,is the primary intermediate snail host for F.hepatica transmission in South-East Australia and we present an in-field loop-mediated isothermal amplification and water filtering method for the detection of A.tomentosa eDNA from water samples to improve current surveillance methods.This methodology is highly sensitive with a detection limit of 5×10^(−6)ng/μL,detected in<20 minutes,with cumulative sample preparation and amplification time under 1 hour.This proposed workflow could assist in monitoring areas to determine the risk of Fascioliasis infection and implement strategies to manage snail populations to ultimately reduce the risk of infection for humans and livestock.

Geochemical Orientation Study of Stream Sediment Samples in the Southern Part of Nuggihalli Schist Belt: Ore Mineral Phases and Their Implications on the Bedrock Potential for Ores

Stream sediment sampling is a significant tool in geochemical exploration. The stream sediment composition reflects the bedrock geology, overburden cover, and metalliferous mineralization. This research article focuses on assessing selected trace element concentrations in stream sediments and interpreting their inter-element relationships using multivariate statistical methods. Tagadur Ranganathaswamy Gudda and its surroundings in the Nuggihalli schist belt of southern India have been investigated in the present work. The geology of the study area is complex, with a diverse range of litho units and evidence of strong structural deformation. The area is known for its mineralization potential for chromite, vanadiferous titanomagnetite, and sulfides. The topography of the region is characterized by an undulating terrain with a radial drainage pattern. Most part of the schist belt is soil covered except the Tagadur Ranganathaswamy Gudda area. For this study, a discrete stream sediment sampling method was adopted to collect the samples. Stream sediment samples were collected using a discrete sampling method and analyzed for trace elements using an ICP-AES spectrophotometer: Fe, Cr, Ti, V, Cu, Ni, Zn, Pb, Mn, Cd, and As have been analyzed. The analytical data were statistically treated using the SPSS software, including descriptive statistics, normalization of data using natural log transformation, and factor analysis with varimax rotation. The transformed data showed a log-normal distribution, indicating the presence of geochemical anomalies. The results of the study provide valuable insights into the geochemical processes and mineralization potential of the study area. The statistical analysis helps in understanding the inter-element relationships and identifying element groups and their implications on bedrock potential mineralization. Additionally, spatial analysis using inverse distance weighting interpolation provides information about the distribution of geochemical parameters across the study area. Overall, this research contributes to the understanding of stream sediment geochemistry and its application in mineral exploration. The findings have implications for future exploration efforts and can aid in the identification of potential ore deposits in the Nuggihalli schist belt and similar geological settings.

Liver Transplantation Outcomes of HBV-,HCV-,and Alcohol-induced Hepatocellular Carcinoma in the United States:Analysis of National Inpatient Samples

Objective Liver transplantation is a current treatment option for hepatocellular carcinoma(HCC).The United States National Inpatient Sample database was utilized to identify risk factors that influence the outcome of liver transplantation,including locoregional recurrence,distant metastasis,and in-hospital mortality,in HCC patients with concurrent hepatitis B infection,hepatitis C infection,or alcoholic cirrhosis.Methods This retrospective cohort study included HCC patients(n=2391)from the National Inpatient Sample database who underwent liver transplantation and were diagnosed with hepatitis B or C virus infection,co-infection with hepatitis B and C,or alcoholic cirrhosis of the liver between 2005 and 2014.Associations between HCC etiology and post-transplant outcomes were examined with multivariate analysis models.Results Liver cirrhosis was due to alcohol in 10.5%of patients,hepatitis B in 6.6%,hepatitis C in 10.8%,and combined hepatitis B and C infection in 24.3%.Distant metastasis was found in 16.7%of patients infected with hepatitis B and 9%of hepatitis C patients.Local recurrence of HCC was significantly more likely to occur in patients with hepatitis B than in those with alcohol-induced disease.Conclusion After liver transplantation,patients with hepatitis B infection have a higher risk of local recurrence and distant metastasis.Postoperative care and patient tracking are essential for liver transplant patients with hepatitis B infection.

Rapid screening of Zr-containing particles from Chang’e-5 lunar soil samples for isotope geochronology:Technical roadmap for future study

New samples returned by China Chang’e-5(CE-5)mission offer an opportunity for studying the lunar geologic longevity,space weathering,and regolith evolution.The age determination of the CE-5 samples was among the first scientific questions to be answered.However,the precious samples,most in the micrometer size range,challenge many traditional analyses on large single crystals of zircon developed for massive bulk samples.Here,we developed a non-destructive rapid screening of individual zirconium-containing particle for isotope geochronology based on a Micro X-ray fluorescence analysis(μXRF).The selected particles were verified via scanning electron microscopy(SEM),3D X-ray microscopy(XRM),and focused ion beam scanning electron microscopy(FIB-SEM)techniques,which showed that zirconium-bearing minerals with several microns were precisely positioned and readily suitable for site-specific isotopic dating by second ion mass spectrometry(SIMS).Such protocol could be also appli-cable in non-destructively screening other types of particles for different scientific purposes.We there-fore proposed a correlative workflow for comprehensively studying the CE-5 lunar samples from single particles on nanometer to atomic scales.Linking various microscopic and spectromicroscopic instru-ments together,this workflow consists of six steps:(1)single-particle selection with non-destructive μXRF technique,(2)2D/3D morphological and structural characterization with a correlative submicron 3D XRM and nanoscale resolution FIB-SEM imaging methods,(3)SEM analysis of the surface morphology and chemistry of the selected particle,(4)a series of microscopic and microbeam analyses(e.g.,SEM,electron probe microanalysis,and SIMS)on the cross-section of the selected particle to obtain structural,mineralogical,chemical,and isotopic features from the micron to nanometer scale,(5)advanced 2D/3D characterization and site-specific sample preparation of thin foil/tip specimens on a microregion of inter-est in the selected particle with FIB-SEM technique,and(6)comprehensive analyses on the FIB-milled specimens at nanometer to atomic scale with synchrotron-based scanning transmission X-ray micro-scopy,analytic transmission electron microscopy,and atom probe tomography.Following this technical roadmap,one can integrate multiple modalities into a uniform frame of multimodal and multiscale cor-related datasets to acquire high-throughput information on the limited or precious terrestrial and extraterrestrial samples.

Virological and histological evaluation of intestinal samples in COVID-19 patients

BACKGROUND Severe acute respiratory syndrome coronavirus-2(SARS-CoV-2)is the pathogen responsible for pandemic coronavirus disease 2019(COVID-19).It is a highly contagious virus which primarily affects the respiratory tract,nevertheless,the lungs are not the only target organs of the virus.The intestinal tract could represent an additional tropism site for SARS-CoV-2.Several observations have collectively suggested that enteric infections can occur in COVID-19 patients.However,the detection of viral RNA in gastrointestinal(GI)tissue samples has not been adequately investigated and results are conflicting.AIM To detect the presence of SARS-CoV-2 RNA in intestinal mucosa samples and to evaluate histological features.METHODS The COVID-19 patients hospitalized at an Italian tertiary hospital from April 2020 to March 2021 were evaluated for enrollment in an observational,monocentric trial.The study population was composed of two groups of adult patients.In the first group(biopsy group,30 patients),patients were eligible for inclusion if they had mild to moderate disease and if they agreed to have a rectal biopsy;in the second group(surgical specimen group,6 patients),patients were eligible for inclusion if they underwent intestinal resection during index hospitalization.Fifty-nine intestinal mucosal samples were analyzed.RESULTS Viral RNA was not detectable in any of the rectal biopsies performed(0/53).Histological examination showed no enterocyte damage,but slight edema of the lamina propria with mild inflammatory lymphoplasmacytic infiltration.There was no difference in inflammatory infiltrates in patients with and without GI symptoms.SARS-CoV-2 RNA was detected in fecal samples in 6 cases out of 14 cases examined(42.9%).In the surgical specimen group,all patients underwent emergency intestinal resection.Viral RNA was detected in 2 surgical specimens of the 6 examined,both of which were from patients with active neoplastic disease.Histological examination also pointed out abundant macrophages,granulocytes and plasma cells infiltrating the muscular layer and adipose tissue,and focal vasculitis.CONCLUSION Mild-moderate COVID-19 may not be associated with rectal infection by the virus.More comprehensive autopsies or surgical specimens are needed to provide histological evidence of intestinal infection.

Experimental and numerical simulation of loading rate effects on failure and strain energy characteristics of coal-rock composite samples

The deformation and failure of coal and rock is energy-driving results according to thermodynamics.It is important to study the strain energy characteristics of coal-rock composite samples to better understand the deformation and failure mechanism of of coal-rock composite structures.In this research,laboratory tests and numerical simulation of uniaxial compressions of coal-rock composite samples were carried out with five different loading rates.The test results show that strength,deformation,acoustic emission(AE)and energy evolution of coal-rock composite sample all have obvious loading rate effects.The uniaxial compressive strength and elastic modulus increase with the increase of loading rate.And with the increase of loading rate,the AE energy at the peak strength of coal-rock composites increases first,then decreases,and then increases.With the increase of loading rate,the AE cumulative count first decreases and then increases.And the total absorption energy and dissipation energy of coal-rock composite samples show non-linear increasing trends,while release elastic strain energy increases first and then decreases.The laboratory experiments conducted on coal-rock composite samples were simulated numerically using the particle flow code(PFC).With careful selection of suitable material constitutive models for coal and rock,and accurate estimation and calibration of mechanical parameters of coal-rock composite sample,it was possible to obtain a good agreement between the laboratory experimental and numerical results.This research can provide references for understanding failure of underground coalrock composite structure by using energy related measuring methods.

Particle flow code simulation of intact and fissured granitic rock samples

This study presents a calibration process of three-dimensional particle flow code(PFC3D)simulation of intact and fissured granite samples.First,laboratory stressestrain response from triaxial testing of intact and fissured granite samples is recalled.Then,PFC3D is introduced,with focus on the bonded particle models(BPM).After that,we present previous studies where intact rock is simulated by means of flatjoint approaches,and how improved accuracy was gained with the help of parametric studies.Then,models of the pre-fissured rock specimens were generated,including modeled fissures in the form of“smooth joint”type contacts.Finally,triaxial testing simulations of 1 t 2 and 2 t 3 jointed rock specimens were performed.Results show that both elastic behavior and the peak strength levels are closely matched,without any additional fine tuning of micro-mechanical parameters.Concerning the postfailure behavior,models reproduce the trends of decreasing dilation with increasing confinement and plasticity.However,the dilation values simulated are larger than those observed in practice.This is attributed to the difficulty in modeling some phenomena of fissured rock behaviors,such as rock piece corner crushing with dust production and interactions between newly formed shear bands or axial splitting cracks with pre-existing joints.

Investigation on Fe,Mn,Zn,Cu,Pb and Cd fractions in the natural surface coating samples and surficial sediments in the Songhua River,China

Natural surface coating samples （NSCSs） from the surface of shingles and surficial sediments （SSs） in the Songhua River, China were employed to investigate the relationship between NSCSs and SSs in fractions of heavy metals （Fe, Mn, Zn, Cu, Pb, and Cd） using the modified sequential extraction procedure （MSEP）. The results show that the differences between NSCSs and SSs in Fe fi＇actions were insignificant and Fe was dominantly present as residual phase （76.22% for NSCSs and 80.88% for SSs） and Fe-oxides phase （20.33% for NSCSs and 16.15% for SSs）. Significant variation of Mn distribution patterns between NSCSs and SSs was observed with Mn in NSCSs mainly present in Mn-oxides phase （48.27%） and that in SSs present as residual phase （45.44%）. Zn, Cu, Pb and Cd were found dominantly in residual fractions （〉48%）, and next in solid oxides/hydroxides for Zn, Pb and Cd and in easily oxidizable solids/compounds form for Cu, respectively. The heavy metal distribution patterns implied that Fe/Mn oxides both in NSCSs and SSs were more important sinks for binding and adsorption of Zn, Pb and Cd than organic matter （OM）, and inversely, higher affinity of Cu to OM than Fe/Mn oxides in NSCSs and SSs was obtained. Meanwhile, it was found that the distributions of heavy metals in NSCSs and SSs were similar to each other and the pseudo-total concentrations of Zn, Cu, Pb and Cd in NSCSs were greater than those in SSs, highlighting the more importance for NSCSs than SSs in controlling behaviours of heavy metals in aquatic environments.

A Rapid and Sensitive One Step-SYBR Green Based Semi Quantitative Real Time RT-PCR for the Detection of peste des petits ruminants Virus in the Clinical Samples

A sensitive and rapid single step real time （rt） RT-PCR was standardized using one-step Brilliant SYBR Green kit for detection and semi-quantitation ofpeste des petitis ruminants virus （PPRV） using the virus RNA and matrix （M） protein gene-specific primers and compared with established conventional RT-PCR and TaqMan RT-PCR. The assay amplifies a 124 bp fragment of the PPRV M gene with Tm of 78.28 to 78.50. The assay was linear within a range of 50 ng to 0.5 fg total virus RNA with a detection limit （sensitivity） of 0.5 fg. Based on the serial dilution of the live-attenuated PPR vaccine virus, the detection limit was -0.0001 cell culture infectious dose 50% units （TCID50）. Additionally, swab materials spiked with known titre of vaccine virus were equally well detected in the assay. The standardized rt RT-PCR was easily employed for the detection of PPRV nucleic acid directly in the field and experimental clinical samples. The assay detected the PPRV nucleic acid as early as 3 day post infection （dpi） and up to 20 dpi in swab materials from the experimental samples. The assay was rapid and more sensitive than TaqMan and conventional RT-PCR in the detection of PPRV nucleic acid from the PPR suspected clinical samples of sheep and goats. Therefore, the established, simplified SYBR green rt RT-PCR is an alternative test to the already existing various diagnostic assays and could be useful for rapid clinical diagnosis with advantage in reducing risk of contamination.

Novel method for the determination of five carbamate pesticides in water samples by dispersive liquid-liquid microextraction combined with high performance liquid chromatography

A novel method for the determination of five carbamate pesticides （metolcarb, carbofuran, carbaryl, isoprocard and diethofencard） in water samples was developed by dispersive liquid-liquid microextraction （DLLME） coupled with high performance liquid chromatography-diode array detector （HPLC-DAD）. Some experimental parameters that influence the extraction efficiency were studied and optimized to obtain the best extraction results. Under the optimum conditions for the method, the calibration curve was linear in the concentration range from 5 to 1000 ng mL^-1 for all the five carbamate pesticides, with the correlation coefficients （r^2） varying from 0.9984 to 0.9994. Good enrichment factors were achieved ranging from 80 to 177- fold, depending on the compound. The limits of detection （LODs） （S/N = 3） were ranged from 0.1 to 0.5 ng mL^-1. The method has been successfully applied to the analysis of the pesticide residues in environmental water samples.

Data processing of small samples based on grey distance information approach

Data processing of small samples is an important and valuable research problem in the electronic equipment test. Because it is difficult and complex to determine the probability distribution of small samples, it is difficult to use the traditional probability theory to process the samples and assess the degree of uncertainty. Using the grey relational theory and the norm theory, the grey distance information approach, which is based on the grey distance information quantity of a sample and the average grey distance information quantity of the samples, is proposed in this article. The definitions of the grey distance information quantity of a sample and the average grey distance information quantity of the samples, with their characteristics and algorithms, are introduced. The correlative problems, including the algorithm of estimated value, the standard deviation, and the acceptance and rejection criteria of the samples and estimated results, are also proposed. Moreover, the information whitening ratio is introduced to select the weight algorithm and to compare the different samples. Several examples are given to demonstrate the application of the proposed approach. The examples show that the proposed approach, which has no demand for the probability distribution of small samples, is feasible and effective.

Magnetic metal organic framework for pre-concentration of ampicillin from cow milk samples

The aim of this study is a present of a simple solvothermal synthesis approach to preparation of Cu-based magnetic metal organic framework(MMOF)and subsequently its application as sorbent for ultrasound assisted magnetic solid phase extraction(UAMSPE)of ampicillin(AMP)from cow milk samples prior to high performance liquid chromatography-Ultraviolet(HPLC-UV)determination.Characteristics of prepared MMOF were fully investigated by different techniques which showed the exclusive properties of proposed sorbent in terms of proper functionality,desirable magnetic property and also high specific surface area.Different influential factors on extraction recovery including sorbent dosage,ultrasonic time,washing solvent volume and eluent solvent volume were assessed using central composite design(CCD)based response surface methodology(RSM)as an operative and powerful optimization tool.This is the first report for determination of AMP using MMOF.The proposed method addressed some drawbacks of other methods and sorbents for determination of AMP.The presented method decreases the extraction time(4 min)and also enhances adsorption capacity(250 mg/g).Moreover,the magnetic property of presented sorbent(15 emu/g)accelerates the extraction process which does not need filtration,centrifuge and precipitation procedures.Under the optimized conditions,the proposed method is applicable for linear range of 1.0-5000.0 μg/L with detection limit of 0.29 μg/L,satisfactory recoveries(≥95.0%)and acceptable repeatability(RSD less than 4.0%).The present study indicates highly promising perspectives of MMOF for highly effective analysis of AMP in complicated matrices.