A Real-time Lithological Identification Method based on SMOTE-Tomek and ICSA Optimization

In petroleum engineering,real-time lithology identification is very important for reservoir evaluation,drilling decisions and petroleum geological exploration.A lithology identification method while drilling based on machine learning and mud logging data is studied in this paper.This method can effectively utilize downhole parameters collected in real-time during drilling,to identify lithology in real-time and provide a reference for optimization of drilling parameters.Given the imbalance of lithology samples,the synthetic minority over-sampling technique(SMOTE)and Tomek link were used to balance the sample number of five lithologies.Meanwhile,this paper introduces Tent map,random opposition-based learning and dynamic perceived probability to the original crow search algorithm(CSA),and establishes an improved crow search algorithm(ICSA).In this paper,ICSA is used to optimize the hyperparameter combination of random forest(RF),extremely random trees(ET),extreme gradient boosting(XGB),and light gradient boosting machine(LGBM)models.In addition,this study combines the recognition advantages of the four models.The accuracy of lithology identification by the weighted average probability model reaches 0.877.The study of this paper realizes high-precision real-time lithology identification method,which can provide lithology reference for the drilling process.

Iris Identification Technology Based on Multiwavelets

A new method for iris identification based on multiwavelets is proposed. By means of the properties of multiwavelets, such as orthogonality, symmetry, vanishing moments and approximation order, the iris texture can be simply presented. A brief overview of muhiwavelets is presented at first. Iris identification system and iris texture feature presentation and recognition based on multiwavelets a,e introduced subsequently. And the experiment indicates the validity of this method finally.

Identification and Quantitation of Cashmere (Pashmina) Fiber and Wool Using Novel Microchip Based Real-Time PCR Technology

The textile industrial chain all over the world is facing a challenge of differentiating cashmere fiber from mixture of wool and other fibers in case cashmere stocks are adulterated with wool or other fibers. For identification of cashmere in such mixtures, the development of microchip based real-time PCR technology offers a very sensitive, specific, and accurate solution. The technology has been validated with cashmere and wool samples procured from distant farms, and from cashmere goats and sheep of different age and sex. Model samples with incremental raw cashmere or wool content were tested. The experimentally determined content was found to be comparable to the weighed content of the respective fibers in the samples. This technology may prove a cost cutter since it needs only 1.2 μl of the PCR reagent mix. It is substantially faster than traditional real-time PCR systems for being carried as miniature reaction volume in metal microchip. These features allow faster thermal equilibrium and thermal uniformity over the entire array of microreactors. For routine tests or in commercial set up, the microchips are available as ready-to-run with lyophilized reagents in its microreactors to which only 1 μl of the 10-fold diluted isolated DNA sample is added. The lyophilized microchips offer user-friendly handling in testing laboratories and help minimize human error.

Intelligent identification and real-time warning method of diverse complex events in horizontal well fracturing

The existing approaches for identifying events in horizontal well fracturing are difficult, time-consuming, inaccurate, and incapable of real-time warning. Through improvement of data analysis and deep learning algorithm, together with the analysis on data and information of horizontal well fracturing in shale gas reservoirs, this paper presents a method for intelligent identification and real-time warning of diverse complex events in horizontal well fracturing. An identification model for "point" events in fracturing is established based on the Att-BiLSTM neural network, along with the broad learning system (BLS) and the BP neural network, and it realizes the intelligent identification of the start/end of fracturing, formation breakdown, instantaneous shut-in, and other events, with an accuracy of over 97%. An identification model for "phase" events in fracturing is established based on enhanced Unet++ network, and it realizes the intelligent identification of pump ball, pre-acid treatment, temporary plugging fracturing, sand plugging, and other events, with an error of less than 0.002. Moreover, a real-time prediction model for fracturing pressure is built based on the Att-BiLSTM neural network, and it realizes the real-time warning of diverse events in fracturing. The proposed method can provide an intelligent, efficient and accurate identification of events in fracturing to support the decision-making.

德国鸢尾(Iris germanica L.)细菌性软腐病病原物初步鉴定

从德国鸢尾(Iris germanicaL.)栽培品种软腐病感病植株上分离到2株病原细菌,经回接试验证明为鸢尾软腐病病原细菌;同时还对2株细菌进行了形态观察、致病性测定、染色反应、培养性状观察和生理生化反应等研究,并将该病原细菌初步鉴定为欧文氏菌菊欧软腐致病型(Erwinia chrysanthemiBurkhdder,Mc Fadden&Di-mock)。

Real-Time Multi-Class Infection Classification for Respiratory Diseases

Real-time disease prediction has emerged as the main focus of study in the field of computerized medicine.Intelligent disease identification framework can assist medical practitioners in diagnosing disease in a way that is reliable,consistent,and timely,successfully lowering mortality rates,particularly during endemics and pandemics.To prevent this pandemic’s rapid and widespread,it is vital to quickly identify,confine,and treat affected individuals.The need for auxiliary computer-aided diagnostic(CAD)systems has grown.Numerous recent studies have indicated that radiological pictures contained critical information regarding the COVID-19 virus.Utilizing advanced convolutional neural network(CNN)architectures in conjunction with radiological imaging makes it possible to provide rapid,accurate,and extremely useful susceptible classifications.This research work proposes a methodology for real-time detection of COVID-19 infections caused by the Corona Virus.The purpose of this study is to offer a two-way COVID-19(2WCD)diagnosis prediction deep learning system that is built on Transfer Learning Methodologies(TLM)and features customized fine-tuning on top of fully connected layered pre-trained CNN architectures.2WCD has applied modifications to pre-trained models for better performance.It is designed and implemented to improve the generalization ability of the classifier for binary and multi-class models.Along with the ability to differentiate COVID-19 and No-Patient in the binary class model and COVID-19,No-Patient,and Pneumonia in the multi-class model,our framework is augmented with a critical add-on for visually demonstrating infection in any tested radiological image by highlighting the affected region in the patient’s lung in a recognizable color pattern.The proposed system is shown to be extremely robust and reliable for real-time COVID-19 diagnostic prediction.It can also be used to forecast other lung-related disorders.As the system can assist medical practitioners in diagnosing the greatest number of patients in the shortestamount of time, radiologists can also be used or published online to assistany less-experienced individual in obtaining an accurate immediate screeningfor their radiological images.

RFID Complex Event Processing: Applications in Real-Time Locating System

Complex event processing (CEP) can extract meaningful events for real-time locating system (RTLS) applications. To identify complex event accurately in RTLS, we propose a new RFID complex event processing method GEEP, which is based on the timed automata (TA) theory. By devising RFID locating application into complex events, we model the timing diagram of RFID data streams based on the TA. We optimize the constraint of the event streams and propose a novel method to derive the constraint between objects, as well as the constraint between object and location. Experiments prove the proposed method reduces the cost of RFID complex event processing, and improves the efficiency of the RTLS.

A Non-Contact Original-State Online Real-Time Monitoring Method for Complex Liquids in Industrial Processes

Failures are very common during the online real-time monitoring of large quantities of complex liquids in industrial processes, and can result in excessive resource consumption and pollution. In this study, we introduce a monitoring method capable of non-contact original-state online real-time monitoring for strongly coated, high-salinity, and multi-component liquids. The principle of the method is to establish the relationship among the concentration of the target substance in the liquid （C）, the color space coor- dinates of the target substance at different concentrations （L＊, a＊, b＊）, and the maximum absorption wave- length （λmax）; subsequently, the optimum wavelength λT of the liquid is determined by a high-precision scanning-type monitoring system that is used to detect the instantaneous concentration of the target substance in the flowing liquid. Unlike traditional monitoring methods and existing online monitoring methods, the proposed method does not require any pretreatment of the samples （i.e., filtration, dilution, oxidation/reduction, addition of chromogenic agent, constant volume, etc.）, and it is capable of original- state online real-time monitoring. This method is employed at a large electrolytic manganese plant to monitor the Fe3. concentration in the colloidal process of the plant＇s aging liquid （where the concentra- tions of Fe3＋, Mn2＋, and （NH4）2SO4 are 0.5-18 mg.L 1, 35-39 g.L 1, and 90-110 g.L 1, respectively）. The relative error of this monitoring method compared with an off-line laboratory monitoring is less than 2%.

Iris Liveness Detection Using Fragmental Energy of Haar Transformed Iris Images Using Ensemble of Machine Learning Classifiers

Contactless verification is possible with iris biometric identification,which helps prevent infections like COVID-19 from spreading.Biometric systems have grown unsteady and dangerous as a result of spoofing assaults employing contact lenses,replayed the video,and print attacks.The work demonstrates an iris liveness detection approach by utilizing fragmental coefficients of Haar transformed Iris images as signatures to prevent spoofing attacks for the very first time in the identification of iris liveness.Seven assorted feature creation ways are studied in the presented solutions,and these created features are explored for the training of eight distinct machine learning classifiers and ensembles.The predicted iris liveness identification variants are evaluated using recall,F-measure,precision,accuracy,APCER,BPCER,and ACER.Three standard datasets were used in the investigation.The main contribution of our study is achieving a good accuracy of 99.18%with a smaller feature vector.The fragmental coefficients of Haar transformed iris image of size 8∗8 utilizing random forest algorithm showed superior iris liveness detection with reduced featured vector size(64 features).Random forest gave 99.18%accuracy.Additionally,conduct an extensive experiment on cross datasets for detailed analysis.The results of our experiments showthat the iris biometric template is decreased in size tomake the proposed framework suitable for algorithmic verification in real-time environments and settings.

Inter-laboratory Validation of Real-time PCR Assays for Goat-,Horse-and Donkey-derived Material in Meat Products

In this study,we performed an inter-laboratory collaborative ring trial to develop and validate specific TaqMan real-time PCR assays for goat-,horse-,and donkey-derived material in meat products.The performances of these assays in different environments and situations were comprehensively evaluated.This ring trial involved the participation of 12 laboratories in Europe and Asia.The results from the participating laboratories were analyzed to determine the specificity,accuracy,false positive rate,limit of detection(LOD),and probability of detection(POD)of the developed assays.Statistical analysis showed that the false positive and negative rates were zero,the LOD was five copies/reaction,and the laboratory standard deviation(σ_(L))was 0.30 for all three assays.Thus,the results demonstrate that the developed methods are robust and suitable for the detection and identification of goat-,horse-,and donkey-derived materials in meat products.

Analysis on Influence Factors of Real-time Identification of Neural Network in Sheet Intelligent Bending

The scheme of intelligent control system of cap-bending has been advanced in this paper using the neural network technology, based on the prominent problem that bending springback difficult to control accurately during the forming process of cap-bending. The key technology of real-time identification for material performance parameter and friction coefficient was researched, and the back-propagation neural network of real-time identification for material performance parameters and friction coefficient was established, which can real-time identify the needed material performance parameters through the real-time monitoring variable. Factors that affecting recognition results of neural network model were analyzed, such as influences of the selection of the sample date and the algorithm for identification result. Factors affecting neural network generalization ability were discussed, such as influences of the selection of the sample date and the node number of the hidden layer for generalization ability. The results provide a guarantee for improving the convergence accuracy and the generalization ability of network, and provide a basis for the building of intelligent bending control of network model.

Establish an allele-specific real-time PCR for Leishmania species identification

Background:Leishmaniasis is a serious neglected tropical disease that may lead to life-threatening outcome, which species are closely related to clinical diagnosis and patient management. The current Leishmania species determination method is not appropriate for clinical application. New Leishmania species identification tool is needed using clinical samples directly without isolation and cultivation of parasites.Methods:A probe-based allele-specific real-time PCR assay was established for Leishmania species identification between Leishmania donovani and L. infantum for visceral leishmaniasis (VL) and among L. major, L. tropica and L. donovani/L. infantum for cutaneous leishmaniasis (CL), targeting hypoxanthine-guanine phosphoribosyl transferase (HGPRT) and spermidine synthase (SPDSYN) gene with their species-specific single nucleotide polymorphisms (SNPs). The limit of detection of this assay was evaluated based on 8 repeated tests with intra-assay standard deviation < 0.5 and inter-assay coefficients of variability < 5%. The specificity of this assay was tested with DNA samples obtained from Plasmodium falciparum, Toxoplasma gondii, Brucella melitensis and Orientia tsutsugamushi. Total 42 clinical specimens were used to evaluate the ability of this assay for Leishmania species identification. The phylogenetic tree was constructed using HGPRT and SPDSYN gene fragments to validate the performance of this assay.Results:This new method was able to detect 3 and 12 parasites/reaction for VL and CL respectively, and exhibited no cross-reaction with P. falciparum, T. gondii, B. melitensis, O. tsutsugamushi and non-target species of Leishmania. Twenty-two samples from VL patients were identified as L. donovani (n = 3) and L. infantum (n = 19), and 20 specimens from CL patients were identified as L. major (n = 20), providing an agreement of 100% compared with sequencing results. For further validation, 29 sequences of HGPRT fragment from nine Leishmania species and 22 sequences from VL patients were used for phylogenetic analysis, which agreed with the results of this new method. Similar results were obtained with 43 sequences of SPDSYN fragment from 18 Leishmania species and 20 sequences from CL patients.Conclusions:Our assay provides a rapid and accurate tool for Leishmania species identification which is applicable for species-adapted therapeutic schedule and patient management.

鸢尾链格孢叶斑病病原菌鉴定

2023年6月发现,在江西省南昌市江西农业大学植物园种植的鸢尾普遍发生链格孢叶斑病。为明确该病害病原菌种类,对发病叶片进行真菌分离,对分离菌株进行致病性测定,共获得24株链格孢,在人工接种试验中均具有致病性;选择3株代表性菌株JXYW1、JXYW2、JXYW3进行培养性状和形态特征观察及多基因(rDNA-ITS、EF-1、GAPDH、Altα1)序列分析,这3株菌株培养性状和形态特征相同,符合文献中对鸢尾生链格孢Alternaria iridicola的描述,3株菌株间rDNA-ITS、EF-1、GAPDH、Altα1基因序列也完全相同,与GenBank中鸢尾生链格孢对应基因序列的相似性为100%,在多基因系统发育树上与鸢尾生链格孢菌株YZU聚于同一个分支。据此,将江西农业大学植物园内鸢尾链格孢叶斑病病原菌鉴定为鸢尾生链格孢。

虹膜定位

为了提高虹膜定位的速度以及虹膜定位算法的健壮性 ,提出了一种粗定位与精定位相结合的两步定位法 ,用以进行虹膜定位 ,并对现有的虹膜定位算法进行了一些改进。实验结果表明 ,用两步法进行虹膜定位可以加快定位速度 ,减少搜索计算的盲目性。

一种虹膜定位算法

针对虹膜二值边缘图像提取的困难 ,提出利用虹膜图像的灰度边缘图像以及虹膜的几何特征进行虹膜定位的快速算法 .首先根据虹膜图像的整体灰度分布信息特征 ,利用改进的 Sobel算子提取虹膜的灰度边缘图像 ,然后利用圆的对称性质以及投票策略提取虹膜的中心 ,最后利用 Hough变换的半径直方图投票提取虹膜内外径 .实验结果证明 ,该算法提高了虹膜定位的速度 ,并且具有较强的抗干扰能力 .

虹膜定位的快速算法

基于眼睛图像的几何特性,提出了一种基于最长弦的瞳孔定位算法,该算法能快速地检测到瞳孔的位置。由于图像中的瞳孔并不是规整的圆形,此方法与传统的基于圆灰度梯度检测算子的瞳孔定位算法相比能够更好地拟合瞳孔边界。在精确地定位瞳孔之后,根据瞳孔和虹膜近似同心的特性,利用圆灰度梯度检测算子在小范围内搜索虹膜的外边沿。与传统的虹膜定位方法相比,此算法避免了搜索的盲目性,提高了虹膜定位的速度和精度。

准葛尔鸢尾叶的化学成分

目的:研究鸢尾科鸢尾属植物准葛尔鸢尾(Iris songaricaSchrenk)叶的化学成分。方法:采用70%乙醇提取,硅胶柱反复层析分离及重结晶等方法从准格尔鸢尾叶中分离其化学成分,通过波谱及化学方法进行结构鉴定。结果:分离并鉴定了18个化合物,其中3个黄酮类化合物:5-羟基-7-甲氧基黄酮(tectochrysin,Ⅰ),5,7-二羟基-8-甲氧基黄酮(wogonin,Ⅱ),蒙花苷(acacetin-7-O-α--Lrhamnopyranosyl-(1→6)-β-D-glucopyranoside,Ⅲ);2个黄酮醇类化合物:良姜素(izapinin,Ⅳ),异鼠李素-葡萄糖苷(isorhamnetin-7-O-β-D-glucoside,Ⅴ);1个二氢黄酮类化合物:5,2′-二羟基-6,7-亚甲二氧基二氢黄酮(Ⅵ);4个异黄酮类化合物:分别为:5,7-二羟基-6,2′-二甲氧基异黄酮(Ⅶ),4′,5,7-三羟基-6-甲氧基异黄酮(tectorigenin,Ⅷ),鸢尾苷(tec-toridin,Ⅸ),4′-羟基-5-甲氧基-6,7-亚甲二氧基异黄酮(irisolone,Ⅹ);3个为peltogynoids型化合物:irisoid A(Ⅺ),irisoid B(Ⅻ),irisoid D(ⅩⅢ);另外5个化合物为三十一烷醇n-hentriacontanol(ⅩⅣ),胡萝卜苷(daucosterol,ⅩⅤ),豆甾醇(stigmasterol,ⅩⅥ),nonadecanoic acid(ⅩⅦ)和谷甾醇(-βsitosterol,ⅩⅧ)。结论:这些化合物均为首次从该植物中分得,其中化合物Ⅲ,Ⅳ和Ⅴ为本属植物中首次发现。

虹膜定位算法研究

针对目前已有的虹膜定位算法的局限性提出一种粗定位和精定位相结合的方法根据对人眼部的生理特性和采集得到的眼部图像进行分析先利用灰度投影量的分布特点进行粗定位再利用变圆模板精定位实验结果表明该方法提高了准确度和速度有效地解决了目前算法处理包含大量脸部区域的虹膜图像时会遇到的困难

射干及类似药用植物叶绿体rbcL基因序列分析

目的 对射干Belamcandachinensis(L .)DC .,鸢尾IristectorumMaxim .,野鸢尾I .dichotomaPall.,蝴蝶花I.japonicaThunb .和德国鸢尾I.germaicaL .等 5种药用植物进行叶绿体rbcL基因序列分析 ,并对其亲缘关系进行探讨。方法 CTAB(Cetyltrimelhylammoniumbromide,CTAB)法提取总DNA ,用作者设计的引物对鸢尾科 5种药用植物的叶绿体rbcL(ribulose 1,5 bisphosphatecarboxyloseLargeGene ,rbcL)基因进行扩增 ,PCR扩增产物纯化后 ,用ABI3 10DNA自动测序仪测序。结果 获得射干和 4种鸢尾属药用植物叶绿体rbcL基因部分序列 (约 75 0bp) ,除德国鸢尾外 ,其余 4种药用植物的rbcL基因序列为首次获得 ;用clustal 8 0 ,MEGA 2 0等软件分析统计获得的目的基因片段 ,得到碱基突变点 ,遗传距离 [碱基差异数 ( 1 0 0 0～ 2 0 0 0 0 )、颠换数为 ( 0 0 0 0～ 9 0 0 0 )、转换数为 ( 0 0 0 0～ 14 0 0 0 ) ],根据rbcL基因部分序列数据建立分子系统树。结论 根据叶绿体rbcL基因序列数据可以很好的鉴别

基于纹理分布特征的虹膜识别算法

二维小波变换方向性差,不能从方向和频率同时描述虹膜纹理。基于此,分析了归一化虹膜纹理的分布特点,提出了基于纹理分布特征的虹膜识别算法。对原始人眼图像进行虹膜定位等预处理操作,得到归一化的虹膜纹理图像;对归一化虹膜图像进行了二维小波多尺度变换,结合虹膜纹理分布特点,选择小波分解水平通道;依据点能量贡献度,消除伪特征点并进行了点能量编码;计算了不同虹膜编码间海明距离,以其为依据进行分类。在给定距离阈值为0.25的前提下,可以达到99.91%的正确识别率。实验证明该算法是有效、可行的,并具有较高的识别率,识别速度也很快。