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.

A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm

Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation.

Stratigraphic identification using real-time drilling data

Identification of stratigraphic interfaces and lithology is a key aspect in geological and geotechnical investigations.In this study,a monitoring while-drilling system was developed,along with a corresponding data pre-processing method.The method can handle invalid drilling data generated during manual operations.The correlation between various drilling parameters was analyzed,and a database of stratigraphic interfaces and key lithology identification based on the monitoring parameters was established.The average drilling speed was found to be the most suitable parameter for stratigraphic and lithology identification,and when the average drilling speed varied over a wide range,it corresponded to a stratigraphic interface.The average drilling speeds in sandy mudstone and sandstone strata were in the ranges of 0.1e0.2 m/min and 0.2e0.29 m/min,respectively.The results obtained using the present method were consistent with geotechnical survey results.The proposed method can be used for realtime lithology identification and represents a novel approach for intelligent geotechnical surveying.

Prevalence and Antibiotic Resistance of Urinary Tract Pathogens, with Molecular Identification of Klebsiella pneumoniae, Klebsiella oxytoca, and Acinetobacter spp., Using Multiplex Real-Time PCR

Urinary tract infections (UTIs) caused by uropathogens are a significant public health problem, and their treatment primarily relies on antibiotic therapy. However, the increasing global development of antibiotic resistance necessitates updating diagnostic techniques to ensure higher sensitivity and specificity, especially with advancements in science and medicine. This study aimed to evaluate the prevalence of UTIs and antibiotic resistance profiles through urine culture, as well as to identify Klebsiella pneumoniae, Klebsiella oxytoca, and Acinetobacter spp. in urine samples using a molecular approach with multiplex real-time PCR. From May 3 to July 25, 2023, at the Pietro Annigoni Biomolecular Research Center (CERBA) and Saint Camille Hospital of Ouagadougou (HOSCO), 209 urine samples collected from patients with suspected UTIs were analyzed using both urine culture and multiplex real-time PCR. Among the 209 patients, 52.15% were male and 47.85% female, with an average age of 46.87 ± 21.33 years. Urine cultures revealed an overall UTI prevalence of 23.44%, with a prevalence of 8.13% in men versus 15.31% in women (P = 0.023). The bacterial prevalence rates were as follows: Escherichia coli (12.92%), Klebsiella spp. (7.18%), Enterobacter cloacae (1.44%), Staphylococcus aureus (0.96%), and other bacteria. Klebsiella spp. demonstrated 100% resistance to Amoxicillin and Amoxicillin/Clavulanic Acid, while Escherichia coli showed 96.2% and 65.4% resistance to Amoxicillin and Amoxicillin/Clavulanic Acid, respectively. PCR analysis of the target bacteria revealed mono-infection prevalence rates of Klebsiella pneumoniae (10.39%), Klebsiella oxytoca (7.79%), and Acinetobacter spp. (7.79%), along with a co-infection prevalence rate of Klebsiella pneumoniae/Acinetobacter spp. (1.30%). This study demonstrated that PCR, with its high sensitivity and specificity, could effectively distinguish Klebsiella pneumoniae from Klebsiella oxytoca and detect Acinetobacter spp. in less than 24 hours—something urine culture alone could not achieve. The relative ease of automating urine PCR testing, combined with its diagnostic accuracy and rapid turnaround time, makes it a valuable addition to modern medical practice for the laboratory diagnosis of UTIs.

A YOLOv8-CE-based real-time traffic sign detection and identification method for autonomous vehicles

Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.

Advancing healthcare through laboratory on a chip technology:Transforming microorganism identification and diagnostics

In a recent case report in the World Journal of Clinical Cases,emphasized the crucial role of rapidly and accurately identifying pathogens to optimize patient treatment outcomes.Laboratory-on-a-chip(LOC)technology has emerged as a transformative tool in health care,offering rapid,sensitive,and specific identification of microorganisms.This editorial provides a comprehensive overview of LOC technology,highlighting its principles,advantages,applications,challenges,and future directions.Success studies from the field have demonstrated the practical benefits of LOC devices in clinical diagnostics,epidemiology,and food safety.Comparative studies have underscored the superiority of LOC technology over traditional methods,showcasing improvements in speed,accuracy,and portability.The future integration of LOC with biosensors,artificial intelligence,and data analytics promises further innovation and expansion.This call to action emphasizes the importance of continued research,investment,and adoption to realize the full potential of LOC technology in improving healthcare outcomes worldwide.

A Novel On-Site-Real-Time Method for Identifying Characteristic Parameters Using Ultrasonic Echo Groups and Neural Network

On-site and real-time non-destructive measurement of elastic constants for materials of a component in a in-service structure is a challenge due to structural complexities,such as ambiguous boundary,variable thickness,nonuniform material properties.This work develops for the first time a method that uses ultrasound echo groups and artificial neural network(ANN)for reliable on-site real-time identification of material parameters.The use of echo groups allows the use of lower frequencies,and hence more accommodative to structural complexity.To train the ANNs,a numerical model is established that is capable of computing the waveform of ultrasonic echo groups for any given set of material properties of a given structure.The waveform of an ultrasonic echo groups at an interest location on the surface the structure with material parameters varying in a predefined range are then computed using the numerical model.This results in a set of dataset for training the ANN model.Once the ANN is trained,the material parameters can be identified simultaneously using the actual measured echo waveform as input to the ANN.Intensive tests have been conducted both numerically and experimentally to evaluate the effectiveness and accuracy of the currently proposed method.The results show that the maximum identification error of numerical example is less than 2%,and the maximum identification error of experimental test is less than 7%.Compared with currently prevailing methods and equipment,the proposefy the density and thickness,in addition to the elastic constants.Moreover,the reliability and accuracy of inverse prediction is significantly improved.Thus,it has broad applications and enables real-time field measurements,which has not been fulfilled by any other available methods or equipment.

Identifying the stability of housekeeping genes to be used for the quantitative real-time PCR normalization in retinal tissue of streptozotocin-induced diabetic rats

AIM:To investigate the stability of the seven housekeeping genes:beta-actin(ActB),glyceraldehyde-3-phosphate dehydrogenase(GAPDH),18s ribosomal unit 5(18s),cyclophilin A(CycA),hypoxanthine-guanine phosphoribosyl transferase(HPRT),ribosomal protein large P0(36B4)and terminal uridylyl transferase 1(U6)in the diabetic retinal tissue of rat model.METHODS:The expression of these seven genes in rat retinal tissues was determined using real-time quantitative reverse transcription polymerase chain reaction(RT-qPCR)in two groups;normal control rats and streptozotocininduced diabetic rats.The stability analysis of gene expression was investigated using geNorm,NormFinder,BestKeeper,and comparative delta-Ct(ΔCt)algorithms.RESULTS:The 36B4 gene was stably expressed in the retinal tissues of normal control animals;however,it was less stable in diabetic retinas.The 18s gene was expressed consistently in both normal control and diabetic rats’retinal tissue.That this gene was the best reference for data normalisation in RT-qPCR studies that used the retinal tissue of streptozotocin-induced diabetic rats.Furthermore,there was no ideal gene stably expressed for use in all experimental settings.CONCLUSION:Identifying relevant genes is a need for achieving RT-qPCR validity and reliability and must be appropriately achieved based on a specific experimental setting.

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.

Construction of multi-factor identification model for real-time monitoring and early warning of mine water inrush

As a new technical means that can detect abnormal signs of water inrush in advance and give an early warning,the automatic monitoring and early warning of water inrush in mines has been widely valued in recent years.Due to the many factors affecting water inrush and the complicated water inrush mechanism,many factors close to water inrush may have precursory abnormal changes.At present,the existing monitoring and early warning system mainly uses a few monitoring indicators such as groundwater level,water influx,and temperature,and performs water inrush early warning through the abnormal change of a single factor.However,there are relatively few multi-factor comprehensive early warning identification models.Based on the analysis of the abnormal changes of precursor factors in multiple water inrush cases,11 measurable and effective indicators including groundwater flow field,hydrochemical field and temperature field are proposed.Finally,taking Hengyuan coal mine as an example,6 indicators with long-term monitoring data sequences were selected to establish a single-index hierarchical early-warning recognition model,a multi-factor linear recognition model,and a comprehensive intelligent early-warning recognition model.The results show that the correct rate of early warning can reach 95.2%.

Real-time Monitoring of Subsea Gas Pipelines,Offshore Platforms,and Ship Inspection Scores Using an Automatic Identification System

The aim of this research is to develop an algorithm and application that can perform real-time monitoring of the safety operation of offshore platforms and subsea gas pipelines as well as determine the need for ship inspection using data obtained from automatic identification system(AIS).The research also focuses on the integration of shipping database,AIS data,and others to develop a prototype for designing a real-time monitoring system of offshore platforms and pipelines.A simple concept is used in the development of this prototype,which is achieved by using an overlaying map that outlines the coordinates of the offshore platform and subsea gas pipeline with the ship’s coordinates(longitude/latitude)as detected by AIS.Using such information,we can then build an early warning system(EWS)relayed through short message service(SMS),email,or other means when the ship enters the restricted and exclusion zone of platforms and pipelines.The ship inspection system is developed by combining several attributes.Then,decision analysis software is employed to prioritize the vessel’s four attributes,including ship age,ship type,classification,and flag state.Results show that the EWS can increase the safety level of offshore platforms and pipelines,as well as the efficient use of patrol boats in monitoring the safety of the facilities.Meanwhile,ship inspection enables the port to prioritize the ship to be inspected in accordance with the priority ranking inspection score.

Development of a real-time PCR assay(SYBR Green I) for rapid identification and quantification of scyphomedusae Aurelia sp.1 planulae

The complicated life cycle ofAurelia spp., comprising benthic asexually-reproducing polyps and sexually-reproducing medusae, makes it hard for researchers to identify and track them, especially for early stage individuals, such as planulae. To solve this problem, we developed a real-time PCR assay （SYBR Green I） to identify planulae in both cultured and natural seawater samples. Species-specific primers targeting Aurelia sp.1 mitochondrial 16S rDNA （mr 16S rDNA） regions were designed. Using a calibration curve constructed with plasmids containing the Aurelia sp. 1 mt 16S rDNA fragment and a standard curve for planulae, the absolute number of mt 16S rDNA copies per planula was determined and from that the total number ofplanulae per sample was estimated. For the field samples, a 100-fold dilution of the sample DNA combined with a final concentration of 0.2 μg/μL BSA in the PCR reaction mixture was used to remove real- time PCR inhibitors. Samples collected in Jiaozhou Bay from July to September 2012 were subsequently analyzed using this assay. Peak Aurelia sp.1 planula abundance occurred in July 2012 at stations near Hongdao Island and Qingdao offshore; abundances were very low in August and September. The real-time PCR assay （SYBR Green I） developed here negates the need for traditional microscopic identification, which is laborious and time-consuming, and can detect and quantify jellyfish planulae in field plankton samples rapidly and specifically.

Identification of circulating miRNA biomarkers based on global quantitative real-time PCR profiling

MicroRNAs （miRNAs） are small noncoding RNAs （18-25 nucleotides） that regulate gene expression at the posttranscriptional level. Recent studies have demonstrated the presence of miRNAs in the blood circulation. Deregulation of miRNAs i n serum or plasma has been associated with many diseases including cancers and cardiovascular diseases, suggesting the possible use of miRNAs as diagnostic biomarkers. However, the detection of the small amount of miRNAs found in serum or plasma requires a method with high sensitivity and accuracy. Therefore, the current study describes polymerase chain reaction （PCR）-based methods for measuring circulating miRNAs. Briefly, the procedure involves four major steps： （1） sample collection and preparation; （2） global miRNAs profiling using quantitative real-time PCR （qRT-PCR）; （3） data normalization and analysis; and （4） selection and validation of miRNA biomarkers. In conclusion, qRT-PCR is a promising method for profiling of circulating miRNAs as biomarkers.

Identification of potential internal control genes for real-time PCR analysis during stress response in Pyropia haitanensis

Pyropia haitanensis has prominent stress-resistance characteristics and is endemic to China. Studies into the stress responses in these algae could provide valuable information on the stress-response mechanisms in the intertidal Rhodophyta. Here, the effects of salinity and light intensity on the quantum yield of photosystem II in Py. haitanensis were investigated using pulse-amplitude-modulation fluorometry. Total RNA and genomic DNA of the samples under different stress conditions were isolated. By normalizing to the genomic DNA quantity, the RNA content in each sample was evaluated. The cDNA was synthesized and the expression levels of seven potential internal control genes were evaluated using qRT-PCR method. Then, we used geNorm, a common statistical algorithm, to analyze the qRT-PCR data of seven reference genes. Potential genes that may constantly be expressed under different conditions were selected, and these genes showed stable expression levels in samples under a salinity treatment, while tubulin, glyceraldehyde- 3-phosphate dehydrogenase and actin showed stability in samples stressed by strong light. Based on the results of the pulse amplitude-modulation fluorometry, an absolute quantification was performed to obtain gene copy numbers in certain stress-treated samples. The stably expressed genes as determined by the absolute quantification in certain samples conformed to the results of the geNorm screening. Based on the results of the software analysis and absolute quantification, we proposed that elongation factor 3 and 18S ribosomal RNA could be used as internal control genes when the Py. haitanensis blades were subjected to salinity stress, and that a-tubulin and 18S ribosomal RNA could be used as the internal control genes when the stress was from strong light. In general, our findings provide a convenient reference for the selection of internal control genes when designing experiments related to stress responses in Py. haitanensis.

Development of a Real-Time PCR Method (Taqman) for Rapid Identification and Quantification of Prorocentrum donghaiense

Prorocentrum donghaiense is a dinoflagellate that is widely distributed in the East China Sea and has become increasingly involved in Harmful Algal Blooms (HABs). Therefore, it is necessary to study this dinoflagellate to monitor HABs. In this study, 13 pairs of primers specific to P. donghaiense (within its internal transcribed spacer (ITS) regions) were designed for SYBR Green I real-time PCR. As the SYBR Green I real-time PCR could not identify P. donghaiense in a specific manner, a Taqman real-time PCR method was developed by designing a set of specific primers and a Taqman probe. A 10-fold serial dilution of recombinant plasmid containing ITS regions of P. donghaiense was prepared as standard samples and the standard curve was established. Additionally, we quantified the genomic DNA in P. donghaiense cells and utilized this DNA to prepare another 10-fold serial dilution of standard sample and accordingly set up the standard curve. The mathematic correlation between the cell number and its corresponding plasmid copy number was also established. In order to test the efficiency of the real-time PCR method, laboratory samples and P. donghaiense HAB field samples were employed for identification and quantitative analysis. As to laboratory samples, as few as 102 cells of P. donghaiense could be quantified precisely utilizing both centrifugation and filtration techniques. The quantification results from field samples by real-time PCR were highly similar to those by light microscopy. In conclusion, the real-time PCR could be applied to identify and quantify P. donghaiense in HABs.

Identification of T-DNA Inserted Mutant Gene Transcription by Using Real-time PCR in Arabidopsis

AtERF4 （ethylene response factor） is a negative regulator in jasmonic acid mediated signal transduction pathway and ethylene mediated signal transduction pathway of Arabidopsis. It could respond to abscisic acid （ABA） and ethylene stimulus ATSYR1 gene encodes a syntaxin localizing at the plasma membrane in Arabidopsis, which can be induced by abiotic stress. To identify mutation lines for gene functional analysis, real-time PCR was employed to detect the expression level of AtERF4 and ATSYR1 in homozygous T-DNA insertion mutant line, respectively. Real-time PCR is a powerful tool which can be used to detect steady-state mRNA levels specifically, sensitively and reproducibly. Comparing to other forms of quantitative RT-PCR, the amount of amplified products can be detected by real-time PCR instantly and thus is a preferable alternative. In this study, RNA with T-DNA inserting into exon could be detected in AtERF4 knock-out mutation line. The results indicated that AtERF4 had been trucked in transcription level. On the other hand, T-DNA inserting into the promoter of gene ATSYR1 had no effect on reducing the expression level ofATSYR1 gene. Further molecular and phenotype studies now are ongoing to clarify the potential consequences of AtERF4 and ATSYR1 deficiency in Arabidopsis

Evaluation of Real-Time 16S rDNA PCR and Pyrosequencing for Routine Identification of Bacteria in Joint Fluid and Tissue Specimens

16S rDNA PCR and sequencing are powerful tools for bacterial detection and identification, although their routine use is not currently widespread in the field of clinical microbiology. The availability of pyrosequencing now makes 16S rDNA assays more accessible to routine diagnostic laboratories, but this approach has had limited evaluation in general diagnostic practice. In this study we evaluated a real-time 16S rDNA PCR and pyrosequencing assay for use in a routine microbiology laboratory, by retrospectively testing joint fluid and joint tissue specimens received for conventional culture. We found that use of the real-time 16S rDNA assay was clinically valuable in this specimen type because it enabled us to identify a small number of culture-negative infections. Although faster and less labour-intensive, we found that the utility of pyrosequencing for pathogen identification is still hampered by shorter read lengths compared to conventional (Sanger) sequencing. Combining results from both molecular and conventional culture methods, bacteria were only detected in 11.8% specimens in this study. However, the detection rate was increased to 18.6% if specimens were only included from patients with a documented clinical suspicion of infection. In conclusion, while pyrosequencing had significant advantages in speed and ease-of-use over conventional sequencing, multiple reactions will be required to deliver comparable species-level identification, thus negating many of the benefits of using the technique. We found that 16S rDNA PCR and sequencing should be rationally targeted on the basis of good clinical information in the routine diagnostic setting, and not used as a general screening test for the exclusion of bacterial infection in joint specimens.

Advances in the Identification of Genetically Modified Rice with Real-time PCR and Multiplex PCR

In recent years, food security and safety have attracted increasing attention due to the worldwide research and development of genetically modified （GM） rice, and the controversy over the commercialization of GM rice. And the identification of GM rice is of great significance. Therefore, in the present study, the po- tential problems in the identification of GM rice with PCR were analyzed both at a technical level and from a theoretical perspective. In addition, PCR detection on the transgenic elements： promoter, terminator, internal reference gene and target gene was discussed, respectively. The possible solutions were proposed based on the principles of plant virology and genetic engineering.

基于Real-time PCR法检测乳粉中牛源性成分定量研究

基于Real-timePCR建立了乳粉中牛源性成分相对定量检测方法,并对牛的特异性引物与探针进行了特异性、灵敏度和稳定性测试。通过模拟不同浓度牛乳粉与马乳粉混合样本,根据其△Ct值的函数关系进行线性拟合进而绘制标准曲线,建立乳粉中牛源性成分的相对定量检测。结果显示,该方法的最低检测限为0.00001 mg/mL,回收率为91.11%~119.2%,组间变异系数≤0.58%、组内变异系数≤1.44%。说明该方法在特异性与稳定性上适用于乳粉中牛源性成分及含量的掺假检测。