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.

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.

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.

The influence of temperature on flow-induced forces on quartz-crystal-microbalance sensors in a Chinese liquor identification electronic-nose: three-dimensional computational fluid dynamics simulation and analysis

An electronic-nose is developed based on eight quartz-crystal-microbalance (QCM) gas sensors in a sensor box, and is used to detect Chinese liquors at room temperature. Each sensor is a highly-accurate and highly-sensitive oscillator that has experienced airflow disturbances under the condition of varying room temperatures due to unstable flow-induced forces on the sensors surfaces. The three-dimensional (3D) nature of the airflow inside the sensor box and the interactions of the airflow on the sensors surfaces at different temperatures are studied by computational fluid dynamics (CFD) tools. Higher simulation accuracy is achieved by optimizing meshes, meshing the computational domain using a fine unstructural tetrahedron mesh. An optimum temperature, 30 ℃, is obtained by analyzing the distributions of velocity streamlines and the static pressure, as well as the flow-induced forces over time, all of which may be used to improve the identification accuracy of the electronic-nose for achieving stable and repeatable signals by removing the influence of temperature.

Identification and Evaluation of Cold Tolerance in Bitter Gourd (Momordica charantia L.) at Bud and Seedling Stages under Different Temperature Conditions

[ Objectives] This study was conducted to screen the temperature index most suitable for identification of cold tolerance in bitter gourd （Momordica charantia L. ） at bud and seedling stages. [ Methods] With six different bitter gourd germplasms as experimental materials, the cold tolerance at bud and seedling stages were identified and evaluated. [ Results] At 18℃ , the largest change range of germination potential among different gernlplasms was 11.3% -96.0%, and the largest change range of germination rate was 13.3% - 100.0% ; and the six germplasms could be divided into three grades of cold tolerance. Therefore, 18 9C is an ideal temperature for the identification of cold tolerance in bitter gourd germplasms at bud stage. At 20℃, the cold tolerance in bitter gourd germplasms at bud stage could also be divided into three grades, and the change ranges of germination potential and germination rate were larger than 50.0%, so 20℃ could serve as the candidate temperature for the identification of cold tolerance in bitter gourd germplasms at bud stage. After 1 d of treatment at 6℃, the six bitter gourd germplasms could be divided into three grades, and the cold injury index had the largest range of 20.31 - 84.38 and could serve as the temperature index for the identification of cold tolerance in bitter gourd at seedling stage. [ Conclusions] This study will provide reference for the identification of cold tolerance in bitter gourd at bud stage and seedling stage.

Parameter identification and state-of-charge estimation approach for enhanced lithium–ion battery equivalent circuit model considering influence of ambient temperatures

It is widely accepted that the variation of ambient temperature has great influence on the battery model parameters and state-of-charge(SOC) estimation, and the accurate SOC estimation is a significant issue for developing the battery management system in electric vehicles. To address this problem, in this paper we propose an enhanced equivalent circuit model(ECM) considering the influence of different ambient temperatures on the open-circuit voltage for a lithium-ion battery. Based on this model, the exponential-function fitting method is adopted to identify the battery parameters according to the test data collected from the experimental platform. And then, the extended Kalman filter(EKF) algorithm is employed to estimate the battery SOC of this battery ECM. The performance of the proposed ECM is verified by using the test profiles of hybrid pulse power characterization(HPPC) and the standard US06 driving cycles(US06) at various ambient temperatures, and by comparing with the common ECM with a second-order resistance capacitor. The simulation and experimental results show that the enhanced battery ECM can improve the battery SOC estimation accuracy under different operating conditions.

Real-time quantitative optical method to study temperature dependence of crack propagation process in colloidal photonic crystal film

A real-time quantitative optical method to characterize crack propagation in colloidal photonic crystal film（CPCF）is developed based on particle deformation models and previous real-time crack observations. The crack propagation process and temperature dependence of the crack propagation rate in CPCF are investigated. By this method, the crack propagation rate is found to slow down gradually to zero when cracks become more numerous and dense. Meanwhile, with the temperature increasing, the crack propagation rate constant decreases. The negative temperature dependence of the crack propagation rate is due to the increase of van der Waals attraction, which finally results in the decrease of resultant force. The findings provide new insight into the crack propagation process in CPCF.

Development of Drone Cargo Bay with Real-Time Temperature Control

In order to deliver medical products (medicines, vaccines, blood packs, etc.) in time for needed areas, a method of transporting goods using drones is being studied. However, temperature-sensitive medical products may decay due to outside temperature changes. The time required to transport over the distance may vary a lot as well. As a result, the likelihood of the goods deteriorating is very high. There is a need for a study on cargo bay to prevent this and to protect the medical goods. In this paper, in order to protect the temperature sensitive medical goods, the inside cargo bay is equipped with the cooling fan device and the electric heating elements. These elements can be monitored and controlled according to the user’s discretion. By using the web server built inside the cloud server, the temperature can be controlled in real-time from anywhere without the limitation of distance. We built the proposed device, and installed it on the drone cargo bay. The test results show that the cargo bay can be temperature-controlled, and the setting can be maintained over a great distance. The user can watch the temperature variations during the transport and ascertain the goodness of the medical supply with the data. It is expected that such development can greatly enhance the utility of the drone operations, especially for the medical supply transport applications.

Model Identification Based on Subspace Model Identification of Main Steam Temperature in Ultra-Supercritical Coal-Fired Power Unit

Ultra-supercritical(USC) unit is more and more popular in coal-fired power industry.In this paper,closed-loop identification based on subspace model identification(SMI) is introduced for superheated steam temperature system of USC unit.Closed-loop SMI is applied to building step response model of the unit directly.The parameters selection method is proposed to deal with the parameter sensitivity and improve the reliability of the model.Finally,the model is used in model identification of real USC unit.

The Fuzzy Cluster Analysis in Identification of Key Temperatures in Machine Tool

The thermal-induced error is a very important sour ce of machining errors of machine tools. To compensate the thermal-induced machin ing errors, a relationship model between the thermal field and deformations was needed. The relationship can be deduced by virtual of FEM (Finite Element Method ), ANN (Artificial Neural Network) or MRA (Multiple Regression Analysis). MR A is on the basis of a total understanding of the temperature distribution of th e machine tool. Although the more the temperatures measured are, the more accura te the MRA is, too more temperatures will hinder the analysis calculation. So it is necessary to identify the key temperatures of the machine tool. The selectio n of key temperatures decides the efficiency and precision of MRA. Because of th e complexities and multi-input and multi-output structure of the relationships , the exact quantitative portions as well as the unclear portions must be taken into consideration together to improve the identification of key temperatures. I n this paper, a fuzzy cluster analysis was used to select the key temperatures. The substance of identifying the key temperatures is to group all temperatures b y their relativity, and then to select a temperature from each group as the repr esentation. A fuzzy cluster analysis can uncover the relationships between t he thermal field and deformations more truly and thoroughly. A fuzzy cluster ana lysis is the cluster analysis based on fuzzy sets. Given U={u i|i=0,...,N}, in which u i is the temperature measured, a fuzzy matrix R can be obta ined. The transfer close package t(R) can be deduced from R. A fuzzy clu ster of U then conducts on the basis of t(R). Based on the fuzzy cluster analysis discussed above, this paper identified the k ey temperatures of a horizontal machining center. The number of the temperatures measured was reduced to 4 from 32, and then the multiple regression relationshi p models between the 4 temperatures and the thermal deformations of the spindle were drawn. The remnant errors between the regression models and measured deform ations reached a satisfying low level. At the same time, the decreasing of tempe rature variable number improved the efficiency of measure and analysis greatly.

Predicting the martensite transformation start-temperature of low alloy steel based on fuzzy identification

A method of fuzzy identification based on T-S fuzzy model was proposed for predicting temperature Ms from chemical composition, austenitizing temperature and time for low alloy steel. The degree of membership of each sample was calculated with fuzzy clustering algorithm. Kalman filtering was used to identify the consequent parameters. Compared with the results obtained by empirical models based on the same data, the results by the fuzzy method showed good precision. The accuracy of the fuzzy model is almost 6 times higher than that of the best empirical model. The influence of alloying elements, austenitizing temperature and time on Ms was analyzed quantitatively by using the fuzzy model. It is shown that there exists a nonlinear relationship between the contents of alloying elements in steels and their Ms, and the effects of austenitizing temperature and time on Ms temperature cannot be neglected.

Morphology and Molecular Identification of Dry Fish Fungus Cunninghamella blakesleeana from Small Indigenous Fish “Kachki” Corica soborna (Hamilton 1822) in Bangladesh

The present experiment was conducted to investigate a dry fish fungus, Cunnighamella blakesleeana, which was identified from the infected part of the Corica soborna, locally named as Kachki fish. Mycelium was hyaline, often with granular content, and conidiophores were erected, with verticillate or solitary branches. Zygospores were globose, tuberculate, suspensors equal, smooth, hyaline and heterothallic. Using ITS4 and ITS5 primers, the 740 bp-long ITS region was amplified and sequenced. The ITS region sequences had reciprocal homologies of 98% to 100%. The findings showed that several species of C. blakesleeana fall into the same cluster. It has been determined by molecular data that the fungus we had studied was C. blakesleeana. The maximum mycelial growth (95.33 mm) was observed in the PDA medium, followed by the PSA medium, and the lowest growth (65.50 mm) was measured in the HPA medium in the study of the impact of culture media on the mycelial growth of C. blakesleeana. The influence of temperature on the radial mycelial growth of C. blakesleeana on PDA medium was investigated through five different temperatures. Although pH is a crucial factor in understanding the ecology of spoilage fungus, the highest mycelial growth of C. blakesleeana (88.25 mm) was seen at pH 7, followed by pH 8 and pH 6, while pH 9 was revealed to have the lowest mycelial growth. The outcome suggested that C. blakesleeana thrived in neutral environments.

Gene Expression and Activities of SOD in Cucumber Seedlings Were Related with Concentrations of Mn^(2+),Cu^(2+),or Zn^(2+) Under Low Temperature Stress

Effects of increasing Mn^2＋, Cu^2＋, or Zn^2＋ on SOD expressions were studied in cucumber seedlings under low temperature stress. Both gene expressions and activities of Cu/Zn-SOD and Mn-SOD in cucumber seedling leaves were induced by increasing Mn^2＋, Cu^2＋, or Zn^2＋ under low temperature stress, especially 48 h afterwards. The activities of Cu/Zn-SOD and Mn-SOD at 0 and 48 h after treatment were in accordance with their gene expression levels, which implied that the transcriptional regulation plays key roles in regulating their activities at the early stage of low temperature stress. Gene expressions of Cu/Zn-SOD and Mn-SOD declined at 96 h, but Cu/Zn-SOD and Mn-SOD activities still remain high, which suggested that Cu/Zn-SOD and Mn-SOD activities might be regulated by other factors after transcription at the later stage of low temperature stress. Therefore, we concluded that the increasing Mn^2＋, Cu^2＋, or Zn^2＋ could increase the capacity of scavenging ROS in cucumber seedlings under low temperature stress by inducing gene expressions of Cu/ Zn-SOD and Mn-SOD, elevating activities of Cu/Zn-SOD, Mn-SOD, or regulating other factors after transcription.

Effects of seasonal air temperature variation on airflow and surrounding rock temperature of mines

In underground mines, high air temperatures in the summer months lead to an increase in inlet airflow temperatures. This leads to seasonal thermal pollution in the mines. This paper examines the dynamics and effects of seasonal variation in surface air temperatures and surrounding rock temperatures in deep coal mines. It also examines temperature variations in the main ventilation circuit, working face, and surrounding rock. The study results revealed that airflow temperatures were significantly affected by seasonal air temperature variations. The greater the distance was between the inlet and the wellhead of the ventilation shaft, the less the effect was on temperature. Moreover, slight temperature variations (1.0-3.0 ℃) were observed between various points on the return route during the summer months. Airflow temperatures along the airflow inlet to the return route of the working face first decreased, but then increased. The temperature field of the surrounding rock increased gradually with increased distance between the mine roadway and inlet, with recorded rock temperatures as high as 40.53 ℃. The radius of the heat-adjusting layer was between 28 and 33 m.

Temperature Stress at Grain Filling Stage Mediates Expression of Three Isoform Genes Encoding Starch Branching Enzymes in Rice Endosperm

An early-maturity indica rice variety Zhefu 49, whose grain quality and starch structure are sensitive to environmental temperature, was subjected to different temperatures （32℃ for high temperature and 22℃ for optimum temperature） at the grain filling stage in plant growth chambers, and the different expressions of three isoform genes （SBEI, SBEIII and SBE/V） encoding starch branching enzyme （SBE） in the endosperms were studied by the real-time fluorescence quantitative PCR （FQ-PCR） method. Effects of high temperature on the SBE expression in developing rice endosperrns were isoform-dependent. High temperature significantly down-regulated the expressions of SBEI and SBEIII, while up-regulated the expression of SBEIV. Compared with SBEIV and SBEIII, the expression of SBEI gene in Zhefu 49 rice endosperms was more sensitive to temperature variation at the grain filling stage. This study indicates that changes in weather/climate conditions especially temperature stress influence rice grain formation and its quality as evidenced by isoform expression.

Auto-Test on Motor Temperature Rising in Electric Vehicles with Mutual MRAS

A new method to calculate the motor temperature rising in electric vehicle （EV） is proposed based on the stator resistance identification. The measure theory of the motor temperature rising with the stator resistance is discussed at first. An enhanced magnetism motor dynamic math model is built which is the research object. Then the resistance identification system model is built on the mutual model reference adaptive,system （MRAS） theory. The simulation diagram of the mutual MRAS model is constructed and the resistance identification performance is studied in different motor states. Simulation results indicate that the stator resistance identification model with the mutual MRAS is effective. At the same time, the identification of motor temperature rising is possible with the identification of the stator resistance.

Methods and parameters of melting curve analysis for identification of Leishmania species:A scoping review

Leishmaniasis is a set of diseases with a worldwide distribution that affects mainly economically underprivileged populations in developing countries. It has a major impact on public health, with a global cost of billions of dollars per year. The treatment and control of leishmaniasis vary according to the Leishmania species involved, which require reliable methods for species identification. Since most of the currently used methods have limitations, there is a need for assays that allow rapid, precise identification of the offending species. Real-time polymerase chain reactions in conjunction with dissociation curve analysis have been used to detect differences in the DNA composition of selected genes of Leishmania spp. Kinetoplast DNA is the main molecular target used because of its high copy number per parasite, but other targets have also been studied. As part of an effort to establish melting temperature standards for each target gene, we have reviewed the pertinent literature available in public databases, including Pub Med, Web of Science, Sci ELO and LILACS, using the keywords "Leishmania", "leishmaniasis", "realtime PCR", "melting temperature", and "melting curve", alone or in combination. After applying eligibility criteria, 27 articles were selected for analysis. A considerable variation in the methodologies analyzed was found regarding molecular targets, standardization of the methods, reproducibility and specificity. Because of this, statistical analysis was not performed. In most cases, the methods were able to differentiate the parasite at the subgenus level or few species regardless of the target chosen.

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.

Rapid genotyping of human rotavirus using SYBR green real-time reverse transcription-polymerase chain reaction with melting curve analysis

AIM: To develop a real-time reverse transcriptionpolymerase chain reaction(RT-PCR) assay to genotype rotavirus(G and P) in Alberta from January 2012 to June 2013. METHODS: We developed and validated a different approach to perform rotavirus G and P genotyping using a two-step SYBR green RT-PCR(rt-g PCR) by selecting genotype-specific primers of published conventional RT nested PCR(cn RT-PCR) assay and optimizing the amplification conditions. c DNA was first synthesized from total RNA with Super Script? Ⅱ reverse transcriptase kit followed by amplication step using monoplex SYBR green real-time PCR. After the PCR reaction, melting curve analysis was used to determine specific genotype. Sixteen samples previously genotyped using cn RT-PCR were tested using the new assay and the genotyping results were compared as sensitivity analysis. Assay specificity was evaluated by testing other gastroenteritis viruses with the new assay. The amplicon size of each available genotype was determined by gelelectrophoresis and DNA sequences were obtained using Sanger-sequencing method. After validation and optimization, the new assay was used to genotype 122 pediatric clinical stool samples previously tested positive for rotavirus using electron microscopy between January2012 and June 2013.RESULTS: The new rt-g PCR assay was validated and optimized. The assay detected G1 to G4, G9, G12 and P[4] and P[8] that were available as positive controls in our laboratory. A single and clear peak of melting curve was generated for each of specific G and P genotypes with a Tm ranging from 80 ℃ to 82 ℃. The sensitivity of rt-g PCR was comparable to cn RT-PCR with 100% correlation of the 16 samples with known G and P genotypes. No cross reaction was found with other gastroenteritis viruses. Using the new rt-g PCR assay, genotypes were obtained for 121 of the 122 pediatric clinical samples tested positive for rotavirus: G1P[8](42.6%), G2P[4](4.9%), G3P[8](10.7%), G9P[8](10.7%), G9P[4](6.6%), G12P[8](23.0%), and unknown GP[8](0.8%). For the first time, G12 rotavirus strains were found in Alberta and G12 was the second most common genotype during the study period. Gel electrophoresis of all the genotypes showed expected amplicon size for each genotype. The sequence data of the two G12 samples along with other genotypes were blasted in NCBI BLAST or analyzed with Rota C Genotyping tool(http://rotac.regatools.be/). All genotyping results were confirmed to be correct.CONCLUSION: rt-g PCR is a useful tool for the genotyping and characterization of rotavirus. Monitoring of rotavirus genotypes is important for the identification of emerging strains and ongoing evaluation of rotavirus vaccination programs.