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。

一种基于real-time PCR技术的TTV检测方法的建立及应用

目的:本研究旨在开发一种具有更高灵敏度和特异性的TTV检测技术,为揭示TTV在多种疾病过程中的作用提供重要的技术支持。方法:为了更精确、灵敏的检测TTV,本研究分析了目前公布的所有亚型的TTV基因序列,在此基础上建立了一种基于UTR区域的real-time PCR检测方法,并与文献报道应用较为广泛的PCR检测方法进行了对比。结果:本研究建立的方法在1×10^(7)~1×10^(1) copies/μL标准品浓度范围内具有良好的线性关系,相关系数为1.000,斜率为-3.446,检测下限为1×10^(1) copies/μL。重复性试验结果显示,组内变异系数为7.22%,表明本方法重复性、稳定性较强。针对30份临床样本,使用本研究建立的real-time PCR检测方法及目前被多个研究所使用的4套引物进行对比。结果表明,本研究所建立的方法灵敏度显著高于文献中报道的4种方法(P<0.01);Sanger测序结果表明,本方法检测出的30份阳性样本均为TTV,检测特异性为100%。结论:本研究采用基于TaqMan探针的real-time PCR检测方法,检测灵敏性高、覆盖基因型范围广,尤其对于TTV病毒载量较低的情况下能够进行定量检测,对于TTV病毒的致病性及作为免疫标志物的应用提供重要的技术支持。

基于MapReduce的负载均衡策略研究

MapReduce是Hadoop集群框架中的重要组件,用于大规模数据集的并行运算。文章针对MapReduce中存在的负载均衡问题,提出基于抽样的两阶段哈希分区策略,采用二层抽样技术进行数据采样。在分区第一阶段使用Hash算法对样本进行初始分区,将各分区大小与阈值比较,以确定是否是异常分区。在分区第二阶段融合了偏移分区和细粒度划分的思想,对异常分区进行二次哈希分区操作。实验结果表明,该策略有效解决了MapReduce中的负载均衡问题,减少了数据不平衡带来的性能损失,提高了资源的利用率。

基于MapReduce的并行化电网运行数据处理方法研究

在大规模新能源的新型电力系统中,由于云端电力数据存在结构复杂、数据量大及多维度的特点,因此不利于发挥数据对运行的指导作用,甚至有可能危害电力系统的安全、稳定运行。针对上述问题,文中提出了一种基于MapReduce的电网数据分析方法。其将云计算应用于新型电力系统,并构建了基于MapReduce云计算模型的并行化处理算法,进而提升了系统的响应速度。通过将该方法应用于电网的数据处理结果表明,所提方法可以有效地提高电网运行数据处理的准确性和工作效率。在海量数据的工况下,其处理效率约为30 min,且随着数据量的增加仍可保持稳定性与准确性,实现了网格化的并行分析。

Integrated strategy for real-time wind power fluctuation mitigation and energy storage system control

To address the impact of wind-power fluctuations on the stability of power systems,we propose a comprehensive approach that integrates multiple strategies and methods to enhance the efficiency and reliability of a system.First,we employ a strategy that restricts long-and short-term power output deviations to smoothen wind power fluctuations in real time.Second,we adopt the sliding window instantaneous complete ensemble empirical mode decomposition with adaptive noise(SW-ICEEMDAN)strategy to achieve real-time decomposition of the energy storage power,facilitating internal power distribution within the hybrid energy storage system.Finally,we introduce a rule-based multi-fuzzy control strategy for the secondary adjustment of the initial power allocation commands for different energy storage components.Through simulation validation,we demonstrate that the proposed comprehensive control strategy can smoothen wind power fluctuations in real time and decompose energy storage power.Compared with traditional empirical mode decomposition(EMD),ensemble empirical mode decomposition(EEMD),and complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)decomposition strategies,the configuration of the energy storage system under the SW-ICEEMDAN control strategy is more optimal.Additionally,the state-of-charge of energy storage components fluctuates within a reasonable range,enhancing the stability of the power system and ensuring the secure operation of the energy storage system.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

基于MapReduce集群框架的外观专利检索系统设计

基于MapReduce集群框架的外观专利检索系统设计旨在提高专利检索的效率和准确性。该系统以Hadoop分布式文件系统(HDFS)为存储基础,利用MapReduce并行计算模型实现专利数据的快速处理和检索。通过搭建Linux集群环境和分布式计算平台Hadoop,系统能有效加速外观专利检索,提高检索效率和准确性。

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.

Pore-pressure and stress-coupled creep behavior in deep coal:Insights from real-time NMR analysis

Understanding the variations in microscopic pore-fracture structures(MPFS) during coal creep under pore pressure and stress coupling is crucial for coal mining and effective gas treatment. In this manuscript, a triaxial creep test on deep coal at various pore pressures using a test system that combines in-situ mechanical loading with real-time nuclear magnetic resonance(NMR) detection was conducted.Full-scale quantitative characterization, online real-time detection, and visualization of MPFS during coal creep influenced by pore pressure and stress coupling were performed using NMR and NMR imaging(NMRI) techniques. The results revealed that seepage pores and microfractures(SPM) undergo the most significant changes during coal creep, with creep failure gradually expanding from dense primary pore fractures. Pore pressure presence promotes MPFS development primarily by inhibiting SPM compression and encouraging adsorption pores(AP) to evolve into SPM. Coal enters the accelerated creep stage earlier at lower stress levels, resulting in more pronounced creep deformation. The connection between the micro and macro values was established, demonstrating that increased porosity at different pore pressures leads to a negative exponential decay of the viscosity coefficient. The Newton dashpot in the ideal viscoplastic body and the Burgers model was improved using NMR experimental results, and a creep model that considers pore pressure and stress coupling using variable-order fractional operators was developed. The model’s reasonableness was confirmed using creep experimental data. The damagestate adjustment factors ω and β were identified through a parameter sensitivity analysis to characterize the effect of pore pressure and stress coupling on the creep damage characteristics(size and degree of difficulty) of coal.

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.

24重荧光real-time PCR技术在食物中毒快速检测中的应用

目的:应用24重荧光real-time PCR检测技术快速筛检食物中毒病原菌,结合国家标准中的培养法探讨24重荧光real-time PCR检测技术符合性和应用价值。方法:采用高灵敏度、高特异性的24重荧光real-time PCR检测技术作为中毒病原菌的初筛方法,国标方法进行细菌分离培养,并对分离出的病原菌进行生化鉴定。结果:5份食物中毒患者肛拭子在增菌前检出4份霍乱弧菌核酸(非O1/非O139群,24重荧光real-time PCR),9份患者肛拭子在增菌后检出5株霍乱弧菌(非O1/非O139群,国标培养法),其中包含4份PCR技术初筛阳性样品,两个方法的符合率为80%。所有样本均未检出沙门氏菌、志贺菌、副溶血性弧菌、致泻性大肠埃希菌以及金黄色葡萄球菌。结论:应用24重荧光real-time PCR检测技术同时检测24种常见致病病原菌,能高效锁定中毒病原菌。将其与国标培养法相结合,对临床治疗和食物中毒快速处置能起到积极作用,值得应用和推广。

基于Hadoop和MapReduce实现词频的统计与排序

在大数据背景下,词频统计是被频繁提及的一个需求。一个词在文档中出现的频率越高就越重要,通过对每个词出现频率的统计而得出质的评价,从而为用户提供大量相关行业的数据信息,最终助力用户简便、快速地找到所需信息,为其做出精确、明智、及时的决策保驾护航。因此,将在Hadoop集群环境下,运用MapReduce计算框架来实现英文单词的统计与排序功能。

基于MapReduce平台的大数据查询与处理优化算法

基于MapReduce编程并行计算框架、RSJ表连接算法等软件技术,构建了混合式MapReduce并行运算处理系统,通过读取元信息管理表的数据、提取表连接属性信息,按照表数据的属性列进行完整的数据复制与存储,得到合并汇总后完整的共享信息,使用改进RSJ的分布式缓存表连接算法滤除不满足连接条件的数据。仿真实验表明,运用MapReduce并行计算框架和RSJ表连接算法进行数据处理,可减少Map端输出的数据量,缩短网络数据传输与处理时长,实现海量化大数据查询与处理的优化目标。

A CNN-Based Single-Stage Occlusion Real-Time Target Detection Method

Aiming at the problem of low accuracy of traditional target detection methods for target detection in endoscopes in substation environments, a CNN-based real-time detection method for masked targets is proposed. The method adopts the overall design of backbone network, detection network and algorithmic parameter optimisation method, completes the model training on the self-constructed occlusion target dataset, and adopts the multi-scale perception method for target detection. The HNM algorithm is used to screen positive and negative samples during the training process, and the NMS algorithm is used to post-process the prediction results during the detection process to improve the detection efficiency. After experimental validation, the obtained model has the multi-class average predicted value (mAP) of the dataset. It has general advantages over traditional target detection methods. The detection time of a single target on FDDB dataset is 39 ms, which can meet the need of real-time target detection. In addition, the project team has successfully deployed the method into substations and put it into use in many places in Beijing, which is important for achieving the anomaly of occlusion target detection.

Cyber Resilience through Real-Time Threat Analysis in Information Security

This paper examines how cybersecurity is developing and how it relates to more conventional information security. Although information security and cyber security are sometimes used synonymously, this study contends that they are not the same. The concept of cyber security is explored, which goes beyond protecting information resources to include a wider variety of assets, including people [1]. Protecting information assets is the main goal of traditional information security, with consideration to the human element and how people fit into the security process. On the other hand, cyber security adds a new level of complexity, as people might unintentionally contribute to or become targets of cyberattacks. This aspect presents moral questions since it is becoming more widely accepted that society has a duty to protect weaker members of society, including children [1]. The study emphasizes how important cyber security is on a larger scale, with many countries creating plans and laws to counteract cyberattacks. Nevertheless, a lot of these sources frequently neglect to define the differences or the relationship between information security and cyber security [1]. The paper focus on differentiating between cybersecurity and information security on a larger scale. The study also highlights other areas of cybersecurity which includes defending people, social norms, and vital infrastructure from threats that arise from online in addition to information and technology protection. It contends that ethical issues and the human factor are becoming more and more important in protecting assets in the digital age, and that cyber security is a paradigm shift in this regard [1].

基于FP-tree和MapReduce的集合相似度自连接算法

利用集合相似度自连接算法找出一个集合集中所有相似度大于给定阈值的集合对有着广泛的应用.基于过滤-验证框架和并行分布式计算框架MapReduce的集合相似度连接是近年来的研究热点.但现有算法在阈值低时产生较大规模的候选集,导致性能不理想.针对这一问题,提出采用频繁模式树FP-tree及其派生结构FP-tree*将数据压缩在内存中计算集合相似度自连接以减小候选集规模.首先设计并讨论基于现有FP-tree*的集合相似度连接计算及其优缺点,提出遍历效率更高的线性频繁模式树结构模型TELP-tree及基于它的算法TELP-SJ(TELP-tree self join),其包括分别面向构建树和遍历树的2阶段过滤算法,这些算法可以减小树规模和减少树遍历.然后,设计基于MapReduce的并行分布式算法FastTELP-SJ.最后,基于4组真实应用数据集进行3组性能比较实验.实验结果表明FastTELP-SJ算法面向高维大规模集合相似度自连接计算时,包括执行时间、内存占用率、磁盘使用量和可扩展性的运行效率最好.

肉中猪源性成分Real-time PCR定量检测技术

【目的】建立一种快速、准确的肉中猪源性成分定量检测方法。【方法】首先从GenBank数据库中筛选猪特异性的微卫星DNA,根据微卫星DNA核酸序列设计引物,对常见10种动物基因组DNA进行PCR扩增,通过有无扩增产物判断筛选的微卫星DNA对猪源性成分的特异性。然后根据微卫星DNA核酸序列,设计特异性引物和探针,建立猪源性成分Real-time PCR检测方法,采用双标准曲线分别对猪源性成分和总动物源性成分进行定量,计算猪源性成分的百分含量。【结果】筛选到猪特异性微卫星DNA(Accession EF172428),根据其序列设计的引物SEQ-sus2-F/R只能从猪基因组DNA中扩增出目的条带,其他动物的基因组均无目的条带扩增。建立的Real-time PCR检测方法灵敏度为0.02 ng/25μL反应体系。该方法能够准确检测出混合DNA样品中猪源性成分和混合肉样品中猪源性成分,百分误差分别约为1.32%和1.06%-7.12%。【结论】本研究利用Real-time PCR技术建立的定量猪源性成分的检测方法可以用来检测猪源性成分在混合样品中的百分含量。

基于Simulink Real-Time的汽车空调半实物仿真试验系统研发

针对多样化汽车空调电子零部件的高效、低成本总成级测试需求,研究基于一汽大众速腾1.6 L车型原型,设计了基于Simulink Real-Time的汽车空调半实物仿真试验系统。该系统集成了真实的汽车空调总成、制冷循环以及包含宿主机和目标机的双机实时仿真系统,用于开发和运行台架监控和空调控制模型,为汽车空调电子零部件提供半实物仿真测试环境。通过系统集成及功能验证,结果表明,汽车空调半实物仿真试验系统能够实现汽车空调基本控制功能,同时在不同工况下实现乘员舱温度控制的稳定性,稳态误差控制在±1℃以内。此外,该系统还具备灵活调整空调运行工况和状态的能力,为汽车空调电子零部件提供了高效、低成本的半实物仿真测试环境。

混合存储模式下MapReduce作业调度

在异构Hadoop集群场景中,为了缓和由于纠删码和副本存储模式混合使用,以及服务器节点本身实时算力差异造成的MapReduce作业处理效率低下的问题,本文实现了一种根据数据存储情况和节点实时负载来在多并发场景下动态调节MapReduce作业任务分配情况的调度策略.该策略通过修改当前Hadoop框架中的数据存储选址策略并对节点任务并发量进行动态控制,在多作业并发时实现更加均衡的作业间资源分配.实验结果表明,相较于Hadoop默认的两种作业调度策略,本文提出的调度模式能够将作业完成时间缩短约17%,并有效避免部分作业面临的饥饿现象.