A dual-RPA based lateral flow strip for sensitive,on-site detection of CP4-EPSPS and Cry1Ab/Ac genes in genetically modified crops

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient.In this study,a one-tube dual recombinase polymerase amplification(RPA)reaction system for CP4-EPSPS and Cry1Ab/Ac was proposed and combined with a lateral flow immunochromatographic assay,named“Dual-RPA-LFD”,to visualize the dual detection of genetically modified(GM)crops.In which,the herbicide tolerance gene CP4-EPSPS and the insect resistance gene Cry1Ab/Ac were selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits.Gradient diluted plasmids,transgenic standards,and actual samples were used as templates to conduct sensitivity,specificity,and practicality assays,respectively.The constructed method achieved the visual detection of plasmid at levels as low as 100 copies,demonstrating its high sensitivity.In addition,good applicability to transgenic samples was observed,with no cross-interference between two test lines and no influence from other genes.In conclusion,this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37°C in a rapid,equipmentfree field manner,providing a new alternative for rapid screening for transgenic assays in the field.

Clinical impact of endoscopy position detecting unit(UPD-3) for a non-sedated colonoscopy

AIM:To evaluate whether an endoscopy position detecting unit(UPD-3) can improve cecal intubation rates, cecal intubation times and visual analog scale(VAS) pain scores, regardless of the colonoscopist's level of experience.METHODS:A total of 260 patients(170 men and 90women)who underwent a colonoscopy were divided into the UPD-3-guided group or the conventional group(no UPD-3 guidance).Colonoscopies were performed by experts(experience of more than 1000colonoscopies)or trainees(experience of less than 100colonoscopies).Cecal intubation rates,cecal intubation times,insertion methods(straight insertion:shortening the colonic fold through the bending technique;roping insertion:right turn shortening technique)and patient discomfort were assessed.Patient discomfort during the endoscope insertion was scored by the VAS that was divided into 6 degrees of pain.RESULTS:The cecum intubation rates,cecal intubation times,number of cecal intubations that were performed in<15 min and insertion methods were not significantly different between the conventional group and the UPD-3-guided group.The number of patients who experienced pain during the insertion was markedly less in the UPD-3-guided group than in the conventional group.Univariate and multivariate analysis showed that the following factors were associated with lower VAS pain scores during endoscope insertion:insertion method(straight insertion)and UPD-3guidance in the trainee group.For the experts group,univariate analysis showed that only the insertion method(straight insertion)was associated with lower VAS pain scores.CONCLUSION:Although UPD-3 guidance did not shorten intubation times,it resulted in less patient painduring endoscope insertion compared with conventional endoscopy for the procedures performed by trainees.

Random Forests Algorithm Based Duplicate Detection in On-Site Programming Big Data Environment

On-site programming big data refers to the massive data generated in the process of software development with the characteristics of real-time,complexity and high-difficulty for processing.Therefore,data cleaning is essential for on-site programming big data.Duplicate data detection is an important step in data cleaning,which can save storage resources and enhance data consistency.Due to the insufficiency in traditional Sorted Neighborhood Method(SNM)and the difficulty of high-dimensional data detection,an optimized algorithm based on random forests with the dynamic and adaptive window size is proposed.The efficiency of the algorithm can be elevated by improving the method of the key-selection,reducing dimension of data set and using an adaptive variable size sliding window.Experimental results show that the improved SNM algorithm exhibits better performance and achieve higher accuracy.

Is every microorganism detected in the intensive care unit a nosocomial infection?Isn’t prevention more important than detection?

The present letter to the editor is related to the study entitled“Multidrug-resistant organisms in intensive care units and logistic analysis of risk factors.”Not every microorganism grown in samples taken from critically ill patients can be considered as an infectious agent.Accurate and adequate information about nosocomial infections is essential in introducing effective prevention programs in hospitals.Therefore,the development and implementation of care bundles for frequently used medical devices and invasive treatment devices(e.g.,intravenous catheters and invasive ventilation),adequate staffing not only for physicians,nurses,and other medical staff but also for housekeeping staff,and infection surveillance and motivational feedback are key points of infection prevention in the intensive care unit.

High Impedance Fault Detection of Distribution Network by Phasor Measurement Units

This paper proposes a new algorithm for High Impedance Fault (HIF) detection using Phasor Measurement Unit (PMU). This type of faults is difficult to detect by over current protection relays because of low fault current. In this paper, an index based on phasors change is proposed for HIF detection. The phasors are measured by PMU to obtain the square summation of errors. Two types of data are used for error calculation. The first one is sampled data and the second one is estimated data. But this index is not enough to declare presence of a HIF. Therefore another index introduces in order to distinguish the load switching from HIF. Second index utilizes 3rd harmonic current angle because this number of harmonic has a special behaviour during HIF. The verification of the proposed method is done by different simulation cases in EMTP/MATLAB.

Novel Approach for Fault Detection and Diagnosis of Sensors in Air Handling Units Using Wavelet Energy Entropy

A novel fault diagnosis method for sensors in air handling unit（AHU） using wavelet energy entropy was presented. Instead of directly comparing the numerous data under noise conditiom, the wavelet energy entropy residual was compared in the proposed method. Three.level wavelet analysis was used to decompose the measurement data under both fault-free and faulty operation conditions. The concept of Shannon entropy was referred to define wavelet energy entropy of the wavelet coefficients. The sensor faults were diagnosed by comparing the deviation of the wavelet energy entropy of the measured signal and the estimated one with the preset threshold. Testing results showed that the wavelet energy entropy was sensitive to diagnose the biased faults. The wavelet energy entropy residuals exceed the threshold significantly when faults occur. In addition, the severer the faults were, the larger the residuals would be. The results prove that the proposed method is feasible and effective for the fault detection and diagnosis of the sensors.

Semantic Units Based Event Detection in Soccer Videos

A semantic unit based event detection scheme in soccer videos is proposed in this paper.The scheme can be characterized as a three-layer framework. At the lowest layer, low-level featuresincluding color, texture, edge, shape, and motion are extracted. High-level semantic events aredefined at the highest layer. In order to connect low-level features and high-level semantics, wedesign and define some semantic units at the intermediate layer. A semantic unit is composed of asequence of consecutives frames with the same cue that is deduced from low-level features. Based onsemantic units, a Bayesian network is used to reason the probabilities of events. The experiments forshoot and card event detection in soccer videos show that the proposed method has an encouragingperformance.

Micro-seismic Event Detection of Hot Dry Rock based on the Gated Recurrent Unit Model and a Support Vector Machine

Micro-seismic monitoring is one of the most critical technologies that guide hydraulic fracturing in hot dry rock resource development. Micro-seismic monitoring requires high precision detection of micro-seismic events with a low signal-to-noise ratio. Because of this requirement, we propose a recurrent neural network model named gated recurrent unit and support vector machine(GRU;VM). The proposed model ensures high accuracy while reducing the parameter number and hardware requirement in the training process. Since micro-seismic events in hot dry rock produce large wave amplitudes and strong vibrations, it is difficult to reverse the onset of each individual event. In this study, we utilize a support vector machine(SVM) as a classifier to improve the micro-seismic event detection accuracy. To validate the methodology, we compare the simulation results of the short-term-average to the long-term-average(STA/LTA) method with GRU;VM method by using hot dry rock micro-seismic event data in Qinghai Province, China. Our proposed method has an accuracy of about 95% for identifying micro-seismic events with low signal-to-noise ratios. By ignoring smaller micro-seismic events, the detection procedure can be processed more efficiently, which is able to provide a real-time observation on the types of hydraulic fracturing in the reservoirs.

Recurrent Autoencoder Ensembles for Brake Operating Unit Anomaly Detection on Metro Vehicles

The anomaly detection of the brake operating unit (BOU) in thebrake systems on metro vehicle is critical for the safety and reliability ofthe trains. On the other hand, current periodic inspection and maintenanceare unable to detect anomalies in an early stage. Also, building an accurateand stable system for detecting anomalies is extremely difficult. Therefore,we present an efficient model that use an ensemble of recurrent autoencodersto accurately detect the BOU abnormalities of metro trains. This is the firstproposal to employ an ensemble deep learning technique to detect BOUabnormalities in metro train braking systems. One of the anomalous caseson metro vehicles is the case when the air cylinder (AC) pressures are less thanthe brake cylinder (BC) pressures in certain parts where the brake pressuresincrease before coming to a halt. Hence, in this work, we first extract the dataof BC and AC pressures. Then, the extracted data of BC and AC pressuresare divided into multiple subsequences that are used as an input for bothbi-directional long short-term memory (biLSTM) and bi-directional gatedrecurrent unit (biGRU) autoencoders. The biLSTM and biGRU autoencodersare trained using training dataset that only contains normal subsequences. Fordetecting abnormalities from test dataset which consists of abnormal subsequences, the mean absolute errors (MAEs) between original subsequences andreconstructed subsequences from both biLSTM and biGRU autoencoders arecalculated. As an ensemble step, the total error is calculated by averaging twoMAEs from biLSTM and biGRU autoencoders. The subsequence with totalerror greater than a pre-defined threshold value is considered an abnormality.We carried out the experiments using the BOU dataset on metro vehiclesin South Korea. Experimental results demonstrate that the ensemble modelshows better performance than other autoencoder-based models, which showsthe effectiveness of our ensemble model for detecting BOU anomalies onmetro trains.

Online Fault Detection Configuration on Equipment Side of a Variable-Air-Volume Air Handling Unit

With the development of the technology of the Internet of Things,more and more operational data can be collected from air conditioning systems.Unfortunately,the most of existing air conditioning controllers mainly provide controlling functions more than storing,processing or computing the measured data.This study develops an online fault detection configuration on the equipment side of air conditioning systems to realize these functions.Modbus communication is served to collect real-time operational data.The calculating programs are embedded to identify whether the measured signals exceed their limits or not,and to detect if sensor reading is frozen and other faults in relation to the operational performance are generated or not.The online fault detection configuration is tested on an actual variable-air-volume(VAV)air handling unit(AHU).The results show that the time ratio of fault detection exceeds 95.00%,which means that the configuration exhibits an acceptable fault detection effect.

Development of RPA-Cas12a-fluorescence assay for rapid and reliable detection of human bocavirus 1

Human bocavirus(HBoV)1 is considered an important pathogen that mainly affects infants aged 6–24 months,but preventing viral transmission in resource-limited regions through rapid and affordable on-site diagnosis of individuals with early infection of HBoV1 remains somewhat challenging.Herein,we present a novel faster,lower cost,reliable method for the detection of HBoV1,which integrates a recombinase polymerase amplification(RPA)assay with the CRISPR/Cas12a system,designated the RPA-Cas12a-fluorescence assay.The RPA-Cas12a-fluorescence system can specifically detect target gene levels as low as 0.5 copies of HBoV1 plasmid DNA per microliter within 40 min at 37℃without the need for sophisticated instruments.The method also demonstrates excellent specificity without cross-reactivity to non-target pathogens.Furthermore,the method was appraised using 28 clinical samples,and displayed high accuracy with positive and negative predictive agreement of 90.9%and 100%,respectively.Therefore,our proposed rapid and sensitive HBoV1 detection method,the RPA-Cas12a-fluorescence assay,shows promising potential for early on-site diagnosis of HBoV1 infection in the fields of public health and health care.The established RPA-Cas12a-fluorescence assay is rapid and reliable method for human bocavirus 1 detection.The RPA-Cas12a-fluorescence assay can be completed within 40 min with robust specificity and sensitivity of 0.5 copies/μl.

Unit-Linking PCNN和图像熵的彩色图像分割与边缘检测

在RGB空间中,将最大香农熵准则和最小交叉熵准则两种评价准则与大概率合并和小概率合并两种合并策略相结合,提出基于Unit-Linking PCNN的四种彩色图像分割方案,并在各分量分割结果基础上利用Unit-Linking PCNN实施边缘检测,合并得到彩色图像的边缘检测结果。分析了各评价准则和合并策略的优劣,比较了各分割方案条件下的图像分割和边缘检测效果。与HSV空间中得到的相关结果进行分析比较,该文分割和边缘检测结果体现了图像的更多的细节,说明了在RGB空间中进行彩色图像分割和边缘检测的合理性。与相关文献结果相比,该方法的模型参数对图像分割结果的影响较不敏感。计算机仿真结果表明,该方法具有较好的彩色图像分割和边缘检测效果,具有较强适用性。

Unit-Linking PCNN中的彩色图像边缘检测

PCNN模型具有相似群神经元同步发放脉冲的特性,适合于图像分割。对彩色图像的亮度分量进行对数变换,使其更符合人眼的视觉特性;在PCNN进行彩色图像R、G、B三分量分割的过程中,利用遗传算法进行神经元关键参数的选择,利用偏态指标进行迭代控制;在Unit-Linking PCNN模型中实现R、G、B三分量分割图的边缘检测,利用加权合并策略得到最终的边缘检测结果。仿真结果表明,该方法得到的结果体现了图像中更多的轮廓细节,具有很好的自适应性。

Traffic Anomaly DetectionMethod Based on Improved GRU and EFMS-Kmeans Clustering

In recent years,with the continuous development of information technology and the rapid growth of network scale,network monitoring and management become more and more important.Network traffic is an important part of network state.In order to ensure the normal operation of the network,improve the availability of the network,find network faults in time and deal with network attacks;it is necessary to detect the abnormal traffic in the network.Abnormal traffic detection is of great significance in the actual network management.Therefore,in order to improve the accuracy and efficiency of network traffic anomaly detection,this paper proposes a comprehensive anomaly detection method based on improved GRU traffic prediction and improved K-means clustering,and cascade the traffic prediction and clustering to achieve the purpose of anomaly detection.Firstly,an improved highway-GRU algorithm HS-GRU(An improved Gate Recurrent Unit neural network based on Highway network and STL algorithm,HS-GRU)is proposed,which combines STL decomposition algorithm with highway GRU neural network and uses this improved algorithm to predict traffic.And then,we proposed the EFMS-Kmeans algorithm(An improved clustering algorithmthat combined Mean Shift algorithmbased on electrostatic force with K-means clustering)to solve the shortcoming of the traditional K-means clustering which cannot automatically determine the number of clustering.The sum of the squared errors(SSE)method and the contour coefficient method were used to double test the clustering effect.After determining the clustering center,the potential energy gradient was directly used for anomaly detection by using the threshold method,which considered the local characteristics of the data and ensured the accuracy of anomaly detection.The simulation results show that the anomaly detection algorithm based on HS-GRU and EFMS-Kmeans clustering proposed in this paper can effectively improve the accuracy of flow anomaly detection and has important application value.

Fault Detection Based on Hierarchical Cluster Analysis in Wide Area Backup Protection System

In wide area backup protection of electric power systems, the prerequisite of protection device's accurate, fast and reliable performance is its corresponding fault type and fault location can be discriminated quickly and defined exactly. In our study, global information will be introduced into the backup protection system. By analyzing and computing real-time PMU measurements, basing on cluster analysis theory, we are using mainly hierarchical cluster analysis to search after the statistical laws of electrical quantities' marked changes. Then we carry out fast and exact detection of fault components and fault sections, and finally accomplish fault isolation. The facts show that the fault detection of fault component (fault section) can be performed successfully by hierarchical cluster analysis and calculation. The results of hierarchical cluster analysis are accurate and reliable, and the dendrograms of hierarchical cluster analysis are in intuition.

Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN

Computer-empowered detection of possible faults for Heating,Ventilation and Air-Conditioning(HVAC)subsystems,e.g.,chillers,is one of the most important applications in Artificial Intelligence(AI)integrated Internet of Things(IoT).The cyber-physical system greatly enhances the safety and security of the working facilities,reducing time,saving energy and protecting humans’health.Under the current trends of smart building design and energy management optimization,Automated Fault Detection and Diagnosis(AFDD)of chillers integrated with IoT is highly demanded.Recent studies show that standard machine learning techniques,such as Principal Component Analysis(PCA),Support Vector Machine(SVM)and tree-structure-based algorithms,are useful in capturing various chiller faults with high accuracy rates.With the fast development of deep learning technology,Convolutional Neural Networks(CNNs)have been widely and successfully applied to various fields.However,for chiller AFDD,few existing works are adopting CNN and its extensions in the feature extraction and classification processes.In this study,we propose to perform chiller FDD using a CNN-based approach.The proposed approach has two distinct advantages over existing machine learning-based chiller AFDD methods.First,the CNN-based approach does not require the feature selection/extraction process.Since CNN is reputable with its feature extraction capability,the feature extraction and classification processes are merged,leading to a more neat AFDD framework compared to traditional approaches.Second,the classification accuracy is significantly improved compared to traditional methods using the CNN-based approach.

GPS Spoofing Attack Detection in Smart Grids Based on Improved CapsNet

This paper analyzes the influence of the global positionong system(GPS)spoofing attack(GSA)on phasor measurement units(PMU)measurements.We propose a detection method based on improved Capsule Neural Network(CapsNet)to handle this attack.In the improved CapsNet,the gated recurrent unit(GRU)is added to the front of the full connection layer of the CapsNet.The improved CapsNet trains and updates the network parameters according to the historical measurements of the smart grid.The detection method uses different structures to extract the temporal and spatial features of the measurements simultaneously,which can accurately distinguish the attacked data from the normal data,to improve the detection accuracy.Finally,simulation experiments are carried out on IEEE 14-,IEEE 118-bus systems.The experimental results show that compared with other detection methods,our method is proved to be more efficient.

基于Object Detection API的物流单元货架目标检测

随着人工智能的兴起,深度学习的方法已经被广泛地应用到各类图像目标的检测当中,并在复杂环境下取得了良好的效果。针对物流仓储环境,该文基于开源框架Tensorflow上的库Object Detection API,选择了Faster R-CNN算法和SSD-MobileNet算法,分别对物流单元货架上摆放的物流周转箱进行目标检测。实验结果表明,相比于Faster R-CNN算法,SSD-MobileNet算法能够同时满足实时性与准确率的要求。将训练所得的SSD-MobileNet模型移植到QT平台,设计了物流单元货架目标检测界面。

Loss-of-Main Monitoring and Detection for Distributed Generations Using Dynamic Principal Component Analysis

In this paper, our previous work on Principal Component Analysis (PCA) based fault detection method is extended to the dynamic monitoring and detection of loss-of-main in power systems using wide-area synchrophasor measurements. In the previous work, a static PCA model was built and verified to be capable of detecting and extracting system faulty events;however the false alarm rate is high. To address this problem, this paper uses a well-known ‘time lag shift’ method to include dynamic behavior of the PCA model based on the synchronized measurements from Phasor Measurement Units (PMU), which is named as the Dynamic Principal Component Analysis (DPCA). Compared with the static PCA approach as well as the traditional passive mechanisms of loss-of-main detection, the proposed DPCA procedure describes how the synchrophasors are linearly auto- and cross-correlated, based on conducting the singular value decomposition on the augmented time lagged synchrophasor matrix. Similar to the static PCA method, two statistics, namely T2 and Q with confidence limits are calculated to form intuitive charts for engineers or operators to monitor the loss-of-main situation in real time. The effectiveness of the proposed methodology is evaluated on the loss-of-main monitoring of a real system, where the historic data are recorded from PMUs installed in several locations in the UK/Ireland power system.

融合CNN-BiGRU和注意力机制的网络入侵检测模型

为提高网络入侵检测模型特征提取能力和分类准确率,提出了一种融合双向门控循环单元(CNN-BiGRU)和注意力机制的网络入侵检测模型.使用CNN有效提取流量数据集中的非线性特征;双向门控循环单元(BiGRU)提取数据集中的时序特征,最后融合注意力机制对不同类型流量数据通过加权的方式进行重要程度的区分,从而整体提高该模型特征提取与分类的性能.实验结果表明:其整体精确率比双向长短期记忆网络(BiLSTM)模型提升了2.25%.K折交叉验证结果表明:该模型泛化性能良好,避免了过拟合现象的发生,印证了该模型的有效性与合理性.