Colorectal cancer screening:The value of early detection and modern challenges

The screening of colorectal cancer(CRC)is pivotal for both the prevention and treatment of this disease,significantly improving early-stage tumor detection rates.This advancement not only boosts survival rates and quality of life for patients but also reduces the costs associated with treatment.However,the adoption of CRC screening methods faces numerous challenges,including the technical limitations of both noninvasive and invasive methods in terms of sensitivity and specificity.Moreover,socioeconomic factors such as regional disparities,economic conditions,and varying levels of awareness affect screening uptake.The coronavirus disease 2019 pandemic further intensified these challenges,leading to reduced screening participation and increased waiting periods.Additionally,the growing prevalence of early-onset CRC necessitates innovative screening approaches.In response,research into new methodologies,including artificial intelligence-based systems,aims to improve the precision and accessibility of screening.Proactive measures by governments and health organizations to enhance CRC screening efforts are underway,including increased advocacy,improved service delivery,and international cooperation.The role of technological innovation and global health collaboration in advancing CRC screening is undeniable.Technologies such as artificial intelligence and gene sequencing are set to revolutionize CRC screening,making a significant impact on the fight against this disease.Given the rise in early-onset CRC,it is crucial for screening strategies to continually evolve,ensuring their effectiveness and applicability.

Application of transient Rayleigh wave in detection of tunnel lining void area

Transient Rayleigh wave detection is a high-precision nondestructive detection method.At present,it has been widely used in shallow exploration,but rarely used in tunnel lining quality detection.Through the tunnel lining physical model experiment,the layout defects of the double-layer reinforcement lining area were detected and the Rayleigh wave velocity profile and dispersion curve were analyzed after data process-ing,which finally verified the feasibility and accuracy of Rayleigh wave method in detecting the tunnel lining void area.The results show that the method is not affected by the reinforcement inside the lining,the shallow detection is less disturbed and the accuracy is higher,and the data will fluctuate slightly with the deepening of the detection depth.At the same time,this method responds quite accurately to the thickness of the concrete,allowing for the assessment of the tunnel lining’s lack of compactness.This method has high efficiency,good reliability,and simple data processing,and is suitable for nondestructive detection of internal defects of tun-nel lining structure.

Deep Transfer Learning Driven Automated Fall Detection for Quality of Living of Disabled Persons

Mobile communication and the Internet of Things(IoT)technologies have recently been established to collect data from human beings and the environment.The data collected can be leveraged to provide intelligent services through different applications.It is an extreme challenge to monitor disabled people from remote locations.It is because day-to-day events like falls heavily result in accidents.For a person with disabilities,a fall event is an important cause of mortality and post-traumatic complications.Therefore,detecting the fall events of disabled persons in smart homes at early stages is essential to provide the necessary support and increase their survival rate.The current study introduces a Whale Optimization Algorithm Deep Transfer Learning-DrivenAutomated Fall Detection(WOADTL-AFD)technique to improve the Quality of Life for persons with disabilities.The primary aim of the presented WOADTL-AFD technique is to identify and classify the fall events to help disabled individuals.To attain this,the proposed WOADTL-AFDmodel initially uses amodified SqueezeNet feature extractor which proficiently extracts the feature vectors.In addition,the WOADTLAFD technique classifies the fall events using an extreme Gradient Boosting(XGBoost)classifier.In the presented WOADTL-AFD technique,the WOA approach is used to fine-tune the hyperparameters involved in the modified SqueezeNet model.The proposedWOADTL-AFD technique was experimentally validated using the benchmark datasets,and the results confirmed the superior performance of the proposedWOADTL-AFD method compared to other recent approaches.

Quality Detection of Fresh Longan Fruit in Hainan

[Objective] This study was conducted to investigate the fresh eating-quality of Hainan Iongan. [Methed] The quality analysis was conducted on on-season and off-season Iongan, which are main cultivars in Hainan consistent in maturity. In order to understand the quality characteristics of Hainan fresh Iongan, saccharide contents, vitamin C content, edible rate, TSS, weight of single fruit, weight per fruit cluster and pesticide residues were detected in this study. [Result] There were no significant differences in quality between on-season and off-season Iongan, and the content of TSS in off-season fruit was slightly higher than that in on-season Iongan. The two main cultivars ‘Shixia＇ and ＇Chuliang＇ in Hainan differed significantly in edible quality. The weight of single fruit and edible rate of ‘Chuliang＇ were slightly higher than those of ‘Shixia＇ Iongan, and their sucrose contents were nearly equivalent. Vitamin C and TSS contents in ‘Shixia＇ Ionganwere higher than those in ‘Chuliang＇.‘Shixia＇ had a monosaccharide content significantly higher than ‘Chuliang＇, while its sucrose content was lower than ‘Chuliang＇. Only low contents of residual cypermethrin and diflubenzuron were detected in pericarp, and the contents of the 9 pesticides in fruit flesh were all lower than their detection limits. [Conclusion] Longan fruit produced in Hainan could all be eaten safely.

Quality Detection of Hainan Honey Pomelo

Quality analysis was performed to major cultivars, ‘Hongxin pomelo’ and ‘Wuzi pomelo＇ with uniform maturity. Total soluble solid （TSS）, titratable acid, vita- min C, juice yield, edible rate, single-fruit weight and pesticide residues of the 2 cultivars were detected. The results showed that： the TSS contents of ‘Hongxin pomelo＇ and ‘Wuzi pomelo＇ were in the range of 8%-12%, the vitamin C contents were in the range of 0.3-0.6 g/L, the juice yields were in the range of 56%-68%, the edible rates were in the range of 52%-68%, and the single-fruit weights were in the range of 1-2.5 kg. The titratable acid content was higher in ‘Hongxin pomelo＇ than in ‘Wuzi pomelo＇. Only Dursban and imidacloprid were detected at low con- centrations in pericarp, and the residues of the 3 pesticides were all lower than their detection limits. Therefore, honey pomelo produced in Hainan all could be ate safely.

An Intelligent Intrusion Detection System in Smart Grid Using PRNN Classifier

Typically,smart grid systems enhance the ability of conventional power system networks as it is vulnerable to several kinds of attacks.These vulnerabil-ities might cause the attackers or intruders to collapse the entire network system thus breaching the confidentiality and integrity of smart grid systems.Thus,for this purpose,Intrusion detection system(IDS)plays a pivotal part in offering a reliable and secured range of services in the smart grid framework.Several exist-ing approaches are there to detect the intrusions in smart grid framework,however they are utilizing an old dataset to detect anomaly thus resulting in reduced rate of detection accuracy in real-time and huge data sources.So as to overcome these limitations,the proposed technique is presented which employs both real-time raw data from the smart grid network and KDD99 dataset thus detecting anoma-lies in the smart grid network.In the grid side data acquisition,the power trans-mitted to the grid is checked and enhanced in terms of power quality by eradicating distortion in transmission lines.In this approach,power quality in the smart grid network is enhanced by rectifying the fault using a FACT device termed UPQC(Unified Power Quality Controller)and thereby storing the data in cloud storage.The data from smart grid cloud storage and KDD99 are pre-pro-cessed and are optimized using Improved Aquila Swarm Optimization(IASO)to extract optimal features.The probabilistic Recurrent Neural Network(PRNN)classifier is then employed for the prediction and classification of intrusions.At last,the performance is estimated and the outcomes are projected in terms of grid voltage,grid current,Total Harmonic Distortion(THD),voltage sag/swell,accu-racy,precision,recall,F-score,false acceptance rate(FAR),and detection rate of the classifier.The analysis is compared with existing techniques to validate the proposed model efficiency.

A Defect Detection Method for the Primary Stage of Software Development

In the early stage of software development,a software requirements specification(SRS)is essential,and whether the requirements are clear and explicit is the key.However,due to various reasons,there may be a large number of misunderstandings.To generate high-quality software requirements specifications,numerous researchers have developed a variety of ways to improve the quality of SRS.In this paper,we propose a questions extraction method based on SRS elements decomposition,which evaluates the quality of SRS in the form of numerical indicators.The proposed method not only evaluates the quality of SRSs but also helps in the detection of defects,especially the description problem and omission defects in SRSs.To verify the effectiveness of the proposed method,we conducted a controlled experiment to compare the ability of checklist-based review(CBR)and the proposed method in the SRS review.The CBR is a classicmethod of reviewing SRS defects.After a lot of practice and improvement for a long time,CBR has excellent review ability in improving the quality of software requirements specifications.The experimental results with 40 graduate studentsmajoring in software engineering confirmed the effectiveness and advantages of the proposed method.However,the shortcomings and deficiencies of the proposed method are also observed through the experiment.Furthermore,the proposed method has been tried out by engineers with practical work experience in software development industry and received good feedback.

Realtime Object Detection Through M-ResNet in Video Surveillance System

Object detection plays a vital role in the video surveillance systems.To enhance security,surveillance cameras are now installed in public areas such as traffic signals,roadways,retail malls,train stations,and banks.However,monitor-ing the video continually at a quicker pace is a challenging job.As a consequence,security cameras are useless and need human monitoring.The primary difficulty with video surveillance is identifying abnormalities such as thefts,accidents,crimes,or other unlawful actions.The anomalous action does not occur at a high-er rate than usual occurrences.To detect the object in a video,first we analyze the images pixel by pixel.In digital image processing,segmentation is the process of segregating the individual image parts into pixels.The performance of segmenta-tion is affected by irregular illumination and/or low illumination.These factors highly affect the real-time object detection process in the video surveillance sys-tem.In this paper,a modified ResNet model(M-Resnet)is proposed to enhance the image which is affected by insufficient light.Experimental results provide the comparison of existing method output and modification architecture of the ResNet model shows the considerable amount improvement in detection objects in the video stream.The proposed model shows better results in the metrics like preci-sion,recall,pixel accuracy,etc.,andfinds a reasonable improvement in the object detection.

Probabilistic Automatic Outlier Detection for Surface Air Quality Measurements from the China National Environmental Monitoring Network

Although quality assurance and quality control procedures are routinely applied in most air quality networks, outliers can still occur due to instrument malfunctions, the influence of harsh environments and the limitation of measuring methods. Such outliers pose challenges for data-powered applications such as data assimilation, statistical analysis of pollution characteristics and ensemble forecasting. Here, a fully automatic outlier detection method was developed based on the probability of residuals, which are the discrepancies between the observed and the estimated concentration values. The estimation can be conducted using filtering—or regressions when appropriate—to discriminate four types of outliers characterized by temporal and spatial inconsistency, instrument-induced low variances, periodic calibration exceptions, and less PM_(10) than PM_(2.5) in concentration observations, respectively. This probabilistic method was applied to detect all four types of outliers in hourly surface measurements of six pollutants(PM_(2.5), PM_(10),SO_2,NO_2,CO and O_3) from 1436 stations of the China National Environmental Monitoring Network during 2014-16. Among the measurements, 0.65%-5.68% are marked as outliers. with PM_(10) and CO more prone to outliers. Our method successfully identifies a trend of decreasing outliers from 2014 to 2016,which corresponds to known improvements in the quality assurance and quality control procedures of the China National Environmental Monitoring Network. The outliers can have a significant impact on the annual mean concentrations of PM_(2.5),with differences exceeding 10 μg m^(-3) at 66 sites.

Image Tampering Detection Using No-Reference Image Quality Metrics

In this paper,a new approach is proposed to determine whether the content of an image is authentic or modified with a focus on detecting complex image tampering.Detecting image tampering without any prior information of the original image is a challenging problem since unknown diverse manipulations may have different characteristics and so do various formats of images.Our principle is that image processing,no matter how complex,may affect image quality,so image quality metrics can be used to distinguish tampered images.In particular,based on the alteration of image quality in modified blocks,the proposed method can locate the tampered areas.Referring to four types of effective no-reference image quality metrics,we obtain 13 features to present an image.The experimental results show that the proposed method is very promising on detecting image tampering and locating the locally tampered areas especially in realistic scenarios.

A Double Threshold Energy Detection-Based Neural Network for Cognitive Radio Networks

In cognitive radio networks(CoR),the performance of cooperative spectrum sensing is improved by reducing the overall error rate or maximizing the detection probability.Several optimization methods are usually used to optimize the number of user-chosen for cooperation and the threshold selection.However,these methods do not take into account the effect of sample size and its effect on improving CoR performance.In general,a large sample size results in more reliable detection,but takes longer sensing time and increases complexity.Thus,the locally sensed sample size is an optimization problem.Therefore,optimizing the local sample size for each cognitive user helps to improve CoR performance.In this study,two new methods are proposed to find the optimum sample size to achieve objective-based improved(single/double)threshold energy detection,these methods are the optimum sample size N^(*)and neural networks(NN)optimization.Through the evaluation,it was found that the proposed methods outperform the traditional sample size selection in terms of the total error rate,detection probability,and throughput.

Optical techniques in non-destructive detection of wheat quality:A review

Wheat quality detection is essential to ensure the safety ofwheat circulation and storage.The traditional wheat quality detection methods mainly include artificial sensory evaluation and physicochemical index analysis,which are difficult to meet the requirements for high accuracy and efficiency in modern wheat quality detection due to the disadvantages of subjectivity,destruction of sample integrity and low efficiency.With the rapid development of optical technology,various optical-based methods,using near-infrared spectroscopy technology,hyperspectral imaging technology and terahertz,etc.,have been proposed for wheat quality detection.These methods have the characteristics of nondestructiveness and high efficiency which make them popular in wheat quality detection in recent years.In this paper,various state-of-the-art optical-based techniques of wheat quality detection are analyzed and summarized in detail.Firstly,the principle and process of common optical non-destructive detection methods for wheat quality are introduced.Then,the optical techniques used in these detection methods are divided into seven categories,and the comparison of these technologies and their advantages and disadvantages are further discussed.It shows that terahertz technology is regarded as the most promising wheat quality detection method compared with other optical detection technologies,because it can not only detect most types of wheat deterioration,but also has higher accuracy and efficiency.Finally,the research of optical technology in wheat quality detection is prospected.The future research of optical technology-based wheat quality detection mainly includes the construction of wheat quality optical detection standardization database,the fusion of multiple optical detection technologies and multiple quality index information,the improvement of the anti-interference of optical technology and the industrialization of optical inspection technology for wheat quality.These studies are of great significance to improve the detection technology of wheat and ensure the storage safety of wheat in the future.

Research on Infrared Image Fusion Technology Based on Road Crack Detection

This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.

Quality Analysis of Herbal Medicine Products Prepared from Herba Sarcandrae by Capillary Electrophoresis with Electrochemical Detection

A capillary electrophoresis with electrochemical detection（CE-ED） method was developed for the quality analysis of herbal medicine products prepared from the same herb of Herba Sarcandrae： Fufang Caoshanhu tablets, Qingrexiaoyanning capsules, and Xuekang oral liquids. Under the optimal analysis conditions, the low detection limit[l.0×10^-7 mol/L（S/N=3）] and the wide linear range（1.0×10^-7-1.0×10^-4 mol/L） were obtained for quality standard compound of isofraxidin. The precisions of the peak current and the migration time（as RSDs） for the real sample analysis were 2.0%-2.6%, and 1.2%-1.8% for isofraxidin, respectively. The contents of isofraxidin detected were 15.77 μg/tablet, 0.48 mg/capsule, 1.2 mg/ampoule（Jiangxi）, and 0.44 mg/ampoule（Dalian） for Fufang Caoshanhu tablets, Qingrexiaoyanning capsules, and Xuekang oral liquids from different manufacturers, respectively. Quality estimate was conducted by comparing the contents of isofraxidin in the herbal medicine products with the demanded values of Chinese pharmacopeia. In addition, based on their own unique CE-ED profiles（namely, CE-ED electropherograms） the Xuekang oral liquids from the different manufacturers could be easily identified.

Photodetection of the Quality on Internal Surface of Steel Pipe

In this paper,a photoelectric device is introduced,which is used in detecting the quality on internal surface of thin and long steel pipe.In this device,the CCTV lens is used for extract tile flaw information on internal surface of the pipe,and make IBM-PC/AT 486 computer as controlling and image processing system.By this instrument,the functions,such as the digital conversion of input information,image processing,classification of recognition and output display can be obtained.In the petroleum and chemical industry,by using this apparatus,we can detect the quality on internal surface of various metal pipes with real-time automatically.

An Intelligent Early Warning Method of Press-Assembly Quality Based on Outlier Data Detection and Linear Regression

Focusing on controlling the press-assembly quality of high-precision servo mechanism,an intelligent early warning method based on outlier data detection and linear regression is proposed.Linear regression is used to deal with the relationship between assembly quality and press-assembly process,then the mathematical model of displacement-force in press-assembly process is established and a qualified press-assembly force range is defined for assembly quality control.To preprocess the raw dataset of displacement-force in the press-assembly process,an improved local outlier factor based on area density and P weight(LAOPW)is designed to eliminate the outliers which will result in inaccuracy of the mathematical model.A weighted distance based on information entropy is used to measure distance,and the reachable distance is replaced with P weight.Experiments show that the detection efficiency of the algorithm is improved by 5.6 ms compared with the traditional local outlier factor(LOF)algorithm,and the detection accuracy is improved by about 2%compared with the local outlier factor based on area density(LAOF)algorithm.The application of LAOPW algorithm and the linear regression model shows that it can effectively carry out intelligent early warning of press-assembly quality of high precision servo mechanism.

Quality control before chromosome detection in peripheral blood

The chromosome detection in peripheral blood of 1,863 patients in our hospital from January 2011 to June 2016 were analyzed. The quality control and precautions before chromosome detection in peripheral blood were reported as follows.

Spatial-Temporal Event Detection Method with Multivariate Water Quality Data

With the rapid development of the society,water contamination events cause great loss if the accidents happen in the water supply system.A large number of sensor nodes of water quality are deployed in the water supply network to detect and warn the contamination events to prevent pollution from speading.If all of sensor nodes detect and transmit the water quality data when the contamination occurs,it results in the heavy communication overhead.To reduce the communication overhead,the Connected Dominated Set construction algorithm-Rule K,is adopted to select a part fo sensor nodes.Moreover,in order to improve the detection accuracy,a Spatial-Temporal Abnormal Event Detection Algorithm with Multivariate water quality data(M-STAEDA)was proposed.In M-STAEDA,first,Back Propagation neural network models are adopted to analyze the multiple water quality parameters and calculate the possible outliers.Then,M-STAEDA algorithm determines the potential contamination events through Bayesian sequential analysis to estimate the probability of a contamination event.Third,it can make decision based on the multiple event probabilities fusion.Finally,a spatial correlation model is applied to determine the spatial-temporal contamination event in the water supply networks.The experimental results indicate that the proposed M-STAEDA algorithm can obtain more accuracy with BP neural network model and improve the rate of detection and the false alarm rate,compared with the temporal event detection of Single Variate Temporal Abnormal Event Detection Algorithm(M-STAEDA).

Numerical Simulation Research on Response Characteristics of Grouting Defects of Ground Penetrating Radar for Detection of Grouting Quality behind Tunnel Wall

The effect of grouting behind tunnel wall directly affects the surrounding ground settlement and the stability of tunnel structure,so the grouting quality detection is very necessary.As an efficient and convenient shallow geophysical exploration method,ground-penetrating radar can meet the high-resolution and non-destructive requirements of grouting quality detection behind the tunnel wall,so it is widely used in engineering in recent years.Most of the existing studies have obvious regional pertinence and special geological conditions,and there are few universal studies on the characteristics of the ground penetrating radar reflection image of the grouting defect behind the tunnel wall.In view of this,this paper uses the finite difference time domain method to simulate several grouting defects behind the wall,such as voids,water-bearing anomaly,cracks,and other grouting defects.The simulation results show that the reflection image of the direct wave is characterized by a white band with strong amplitude;the interface between primary support and second lining,primary support,and surrounding rock is also banded;the circular cavity and water anomaly characteristics are all hyperbolic,the difference is that the phase of the lower part of the radar image of the cavity anomaly is 0,and there are only hyperbolic tails on both sides,and the water-bearing anomaly also has obvious hyperbolic characteristics at each interface;the reflected wave characteristics of the rectangular crack are striped and watery and the reflected wave characteristic of rectangular cracks is striped,and the abnormal range of water-bearing cracks on the radar image is larger than that of air.The research results can provide an effective theoretical reference for the engineering application of ground penetrating radar detection of grouting defects behind the tunnel wall.

Food Safety Detection Methods Applied to National Special Rectification of Product Quality and Food Safety

Afour-month period of national special rectification for product quality and food safety officially started on August 25, and was focused on eight fields, including those of agricultural products and processed foods.……