Finite element model updating for structural damage detection using transmissibility data

This paper presents a new finite element model updating method for estimating structural parameters and detecting structural damage location and severity based on the structural responses(output-only data).The method uses the sensitivity relation of transmissibility data through a least-squares algorithm and appropriate normalization of the extracted equations.The proposed transmissibility-based sensitivity equation produces a more significant number of equations than the sensitivity equations based on the frequency response function(FRF),which can estimate the structural parameters with higher accuracy.The abilities of the proposed method are assessed by using numerical data of a two-story two-bay frame model and a plate structure model.In evaluating different damage cases,the number,location,and stiffness reduction of the damaged elements and the severity of the simulated damage have been accurately identified.The reliability and stability of the presented method against measurement and modeling errors are examined using error-contaminated data.The parameter estimation results prove the method’s capabilities as an accurate model updating algorithm.

Anomaly Detection Using Data Rate of Change on Medical Data

The identification and mitigation of anomaly data,characterized by deviations from normal patterns or singularities,stand as critical endeavors in modern technological landscapes,spanning domains such as Non-Fungible Tokens(NFTs),cyber-security,and the burgeoning metaverse.This paper presents a novel proposal aimed at refining anomaly detection methodologies,with a particular focus on continuous data streams.The essence of the proposed approach lies in analyzing the rate of change within such data streams,leveraging this dynamic aspect to discern anomalies with heightened precision and efficacy.Through empirical evaluation,our method demonstrates a marked improvement over existing techniques,showcasing more nuanced and sophisticated result values.Moreover,we envision a trajectory of continuous research and development,wherein iterative refinement and supplementation will tailor our approach to various anomaly detection scenarios,ensuring adaptability and robustness in real-world applications.

Adaptive detection of range-spread targets in homogeneous and partially homogeneous clutter plus subspace interference

Adaptive detection of range-spread targets is considered in the presence of subspace interference plus Gaussian clutter with unknown covariance matrix.The target signal and interference are supposed to lie in two linearly independent subspaces with deterministic but unknown coordinates.Relying on the two-step criteria,two adaptive detectors based on Gradient tests are proposed,in homogeneous and partially homogeneous clutter plus subspace interference,respectively.Both of the proposed detectors exhibit theoretically constant false alarm rate property against unknown clutter covariance matrix as well as the power level.Numerical results show that,the proposed detectors have better performance than their existing counterparts,especially for mismatches in the signal steering vectors.

Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

Early diagnosis of esophageal cancer:How to put“early detection”into effect?

This editorial comments on the article by Qu et al in a recent edition of World Journal of Gastrointestinal Oncology,focusing on the importance of early diagnosis in managing esophageal cancer and strategies for achieving“early detection”.The five-year age-standardized net survival for esophageal cancer patients falls short of expectations.Early detection and accurate diagnosis are critical strategies for improving the treatment outcomes of esophageal cancer.While advancements in endoscopic technology have been significant,there seems to be an excessive emphasis on the latest high-end endoscopic devices and various endoscopic resection techniques.Therefore,it is imperative to redirect focus towards proactive early detection strategies for esophageal cancer,investigate the most cost-effective screening methods suitable for different regions,and persistently explore practical solutions to improve the five-year survival rate of patients with esophageal cancer.

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.

Long-term impact of artificial intelligence on colorectal adenoma detection in high-risk colonoscopy

BACKGROUND Improved adenoma detection rate(ADR)has been demonstrated with artificial intelligence(AI)-assisted colonoscopy.However,data on the real-world appli-cation of AI and its effect on colorectal cancer(CRC)screening outcomes is limited.AIM To analyze the long-term impact of AI on a diverse at-risk patient population undergoing diagnostic colonoscopy for positive CRC screening tests or sympt-oms.METHODS AI software(GI Genius,Medtronic)was implemented into the standard proced-ure protocol in November 2022.Data was collected on patient demographics,procedure indication,polyp size,location,and pathology.CRC screening outcomes were evaluated before and at different intervals after AI introduction with one year of follow-up.RESULTS We evaluated 1008 colonoscopies(278 pre-AI,255 early post-AI,285 established post-AI,and 190 late post-AI).The ADR was 38.1%pre-AI,42.0%early post-AI(P=0.77),40.0%established post-AI(P=0.44),and 39.5%late post-AI(P=0.77).There were no significant differences in polyp detection rate(PDR,baseline 59.7%),advanced ADR(baseline 16.2%),and non-neoplastic PDR(baseline 30.0%)before and after AI introduction.CONCLUSION In patients with an increased pre-test probability of having an abnormal colonoscopy,the current generation of AI did not yield enhanced CRC screening metrics over high-quality colonoscopy.Although the potential of AI in colonoscopy is undisputed,current AI technology may not universally elevate screening metrics across all situations and patient populations.Future studies that analyze different AI systems across various patient populations are needed to determine the most effective role of AI in optimizing CRC screening in clinical practice.

Efficacy of image-enhanced endoscopy for colorectal adenoma detection:A multicenter,randomized trial

BACKGROUND Improved adenoma detection at colonoscopy has decreased the risk of developing colorectal cancer.However,whether image-enhanced endoscopy(IEE)further improves the adenoma detection rate(ADR)is controversial.AIM To compare IEE with white-light imaging(WLI)endoscopy for the detection and identification of colorectal adenoma.METHODS This was a multicenter,randomized,controlled trial.Participants were enrolled between September 2019 to April 2021 from 4 hospital in China.Patients were randomly assigned to an IEE group with WLI on entry and IEE on withdrawal(n=2113)or a WLI group with WLI on both entry and withdrawal(n=2098).The primary outcome was the ADR.The secondary endpoints were the polyp detection rate(PDR),adenomas per colonoscopy,adenomas per positive colonoscopy,and factors related to adenoma detection.RESULTS A total of 4211 patients(966 adenomas)were included in the analysis(mean age,56.7 years,47.1%male).There were 2113 patients(508 adenomas)in the IEE group and 2098 patients(458 adenomas)in the WLI group.The ADR in two group were not significantly different[24.0%vs 21.8%,1.10,95%confidence interval(CI):0.99-1.23,P=0.09].The PDR was higher with IEE group(41.7%)than with WLI group(36.1%,1.16,95%CI:1.07-1.25,P=0.01).Differences in mean withdrawal time(7.90±3.42 min vs 7.85±3.47 min,P=0.30)and adenomas per colonoscopy(0.33±0.68 vs 0.28±0.62,P=0.06)were not significant.Subgroup analysis found that with narrowband imaging(NBI),between-group differences in the ADR,were not significant(23.7%vs 21.8%,1.09,95%CI:0.97-1.22,P=0.15),but were greater with linked color imaging(30.9%vs 21.8%,1.42,95%CI:1.04-1.93,P=0.04).the second-generation NBI(2G-NBI)had an advantage of ADR than both WLI and the first-generation NBI(27.0%vs 21.8%,P=0.01;27.0%vs 21.2.0%,P=0.01).CONCLUSION This prospective study confirmed that,among Chinese,IEE didn’t increase the ADR compared with WLI,but 2G-NBI increase the ADR.

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.

Modeling Target Detection and Performance Analysis of Electronic Countermeasures for Phased Radar

Interference is a key factor in radar return misdetection.Strong interference might make it difficult to detect the signal or targets.When interference occurs in the sidelobes of the antenna pattern,Sidelobe Cancellation(SLC)and Sidelobe Blanking are two unique solutions to solve this problem(SLB).Aside from this approach,the probability of false alert and likelihood of detection are the most essential parameters in radar.The chance of a false alarm for any radar system should be minimal,and as a result,the probability of detection should be high.There are several interference cancellation strategies in the literature that are used to sustain consistent false alarms regardless of the clutter environment.With the necessity for interference cancellation methods and the constant false alarm rate(CFAR),the Maisel SLC algorithm has been modified to create a new algorithm for recognizing targets in the presence of severe interference.The received radar returns and interference are simulated as non-stationary in this approach,and side-lobe interference is cancelled using an adaptive algorithm.By comparing the performance of adaptive algorithms,simulation results are shown.In a severe clutter situation,the simulation results demonstrate a considerable increase in target recognition and signal to noise ratio when compared to the previous technique.

Salient Object Detection Based on a Novel Combination Framework Using the Perceptual Matching and Subjective-Objective Mapping Technologies

The integrity and fineness characterization of non-connected regions and contours is a major challenge for existing salient object detection.The key to address is how to make full use of the subjective and objective structural information obtained in different steps.Therefore,by simulating the human visual mechanism,this paper proposes a novel multi-decoder matching correction network and subjective structural loss.Specifically,the loss pays different attentions to the foreground,boundary,and background of ground truth map in a top-down structure.And the perceived saliency is mapped to the corresponding objective structure of the prediction map,which is extracted in a bottom-up manner.Thus,multi-level salient features can be effectively detected with the loss as constraint.And then,through the mapping of improved binary cross entropy loss,the differences between salient regions and objects are checked to pay attention to the error prone region to achieve excellent error sensitivity.Finally,through tracking the identifying feature horizontally and vertically,the subjective and objective interaction is maximized.Extensive experiments on five benchmark datasets demonstrate that compared with 12 state-of-the-art methods,the algorithm has higher recall and precision,less error and strong robustness and generalization ability,and can predict complete and refined saliency maps.

Study on Real-Time Heart Rate Detection Based on Multi-People

Heart rate is an important vital characteristic which indicates physical and mental health status.Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly.Therefore,the study of non-contact heart rate measurement methods is of great importance.Based on the principles of photoelectric volumetric tracing,we use a computer device and camera to capture facial images,accurately detect face regions,and to detect multiple facial images using a multi-target tracking algorithm.Then after the regional segmentation of the facial image,the signal acquisition of the region of interest is further resolved.Finally,frequency detection of the collected Photo-plethysmography(PPG)and Electrocardiography(ECG)signals is completed with peak detection,Fourier analysis,and a Waveletfilter.The experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.

Intrusion Detection Using Federated Learning for Computing

The integration of clusters,grids,clouds,edges and other computing platforms result in contemporary technology of jungle computing.This novel technique has the aptitude to tackle high performance computation systems and it manages the usage of all computing platforms at a time.Federated learning is a collaborative machine learning approach without centralized training data.The proposed system effectively detects the intrusion attack without human intervention and subsequently detects anomalous deviations in device communication behavior,potentially caused by malicious adversaries and it can emerge with new and unknown attacks.The main objective is to learn overall behavior of an intruder while performing attacks to the assumed target service.Moreover,the updated system model is send to the centralized server in jungle computing,to detect their pattern.Federated learning greatly helps the machine to study the type of attack from each device and this technique paves a way to complete dominion over all malicious behaviors.In our proposed work,we have implemented an intrusion detection system that has high accuracy,low False Positive Rate(FPR)scalable,and versatile for the jungle computing environment.The execution time taken to complete a round is less than two seconds,with an accuracy rate of 96%.

一种改进的概率霍夫直线检测算法

针对概率霍夫直线检测算法检测速率不高、检测精度易受其他特征和连续间断线干扰的缺点,提出了一种改进的概率霍夫直线检测算法。改进后的算法将注意力集中在直线可能存在的若干个区间,提升了检测速率与精度,在直线检测全过程监控并剔除连续间断线,提升了算法的抗干扰能力。经过100组铝锭边缘线检测的实验验证,改进后算法相较原算法平均检测速率提升至1.77倍,查准率提升至1.45倍,且改进后算法在其他图片上的检测速率和检测效果相较原算法也有明显提升。

Evaluation of computer aided detection during colonoscopy among Veterans:Randomized clinical trial

BACKGROUND There has been significant interest in use of computer aided detection(CADe)devices in colonoscopy to improve polyp detection and reduce miss rate.AIM To investigate the use of CADe amongst veterans.METHODS Between September 2020 and December 2021,we performed a randomized controlled trial to evaluate the impact of CADe.Patients at Veterans Affairs Palo Alto Health Care System presenting for screening or low-risk surveillance were randomized to colonoscopy performed with or without CADe.Primary outcomes of interest included adenoma detection rate(ADR),adenomas per colonoscopy(APC),and adenomas per extraction.In addition,we measured serrated polyps per colonoscopy,non-adenomatous,non-serrated polyps per colonoscopy,serrated polyp detection rate,and procedural time.RESULTS A total of 244 patients were enrolled(124 with CADe),with similar patient characteristics(age,sex,body mass index,indication)between the two groups.Use of CADe was found to have decreased number of adenomas(1.79 vs 2.53,P=0.030)per colonoscopy compared to without CADe.There was no significant difference in number of serrated polyps or non-adenomatous non-serrated polyps per colonoscopy between the two groups.Overall,use of CADe was found to have lower ADR(68.5%vs 80.0%,P=0.041)compared to without use of CADe.Serrated polyp detection rate was lower with CADe(3.2%vs 7.5%)compared to without CADe,but this was not statistically significant(P=0.137).There was no significant difference in withdrawal and procedure times between the two groups or in detection of adenomas per extraction(71.4%vs 73.1%,P=0.613).No adverse events were identified.CONCLUSION While several randomized controlled trials have demonstrated improved ADR and APC with use of CADe,in this RCT performed at a center with high ADR,use of CADe was found to have decreased APC and ADR.Further studies are needed to understand the true impact of CADe on performance quality among endoscopists as well as determine criteria for endoscopists to consider when choosing to adopt CADe in their practices.

Major allergens in 8 types of allergenic foods and allergen detection techniques

In recent years,the prevalence of allergens in food warning notices,both domestically and internationally,has become the second leading concern after microbial contamination.Among the various factors that threaten human health reported by the World Health Organization,food allergy ranks fourth,and food allergy has become a global security problem.As of now,no definitive treatment for food allergies exists,making the avoidance of allergen-containing foods the most effective prevention method.Consequently,labeling foods with allergen information serves as a crucial warning for allergic populations.Moreover,to enhance comprehension of food allergies,this article provides a brief overview of their definition and sensitization mechanisms.The main focus lies in highlighting the structure of primary allergens found in eight commonly allergenic foods and the resulting clinical symptoms they cause.Additionally,a summary of commonly employed allergen detection techniques is presented,with an analysis of their principles,advantages,and limitations.Looking ahead,the integration of diverse technological approaches to establish an efficient,accurate,and affordable allergen detection method remains a significant trend.This article has certain reference value for understanding the direction of food allergies.

Iterative Dichotomiser Posteriori Method Based Service Attack Detection in Cloud Computing

Cloud computing(CC)is an advanced technology that provides access to predictive resources and data sharing.The cloud environment represents the right type regarding cloud usage model ownership,size,and rights to access.It introduces the scope and nature of cloud computing.In recent times,all processes are fed into the system for which consumer data and cache size are required.One of the most security issues in the cloud environment is Distributed Denial of Ser-vice(DDoS)attacks,responsible for cloud server overloading.This proposed sys-tem ID3(Iterative Dichotomiser 3)Maximum Multifactor Dimensionality Posteriori Method(ID3-MMDP)is used to overcome the drawback and a rela-tively simple way to execute and for the detection of(DDoS)attack.First,the pro-posed ID3-MMDP method calls for the resources of the cloud platform and then implements the attack detection technology based on information entropy to detect DDoS attacks.Since because the entropy value can show the discrete or aggregated characteristics of the current data set,it can be used for the detection of abnormal dataflow,User-uploaded data,ID3-MMDP system checks and read risk measurement and processing,bug ratingfile size changes,orfile name changes and changes in the format design of the data size entropy value.Unique properties can be used whenever the program approaches any data error to detect abnormal data services.Finally,the experiment also verifies the DDoS attack detection capability algorithm.

腹膜透析相关性腹膜炎的致病菌及药敏试验分析

目的回顾性分析该院腹膜透析相关性腹膜炎(PDAP)的病原菌谱特点及预后转归情况,为该院腹膜炎的防治提供科学依据。方法回顾性选取2017年1月至2023年2月该院收治的69例PDAP患者的临床资料,分析其发病原因、病原菌谱特点及预后转归情况。结果69例PDAP患者腹膜透析透出液共培养出病原菌43例,阳性率为62.32%,43例病原菌中革兰阳性菌23例(53.49%),革兰阴性菌17例(39.53%),真菌3例(6.98%)。43例病原菌感染原因包括肠道感染18例(41.86%),换液操作不规范11例(25.58%),呼吸道感染8例(18.60%),原因不明6例(13.95%)。革兰阳性菌以表皮葡萄球菌(10例)为主,革兰阴性菌以大肠埃希菌(7例)为主。革兰阳性菌对万古霉素、利奈唑胺、替加环素、替考拉宁、莫西沙星均敏感,对克林霉素(52.17%)、红霉素(56.52%)、阿莫西林/克拉维酸(60.87%)、青霉素G(78.26%)耐药;革兰阴性菌对美罗培南、亚胺培南、黏菌素均敏感,对阿莫西林/克拉维酸(47.06%)、头孢呋辛(47.06%)、头孢唑啉(52.94%)、氨苄西林(82.35%)耐药;真菌对氟康唑、伏立康唑、两性霉素B和卡泊芬净均敏感。69例PDAP患者共治愈53例,退出14例,死亡2例,治愈率为76.81%,真菌性PDAP患者退出率明显高于革兰阳性菌、革兰阴性菌及培养阴性的PDAP患者。结论该院腹膜透析患者发生PDAP的主要原因为换液操作不规范和肠道感染,PDAP相关致病菌以革兰阳性菌为主,真菌性PDAP患者退出率较高。

健康体检人群甲状腺结节检出情况及发病的影响因素

目的调查健康体检人群甲状腺结节(TNs)检出情况,并分析其影响因素,为早期防治TNs提供参考依据。方法选取2022年1月1日至2023年4月30日15393例健康体检人群,均进行多普勒超声检查,按年龄分层及性别统计TNs患病率,通过单因素及多因素logistic回归方程分析TNs影响因素。结果15393例健康体检者,共检查出5001例TNs,检出率为32.49%(5001/15393)。男性不同年龄组TNs检出率均低于女性(P<0.05)。健康人群随着年龄增长TNs检出率呈升高趋势(P<0.05)。两组体重指数、收缩压、舒张压、空腹血糖(FPG)、总胆固醇(TC)、低密度脂蛋白胆固醇(LDL-C)、性别、年龄、吸烟、饮酒、高脂血症、糖尿病、代谢综合征、脂肪肝、焦虑、抑郁比较,差异有统计学意义(P<0.05)。年龄、性别、糖尿病、脂代谢综合征、脂肪肝、焦虑、抑郁均为TNs独立影响因素(P<0.05)。结论健康体检人群TNs检出率较高,主要与年龄、性别、糖尿病、脂代谢综合征、脂肪肝、焦虑、抑郁等因素有关,临床应在健康体检中普及甲状腺彩超检查项目,以实现对甲状腺疾病的尽早防治。

不同冲击气压下煤样动态剪切强度的长径比效应

采用Φ50mm分离式霍普金森压杆(SHPB)试验系统,开展了不同冲击气压下直径75mm,长径比分别为0.20,0.27,0.33,0.40和0.47的5组煤样的动态剪切试验,划分了煤动态剪应力时程曲线的阶段,探讨了冲击气压对煤样动态剪切强度的影响,分析了煤样动态剪切强度和加载率的长径比效应,并建立了长径比效应理论模型。研究结果表明:①煤样动态剪应力时程曲线可分为应力初始上升、应力线性增长、应力缓慢上升和应力下降4个阶段;②煤样动态剪切强度与冲击气压呈正线性相关,但不同长径比下增加幅度存在差异,具体表现为:相同冲击气压增量下,煤样长径比越小,动态剪切强度的增加幅度越大;③煤样动态剪切强度和加载率均与长径比有关,在0.25,0.35 MPa较低冲击气压与0.45,0.55 MPa较高冲击气压下分别呈现出正、负长径比效应,并通过方差分析确定了长径比对其影响最小的冲击气压为0.376MPa;④建立了不同冲击气压下煤样动态剪切强度长径比效应理论模型,通过加载率效应推导出加载率长径比效应理论模型,并验证了模型的合理性和准确性。