Quality indicators for colonoscopy: Current insights and caveats

Colonoscopy is the diagnostic modality of choice for investigation of symptoms suspected to be related to the colon and for the detection of polyps and colorectal cancer(CRC). Colonoscopy with removal of detected polyps has been shown to reduce the incidence and mortality of subsequent CRC. In many countries, population screening programs for CRC have been initiated, either by selection of patients for colonoscopy with fecal occult blood testing or by offering colonoscopy directly to average-risk individuals. Several endoscopy societies have formulated quality indicators for colonoscopy. These quality indicators are almost always incorporated as process indicators, rather than outcome measures. This review focuses on the quality indicators bowel preparation, cecal intubation rate, withdrawal time, adenoma detection rate, patient comfort, sedation and complication rate, and discusses the scientific evidence supporting them,as well as their potential shortcomings and issues that need to be addressed. For instance, there is still no clear and generally accepted definition of adequatebowel preparation, no robust scientific evidence is available supporting a cecal intubation rate ≥ 90% and the association between withdrawal time and occurrence of interval cancers has not been clarified. Adenoma detection rate is currently the only quality indicator that has been shown to be associated with interval colorectal cancer, but as an indicator it does not differentiate between subjects with one or more adenoma detected.

Effect of quality of bowel preparation on quality indicators of adenoma detection rates and colonoscopy completion rates

Background and aim:Adequate bowel preparation is important for safe and effective colonoscopy.Quality indicators(QI)for colonoscopy include achieving at least 95%completion rate and an adenoma detection rate(ADR)of at least 25%in average-risk men and 15%in average-risk women aged over 50.Our aim was to investigate the impact of bowel preparation on ADR and colonoscopy completion rates.Methods:This retrospective cohort study included patients who underwent colonoscopy between January 2008 and December 2009.The main outcome measurements were ADR and colonoscopy completion rates to the cecum.Results:A total of 2519 patients was included;1030(41.0%)had excellent preparation,1145(45.5%)good-,240(9.5%)fair-,and 104(4.1%)poor preparation.Colonoscopy completion rates were significantly lower in patients with poor or fair preparation(72.1%and 75.4%,respectively)than in those with good and excellent preparation(99.7%and 99.9%,respectively;P<0.001),and significantly lower than the QI of 95%(P<0.001).ADR in men and women combined was similar in all four grades of preparation(excellent,good,fair and poor)at 24.2%vs.26.8%vs.32.1%vs.22.1%,respectively;P¼0.06.All the groups had ADR above the QI(25%for men and 15%for women)with evidence of significantly higher ADR in the women with excellent or good preparation and in men with excellent,good or fair preparation.On multivariate analysis,male gender was significantly associated with increased ADR(P<0.001),while the quality of bowel preparation did not influence ADR.Conclusions:Patients with fair and poor standards of preparation have significantly lower colonoscopy completion rates than those with excellent and good preparation.However,there was no difference in ADR between the different grades of preparation.

Marine target detection based on Marine-Faster R-CNN for navigation radar plane position indicator images

As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image detection.However,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good performance.In this paper,taking navigation radar PPI images as an example,a marine target detection method based on the Marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target characteristics.The method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the Marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale normalization.Then,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target dataset.Finally,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation radar.Its performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).

Anomalous Cell Detection with Kernel Density-Based Local Outlier Factor

Since data services are penetrating into our daily life rapidly, the mobile network becomes more complicated, and the amount of data transmission is more and more increasing. In this case, the traditional statistical methods for anomalous cell detection cannot adapt to the evolution of networks, and data mining becomes the mainstream. In this paper, we propose a novel kernel density-based local outlier factor(KLOF) to assign a degree of being an outlier to each object. Firstly, the notion of KLOF is introduced, which captures exactly the relative degree of isolation. Then, by analyzing its properties, including the tightness of upper and lower bounds, sensitivity of density perturbation, we find that KLOF is much greater than 1 for outliers. Lastly, KLOFis applied on a real-world dataset to detect anomalous cells with abnormal key performance indicators(KPIs) to verify its reliability. The experiment shows that KLOF can find outliers efficiently. It can be a guideline for the operators to perform faster and more efficient trouble shooting.

Novel Indicators for Adverse Glycemic Events Detection Analysis Based on Continuous Glucose Monitoring Neural Network Predictive Models

This paper proposes five indicators to evaluate the effectiveness and viability for adverse glycemic events detection based on predicted blood glucose(BG)values.False negative rate(FNR)and false positive rate(FPR)are defined to evaluate whether it can detect adverse glycemic events(AGEs)based on the predicted value.The temporal overlap(TO)and time difference(TD)are proposed to evaluate whether the predicted model can capture the accurate time duration of AGEs.The sum of squared percent(SSP)measures comprehensive similarity between prediction values and true values.We examined 328 patients with type 2 diabetes,containing real continuous glucose monitoring data with 5-minute time intervals.Autoregressive integrated moving average model has lower FNR and FPR.The gated recurrent unit has better temporal behavior where the mean TO with standard deviation is calculated as 0.84±0.18,and the mean TD with standard deviation is(4.39±4.01)min.Neural models have better effects on SSP(for hypoglycemia,long-short tern memory possesses 0.149 and 0.246).These five indicators are able to evaluate whether we can detect abnormal BG levels and reveal the temporal behavior of AGEs effectively.The proposed neural predictive models have more promising application in AGE detection.

Damage Detection Method Using Support Vector Machine and First Three Natural Frequencies for Shear Structures

A method is proposed for detecting damage to shear structures by using Support Vector Machine (SVM) and only the first three natural frequencies of the translational modes. This method is able to determine the damage location in any story of a shear building with only two vibration sensors;to obtain modal frequencies, one sensor on the ground detects an input and the other on the roof detects the output. Based on the shifts in the first three natural frequencies, damage location indicators are proposed, and used as new feature vectors for SVM. Simulations of five-story, nine-story and twenty-one-story shear structures and experiments on a five-story steel model were used to test the performance of the proposed method.

Approach to Anomaly Detection in Microservice System with Multi-Source Data Streams

Microservices have become popular in enterprises because of their excellent scalability and timely update capabilities.However,while fine-grained modularity and service-orientation decrease the complexity of system development,the complexity of system operation and maintenance has been greatly increased,on the contrary.Multiple types of system failures occur frequently,and it is hard to detect and diagnose failures in time.Furthermore,microservices are updated frequently.Existing anomaly detection models depend on offline training and cannot adapt to the frequent updates of microservices.This paper proposes an anomaly detection approach for microservice systems with multi-source data streams.This approach realizes online model construction and online anomaly detection,and is capable of self-updating and self-adapting.Experimental results show that this approach can correctly identify 78.85%of faults of different types.

Identifying Money Laundering Process Using Indicators： The Case of Albania

The main purpose of this study is to develop a mathematical model for calculating the probability of money laundering process, by monitoring the behavior of the client using 70 indicators of money laundering. The scientific method used in this study （received from the Modern Criminology） has great investigative power and it is widely applicable. Hopefully the practical application of this study will increase greatly the probability of detection and punishment of the clients who are implicated in the process of money laundering. In particular, this study will be useful for banks, Financial Intelligence Unit （FIU） of Albania, Department of Economic Crime at the Ministry of Domestic Affairs and Albanian State Intelligence Service （SIS）. Also, the investigation of money laundering will be a useful tool to detect other crimes, such as drug trafficking, human trafficking, illegal arms trade, etc. The prevention of money laundering is simultaneously a powerful strike against terrorism both on national and international levels.

Forest Cover Monitoring and Change Detection in Nfifikh Forest (Morocco)

Peri-urban forests are subject to different dynamics due to several factors. Nfifikh forest is a man-made space, located in suburban of Mohammedia City, belonging to Casablanca, Settat Region, and geographically between Casablanca, the economic and business Capital of Morocco and Rabat, the national political capital. Over the past three decades, it has experienced several significant degradations. The aim of this study is to evaluate and quantify the deforestation within the study area using a forest cover change detection of various vegetation indices and subpixel classification to pick out high density plots with Landsat images TM, ETM+ and OLI. Remote sensing is used to highlight the changes caused through Space-Time. This monitoring might help managers to generate forest management plans and to moderate the speed of deforestation and degradation. The results show a significant change in vegetation cover detected between 1987 and 2015. The Density increased in 2001 while it decreased considerably in 2015.

Three Indication Variables and Their Performance for the Troubled-Cell Indicator using K-Means Clustering

In Zhu,Wang and Gao(SIAM J.Sci.Comput.,43(2021),pp.A3009–A3031),we proposed a new framework of troubled-cell indicator(TCI)using K-means clustering and the numerical results demonstrate that it can detect the troubled cells accurately using the KXRCF indication variable.The main advantage of this TCI framework is its great potential of extensibility.In this follow-up work,we introduce three more indication variables,i.e.,the TVB,Fu-Shu and cell-boundary jump indication variables,and show their good performance by numerical tests to demonstrate that the TCI framework offers great flexibility in the choice of indication variables.We also compare the three indication variables with the KXRCF one,and the numerical results favor the KXRCF and the cell-boundary jump indication variables.

Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises

As cyber threats keep changing and business environments adapt, a comprehensive approach to disaster recovery involves more than just defensive measures. This research delves deep into the strategies required to respond to threats and anticipate and mitigate them proactively. Beginning with understanding the critical need for a layered defense and the intricacies of the attacker’s journey, the research offers insights into specialized defense techniques, emphasizing the importance of timely and strategic responses during incidents. Risk management is brought to the forefront, underscoring businesses’ need to adopt mature risk assessment practices and understand the potential risk impact areas. Additionally, the value of threat intelligence is explored, shedding light on the importance of active engagement within sharing communities and the vigilant observation of adversary motivations. “Beyond Defense: Proactive Approaches to Disaster Recovery and Threat Intelligence in Modern Enterprises” is a comprehensive guide for organizations aiming to fortify their cybersecurity posture, marrying best practices in proactive and reactive measures in the ever-challenging digital realm.

国内外食品快速检测方法评价技术研究

相比于国外成熟的快速检测方法评价技术体系,我国的评价技术体系发展滞后且有较多问题,严重制约了快检行业的发展。本文主要从验证方法、性能指标、测试要求等方面对各个评价技术体系做了对比分析。同时梳理了国内不同监管部门的评价技术体系发展与侧重,以期为我国食品快速检测方法确认政策和技术研究提供良好的借鉴,引导食品快速检测方法及产品的评价标准进一步规范,形成完善的科学评价方法和评价技术体系,促进我国食品安全及快速检测行业的健康发展。

计算机视觉的电站锅炉水冷壁缺陷检测方法

发电厂锅炉巡检可有效避免安全事故发生,针对现场巡检过程中,锅炉水冷壁巡检区域较大,部分区域检测困难问题,开发一种基于YOLOv3模型的水冷壁缺陷检测系统。无人机携带视觉采集装置,对豫能集团某电厂锅炉水冷壁进行图像采集,画面经压缩后实时无线传输到检测末端装置,采用YOLOv3算法对水冷壁数据进行分析,对模型重要参数进行调整并做出样本增广与平衡化改进处理,提高检测效果,共测出磨损、裂缝、氧化等106处失效部位,与人工检测对比,成功率达77.9%。该方法解决了在巡检区域大、部分区域检测困难问题,使大型电站锅炉在开展水冷壁检测方面实际付出的成本得到有效缩减。

Systematic exploration of signal-based indicators for failure diagnosis in the context of cyber-physical systems

Malfunction or breakdown of certain mission critical systems(MCSs) may cause losses of life, damage the environments, and/or lead to high costs. Therefore, recognition of emerging failures and preventive maintenance are essential for reliable operation of MCSs. There is a practical approach for identifying and forecasting failures based on the indicators obtained from real life processes. We aim to develop means for performing active failure diagnosis and forecasting based on monitoring statistical changes of generic signal features in the specific operation modes of the system. In this paper, we present a new approach for identifying emerging failures based on their manifestations in system signals. Our approach benefits from the dynamic management of the system operation modes and from simultaneous processing and characterization of multiple heterogeneous signal sources. It improves the reliability of failure diagnosis and forecasting by investigating system performance in various operation modes, includes reasoning about failures and forming of failures using a failure indicator matrix which is composed of statistical deviation of signal characteristics between normal and failed operations, and implements a failure indicator concept that can be used as a plug and play failure diagnosis and failure forecasting feature of cyber-physical systems. We demonstrate that our method can automate failure diagnosis in the MCSs and lend the MCSs to the development of decision support systems for preventive maintenance.

Proposing new indicators for glaucoma healthcare service

Glaucoma is the first leading cause of irreversible blindness worldwide with increasing importance in public health.Indicators of glaucoma care quality as well as efficiency would benefit public health assessments,but are lacking.We propose three such indicators.First,the glaucoma coverage rate(GCR),which is the number of people known to have glaucoma divided by the total number of people with glaucoma as estimated from population-based studies multiplied by 100%.Second,the glaucoma detection rate(GDR),which is number of newly diagnosed glaucoma patients in one year divided by the population in a defined area in millions.Third,the glaucoma follow-up adherence rate(GFAR),calculated as the number of patients with glaucoma who visit eye care provider(s)at least once a year over the total number of patients with glaucoma in given eye care provider(s)in a specific period.Regularly tracking and reporting these three indicators may help to improve the healthcare system performance at national or regional levels.

乌梁素海修复水体主要理化指标检测分析

近年来,由于工业的快速发展乌梁素海水体污染严重,水体中氮、磷、氨氮等营养元素严重超标,致使该湖泊富营养化严重。本论文对湖面试验区内修复水体的主要理化指标进行检测,包括化学需氧量(COD)、总氮(TN)、总磷(TP)、氨氮浓度,依次采用重铬酸钾法、碱性过硫酸钾消解紫外分光光度法、钼酸铵分光光度法、纳氏试剂分光光度法对上述指标进行测定。分析测定结果显示总氮、总磷、氨氮含量呈下降趋势,说明水体污染物在逐步降解。

机器视觉判别牛肉新鲜度的多模型定量分析

色泽是判断生鲜牛肉新鲜度的重要指标之一。随着人工智能的发展,机器视觉为生鲜肉新鲜度判别提供了可量化的解决方案。该研究通过智能手机获取牛肉图像,利用灰度化、二值化、图像分割与提取,获取不同新鲜度牛肉图像在Red-Green-Blue(RGB)、L^(*)a^(*)b^(*)和Hue-Saturation-Intensity(HSI)颜色空间的颜色参数,并与常规牛肉新鲜度评价指标挥发性盐基氮(total volatile base nitrogen,TVB-N)、硫代巴比妥酸(thiobarbituric acid,TBA)、菌落总数(total viable count,TVC)和感官评分(sensory index,SI)相关联,建立多元线性回归(multiple linear regression,MLR)、反向传播神经网络(back propagation neural network,BPNN)和支持向量回归(support vector regression,SVR)牛肉新鲜度定量预测模型。结果表明,MLR模型对TVB-N、TBA、TVC和SI值的预测模型决定系数(R^(2))分别为0.9406、0.9316、0.9582和0.9548。BPNN模型R^(2)为0.9627、0.9641、0.9920和0.9864,SVR模型的R^(2)为0.9712、0.9679、0.9928和0.9883。3种模型中SVR模型新鲜度预测性能最优,且预测相对误差均在±10%之内。在此基础上,建立了基于颜色参数的SVR牛肉货架期预测模型(R^(2)=0.9648),该模型的预测值与真实货架期平均绝对误差<0.5 d,优于传统货架期模型,提供了一种无损、快速确定牛肉新鲜度和货架期的新方法。

基于传递率函数的预制混凝土夹心保温墙板连接件损伤识别

为解决预制混凝土夹心保温墙板中连接件检测困难问题,提出了基于传递率函数的连接件损伤识别方法。首先介绍了传递率函数原理,建立了用于检测预制混凝土夹心保温墙板中损伤连接件的传递率函数理论模型,得出可用预制混凝土夹心保温墙板物理参数表示的传递率损伤指标(transmissibility damage indicator,TDI)。分析理论模型的检测机理表明,该方法不仅可以判断连接件损伤程度而且检测结果不受未被检测损伤连接件干扰。分别设置单损伤和多损伤工况,通过缩尺模型试验,验证了所提出检测方法的可行性,试验结果表明,损伤连接件和无损连接件的TDI指标之间存在着明显的不同,差异达到39.2%,所提出方法可以有效确定连接件的损伤状态与程度,且多损伤工况下其他未被检测的损伤连接件对检测结果无影响。进一步通过数值模拟结果测试所提出方法的抗噪性,建议使用该方法时,保证信噪比在40 dB以上,从而保障方法的鲁棒性。

基于回波模拟法的激光多普勒测速仪示值误差检测方法研究

针对高速激光多普勒测速仪(速度上限超过1 km/s)示值误差检测中参考速度难以获得导致测速仪示值误差无法有效检测的问题,提出采用光学回波模拟法对其进行示值误差检测。通过对测速仪的出射光进行光学移频,以此模拟运动物体的激光多普勒效应,从而解决高速多普勒测速仪检测所需速度源的问题;通过设置波长检测模块与移频量检测模块,获得准确的工作波长与移频频率,进而计算出准确的参考速度信息,保证激光多普勒测速仪示值误差检测的准确性。实验结果表明:基于回波模拟法的激光多普勒测速仪示值误差检测方法,可以实现速度上限高达2.7 km/s的测速仪示值误差的检测,示值误差测量不确定度U_(r)=0.3%(k=2)。实验结果表明基于回波模拟法的激光多普勒测速仪示值误差检测技术具有很好的工程应用前景。

肝脏疾病成分输血与相关检验指标主成分分析和预测研究

目的研究相关检验指标在肝脏疾病成分输血的主成分分析和预测模型。方法采用回顾性方法,收集2017年1月至2022年12月期间住院接受成分输血的肝脏疾病患者与非肝脏疾病患者作为研究对象,根据接受成分输血的类型分为输注悬浮红细胞组、输注病毒灭活冰冻血浆组和输注单采血小板组。收集患者的一般资料和输血前相关实验室指标,包括血红蛋白(Hb)、红细胞压积(HCT)、血小板计数(PLT)、凝血功能指标、肝功能指标以及输血情况,通过t检验与方差检验比较肝脏疾病与非肝脏疾病不同成分输血组上述指标的差异。采用KMO检验、Bartlett球形检验和碎石检验(Scree Test)验证多因子分析的适宜性,利用主成分分析(PCA)对各指标的方差贡献进行观察,评估各指标间的相关性。通过受试者工作曲线(ROC)分析评估各检验指标对于不同成分输血的预测价值。结果共纳入96例肝脏疾病患者与216例非肝脏疾病患者,肝脏疾病中57.3%患者输注血浆(55/96例),非肝脏疾病中54.2%患者接受红细胞输血(117/216例)。输注红细胞组肝病与非肝病患者Hb平均值分别为70.61 g/L和82.82 g/L;HCT平均值分别为20.80%和24.47%;丙氨酸氨基转移酶(ALT)平均值分别为45.94 U/L和25.43 U/L;总胆红素平均值为44.38μmol/L和19.31μmol/L,这四项指标两组患者中存在显著差异(P<0.05)。在输注血浆组肝病与非肝病患者Hb平均值分别为73.45 g/L和111.43 g/L;HCT平均值分别为21.70%和31.06%;ALT平均值分别为59.33 U/L和28.33 U/L;天冬氨酸氨基转移酶(AST)分别为44.35 U/L和22.52 U/L;INR平均值分别为1.43和1.07;以上指标存在显著差异(P<0.05)。输血血小板组肝病与非肝病患者PLT平均值分别为36.70×10^(9)/L和50.76×10^(9)/L;AST平均值分别为54.20 U/L和31.19 U/L;PT平均值分别为15.95 s和12.98 s;APTT平均值分别为54.42 s和29.90 s;INR平均值分别为1.36和1.11;以上五项指标存在显著差异(P<0.05)。PCA分析肝病患者不同成分输血前检验指标显示,血液指标和肝功能指标分布为第一和第二主要成分,非肝病患者输血前检验指标中肝功能和凝血指标为第一和第二主要成分。通过ROC曲线分析肝病患者接受红细胞输血组,HCT曲线下面积为0.912;血浆输血组中,INR和PT曲线下面积为0.964和0.953;在输注单采血小板组中,INR曲线下面积分别为0.938。结论本研究对于不同成分输血前各项指标相关性分析和模型预测,尤其对于肝病患者选择不同成分输血可以提供研究依据。