Comparison of presentation and impact on quality of life of gastroesophageal reflux disease between young and old adults in a Chinese population

AIM:To compare the presentation and impact on qual-ity of life of gastroesophageal reflux disease(GERD)in old and young age groups.METHODS:Data from adult patients with GERD di-agnosed by endoscopic and symptomic characteristics were collected between January and November 2009.Exclusion criteria included combined peptic ulcers,ma-lignancy,prior surgery,antacid medication for more than 2 mo,and pregnancy.Enrolled patients were assigned to the elderly group if they were 65 years or older,or the younger group if they were under 65 years.They had completed the GERD impact scale,the Chinese GERD questionnaire,and the SF-36 question-naire.Data from other cases without endoscopic f ind-ings or symptoms were collected and these subjects comprised the control group in our study.RESULTS:There were 111 patients with GERD and 44 normal cases:78(70.3%)and 33 patients(29.7%)were in the younger and elderly groups,respectively.There were more female patients(60.3%)in the younger group,and more males(72.7%)in the elderly group.The younger cases had more severe and fre-quent typical symptoms than the elderly patients.Sig-nif icantly more impairment of daily activities was noted in the younger patients compared with the elderly group,except for physical functioning.CONCLUSION:Elderly patients with GERD were pre-dominantly male with rare presentation of typical symp-toms,and had less impaired quality of life compared with younger patients in a Chinese population.

采用Kalman滤波器的实时混合试验方法

等效力控制方法采用力反馈控制代替隐式积分方法中的迭代过程,从而使等效力控制方法在避免隐式积分方法的迭代过程的同时仍具有隐式积分方法的无条件稳定性。利用该力反馈控制,等效力控制方法还具有对实时子结构试验中的时滞进行补偿的作用。但是,在实际的实时子结构试验中,能够影响等效力控制方法时滞补偿效果的因素有很多,噪声就是其中之一。本文研究了噪声对等效力控制方法稳定性的影响。提高等效力PID控制器的比例增益可对系统时滞起到补偿作用,但是当比例增益较大时,等效力反馈中的噪声会降低等效力控制方法的稳定性。为了对等效力反馈中的噪声进行滤波,本文采用了基于作动器模型设计的Kalman滤波器,并通过实时子结构实验方法对其滤波效果进行研究。研究结果表明此滤波器能够有效地过滤噪声,消除噪声对等效力控制方法稳定性的影响。

A phased approach to enable hybrid simulation of complex structures

Hybrid simulation has been shown to be a cost-effective approach for assessing the seismic performance of structures. In hybrid simulation,critical parts of a structure are physically tested,while the remaining portions of the system are concurrently simulated computationally,typically using a finite element model. This combination is realized through a numerical time-integration scheme,which allows for investigation of full system-level responses of a structure in a cost-effective manner. However,conducting hybrid simulation of complex structures within large-scale testing facilities presents significant challenges. For example,the chosen modeling scheme may create numerical inaccuracies or even result in unstable simulations; the displacement and force capacity of the experimental system can be exceeded; and a hybrid test may be terminated due to poor communication between modules(e.g.,loading controllers,data acquisition systems,simulation coordinator). These problems can cause the simulation to stop suddenly,and in some cases can even result in damage to the experimental specimens; the end result can be failure of the entire experiment. This study proposes a phased approach to hybrid simulation that can validate all of the hybrid simulation components and ensure the integrity largescale hybrid simulation. In this approach,a series of hybrid simulations employing numerical components and small-scale experimental components are examined to establish this preparedness for the large-scale experiment. This validation program is incorporated into an existing,mature hybrid simulation framework,which is currently utilized in the Multi-Axial Full-Scale Sub-Structuring Testing and Simulation(MUST-SIM) facility of the George E. Brown Network for Earthquake Engineering Simulation(NEES) equipment site at the University of Illinois at Urbana-Champaign. A hybrid simulation of a four-span curved bridge is presented as an example,in which three piers are experimentally controlled in a total of 18 degrees of freedom(DOFs). This simulation illustrates the effectiveness of the phased approach presented in this paper.

Defect inspection of indoor components in buildings using deep learning object detection and augmented reality

Visual inspection is commonly adopted for building operation,maintenance,and safety.The durability and defects of components or materials in buildings can be quickly assessed through visual inspection.However,implementations of visual inspection are substantially time-consuming,labor-intensive,and error-prone because useful auxiliary tools that can instantly highlight defects or damage locations from images are not available.Therefore,an advanced building inspection framework is developed and implemented with augmented reality(AR)and real-time damage detection in this study.In this framework,engineers should walk around and film every corner of the building interior to generate the three-dimensional(3D)environment through ARKit.Meanwhile,a trained YOLOv5 model real-time detects defects during this process,even in a large-scale field,and the defect locations indicating the detected defects are then marked in this 3D environment.The defects areas can be measured with centimeter-level accuracy with the light detection and ranging(LiDAR)on devices.All required damage information,including defect positions and sizes,is collected at a time and can be rendered in the 2D and 3D views.Finally,this visual inspection can be efficiently conducted,and the previously generated environment can also be loaded to re-localize existing defect marks for future maintenance and change observation.Moreover,the proposed framework is also implemented and verified by an underground parking lot in a building to detect and quantify surface defects on concrete components.As seen in the results,the conventional building inspection is significantly improved with the aid of the proposed framework in terms of damage localization,damage quantification,and inspection efficiency.

Construction of Wind Turbine Bearing Vibration Monitoring and Performance Assessment System

This study is primary to develop relevant techniques for the bearing of wind turbine, such as the intelligent monitoring system, the performance assessment, future trend prediction and possible fault classification etc. The main technique of system monitoring and diagnosis is divided into three algorithms, such as the performance assessment, performance prediction and fault diagnosis, respectively. Among them, the Logistic Regression (LR) is adopted to assess the bearing performance condition, the Autoregressive Moving Average (ARMA) is adopted to predict the future variation trend of bearing, and the Support Vector Machine (SVM) is adopted to classify and diagnose the possible fault of bearing. Through testing, this intelligent monitoring system can achieve real-time vibration monitoring, current performance assessment, future performance trend prediction and possible fault classification for the bearing of wind turbine. The monitor and analysis data and knowledge not only can be used as the basis of predictive maintenance, but also can be stored in the database for follow-up off-line analysis and used as the reference for improvement of operation parameter and wind turbine system design.