Clinical characteristics of myelin oligodendrocyte glycoprotein antibody-associated disease according to their epitopes

BackgroundThe detection of myelin oligodendrocyte glycoprotein autoantibodies (MOG-Ab) is essential for the diagnosis of MOG-Ab-associated disease (MOGAD). The clinical implications of different epitopes recognized by MOG-Ab are largely unknown. In this study, we established an in-house cell-based immunoassay for detecting MOG-Ab epitopes and examined the clinical characteristics of patients with MOG-Ab according to their epitopes.MethodsWe conducted a retrospective review of patients with MOG-Ab-associated disease (MOGAD) in our single center registry, and collected serum samples from enrolled patients. Human MOG variants were generated to detect epitopes recognized by MOG-Ab. The differences in clinical characteristics according to the presence of reactivity to MOG Proline42 (P42) were evaluated.ResultsFifty five patients with MOGAD were enrolled. Optic neuritis was the most common presenting syndrome. The P42 position of MOG was a major epitope of MOG-Ab. The patients with a monophasic clinical course and childhood-onset patients were only observed in the group that showed reactivity to the P42 epitope.ConclusionWe developed an in-house cell-based immunoassay to analyze the epitopes of MOG-Ab. The P42 position of MOG is the primary target of MOG-Ab in Korean patients with MOGAD. Further studies are needed to determine the predictive value of MOG-Ab and its epitopes

Characteristics of three-dimensional stress fields in cracked plates under general loadings

Three-dimensional finite element analyses were performed on plates with a through-the-thickness crack. The global-local finite element technique with sub-modeling was used to achieve the refinement required to obtain an accurate stress field. A model was proposed to explain the behavior of stresses in the boundary layer. This model is able to account for the competing interaction between the inverse square root singular term and vertex singular term. The strain energy release rate was calculated using the modified crack closure method and energy balance. A simple technique without 3-D calculation was suggested for evaluating an approximate 3-D stress intensity factor at the mid-plane. Three-dimensional stress fields for the orthotropic cracked plate were investigated. The stress intensity profiles along the thickness direction were obtained using both the stress method and the modified crack closure method. The equation for the relation between three-dimensional stress intensity factor and its two-dimensional counterpart was derived for orthotropic materials. The cracked plates subjected to out-of-plane tearing loads were investigated. Three-dimensional finite element analyses were performed and compared to results from analyses with shell elements based on Reissner\u27s plate theory. New relations between the strain energy release rate and the stress intensity factor were derived from 3-D results. Also, out-of-plane tearing tests were conducted and some experimental observation on the 3-D crack front was carried out

InterPACK2003-35325 ADVANCED MICRO SHEAR TESTING FOR SOLDER ALLOY USING DIRECT LOCAL MEASUREMENT

ABSTRACT A modified single lap shear test configuration, based on the Iosipescu geometry, is proposed for determination of the constitutive properties of solder alloys. An auxiliary device (extension unit) is introduced to improve the accuracy of measurement. The extension unit is attached directly to the specimen and it converts shear displacements to axial displacements, which are subsequently captured by a highresolution extensometer. With aid of the extension unit, shear deformations are measured without compensating machine and grip compliance. The specimen configuration includes geometrical constraints at the solder/substrate interfaces in most electronic assemblies. Consequently, the results represent pseudo-continuum properties that take account for grain constraints at the solder/pad interface. They are properties that are more realistic for continuum mechanics based stress studies such as an FEM analysis

BUSINESS MODELS FOR CONVERGENCE OF CONSTRUCTION AND INFORMATION TECHNOLOGY -A SENARIO PLANNING-BASED APPROACH

ABSTRACT: Currently, the construction industry, which is already both high-innovated and sophisticated, is searching for new solutions in accordance with the incorporation of information technology. The objective is to create a high value construction industry through convergence with IT technology. However, there is no business model which is practical, profitable and marketable. Also, valid pre-research for development and study have not been carried out. Therefore, in this paper, we suggest a direction for investigation, applying the scenario planning method which is one of the methods of future study in order to develop a useful business model where construction and IT are integrated

CNN-Based Automatic Mobile Reporting System and Quantification for the Concrete Crack Size of the Precast Members of OSC Construction

Civil infrastructure over the years has experienced a dominant reliance on concrete material compared to other construction materials. Human inspection is the main mode of inspection for such structures, including concrete columns, which has been proven to be inaccurate and time-consuming. Convolutional neural networks (CNNs) are a substitute for such problems for both detection and quantification. However, storing the results and visualizing them at a later stage has always been a challenge. Additionally, integration of the concrete crack deep learning model to a mobile platform is an area that has received less attention. This study focuses on segmenting the concrete crack sections using the latest state-of-the-art (YOLOv7) neural network, which is then used to obtain the quantification data about the length and width of the detected crack using image binarization, and finally the results are published using a reporting system integrated to a mobile platform using a web and IoT system. The published report uses a checklist from the quantification results to grade the crack as well as its structure. The results show a mAP of 0.85, while the quantification results show a 10.82% absolute error, respectively. The reporting system takes a combined average of 5940 ms to store the data inside a database, which is then published through a mobile device. It has been demonstrated through this study that an automatic mobile reporting system is feasible to be used on buildings for maintenance, which can be further applied across other sectors of construction for monitoring and repair purposes

Deep Learning-Based PC Member Crack Detection and Quality Inspection Support Technology for the Precise Construction of OSC Projects

Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site Construction (OSC)”, a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement. The problem with OSC construction is that the quality inspection of Precast Concrete (PC) members produced at the factory and brought to the construction site is not carried out accurately and systematically. Due to the shortage of quality inspection manpower, a lot of time and money is wasted on inspecting PC members on-site, compromising inspection efficiency and accuracy. In this study, the major inspection items to be checked during the quality inspection are classified based on the existing PC member quality inspection checklist and PC construction specifications. Based on the major inspection items, the items to which AI technology can be applied (for automatic quality inspection) were identified. Additionally, the research was conducted focusing on the detection of cracks, which are one of the major types of defects in PC members. However, accurate detection of cracks is difficult since the inspection mostly relies on a visual check coupled with subjective experience. To automate the detection of cracks for PC members, video images of cracks and non-cracks on the surface were collected and used for image training and recognition using Convolutional Neural Network (CNN) and object detection, one of the deep learning technologies commonly applied in the field of image object recognition. Detected cracks were classified according to set thresholds (crack width and length), and finally, an automated PC member crack detection system that enables automatic crack detection based on mobile and web servers using deep learning and imaging technologies was proposed. This study is expected to enable more accurate and efficient on-site PC member quality inspection. Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspection work was reduced. This led to a reduction of 13 min of total work time, thereby improving work efficiency and convenience. Since quality inspection information can be stored and managed in the system database, human errors can be reduced while managing the quality of OSC work systematically and accurately. It is expected that through optimizing and upgrading our proposed system, quality work for the precise construction of OSC projects can be ensured. At the same time, systematic and accurate quality management of OSC projects is achievable through inspection data. In addition, the smart quality inspection system is expected to establish a smart work environment that enables efficient and accurate quality inspection practices if applied to various construction activities other than the OSC projects

Synthetic Data and Computer-Vision-Based Automated Quality Inspection System for Reused Scaffolding

Regular scaffolding quality inspection is an essential part of construction safety. However, current evaluation methods and quality requirements for temporary structures are based on subjective visual inspection by safety managers. Accordingly, the assessment process and results depend on an inspector’s competence, experience, and human factors, making objective analysis complex. The safety inspections performed by specialized services bring additional costs and increase evaluation times. Therefore, a temporary structure quality and safety evaluation system based on experts’ experience and independent of the human factor is the relevant solution in intelligent construction. This study aimed to present a quality evaluation system prototype for scaffolding parts based on computer vision. The main steps of the proposed system development are preparing a dataset, designing a neural network (NN) model, and training and evaluating the model. Since traditional methods of preparing a dataset are very laborious and time-consuming, this work used mixed real and synthetic datasets modeled in Blender. Further, the resulting datasets were processed using artificial intelligence algorithms to obtain information about defect type, size, and location. Finally, the tested parts’ quality classes were calculated based on the obtained defect values

BIM Environment Based Virtual Desktop Infrastructure (VDI) Resource Optimization System for Small to Medium-Sized Architectural Design Firms

The recent fourth industrial revolution and the era of post-COVID-19 have ushered in a series of technologies including a 5G network and online systems, such as cloud computing technology. In other industries, extensive studies on cloud platforms utilizing such technologies were conducted. Although the cloud environment has taken on greater importance in the construction sector as well, it was used only for servers, failing to fully reflect the characteristics of the cloud system. In particular, compared to large architectural design firms, it is challenging for small to medium-sized design firms to establish a virtual cloud computing environment, which requires high capital investment. Targeting small to medium-sized architectural design firms in Korea, this study was conducted to introduce the VDI system, one of the cloud computing technologies that was recently used in other industries, to the BIM environment for initial application, operation, and management. Specifically, after an analysis was carried out to see if the VDI system utilized in other industries may resolve the hindrance faced with the BIM environment in the construction industry, the KBimVdi system was created based on an algorithm for estimating server scales by analyzing the VDI system suitable for the BIM work environment. This was followed by a validation of the KBimVdi system based on selected projects carried out by small to medium-sized architectural firms where BIM was used for design work

Deep Learning-Based PC Member Crack Detection and Quality Inspection Support Technology for the Precise Construction of OSC Projects

Recently, the construction industry has benefited from the increased application of smart construction led by the core technologies of the fourth industrial revolution, such as BIM, AI, modular construction, and AR/VR, which enhance productivity and work efficiency. In addition, the importance of “Off-Site Construction (OSC)”, a factory-based production method, is being highlighted as modular construction increases in the domestic construction market as a means of productivity enhancement. The problem with OSC construction is that the quality inspection of Precast Concrete (PC) members produced at the factory and brought to the construction site is not carried out accurately and systematically. Due to the shortage of quality inspection manpower, a lot of time and money is wasted on inspecting PC members on-site, compromising inspection efficiency and accuracy. In this study, the major inspection items to be checked during the quality inspection are classified based on the existing PC member quality inspection checklist and PC construction specifications. Based on the major inspection items, the items to which AI technology can be applied (for automatic quality inspection) were identified. Additionally, the research was conducted focusing on the detection of cracks, which are one of the major types of defects in PC members. However, accurate detection of cracks is difficult since the inspection mostly relies on a visual check coupled with subjective experience. To automate the detection of cracks for PC members, video images of cracks and non-cracks on the surface were collected and used for image training and recognition using Convolutional Neural Network (CNN) and object detection, one of the deep learning technologies commonly applied in the field of image object recognition. Detected cracks were classified according to set thresholds (crack width and length), and finally, an automated PC member crack detection system that enables automatic crack detection based on mobile and web servers using deep learning and imaging technologies was proposed. This study is expected to enable more accurate and efficient on-site PC member quality inspection. Through the smart PC member quality inspection system proposed in this study, the time required for each phase of the existing PC member quality inspection work was reduced. This led to a reduction of 13 min of total work time, thereby improving work efficiency and convenience. Since quality inspection information can be stored and managed in the system database, human errors can be reduced while managing the quality of OSC work systematically and accurately. It is expected that through optimizing and upgrading our proposed system, quality work for the precise construction of OSC projects can be ensured. At the same time, systematic and accurate quality management of OSC projects is achievable through inspection data. In addition, the smart quality inspection system is expected to establish a smart work environment that enables efficient and accurate quality inspection practices if applied to various construction activities other than the OSC projects