Validation of a Novel Fluorescent Lateral Flow Assay for Rapid Qualitative and Quantitative Assessment of Total Anti-SARS-CoV-2 S-RBD Binding Antibody Units (BAU) from Plasma or Fingerstick Whole-Blood of COVID-19 Vaccinees

Background: Limited commercial LFA assays are available to provide a reliable quantitative measurement of the total binding antibody units (BAU/mL) against the receptor-binding domain of the SARS-CoV-2 spike protein (S-RBD). Aim: This study aimed to evaluate the performance of the fluorescence LFA FinecareTM 2019-nCoV S-RBD test along with its reader (Model No.: FS-113) against the following reference methods: (i) the FDA-approved GenScript surrogate virus-neutralizing assay (sVNT); and (ii) three highly performing automated immunoassays: BioMérieux VIDAS®3, Ortho VITROS®, and Mindray CL-900i®. Methods: Plasma from 488 vaccinees was tested by all aforementioned assays. Fingerstick whole-blood samples from 156 vaccinees were also tested by FinecareTM. Results and conclusions: FinecareTM showed 100% specificity, as none of the pre-pandemic samples tested positive. Equivalent FinecareTM results were observed among the samples taken from fingerstick or plasma (Pearson correlation r = 0.9, p < 0.0001), suggesting that fingerstick samples are sufficient to quantitate the S-RBD BAU/mL. A moderate correlation was observed between FinecareTM and sVNT (r = 0.5, p < 0.0001), indicating that FinecareTM can be used for rapid prediction of the neutralizing antibody (nAb) post-vaccination. FinecareTM BAU results showed strong correlation with VIDAS®3 (r = 0.6, p < 0.0001) and moderate correlation with VITROS® (r = 0.5, p < 0.0001) and CL-900i® (r = 0.4, p < 0.0001), suggesting that FinecareTM can be used as a surrogate for the advanced automated assays to measure S-RBD BAU/mL.This work was made possible by grant number UREP28-173-3-057 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors

Experimental Quantification of Punching Shear Capacity for Large-Scale GFRP-Reinforced Flat Slabs Made of Synthetic Fiber-Reinforced Self-Compacting Concrete_Dataset

This dataset contains Experimental data for punching shear behavior of synthetic fiber-reinforced slabs reinforced with GFRP bars and cast from SCC. All data is contained in the Excel file "SpecimenDetails.xlsx". A Matlab script is also included ["Plots.m"]; when run, it reads then plots the Load-Deflection curves. Finally, High-Resolution images of the plots are available in individual "*.tiff" files

Experimentally-Measured and Analytically Derived Data on Punching Shear of GFRP-Reinforced Flat Slabs made from SCC and SNFRC_Dataset

This dataset contains Experimental data for punching shear behavior of synthetic fiber-reinforced slabs reinforced with GFRP bars and cast from SCC. All data is contained in the Excel file "SpecimenDetails.xlsx". A Matlab script is also included ["Plots.m"]; when run, it reads then plots the Load-Deflection curves. Finally, High-Resolution images of the plots are available in individual "*.tiff" files

Seismic Risk Quantification and GIS-Based Seismic Risk Maps for Dubai-UAE_Dataset

This dataset contains seismic hazard assessment and fragility relationships for Dubai, UAE. It is derived from incremental dynamic analyses via nonlinear dynamic response history analyses and simulations. The results are contained within an Excel file "Data.xlsx". The first tab in the data file provides the probability of exceeding the three performance levels in conformance to the ASCE41-16 procedure. Namely: Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP), for the different types of multi-story buildings. Moreover, these results are visualized on city-wide GIS maps developed via the ArcGIS platform and ArcMap library. All files are organized in subfolders within a single compressed file: "GIS maps.rar". Finally, High-Resolution images of the GIS maps are available in individual "*.tiff" files

Data for Seismic Risk Quantification and GIS-Based Seismic Risk Maps for Dubai-UAE

This dataset contains seismic hazard assessment and fragility relationships for Dubai, UAE. It is derived from incremental dynamic analyses via nonlinear dynamic response history analyses and simulations. The results are contained within an Excel file &quot;Data.xlsx&quot;. The first tab in the data file provides the probability of exceeding the three performance levels in conformance to the ASCE41-16 procedure. Namely: Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP), for the different types of multi-story buildings. Moreover, these results are visualized on city-wide GIS maps developed via the ArcGIS platform and ArcMap library. All files are organized in subfolders within a single compressed file: &quot;GIS maps.rar&quot;. Finally, High-Resolution images of the GIS maps are available in individual &quot;*.tiff&quot; files.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Experimentally-Measured and Analytically Derived Data on Punching Shear of GFRP-Reinforced Flat Slabs made from SCC and SNFRC_Dataset

This dataset contains Experimental data for punching shear behavior of synthetic fiber-reinforced slabs reinforced with GFRP bars and cast from SCC. All data is contained in the Excel file "SpecimenDetails.xlsx". A Matlab script is also included ["Plots.m"]; when run, it reads then plots the Load-Deflection curves. Finally, High-Resolution images of the plots are available in individual "*.tiff" files

Experimental Quantification of Punching Shear Capacity for Large-Scale GFRP-Reinforced Flat Slabs Made of Synthetic Fiber-Reinforced Self-Compacting Concrete_Dataset

This dataset contains Experimental data for punching shear behavior of synthetic fiber-reinforced slabs reinforced with GFRP bars and cast from SCC. All data is contained in the Excel file "SpecimenDetails.xlsx". A Matlab script is also included ["Plots.m"]; when run, it reads then plots the Load-Deflection curves. Finally, High-Resolution images of the plots are available in individual "*.tiff" files

Seismic Risk Quantification and GIS-Based Seismic Risk Maps for Dubai-UAE_Dataset

This dataset contains seismic hazard assessment and fragility relationships for Dubai, UAE. It is derived from incremental dynamic analyses via nonlinear dynamic response history analyses and simulations. The results are contained within an Excel file "Data.xlsx". The first tab in the data file provides the probability of exceeding the three performance levels in conformance to the ASCE41-16 procedure. Namely: Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP), for the different types of multi-story buildings. Moreover, these results are visualized on city-wide GIS maps developed via the ArcGIS platform and ArcMap library. All files are organized in subfolders within a single compressed file: "GIS maps.rar". Finally, High-Resolution images of the GIS maps are available in individual "*.tiff" files

Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights

Conventional damage detection techniques are gradually being replaced by state-of-the-art smart monitoring and decision-making solutions. Near real-time and online damage assessment in structural health monitoring (SHM) systems is a promising transition toward bridging the gaps between the past's applicative inefficiencies and the emerging technologies of the future. In the age of the smart city, Internet of Things (IoT), and big data analytics, the complex nature of data-driven civil infrastructures monitoring frameworks has not been fully matured. Machine learning (ML) algorithms are thus providing the necessary tools to augment the capabilities of SHM systems and provide intelligent solutions for the challenges of the past. This article aims to clarify and review the ML frontiers involved in modern SHM systems. A detailed analysis of the ML pipelines is provided, and the in-demand methods and algorithms are summarized in augmentative tables and figures. Connecting the ubiquitous sensing and big data processing of critical information in infrastructures through the IoT paradigm is the future of SHM systems. In line with these digital advancements, considering the next-generation SHM and ML combinations, recent breakthroughs in (1) mobile device-assisted, (2) unmanned aerial vehicles, (3) virtual/augmented reality, and (4) digital twins are discussed at length. Finally, the current and future challenges and open research issues in SHM-ML conjunction are examined. The roadmap of utilizing emerging technologies within ML-engaged SHM is still in its infancy; thus, the article offers an outlook on the future of monitoring systems in assessing civil infrastructure integrity