Impact Compression Test on Concrete after High-Temperature Treatment and Numerical Simulation of All Feasible Loading Rates

Concrete materials are important in infrastructure and national defence construction. These materials inevitably bear complicated loads, which include static load, high temperature, and high strain rate. Therefore, the dynamic responses and fragmentation of concrete under high temperatures and loading rates should be investigated. However, the compressive properties of rock materials under ultrahigh loading rates (>20 m/s) are difficult to investigate using the split Hopkinson pressure bar. Impact compression tests were conducted on concrete specimens processed at different temperatures (20-800 °C) under three loading rates in this study to discuss the variation law of the impact compression strength of concrete materials after high-temperature treatment. On this basis, numerical simulation was conducted on impact compression test under all feasible loading rates (10-110 m/s). The results demonstrate that the peak stress of all concrete specimens increases linearly with loading rate before 21 m/s and gradually decreases after 21 m/s. Peak stress shows an inverted V-shaped variation law. Moreover, the temperature-induced weakening effect exceeds the strengthening effect caused by loading rate with the increase in temperature. The growth of peak stress decreases considerably, especially under an ultrahigh loading rate (>50 m/s). These conclusions can provide theoretical references for the design of the ultimate strength of concrete materials for practical applications, such as fire and explosion prevention

New research progress on 18F-FDG PET/CT radiomics for EGFR mutation prediction in lung adenocarcinoma: a review

Lung cancer, the most frequently diagnosed cancer worldwide, is the leading cause of cancer-associated deaths. In recent years, significant progress has been achieved in basic and clinical research concerning the epidermal growth factor receptor (EGFR), and the treatment of lung adenocarcinoma has also entered a new era of individualized, targeted therapies. However, the detection of lung adenocarcinoma is usually invasive. 18F-FDG PET/CT can be used as a noninvasive molecular imaging approach, and radiomics can acquire high-throughput data from standard images. These methods play an increasingly prominent role in diagnosing and treating cancers. Herein, we reviewed the progress in applying 18F-FDG PET/CT and radiomics in lung adenocarcinoma clinical research and how these data are analyzed via traditional statistics, machine learning, and deep learning to predict EGFR mutation status, all of which achieved satisfactory results. Traditional statistics extract features effectively, machine learning achieves higher accuracy with complex algorithms, and deep learning obtains significant results through end-to-end methods. Future research should combine these methods to achieve more accurate predictions, providing reliable evidence for the precision treatment of lung adenocarcinoma. At the same time, facing challenges such as data insufficiency and high algorithm complexity, future researchers must continuously explore and optimize to better apply to clinical practice

An Expanded Three Band Model to Monitor Inland Optically Complex Water Using Geostationary Ocean Color Imager (GOCI)

Due to strict spectral band requirements, the three-band (TB) chlorophyll-a concentration (Cchla) estimation algorithm cannot be applied to GOCI image, which has great potential in frequently monitoring inland complex waters. In this study, the TB algorithm was expanded and applied to GOCI data. The GOCI TB algorithm was subsequently calibrated using an in-situ dataset which contains 281 samples collected from 17 inland lakes in China between 2013 and 2020. MERIS TB and GOCI band ratio (BR) models were selected as comparisons to assess the proposed model. The results showed that the proposed GOCI TB model has similar accuracy with MERIS TB model and overperformed GOCI BR model. The root mean square error (RMSE) of the GOCI TB, MERIS TB, and GOCI BR algorithms are 14.212 μg/L, 12.096 μg/L, and 20.504 μg/L, respectively. The mean absolute percentage error (MAPE) (when Cchla is larger than 10 μg/L) of the three models were 0.377, 0.250, and 0.453, respectively. Similar conclusion could be drawn from a match-up dataset containing 40 samples. Finally, a simulation experiment was carried out to analyze the robustness of the models under various total suspended matter concentration (CTSM) conditions. Both the in-situ validation and simulation experiment indicated that the GOCI TB factor could effectively eliminate the optical influence of CTSM. Furthermore, the broader spectral range requirement of GOCI TB model made it proper for many other multispectral sensors such as Sentinel two Multispectral Instrument (S2 MSI), Moderate Resolution Imaging Spectroradiometer (MODIS) (onboard the Terra/Aqua satellite), and Visible Infrared Imaging Radiometer Suite (VIIRS) (onboard the National Polar-orbiting Partnership satellite). Compared with the GOCI BR algorithm, the GOCI TB algorithm has stronger stability, better accuracy, and greater potential in practice

An MVC-based Intelligent Document Model Using UIML

Aiming at the common problems of intelligent document platform-dependency, this paper proposes an MVC-based (Model View Controller-based) intelligent document model using UIML (User Interface Markup Language). The model is made on the basis of the previous work of our team, and the difference is that the new model separates user interface and interaction descriptions from the view component to make the intelligent document model much more independent of platform and programming language. To verify the intelligent document model, we implemented a prototype, which can support intelligent operations. The test result shows that our approach is correct. The model not only follows MVC framework, but also provides good flexibility and independence

An MVC-based Intelligent Document Model Using UIML

Aiming at the common problems of intelligent document platform-dependency, this paper proposes an MVC-based (Model View Controller-based) intelligent document model using UIML (User Interface Markup Language). The model is made on the basis of the previous work of our team, and the difference is that the new model separates user interface and interaction descriptions from the view component to make the intelligent document model much more independent of platform and programming language. To verify the intelligent document model, we implemented a prototype, which can support intelligent operations. The test result shows that our approach is correct. The model not only follows MVC framework, but also provides good flexibility and independence

ODQ: A Fluid Office Document Query Language

Fluid office documents, as semi-structured data often represented by Extensible Markup Language (XML) are important parts of Big Data. These office documents have different formats, and their matching Application Programming Interfaces (APIs) depend on developing platform and versions, which causes difficulty in custom development and information retrieval from them. To solve this problem, we have been developing an office document query (ODQ) language which provides a uniform method to retrieve content from documents with different formats and versions. ODQ builds common document model ontology to conceal the format details of documents and provides a uniform operation interface to handle office documents with different formats. The results show that ODQ has advantages in format independence, and can facilitate users in developing documents processing systems with good interoperability