62 research outputs found
sj-xlsx-2-cath-10.1177_10760296211068487 - Supplemental material for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review
No full textSupplemental material, sj-xlsx-2-cath-10.1177_10760296211068487 for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review by Usama Waqar, Shaheer Ahmed, Syed M.H.Ali Gardezi, Muhammad Sarmad Tahir, Zain ul Abidin, Ali Hussain, Natasha Ali and Syed Faisal Mahmood in Clinical and Applied Thrombosis/Hemostasis</p
sj-docx-1-cath-10.1177_10760296211068487 - Supplemental material for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review
No full textSupplemental material, sj-docx-1-cath-10.1177_10760296211068487 for Thrombosis with Thrombocytopenia Syndrome After Administration of AZD1222 or Ad26.COV2.S Vaccine for COVID-19: A Systematic Review by Usama Waqar, Shaheer Ahmed, Syed M.H.Ali Gardezi, Muhammad Sarmad Tahir, Zain ul Abidin, Ali Hussain, Natasha Ali and Syed Faisal Mahmood in Clinical and Applied Thrombosis/Hemostasis</p
Machine Learning-Driven Resilient Modulus Prediction for Flexible Pavements Across Mountainous and Other Regions
No full textAccurate estimation of the elastic modulus (Mr) in the com- pacted subgrade soil is essential for the design of flexible pavement systems that are both reliable and environmentally friendly. Mr significantly affects the structural integrity of the pavement, especially in hilly areas with varying loads and climatic conditions. This study collects 2813 data points from pre- vious research results to create an accurate prediction model. The gradient boosted (GB) machine learning (ML) approach is selected to predict the Mr of the compacted subgrade soil. The accuracy and predictive performance of the GB model were evaluated using statistical analysis that includes fun- damental metrics such as root mean square error, mean absolute error, and relative squared error. The model obtained R² values of 0.96 and 0.94 for the training and testing datasets. The RMSE was 5 MPa for training and 7.48 MPa for testing, while the MAE was 3.18 MPa and 5.55 MPa. These results highlight the potential of GB in predicting soil Mr, thereby supporting the development of more accurate and efficient Mr prediction, ultimately reduc- ing time and cost
Investigating Vapour Cloud Explosion Dynamic Fatality Risk on Offshore Platforms by Using a Grid-Based Framework
No full textThe reliability of petroleum offshore platform systems affects human safety and well-being; hence, it should be considered in plant design and operation in order to determine its effect on human fatality risk. Methane Vapour Cloud Explosions (VCE) in offshore platforms are known to be one of the fatal potential accidents that can be attributed to failure in plant safety systems. Traditional Quantitative Risk Analysis (QRA) lacks in providing microlevel risk assessment studies and are unable to update risk with the passage of time. This study proposes a grid-based dynamic risk analysis framework for analysing the effect of VCEs on the risk of human fatality in an offshore platform. Flame Acceleration Simulator (FLACS), which is a Computational Fluid Dynamics (CFD) software, is used to model VCEs, taking into account different wind and leakage conditions. To estimate the dynamic risk, Bayesian Inference (BI) is utilised using Accident Sequence Precursor (ASP) data. The proposed framework offers the advantage of facilitating microlevel risk analysis by utilising a grid-based approach and providing grid-by-grid risk mapping. Increasing the wind speed (from 3 to 7 m/s) resulted in maximum increase of 21% in risk values. Furthermore, the integration of BI with FLACS in the grid-based framework effectively estimates risk as a function of time and space; the dynamic risk analysis revealed up to 68% increase in human fatality risk recorded from year one to year five
Bound States of Atomic Josephson Vortices
No full textWe study the existence and stability of the bound state Josephson vortices solution in two parallel quasi one-dimensional coupled Bose-Einstein condensates. The system can be elucidated by linearly coupled Gross-Pitaevskii equations. The purpose of this study is to investigate the effects of altering the strength of coupling between the two condensates over the stability of the bound state Josephson vortices. It is found that the stability of bound state Josephson vortices depends on the value of coupling strength. However, at a critical value of coupling parameter, the Josephson vortices solution transforms into a coupled dark soliton.The presentation of the authors' names and (or) special characters in the title of the pdf file of the accepted manuscript may differ slightly from what is displayed on the item page. The information in the pdf file of the accepted manuscript reflects the original submission by the author
Predictive Modeling and Experimental Validation for Assessing the Mechanical Properties of Cementitious Composites Made with Silica Fume and Ground Granulated Blast Furnace Slag
No full textUsing sustainable cement-based alternatives, such as secondary cementitious raw materials (SCMs), could be a viable option to decrease CO2 emissions resulting from cement production. Previously conducted studies to determine the optimal mix designs of concrete primarily focused on either experimental approaches or empirical modeling techniques. However, in these experimental approaches, few tests could be performed for optimization due to time restrictions and lack of resources, and empirical modeling methods cannot be relied on without external validation. The machine learning-based approaches are further characterized by certain shortcomings, including a smaller number of data points, a less robust connection among the controlling factors, and a lack of comparative analyses among machine learning models. Furthermore, the literature on predicting the performance of concrete utilizing binary SCMs (silica fume (SF) and ground granulated blast furnace slag (GGBS)) is not available. Therefore, to address these drawbacks, this research aimed to integrate ML-based models with experimental validations for accurate predictions of the compressive strength (CS) and tensile strength (TS) of concrete that includes SF and GGBS as SCMs. Three soft computing techniques, namely the ANN, ANFIS, and GEP methods, were used for prediction purposes. Eight major input parameters, including the W/B ratio, cement, GGBS, SF, coarse aggregates, fine aggregates, superplasticizer, and the age of the specimens, were considered for modeling. The validity of the established models was assessed by using external experimental validation criteria, statistical metrics, and performance measures. In addition, sensitivity and parametric analyses were performed. Based on statistical measures, the ANFIS models outperformed other models with higher correlation and lower statistical error values. However, the GEP models exhibited superior performance compared to ANFIS and ANN with respect to the closeness of the RMSE, MAE, RSE, and R2 values between the training, validation, and testing sets for both the CS and TS models. Experimental validation showed strong evidence for the applicability of the proposed models with an R2 of 0.88 and error percentages of less than 10%. Sensitivity and parametric investigations demonstrated that the input variables exhibited the patterns described in the experimental dataset and the available literature. Hence, the proposed models are accurate, have better prediction performance, and can be used for design purposes
Factores que influyen en el aprendizaje en línea de estudiantes universitarios bajo la pandemia covid-19
No full textOnline learning systems owing to their nature are free of restrictions of time or place and can prove to be a useful platform for students where they can continue their studies when it is not possible for them to go to a university in person owing to different reasons. Such systems have also been used in Pakistan, particularly in private sector, for university and school education. This paper attempts to highlight various issues that the students are facing and the factors that have a significant effect on their online learning experience. We collected data through online questionnaires distributed to 1200 students enrolled in six private universities in Pakistan. This study employed the Structure Equation Modelling (SEM) to examine factors that influenced online learning. The results showed that teaching and professional behaviour, course instructional planning and methodology and online connectivity were significantly positively associated with online learning. With the identification of key factors that affects online learning of students, it will be more helpful to provide improved services for effective student leaning. Other crucial implications and a way forward are also discussed in the paper.Los sistemas de aprendizaje en línea, por su naturaleza, están libres de restricciones de tiempo o de lugar y pueden resultar una plataforma útil para los estudiantes en la que pueden continuar sus estudios cuando no les es posible ir a una universidad en persona por diferentes razones. Esos sistemas también se han utilizado en el Pakistán, en particular en el sector privado, para la educación universitaria y escolar. Este artículo intenta destacar varios problemas que enfrentan los estudiantes y los factores que tienen un efecto significativo en su experiencia de aprendizaje en línea. Recopilamos datos a través de cuestionarios en línea distribuidos a 1200 estudiantes matriculados en seis universidades privadas en Pakistán. Este estudio empleó el Modelado de Ecuación de Estructura (SEM) para examinar factores que influyeron en el aprendizaje en línea. Los resultados mostraron que la enseñanza y el comportamiento profesional, la planificación y metodología de la enseñanza de cursos y la conectividad en línea se asociaban significativamente positivamente con el aprendizaje en línea. Con la identificación de los factores clave que afectan el aprendizaje en línea de los estudiantes, será más útil ofrecer mejores servicios para una efectiva formación de los estudiantes. En el documento también se examinan otras consecuencias cruciales y un camino a seguir
Synthesis, Identification, and Characterization of a Novel 1,2,5-Selenadiazole Derivative as a Microtubule Targeting Agent That Overcomes Multidrug Resistance
No full textABSTRACTMicrotubules are crucial for various cellular processes, including cell division, where they form highly dynamic spindle fibers for chromosomal alignment and segregation. Interference with microtubule dynamics through microtubule targeting agents (MTAs) blocks progression through mitosis, ultimately resulting in apoptosis. Although MTAs have been effectively used as a frontline treatment for various cancers, multidrug resistance (MDR) often limits their effectiveness. This study focuses on selenadiazoles, a group of organic selenium compounds with anticancer activities. Eighteen novel 1,2,5-selenadiazole derivatives were synthesized, three of which (9d, 9f, and 9i) showed potent antiproliferative activity in HCT116 colorectal cancer cells. Treatment of cells with 9f (SSE1706), one of the most potent compounds (GI50 value of 1.89 ± 0.99 µM), disrupted mitotic spindle formation, leading to G2/M arrest. 9f inhibited microtubule polymerization in cell-based assays, and long-term treatment with 9f stabilized p53 and induced apoptosis. Moreover, 9f effectively inhibited the growth of mouse and human colon cancer-derived organoids. Finally, 9f exhibited potent antiproliferative activity against MDR-1 overexpressing KB-V1 cells, highlighting its potential to overcome MDR. These findings suggest 9f as a lead compound for further optimization studies, particularly targeting MDR.This study received support from two Faculty Initiatives Fund grants (FIF-842) and (FIF-406) awarded by the Lahore University of ManagementSciences (LUMS) to Rahman Shah Zaib Saleem and Amir Faisal, respectively. Further support came from the Pakistan Science Foundation grant PSF/Res/P-LUMS/Chem (617) which was also awarded to Rahman Shah Zaib Saleem and Amir Faisal.This study received support from two Faculty Initiatives Fund grants(FIF-842) and (FIF-406) awarded by the Lahore University of Management Sciences (LUMS) to Rahman Shah Zaib Saleem and AmirFaisal, respectively. Further support came from the Pakistan ScienceFoundation grant PSF/Res/P-LUMS/Chem (617) which was alsoawarded to Rahman Shah Zaib Saleem and Amir Faisa
A Comparative Analysis of Camera, LiDAR and Fusion Based Deep Neural Networks for Vehicle Detection
No full textSelf-driving cars are an active area of interdisciplinary research spanning Artificial Intelligence (AI), Internet of Things (IoT), embedded systems, and control engineering. One crucial component needed in ensuring autonomous navigation is to accurately detect vehicles, pedestrians, or other obstacles on the road and ascertain their distance from the self-driving vehicle. The primary algorithms employed for this purpose involve the use of cameras and Light Detection and Ranging (LiDAR) data. Another category of algorithms consists of a fusion between these two sensor data. Sensor fusion networks take input as 2D camera images and LiDAR point clouds to output 3D bounding boxes as detection results. In this paper, we experimentally evaluate the performance of three object detection methods based on the input data type. We offer a comparison of three object detection networks by considering the following metrics - accuracy, performance in occluded environment, and computational complexity. YOLOv3, BEV network, and Point Fusion were trained and tested on the KITTI benchmark dataset. The performance of a sensor fusion network was shown to be superior to single-input networks.
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