1,723,236 research outputs found
Biological science 3D modelling
Get PDFFunded by the Government of Ontario.This project creates 3D printable models that instructors can use to illustrate lecture content, and students and teaching assistants can manipulate the models to better facilitate understanding of the physical space and relationships of biological systems, organs, and cells. It supports experiential learning for biological science courses and increases access to enhanced learning for diverse groups of students. In order to help learning in biological science courses, we will offer a library of 3D-printable files for significant biology models. These files can be used to both augment textbook content and improve hands-on learning in the lab.The views expressed in this publication are the views of the author(s) and do not necessarily reflect those of the Government of Ontario or the Ontario Online Learning Consortium (eCampusOntario)
Transcriptomic analysis of Ovarian Cancer Progression and Treatment (combination of Pembrolizumab with Bevacizumab and Metronomic Cyclophosphamide) Response: Identifying DEGs and Pathways Across the Three Time Points.
No full textThe three drugs have shown to enhance the immune response against cancer cells giving pembrolizumab, bevacizumab, and cyclophosphamide together work better in treating patients with recurrent ovarian cancer. The patient were divided into three groups and their samples were treated across the three time points i.e. baseline(BLBX), after four cycles of treatment(C4BX) and the end of treatment(EOTBX). The data was analyzed using various techniques such as machine learning, clustering, and pathway analysis to identify biomarkers that are associated with treatment response. Network and Pathway analysis was also performed to determine the significance of the results obtained
An experimental and simulation-based approach for a La0.875Sr0.125MnO3 (LSMO) characterization
Get PDFTreballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2024, Tutors: Francesca Peiró, Lluís Yedra, Pranjal NandiLSMO is a material renowned for its electronic, magnetic, and transport characteristics, having numerous technological applications. This work presents a characterization of this material using experimental techniques such as transmission electron microscopy, theoretical modelling and simulation to characterise a thin layer, finding the coexistance of two crystalline orientations within the layer
Damage detection in mooring systems of floating offshore wind turbines using semi-supervised GAN with image-transformed limited labelled data
Get PDFDetection of damage in the mooring systems of Floating Offshore Wind Turbines (FOWTs) is essential to guarantee operational reliability and reduce corrective maintenance costs. However, the complex nature of environmental conditions, the high costs of data collection, and the rarity of damage events make it challenging to obtain extensive labelled datasets. As a result, addressing damage detection from limited labelled data is necessary, yet it remains a relatively under-explored area in the literature. To tackle these challenges, this paper introduces an image-transformed semi-supervised generative adversarial network (ITSGAN) technique based on deep generative models. The method transforms time series data into multichannel image representations, enabling deep learning models to more effectively capture both spatial and temporal features. By combining adversarial training with supervised learning, ITSGAN leverages both labelled and unlabelled data to improve damage detection ability, particularly in scenarios where labelled data is scarce. A comparative analysis with established models such as traditional semi-supervised GAN, Deep Convolutional Neural Networks (DCNN), Support Vector Machines (SVM), and Extreme Gradient Boosting (XGB) shows that ITSGAN consistently outperforms these models in accuracy, precision, recall, and F1 score. It is also demonstrated that the proposed ITSGAN model preserves richer feature representations by transforming time series data into images, resulting in enhanced performance in damage detection task
Probing hidden sectors with early universe cosmology
Get PDFSubmission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo termsThe student, Pranjal Ralegankar, accepted the attached license on 2022-09-12 at 13:26.The student, Pranjal Ralegankar, submitted this Dissertation for approval on 2022-09-12 at 13:38.This Dissertation was approved for publication on 2022-09-16 at 11:51.DSpace SAF Submission Ingestion Package generated from Vireo submission #18486 on 2023-04-12 at 07:23:28There is no fundamental requirement for all particles to be charged under the Standard Model gauge symmetries. Consequently, there could naturally be a hidden sector of particles that have gone undetected and dark matter could reside in it. A hidden sector of particles could naturally have a different temperature than the plasma formed by the Standard Model particles in the early universe. Such a hidden sector can alter early universe cosmology from the assumed behavior in a variety of ways, which we explore in this thesis. First, we discuss how both hidden sector and Standard Model sector can be populated via inflaton decays after inflation. Next, we explore how thermally decoupled hidden sector with a massive lightest particle (m ≫ MeV) can enhance the abundance of sub-Earth mass dark matter microhalos today. We then explore how hidden sectors with a massless lightest particle can cause an inhomogeneous distribution of helium to hydrogen ratios if the hidden sector never had any interaction with the Standard Model sector. If instead the hidden sector had some interaction, then we show how the constraints on dark radiation energy density can be employed to constrain the interaction strength between the two sectors
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Optimizing tensor contractions for nuclear correlation functions
No full textThesis: S.B., Massachusetts Institute of Technology, Department of Physics, 2014.Cataloged from PDF version of thesis.Includes bibliographical references (pages 37-38).Nuclear correlation functions reveal interesting physical properties of atomic nuclei, including ground state energies and scattering potentials. However, calculating their values is computationally intensive due to the fact that the number of terms from quantum chromodynamics in a nuclear wave function scales exponentially with atomic number. In this thesis, we demonstrate two methods for speeding up this computation. First, we represent a correlation function as a sum of the determinants of many small matrices, and exploit similarities between the matrices to speed up the calculations of the determinants. We also investigate representing a correlation function as a sum of functions of bipartite graphs, and use isomorph-free exhaustive generation techniques to find a minimal set of graphs that represents the computation.by Pranjal Vachaspati.S.B
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