73 research outputs found
Elasto-viscoplastic modelling of unsaturated soils under static and dynamic loading in 3D stress space
No full textConsolidation, dynamic analysis, and wave propagation are some of the topics in geomechanics in which a complete characterization of coupling of the solid skeleton deformation and fluid flows is necessary for an accurate evaluation of material response. Dynamic behavior of soils is widely investigated in the past decades; however, they have mainly concerned the behaviour of dry or fully saturated porous media. Considering a three-phase continuum system which accounts for the interactions between the phases is crucial for investigating dynamic behavior of real soils which are invariably in an unsaturated state. Deposits located near the surface of the earth with relatively low water content, highly plastic clays which undergo changing environment or loose silty sands which collapse under wetting process are examples of unsaturated soils and experience severe situations especially under dynamic conditions.
This thesis presents an elasto-visco-plastic flow-deformation model for dynamic analysis of unsaturated soils including mechanical and hydraulic hysteresis. Governing equations of fluid and solid phases are derived based on theory of continuum mechanics considering phase interaction, and nonlinear deformation of solid skeleton subject to dynamic loading. A numerical scheme is developed using a robust Finite Element method as the global solution to solve various boundary value problems. For the local solution, a comprehensive bounding surface viscoplastic model is presented for unsaturated soils which accounts for suction hardening and rate effects and can simulate monotonic and cyclic loading paths. Consistency condition theory is used to describe the viscosity behaviour of the material. A unique relationship between stress, strain, and strain rate of the material is also defined to perfectly describe the effect of the strain rate hardening. Several examples are solved to validate the model and demonstrate the capability of the proposed framework for investigating behaviour of soils in complex hydro-mechanical conditions
Corrigendum to “Impact of chronic opioid on cognitive function and spermatogenesis in rat: An experimental study” [Int J Reprod BioMed 2024; 22: 579-592]
No full textThe publisher has been informed of an error that occurred on page 579 in which the last authors affiliation must be changed to Department of Biology, Faculty of Sciences, Malayer University, Malayer, Iran. On behalf of the author, the publisher wishes to apologize for this error. The online version of the article has been updated on October 31, 2024 and can be found at https://doi.org/10.18502/ijrm.v22i7.16971
Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method
No full textThe World Health Organization (WHO) introduced “Coronavirus disease 19” or “COVID-19” as a novel coronavirus in March 2020. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide crisis. Artificial intelligence and bioinformatics analysis pipelines can assist with finding biomarkers, explanations, and cures. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data. On the other hand, pathway enrichment analysis, as a dominant tool, could help researchers discover potential key targets present in biological pathways of host cells that are targeted by SARS-CoV-2. In this work, we propose a two-stage machine learning approach for pathway analysis. During the first stage, four informative gene sets that can represent important COVID-19 related pathways are selected. These “representative genes” are associated with the COVID-19 pathology. Then, two distinctive networks were constructed for COVID-19 related signaling and disease pathways. In the second stage, the pathways of each network are ranked with respect to some unsupervised scorning method based on our defined informative features. Finally, we present a comprehensive analysis of the top important pathways in both networks. Materials and implementations are available at: https://github.com/MahnazHabibi/Pathway
Automated Liquid Unloading in Low-Pressure Gas Wells Using Intermittent and Distributed Heating of Wellbore Fluid
No full textVision-based Cost Maps for Safe Autonomous Navigation : Design and Evaluation of Vision-based Control Barrier Functions
No full textDesigning safety-critical systems for unfamiliar environments is a substantial challenge in the field of robotics. Control Barrier Functions (CBF) serve as a common tool for addressing this challenge. However, the definition of CBF based on perceptual input remains a relatively unexplored and complex area of research.
This thesis extends prior work, where the authors, including the author of this thesis, introduced the innovative concept of Vision-based Control Barrier Functions (V-CBF). V-CBF defines Control Barrier Functions using perceptual input obtained from an RGBD camera, enabling the avoidance of obstacles with arbitrary shapes in unknown environments. A pivotal element of V-CBF is the 2D customized cost map, which transforms the segmented unsafe sets into an appropriate format that satisfies the requirements of CBF. The design and evaluation of these cost maps play a crucial role in the proper generation of V-CBFs. However, this aspect has not been thoroughly extensively in previous research.
The thesis embarks on a comprehensive investigation of diverse methodologies for generating these essential cost maps. The proposed methods are rigorously implemented and assessed within the CARLA simulator. To offer a thorough evaluation, both qualitative and quantitative comparisons are conducted, drawing from industry-standard ISO 22737 guidelines and custom-designed metrics within the CARLA simulator environment.
Furthermore, to substantiate the practical applicability of V-CBF, it is implemented on an industrial mobile robot. This real-world deployment serves as a tangible demonstration of the effectiveness of V-CBF in unknown environments, emphasizing its potential beyond simulated contexts. The transition from simulation to tangible real-world implementation underscores the portability and robustness of V-CBF, signifying its relevance and promise in real-world scenarios
Post-Colonial Reading of Isabel Allende’s The Japanese Lover
No full textThis research will explore the result of studying different aspects of identity seeking and establishing it as a liminal-prone one in a hybridized atmosphere among the colonized in terms of post-colonial discourse, based on Bhabha’s theories in his book, The Location of Culture, and on Isabel Allende’s novel, The Japanese Lover (2015). This study strives to expose the way through which the colonized characters’ identities in the novel undergo radical transformation through the third space which is heavily laced with qualities like ambivalence, stereotype, mimicry, and unhomeliness. Isabel Allende is an author whose novels mostly are an attempt to delineate the process of identity shaping particularly in the USA, since identity has always been an obsession for human which is defined based on different properties, one of which refers to the nation, culture and the territories based on Bhabha’s notion of hybridity which stems from confrontation of the cultures of the oppressor and the oppressed in the process of colonization. Generally, subject of identity in post-colonialism discourse is one in which people especially the colonized seeks for attachment. It will be divulged through this analysis that how liminal quality which is created as the consequence of colonial discourses will result in creating a space in which the oppressed one undergo radical changes in forming identity and how their identities are susceptible to alteration and likely to be unstable and fugitive
An Experimental and Numerical Investigation into the Durability of Fibre/Polymer Composites with Synthetic and Natural Fibres
No full textProgress in engineering research has shifted the interest from traditional monolithic materials to modern materials such as fibre reinforced composites (FRC). This paradigm shift can be attributed to the unique mechanical characteristics of FRCs such as high strength to weight ratio, good flexural strength, and fracture toughness. At present, synthetic composites dominate the automotive, aerospace, sporting, and construction industries despite serious drawbacks such as costly raw materials, high manufacturing costs, non-recyclability, toxicity, and non-biodegradability. To address these issues, naturally occurring plant fibres (such as jute, hemp, sisal) are being increasingly researched as potential reinforcements for biodegradable or non-biodegradable polymer matrices to produce environmentally friendly composites. In this study, sisal fibres were selected owing to their low production costs, sustainability, recyclability, and biodegradability. The hydrothermal ageing and mechanical characteristics of sisal fibre-reinforced epoxy (SFRE) composites were determined and compared with glass fibre-reinforced epoxy (GFRE) synthetic composites. Moreover, a first-of-its-kind numerical model have been developed to study the hydrothermal ageing and mechanical characteristics of SFRE, along with GFRE, using ANSYS software. Moreover, microstructural analysis of flexural tested GFRE and SFRE samples were carried out to identify the microstructural properties of the composites. Both experimental and numerical results exhibited an influence of short- or long-term hydrothermal treatment on the flexural properties of glass and sisal fibre-based composites. In the case of GFRE, the moisture uptake and fibre-matrix de-bonding existed, but it is less severe as compared to the SFRE composites. It was found that the dosage of sisal fibres largely determines the ultimate mechanical performance of the composite. Nonetheless, the experimental and numerical flexural strengths of SFRE were comparable to GFRE composites. This exhibited that the SFRE composites possess the potentiality as a sustainable material for advanced applications
Applications of image processing and machine learning techniques in wildlife monitoring and cancer cell characterization
No full textThis dissertation presents the application of state-of-art image classification and object detection techniques to two projects in the areas of wildlife monitoring and cancer cell characterization. The following presents a summary of the problem statement, solution approach, and major contributions for each of the two projects.
In the wildlife monitoring project, an automated vision system is proposed for animal detection in trail-camera images taken from a field under the administration of the Texas Parks and Wildlife Department. As traditional wildlife counting techniques are intrusive and labor-intensive to conduct, trail-camera imaging is a comparatively non-intrusive method for capturing wildlife activity. However, given the large volume of images produced from trail cameras, manual analysis of the images remains time-consuming and inefficient. For this purpose, a two-stage deep convolutional neural network pipeline is implemented to find animal-containing images in the first stage and then process these images to detect birds in the second stage. The animal classification system classifies animal images with 93% sensitivity and 96% specificity. The bird detection system achieves better than 93% sensitivity, 92% specificity, and 68% average Intersection-over-Union rate. This dissertation also addresses post-deployment issues related to data drift for the animal classification system as image features vary with seasonal changes. This system utilizes an automatic retraining algorithm to detect data drift and to update the system when necessary. Moreover, two statistical experiments are presented to explain the prediction behavior of the animal classification system. These experiments investigate the cues that steer the system towards a particular decision. Statistical hypothesis testing demonstrates that the presence of an animal in the input image significantly contributes to the system’s decisions.
The cell characterization study investigates the automatic detection and enumeration of circulating tumor cells in patient blood samples. Circulating tumor cells (CTCs) are invaluable biomarkers used in the early diagnosis and treatment of cancer. Microscopy images of isolated CTCs from patient blood samples are routinely acquired and analyzed for CTC detection and enumeration purposes. Due to the scarcity of CTCs in the patient blood sample, their manual characterization is a challenging task that involves a series of tedious cell staining and labeling procedures and laborious manual identification of the cells. This study proposes an automated detection and enumeration system to alleviate the lag in the enumeration process. The core of this system is an efficient and accurate convolutional neural network (CNN)-based model that performs label-free detection of MCF-7 breast cancer cells, as a proxy to CTCs, in brightfield images. The MCF-7 detection model achieves above 99% sensitivity and specificity. In addition, the average Intersection-over-Union rate of the proposed detector is better than 80%. For the training set generation, a fully automated workflow is presented that facilitates the efficient and easy labeling of brightfield images. Additionally, multiple experiments are designed and implemented to explore the prominent features that the CNN extracts and uses for distinguishing MCF-7 cells from white blood cells. The results of the experiments indicate that the designed CNN uses the size of the cell as the prominent distinguishing feature. Furthermore, if the size feature is eliminated, the CNN is still capable of extracting other features to distinguish MCF-7 cells, but with a 3% accuracy reduction. Finally, the robustness of the proposed MCF-7 detection model in the presence of various image intensity transformations is investigated. The results indicate that the F1-score of the detection model deteriorates by less than 0.2% in the presence of image intensity and contrast transformations. Therefore, the MCF-7 cell detection model has sufficient robustness with respect to variations in the intensity and contrast of the brightfield images.Embargo status: Restricted until 06/2022. To request the author grant access, click on the PDF link to the left
A Multidimensional Analytical Approach for Identifying and Locating Large Utility Pipes in Underground Infrastructure
No full textThe population growth, technological improvements, and the need for repairing old or installing new utilities result in a high demand for trenching and drilling activities. However, penetrating the subsurface incurs the risk of damaging existing underground facilities because they were not properly documented, if at all. Ground Penetrating Radar (GPR) constitutes a well-established technology that uses electromagnetic waves to identify objects underground by detecting their reflections. The work presented in this paper focuses on the timing and other characteristics of radar pulses reflected from the buried utilities. It is hypothesised that integrating the knowledge of construction practice, geophysical principles, and electromagnetic wave propagation behaviour in various soil conditions will improve the reliability and accuracy of GPR. This paper presents the results of field experiments that studied the effects of large void such as sinkholes or drainage pipes in several undergrounds. It provides important insights into the features and patterns that can be used to improve current methods
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