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Title: SAFETY TRAINING IN CONSTRUCTION
Creator: Demirkesen, Sevilay
Date: 2011-11-29, 2011-12
Description: The construction industry is one of the most hazardous industries. Therefore, the ways to reduce the number of risks have been a concern for...
Show moreThe construction industry is one of the most hazardous industries. Therefore, the ways to reduce the number of risks have been a concern for construction companies. Safety training is considered as one of the most efficient ways of improving safety record. Therefore, this study aimed to describe the best safety training methods and the most efficient organization in safety training. The study also aimed to show how to take best safety measures to protect construction workers. In this thesis study, a questionnaire was conducted in order to investigate the achievement of safety learning, challenges in a safety training program and the methods of improving safety record. Thus, the questionnaire was e-mailed to 400 contractors in US. The data collected also indicated the importance of safety training in companies’ success in the industry. Additionally, this study presents recommendations on how companies could improve their safety record.
M.S. in Civil Engineering, December 2011
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Title: POINT CLOUD FUSION BETWEEEN AERIAL AND VEHICLE LIDAR
Creator: Guangyao, Ma
Date: 2015, 2015-05
Description: Because of the increasing requirement of precision in region of 3-D map, we began to use LiDAR to establish a more accurate map. There still...
Show moreBecause of the increasing requirement of precision in region of 3-D map, we began to use LiDAR to establish a more accurate map. There still exist some problems although we have already made a great progress in this area. One of them, which I tried to process during my thesis study, is that we have two points source - Aerial LiDAR Data( Points gotten by Airplane ) and Vehicle LiDAR Data( Points gotten by Vehicle ) - while both of them have a different density and cannot be merged well. This process - Fusion-is kindly similar to registration, the difference is that the points we would like to merge are generated from different devices and have only few points pairs in the same region. For example, the Aerial LiDAR data has a higher points density in the roofs and ground, but lower in the walls. In the meanwhile, the Vehicle LiDAR data has a lot of points in the walls and ground region. It is beneficial to minimize the difference between these two point sets since the process is necessary for modeling, registration and so on. Therefore, my thesis is to minimize the difference between these two data sources, a procedure of Fusion. The main idea is to read the LiDAR data into data structure of Point Cloud, sample their density to the similar level, and select several corresponding special region pairs( we named these regions -chunks, e.g. Median strip and boundaries of road ) with sufficient interesting points to do fusion. Interesting points indicate the points with one and more special features among all points. And, the algorithm we used to implement the fusion is ICP( Iterative Closet Point Algorithm). Not similar to Registration of Point Cloud, research in the Fusion area is rare. Therefore, the existing algorithms are not well suitable in this project. I deduce some new algorithms during my research since the original ICP Algorithm cannot work well. Both Update Equation and Objective Function are modified. In this thesis, PCL( Point Cloud Library ) is mainly used to implement the basic function, such as nding the nearest points and sampling point cloud, and Eigen library to write the core functions( e.g. Modified Iterative Closest Point Alg ). I also use libLAS library to implement the IO operations and MeshLab to visualize the point cloud after modification.
M.S. in Computer Science, May 2015
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Title: THE RELATIONSHIP BETWEEN WORK-FAMILY CONFLICT, SOCIAL SUPPORT AND PERFORMANCE AMONG HEALTHCARE MANAGERS
Creator: Hunt, Mary K.
Date: 2011-05-16, 2011-05
Description: As high performers are of great value in all organizations, understanding factors influencing their ability to maintain strong performance can...
Show moreAs high performers are of great value in all organizations, understanding factors influencing their ability to maintain strong performance can have useful implications for leaders. This study examines the relationship between employees’ performance, their stressors and coping approaches as defined by work-family conflict and perception and enactment of social support. Three hundred and twelve managers in a healthcare organization were categorized based on their performance in the year prior to the study. Results showed that high performers had higher satisfaction and use of supervisor support than moderate performers. The more satisfied managers were with support from their supervisor, coworkers, and friends, the less they reported work-family conflict. Satisfaction and use of support from friends influenced both work-to-family and family-to- work conflict.
M.S. in Psychology, May 2011
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Title: A NEURAL NETWORK BASED MODEL FOR BIOMASS GASIFICATION IN FLUIDIZED BED
Creator: Dirbaz, Mohsen
Date: 2020
Description: Biomass is a renewable energy resource and its utilization has received great attention due to its life cycle carbon-neutrality and the...
Show moreBiomass is a renewable energy resource and its utilization has received great attention due to its life cycle carbon-neutrality and the potential to substitute fossil fuel to produce a variety of energy-related products. Thermochemical gasification is an important route for conversion of biomass that results in a product gas mainly consisting of H2, CO, CO2, CH4 and other light hydrocarbons that can be used as fuel gas to generate power, or as well as raw material to produce a variety of chemicals. Among the existing gasifiers, fluidized beds (FB) offer many advantages such as high conversion efficiency and great flexibility over types of feedstock.More than 200 data sets of biomass gasification in fluidized bed were collected featuring a wide range of operating condition and fuel types. An axiom-based reasoning was used to develop a multiphase statistical pathway needed as a precondition to effectively quantify the entanglements of different important factors in the process.Specifically, by creating an interconnected chain of analysis based on trigonometric functions, geometric projections, and design of a statistical inference tool utilizing neural network units, multiple partial measures of associations between biomass constituents, and operating condition were effectively consolidated and embedded in a single characteristic matrix that consequently led to detection of monotonic relationships for prediction of carbon conversion efficiency and product gas yield. The black box model in comparison to three different models showed better accuracy in predicting four major components of product gas, over the largest applicable range of all the influential parameters of the process, namely, temperature, air equivalent ratio, steam to biomass ratio, and type of fuel. In part of our methodology, we introduce a novel technique for obtaining a dynamical property value for stationary objects, based on a “specific computational time” of an “abstract mechanical operation on characteristics matrices”. The specific computational time (sct) showed excellent capability in capturing the non-equilibrium factor of the process which itself was function of several interrelated variables.
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Title: Automated Successive Baseline Schedule Model
Creator: Patel, Mihir Prakashbhai
Date: 2021
Description: The construction project involves many stakeholders and diverse phases. Usually, a construction schedule is initially set up as a simple ideal...
Show moreThe construction project involves many stakeholders and diverse phases. Usually, a construction schedule is initially set up as a simple ideal case scenario, but then, during construction, the project faces modifications such as delay, acceleration, and change in logic caused by the project’s complexity and inherent risk. To recover the damage(s) caused by these modifications, the parties responsible for them should be identified accurately. Researchers and practitioners developed and used various delay analysis models to quantify delays, but the selection of the model depends on the time of analysis, available information, and expertise of the analyst. So, the results can be biased. The general problem is that most delay analysis models consider only delays in quantifying impacts rather than every type of modification that impacted the project, including CPM logic changes and adding/removing activities during construction. This study proposes a new successive baseline model to enable the precise analysis of the impacts of all sorts of modifications that occur during construction. This model can achieve unbiased and accurate results. The analysis process can also be computerized into a web application to improve efficiency and productivity. The fundamental concepts of the various modifications that can occur in the work schedule during construction and the analysis of the modifications’ impacts are presented in this study. Issues related to concurrency, float ownership, type of modification, selection of delay analysis model, and challenges with automation are also highlighted to broaden the understanding disagreements of the parties to a construction contract. A case example is presented to prove the accuracy and usefulness of the proposed model and web application.
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Title: DEVELOPMENT AND EVALUATION OF MRI TEMPLATES OF THE MIITRA ATLAS
Creator: RIDWAN, ABDUR RAQUIB
Date: 2021
Description: Digital human brain atlases play a pivotal role in conducting wide range of neuroimaging studies and are commonly used as references for...
Show moreDigital human brain atlases play a pivotal role in conducting wide range of neuroimaging studies and are commonly used as references for spatial normalization in voxel-wise analysis, region-of interest analyses, automated tissue-segmentation, functional connectivity analyses, etc. A brain atlas typically consists of MRI-based multi-modal templates and semantic labels delineating brain regions according to the characteristics of the underlying tissue. In recent times there has been a plethora of magnetic resonance imaging (MRI) studies on older adults without dementia to explore the role of brain characteristics associated with cognitive functions in old age with the ultimate goal to develop strategies for prevention of cognitive decline. Increasing the accuracy in terms of sensitivity and specificity of such neuroimaging studies require an atlas with a comprehensive set of high-quality templates representative of the brain characteristics typical of older adults and detailed labels accurately mapping brain regions of interest. However, such an atlas has not been constructed for older adults without dementia. Hence this thesis aims to build high quality MRI templates which are the cornerstone resources needed for the development of a comprehensive, high quality, multi-channel, longitudinal, probabilistic digital human brain atlas for older adults termed as Multi-channel Illinois Institute of Technology and Rush University Aging (MIITRA) atlas. This dissertation focuses on a) to develop and evaluate a high performing 1mm isotropic structural T1-weighted brain template, b) to investigate the development and evaluation of a spatio-temporally consistent longitudinal structural T1-weighted template of the older adult brain, c) to develop and evaluate an unbiased 0.5 mm isotropic super-resolved high resolution and detail-preserving structural T1 weighted template of the older adult brain, d) to develop an unbiased 0.5 mm super-resolved high resolution and detail-preserving structural PD weighted and T2-weighted template of the older adult brain, e) to investigate and provide future directions in the development of a 0.5 mm super-resolved high resolution DTI template of the older adult brain, and f) to construct a novel approach in the development of MRI templates using both space and frequency information of spatially normalized older adult data. The thesis based on the aforementioned foundational points was constructed as follows: Firstly, this thesis presents the development of a 1mm isotropic T1-weighted structural template of the older adult brain utilizing state of the art registration algorithm ANTs with parameters carefully optimized for older adults, in an iterative groupwise spatial normalization framework. The preprocessing steps were also thoroughly investigated to ensure high quality data. It was demonstrated through systematic comparison of this new template to several other standardized and study-specific T1-weighted templates that a) it exhibited high image sharpness, b) allowed for high spatial normalization accuracy and detection of smaller inter-group morphometric differences compared to other standardized templates, c) had similar performance to that of study-specific templates and d) was highly representative of the older adult brain. Secondly, with the acquired technical know-how from the aforementioned research findings a new method was introduced for the construction of a spatio-temporally consistent longitudinal template based on high quality cross-sectional older adult data from a large cohort. The new template was compared to templates generated with previously published methods in terms of spatio-temporal consistency and image quality and was shown to have superior performance. In addition, a novel approach was introduced for image quality enhancement of the longitudinal templates utilizing both space and frequency information. Thirdly, the thesis presents a method that involves a) thoroughly refining registration parameters, b) patch-based tissue-guided sparse-representation approach in a super-resolved unbiased minimum deformation space to construct and evaluate an unbiased 0.5 mm isotropic super-resolved high resolution and detail-preserving structural T1 weighted template of the older adult brain. This method accounts for misregistration specially in the cortical regions, ensuring sharp delineation of structures representative of the older adult brain. The new template developed using this approach maintained high anatomical consistency with sharp and detailed cortical features in the brain and exhibited higher image sharpness compared to other high-resolution standardized templates and allowed for high spatial normalization accuracy when used as a reference for normalization of older adult data. Additionally, this approach of template building was investigated on DTI tensors of older adult participants, and the constructed DTI template was shown to perform better than templates developed using the best approach currently present in the literature. Finally, the thesis presents the development of an unbiased 0.5 mm super-resolved high resolution and detail-preserving structural PD weighted and T2-weighted template of the older adult brain, from nonlocal super-resolution based upsampled PD and T2w older adult participant data, using this new template building approach.
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Title: Distribution-aware Visual Semantic Understanding
Creator: Chen, Ying
Date: 2021
Description: Understanding visual semantics, including change detection and semantic segmentation, is an essential task in many computer vision and image...
Show moreUnderstanding visual semantics, including change detection and semantic segmentation, is an essential task in many computer vision and image processing applications. Examples of visual semantics understanding in images include land cover monitoring, urban expansion evaluation, autonomous driving, and scene understanding. The goal is to locate and recognize appropriate pixel-wise semantic labels in images. Classical computer vision algorithms involve sophisticated semi-heuristic pre-processing steps and potentially manual interaction. In this thesis, I propose and evaluate end-to-end deep neural approaches for processing images which achieve better performance compared with existing approaches. Supervised semantic segmentation has been widely studied and achieved great success with deep learning. However, existing deep learning methods typically suffer from generalization issues where a well-trained model may not work well on unseen samples from a different dataset. This is due to a distribution change or domain shift between the training and test sets that can degrade performance. Providing more labeled samples covering many possible variations can further improve the generalization of models, but acquiring labeled data is typically time-consuming, labor-intensive and requires domain knowledge. To tackle this label scarcity bottleneck for supervised learning, we propose to apply unsupervised domain adaptation, semi-supervised learning, and semi-supervised domain adaptation for neural semantic segmentation. The motivation behind unsupervised domain adaptation for semantic segmentation is to transfer learned knowledge from one or more source domains with sufficient labeled samples to a different but relevant target domain where labeled data is sparse or non-existent. The adaptation algorithm tends to learn a common representation space where the distributions over both source and target domains are matched. In this way, we expect a classifier working well in the source domain to generalize well to the target domain. More specifically, we try to learn class-aware source-target domain distribution differences, and transfer the knowledge learned from labeled synthetic data on the source domain to the unlabeled real data on the target domain. Different from domain adaptation, semi-supervised semantic segmentation aims at utilizing a large amount of unlabeled data to improve semantic classification trained on a small amount of labeled data from the same distribution. Specifically, supervised semantic segmentation is trained together with an unsupervised model by applying perturbations on encoded states of the network instead of the input, or using mask-based data augmentation techniques to encourage consistent predictions over mixed samples. In this way, learned representation which capture many kinds of unseen variations in unlabeled data, benefit the supervised semantic classifier. We propose a mask-based data augmentation semi-supervised learning network to utilize structure information from a variety of unlabeled examples to improve the learning on a limited number of labeled examples.Both unsupervised domain adaptation (UDA) with full source supervision but without target supervision and semi-supervised learning (SSL) with partial supervision have shown to be able to address the generalization problem to some extent. While such methods are effective at aligning different feature distributions, their inability to efficiently exploit unlabeled data leads to intra-domain discrepancy in the target domain, where the target domain is separated into two unaligned sub-distributions due to source-aligned and target-aligned data. That is, enforcing partial alignment between full labeled source data and a few labeled target data does not guarantee that the remaining unlabeled target samples will be aligned with source feature clusters, thus leaving them unaligned. Hence, I propose methods for incorporating the advantages of both UDA and SSL, termed semi-supervised domain adaptation (SSDA), with a goal to align cross-domain features as well as addressing the intra-domain discrepancy within the target domain. I propose a simple yet effective semi-supervised domain adaptation approach by utilizing a two-step domain adaptation addressing both cross-domain and intra-domain shifts.
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Title: Constellation and Detection Design for Non-orthogonal Multiple Access System
Creator: Hao, Xing
Date: 2022
Description: It is well known that the Non-Orthogonal Multiple Access (NOMA) system has the capability to achieve higher spectral efficiency and massive...
Show moreIt is well known that the Non-Orthogonal Multiple Access (NOMA) system has the capability to achieve higher spectral efficiency and massive connectivity. In this thesis, some optimized designs in both code-domain and power-domain NOMA systems are studied. Overall, the main contributions are listed as follows:Firstly, we investigate a NOMA system based on the combinatorial design with a novel constellation design for eliminating the surjective mapping from the linear adding data of multiuser and lowering the complexity of constellation design and Multiuser Detection (MUD). And for further enlarge the connectivity, we propose a Low-Density Codes structure to build a trade-off between the diversity and multiusers in resources by expurgating excessive interference on coding matrices. Therefore, our scheme can not only provide a one-to-one mapping pattern with a sparser multiple access structure but also be adjusted with more flexibility to achieve diversity and transmit a large number of users.Secondly, we proposed a constellation mapping scheme based on sub-optimized signal constellation designs by shaping the receiver’s constellation with a strategy that allows differentiated users by which resolvable points will be received allowing simpler detection and design.Thirdly, a novel NOMA system in uplink with time-delayed symbols is investigated, in which a modified Successive Interference Cancellation (SIC) scheme is used at the receiver side. In conventional SIC, when the transmission power is distributed to one user with trivial shifts to other users, the Bit Error Rate (BER) performance will be decreased significantly. Thus, we evaluated a modified SIC by adding artificial time offsets to the conventional power domain-NOMA (PD-NOMA) between users, which can provide higher degrees of freedom for power allocation of users and reduce mutual interference. And then, the added time offsets can provide additional resources to detect the superimposed signals, then the combination of users’ estimations of overall time slots will be considered to get detection improvements. Numerical results demonstrate that the BER performance of our modified SIC outperforms the PD-NOMA with other SIC-based schemes.Thirdly, we propose a new modulation scheme based on polynomial phase signals (PPS) for downlink and uplink non-orthogonal multiple-access (NOMA) transceivers in both the code and power domains. The PPS leads to outstanding spectral efficiency and bit error rate (BER) performance. We also propose a design criterion for CD-NOMA systems to enable the NOMA system to deploy a large number of users with more flexibility as well as lower design and detection complexity than traditional CD-NOMA systems, such as SCMA and PDMA.
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Title: Synthesis and Photophysical Characterization of Novel Aromatic Triplet Dyes for Photodynamic Therapy Applications
Creator: Morgan, Jayla A
Date: 2022
Description: Photodynamic therapy is a biomedical approach to treating specific types of cancerous tumor cells and harmful bacteria. The core principle of...
Show morePhotodynamic therapy is a biomedical approach to treating specific types of cancerous tumor cells and harmful bacteria. The core principle of photodynamic therapy involves the usage of a photosensitizer, which is an agent with the capability of transforming molecular, triplet state oxygen, into a reactive oxygen species upon a reaction with near-infrared (NIR) light. The reactive oxygen species has been demonstrated to cause apoptosis among harmful cells without damaging cancer free cells. The effectiveness of photodynamic is highly dependent upon the identity of the photosensitizer; a powerful and efficient photosensitizer should be non-toxic, exhibit high light absorption capabilities, and should produce large amounts of the reactive oxygen species. A novel chromophore bis-iodo-dipyrrolonaphthyridine-dione was demonstrated to have all vital characteristics of an ideal photosensitizer, however produced low amounts of the reactive oxygen species of interest due to the chemical instability of a carbon-halogen bond present in the molecule. Various subsequent halogenations (bis-bromo and bis-chloro) completed in order to remedy this instability revealed specific regioselectivity in regards to the dipyrrolonaphthyridinedione parent that are exhibited upon substituents effects by the substrate, electronic effects exhibited by the reagents of interest, and overall photophysical characterization of the molecules.
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Title: DEVELOPMENT OF BIOMARKERS OF SMALL VESSEL DISEASE IN AGING
Creator: Makkinejad, Nazanin
Date: 2021
Description: Age-related neuropathologies including cerebrovascular and neurodegenerative diseases play a critical role in cognitive dysfunction, and...
Show moreAge-related neuropathologies including cerebrovascular and neurodegenerative diseases play a critical role in cognitive dysfunction, and development of dementia. Designing methodologies for early prediction of these diseases are much needed. Since multiple pathologies commonly coexist in brains of older adults, clinical diagnosis lacks the specificity to isolate the pathology of interest, and gold standard is determined only at autopsy. Magnetic resonance imaging (MRI) provides a non-invasive tool to study abnormalities in brain characteristics that is unique to each pathology. Utilizing ex-vivo MRI for brain imaging proves to be useful as it eliminates two important biases of in-vivo MRI. First, no additional pathology would develop between imaging and pathologic examination, and second, frail older adults would not be excluded from MRI.Hence, the aims of this dissertation were two-fold: to study brain correlates of age- related neuropathologies, and to develop and validate classifiers of small vessel diseases by combining ex-vivo MRI and pathology in a large community cohort of older adults. The structure of the project is as follows.First, the association of amygdala volume and shape with transactive response DNA-binding protein 43 (TDP-43) pathology was investigated. Using a regularized regression technique, higher TDP-43 was associated with lower amygdala volume. Also, shape analysis of amygdala showed unique patterns of spatial atrophy associated with TDP-43 independent of other pathologies. Lastly, using linear mixed effect models, amygdala volume was shown to explain an additional portion of variance in cognitive decline above and beyond what was explained by the neuropathologies and demographics.Second, the previous study was extended to analyze other subcortical regions including the hippocampus, thalamus, nucleus accumbens, caudate, and putamen, and was also conducted in a larger dataset. The results showed unique contribution of TDP-43, neurofibrillary tangles (hallmark characteristic of Alzheimer’s disease pathology), and atherosclerosis (a cerebrovascular pathology) to atrophy on the surface of subcortical structures. Understanding the independent effects of each pathology on volume and shape of different brain regions can form a basis for the development of classifiers of age-related neuropathologies.Third, an in-vivo classifier of arteriolosclerosis was developed and validated. Arteriolosclerosis is one of the main pathologies of small vessel disease, is associated with cognitive decline and dementia, and currently has no standard biomarker available. In this work, the classifier was developed ex-vivo using machine learning (ML) techniques and was then translated to in-vivo. The in-vivo classifier was packaged as a software called ARTS, which outputs a score that is the likelihood of arteriolosclerosis when the required input is given to the software. It was tested and validated in various cohorts and showed to have high performance in predicting the pathology. It was also shown that higher ARTS score was associated with greater cognitive decline in domains that are specific to small vessel disease.Fourth, motivated by current trends and superiority of deep learning (DL) techniques in classification tasks in computer vision and medical imaging, a preliminary study was designed to use DL for training an ex-vivo classifier of arteriolosclerosis. Specifically, convolutional neural networks (CNNs) were applied on 3 Tesla ex-vivo MR images directly without providing prior information of brain correlates of arteriolosclerosis. One interesting aspect of the results was that the network learnt that white matter hyperintense lesions contributed the most to classification of arteriolosclerosis. These results were encouraging, and more future work will exploit the capability of DL techniques alongside the traditional ML approaches for more automation and possibly better performance.Finally, a preliminary classifier of arteriolosclerosis and small vessel atherosclerosis was developed since the existence of both pathologies in brain have devastating effects on cognition. The methodology was similar to the one used for development of arteriolosclerosis classifier with minor differences. The classifier showed a good performance in-vivo, although the testing needs to be assessed in more cohorts.The comprehensive study of age-related neuropathologies and their contribution to abnormalities of subcortical brain structures offers a great potential to develop a biomarker of each pathology. Also, the finding that the MR-based classifier of arteriolosclerosis showed high performance in-vivo demonstrate the potential of ex-vivo studies for development of biomarkers that are precise (because they are based on autopsy, which is the gold standard) and are expected to work well in-vivo. The implications of this study include development of biomarkers that could potentially be used in refined participant selection and enhanced monitoring of the treatment response in clinical drug and prevention trials.
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Title: Towards Assisting Human-Human Conversations
Creator: Nanaware, Tejas Suryakant
Date: 2021
Description: The idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in...
Show moreThe idea of the research is to understand the open-topic conversations and ways to provide assistance to humans who face difficulties in initiating conversations and overcome social anxiety so as to be able to talk and have successful conversations. By providing humans with assistive conversational support, we can augment the conversation that can be carried out. The AdvisorBot can also help to reduce the time taken to type and convey the message if the AdvisorBot is context aware and capable of providing good responses.There has been a significant research for creating conversational chatbots in open-domain conversations that have claimed to have passed the Turing Test and can converse with humans while not seeming like a bot. However, if these chatbots can converse like humans, can they provide actual assistance in human conversations? This research study observes and improves the advanced open-domain conversational chatbots that are put in practice for providing conversational assistance.While performing this thesis research, the chatbots were deployed to provide conversational assistance and a human study was performed to identify and improve the ways to tackle social anxiety by connecting strangers to perform conversations that would be aided by AdvisorBot. Through the questionnaires that the research subjects filled during their participation, and by performing linguistic analysis, the quality of the AdvisorBot can be improved so that humans can achieve better conversational skills and are able to clearly convey their message while conversing. The results were further enhanced by using transfer learning techniques and quickly improve the quality of the AdvisorBot.
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Title: The Peter Principle and Career Development in Construction Management
Creator: Bolisetty, Lakshmi Satya Bavya
Date: 2023
Description: The construction industry, like any other industry, may benefit from updating its working environment by adopting advances in technology,...
Show moreThe construction industry, like any other industry, may benefit from updating its working environment by adopting advances in technology, updating the qualifications of its workforce, and ensuring that the right professionals are employed at each level. Having effective career development programs, filling project management roles with competent professionals, and promoting competent professionals ensures a successful project delivery. Promotion typically ensures candidates are rewarded for their performance and motivates them towards achieving higher success. Promoting the wrong person or having an incompetent person at any management level has detrimental effects to the project’s success. However, according to the “Peter Principle” (Peter, 1969), incumbents in a hierarchy tend to rise to “a level of respective incompetence”. That is, they are promoted based on their success in their current role, rather than their ability to perform in the new one. Through a review of the literature and a critical analysis of the existing research, this study examines the strengths and weaknesses of the Peter Principle and its relevance to professionals employed by construction companies and construction management firms in contemporary management practice. It also explores the reasons why an incumbent is stuck in a position for a long time. The findings of this research suggest that while the Peter Principle may have some limitations, it remains an important concept for managers to consider when making decisions about employee promotion and development. It concludes that while the Peter Principle may have a significant effect in promotion decisions, there are also external circumstances unique to each individual that may affect their performance.
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Title: THE EFFECTS OF PHYSICAL AND CHEMICAL PROPERTIES OF DOLOMITE ON DOLOMITE DECOMPOSITION
Creator: Huang, Hsiang-Jung
Date: 2020
Description: Dolomite comprises approximately two percent of the Earth’s crust and has a widespread geological distribution throughout the world. It is an...
Show moreDolomite comprises approximately two percent of the Earth’s crust and has a widespread geological distribution throughout the world. It is an abundant, low cost, and promising raw base material for many applications in industry, such as sorbents for capturing CO2 from coal gas and a heterogeneous catalyst for reducing tar content in biomass gasification. Dolomite decomposition has been intensively studied over the past decades. However, to date, there is hardly any systematic literature available that addresses the effects of naturally occurring impurities on dolomite decomposition due to the difference in various experimental setups, sample size, particle size, and so on. Therefore, this research focuses on employing a systematic and comprehensive investigation to develop a better understanding of the effects of the physical and chemical properties of raw dolomites on dolomite decomposition. This study involves experimental, theoretical, and modeling work. There are several experimental techniques utilized for the exploration of the physical and chemical properties of dolomites from different sources, such as the Thermogravimetric analysis (TGA), the X-ray powder diffraction (XRD) and the Brunauer–Emmett–Teller theory (BET), respectively. In the study, it has been discovered that the excess weight loss of samples during thermal decomposition experiments was owing to the explosive disintegration of the nature of dolomite. The physical properties of dolomites are not the main factor affecting dolomite decomposition but thermodynamic properties and crystal structure. The initial equilibrium constant of dolomite which is dominated by the amount of silicate-based impurities plays a major role in the decomposition rate. A two-stage reaction model was developed that included a reversible reaction of uniform solid ordered-disordered crystal transformation of dolomite followed by a "Quasi-Shrinking Core" reaction of disordered dolomite decomposition. This model is capable of describing the reaction rate of half-calcination of dolomite with acceptable accuracy.
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Title: Developing Leader Identity via Structured Reflection
Creator: Standish, Melanie P.
Date: 2020
Description: As leader identity research in the context of leader development continues to expand, it is important to understand the mechanisms through...
Show moreAs leader identity research in the context of leader development continues to expand, it is important to understand the mechanisms through which leader identity becomes more central to one’s self concept. Structured leadership reflection is proposed to positively impact leader development but has not been experimentally manipulated to understand what its impact is on leader identity change. In this study, 90 participants were assigned into one of three reflection conditions and were asked to respond to reflection prompts over the course of four days. Participants were divided into the control condition, the reflecting on others as leaders condition, or the reflecting on oneself as a leader condition. Results showed no significant differences between reflection groups and their impact on leader identity change. Though our results do not provide support for the use of structured reflection to elicit leader identity development, we suggest future research should further study structured self-reflection over a longer period of time.
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Title: REVEALING LINGUISTIC BIAS
Creator: Karmarkar, Sathyaveer S.
Date: 2021
Description: Readers currently face bias in articles written by writers who focus more on partiality towards any person or organization than showing the...
Show moreReaders currently face bias in articles written by writers who focus more on partiality towards any person or organization than showing the real facts. The study aims to detect and reveal such bias against them and try to portray real facts without any partiality against any person or organization. The data is fetched by selecting various articles from Google, especially those containing some bias in them. The bias was checked by measuring the subjectivity and polarity of the article using multiple libraries such as NLTK etc. We created a google form to take readers’ views showing them randomly either the biased article or the improved article after changing bias and getting their opinions.
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Title: Leader Identity Claiming and Granting Process: The Role of Gender on Perceptions of Leadership
Creator: Standish, Melanie P.
Date: 2023
Description: Ely, Ibarra, and Kolb (2011) theorize that the leader identity work among women is an area of work wherein subtle gender bias is pervasive and...
Show moreEly, Ibarra, and Kolb (2011) theorize that the leader identity work among women is an area of work wherein subtle gender bias is pervasive and impacting women’s advancement in the workplace. Interferences with the leader identity development process not only impact how a woman views herself as a leader, but how others collectively come to endorse her as a leader. Simply observing an individual claiming leadership and having that leadership be granted by someone else is known to influence how an observer classifies an individual as a leader or a non-leader. This study examines how the gender of an individual claiming leadership impacts external perceptions of how leader-like they are to others, when they are granted vs. not granted leadership. To examine this gap, this study uses an experimental vignette methodology to explore the impact of gender on leadership perceptions, during a claiming and granting process. Specifically, this work examines the mediating roles of competence and likability, as potential drivers through which differences may occur. Though women today are evaluated as equally competent as their male counterparts, engaging in dominant, agentic, behaviors, may make them less likable, and rated less leader-like as a result. The results of this study did not find an interaction between gender and granting, on perceived likability. The results did replicate existing findings that claiming leadership is not enough to be relationally recognized as a leader, and that granting from others plays an important role in how competent, and subsequently leader-like, an individual is perceived to be.
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Title: FUNCTIONAL CONNECTIVITY LABELS FOR THE MULTICHANNEL IIT AND RUSH UNIVERSITY AGING (MIITRA) ATLAS
Creator: Badhon, Rashadul Hasan
Date: 2022
Description: In the field of medical imaging, a brain atlas refers to a specific model of the brain of a population where different parts of the atlas...
Show moreIn the field of medical imaging, a brain atlas refers to a specific model of the brain of a population where different parts of the atlas correspond to different anatomical parts of the average brain of the population. A brain atlas is composed of MRI templates and semantic labels and is a crucial component of neuroscience for its critical role in facilitating spatial normalization, temporal characterization and automated segmentation for the purposes of voxel-wise, region of interest and network analyses. Building a brain atlas requires registering multi-dimensional brain datasets from a population into a reference space and, during the last decade, the advent of new technologies and computational modeling approaches has made it possible to build high-quality, detailed brain atlases. At the same time developments in data acquisition now allow the construction of comprehensive brain atlases containing a variety of information about the brain. The Multichannel Illinois Institute of Technology and Rush university Aging (MIITRA) atlas project is developing a high-quality comprehensive atlas of the older adult brain containing a multitude of templates and labels. These templates are constructed with state-of-the-art spatial normalization of high-quality data and as a result, they are characterized by higher image quality, are more representative of the brain of non-demented older adults and provide higher inter-subject spatial normalization accuracy of older adult data compared to other available templates. The methodology used in the development of the MIITRA templates facilitates the construction of accurate structural and connectivity labels. Functional connectivity MRI reveals sets of functionally connected brain regions, forming networks, by investigating synchronous fluctuations in MRI signal over time across these brain regions during rest. The purpose of this work was to generate functional connectivity labels for several brain networks in MIITRA space.
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Title: Development and evaluation of high resolution MRI templates and labels of the MIITRA atlas
Creator: Niaz, Mohammad Rakeen
Date: 2022
Description: A digital human brain atlas consisting of MRI-based multi-modal templates and semantic labels delineating brain regions are commonly used as...
Show moreA digital human brain atlas consisting of MRI-based multi-modal templates and semantic labels delineating brain regions are commonly used as references for spatial normalization in a wide range of neuroimaging studies. Magnetic resonance imaging (MRI) studies of the aging brain is of significant interest in recent times to explore the role of brain characteristics associated with cognitive functions. The introduction of advanced image reconstruction techniques, and the recent trend in MRI acquisitions at submillimeter in-plane resolution have resulted in an easier availability of MRI data on older adults at high spatial resolution. An atlas with a comprehensive set of high-resolution templates representative of the older adult brain and detailed labels accurately mapping brain regions can increase the sensitivity and specificity of such neuroimaging studies. Additionally, most neuroimaging studies can benefit from a high-resolution atlas with templates where fine brain structures are resolved and, where the transition between different tissue can be more accurately defined. However, such an atlas is not publicly available for older adults. Hence the goal of this thesis is to develop a comprehensive, high-resolution digital human brain atlas for older adults termed as Multi-channel Illinois Institute of Technology and Rush University Aging (MIITRA) atlas.This dissertation aims a) to develop a new technique based on the principles of super-resolution for the construction of high-resolution structural and diffusion tensor templates, and evaluate the templates for use in studies on older adults, b) to construct and evaluate high-resolution structural and diffusion tensor templates constructed using the method developed in (a) for the MIITRA atlas using MRI data collected on 400 nondemented older adults, c) to investigate and develop a technique for the construction of high-resolution labels and evaluate the performance of gray matter labels constructed using this technique in segmenting the gray matter of older adults, and d) to develop and evaluate a comprehensive set of high-resolution labels using the technique developed in (c) for the MIITRA atlas using data on 400 non-demented older adults. Based on the aforementioned points, the thesis is structured as follows: Firstly, this thesis presents a novel approach for the construction of a high-resolution T1-weighted structural template based on the principles of super resolution. This method introduced a forward mapping technique to minimize signal interpolation, and a weighted averaging method to account for residual misregistration. The new template was shown to resolve finer brain structures compared to a lower resolution template constructed using the same data. It was demonstrated through systematic comparison of this new template to several other standardized templates of different resolutions that a) it exhibited high image sharpness, b) was free of image artifacts, c) allowed for high spatial normalization accuracy and detection of smaller inter-group morphometric differences compared to other standardized templates, d) was highly representative of the older adult brain. This novel approach was further modified for the construction of a high spatial resolution diffusion tensor imaging template. The new DTI template is the first high spatial resolution population-based DTI template of the older adult brain and exhibits high image quality, high sharpness, is free of artifacts, resolves fine white matter structures, and provides higher spatial normalization accuracy of older adult DTI data compared to other available DTI templates. Secondly, the aforementioned techniques were utilized in the development of high resolution T1-weighted and DTI templates, and tissue probability maps for the MIITRA atlas using high quality MRI images on 400 diverse, community cohort of non-demented older adults. Thirdly, a novel approach for generating high resolution gray matter labels is presented that involves a) utilization of the super resolution technique to ensure sharp delineation of structures, and b) a multi atlas based correction technique to reduce errors due to misregistration. High-resolution gray matter labels were constructed using the super resolution technique. When used for regional segmentation of the gray matter of older adults, the new gray matter labels of the showed high overlap, high geometric correlation, and low dissimilarity with the manually edited reference labels, demonstrating that there is a high agreement between the new labels and the manually edited Freesurfer labels. Finally, this thesis presents the development of a comprehensive array of gyral-based, cytoarchitecture-based, and functional connectivity-based gray matter labels in MIITRA space utilizing the aforementioned techniques. These labels include gyral-based, cytoarchitecture-based, and functional connectivity-based labels which will enhance the functionality of the MIITRA atlas. The new labels will also enhance the interoperability of MIITRA with the source atlases.
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Title: Image Synthesis with Generative Adversarial Networks
Creator: Ouyang, Xu
Date: 2023
Description: Image synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely...
Show moreImage synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely resemble the target images, learned from the source data distribution. This technique has a wide range of applications, including transforming captions into images, deblurring blurred images, and enhancing low-resolution images. In recent years, deep learning techniques, particularly Generative Adversarial Network (GAN), has achieved significant success in this field. GAN consists of a generator (G) and a discriminator (D) and employ adversarial learning to synthesize images. Researchers have developed various strategies to improve GAN performance, such as controlling learning rates for different models and modifying the loss functions. This thesis focuses on image synthesis from captions using GANs and aims to improve the quality of generated images. The study is divided into four main parts:In the first part, we investigate the LSTM conditional GAN which is to generate images from captions. We use the word2vec as the caption features and combine these features’ information by LSTM and generate images via conditional GAN. In the second part, to improve the quality of generated images, we address the issue of convergence speed and enhance GAN performance using an adaptive WGAN update strategy. We demonstrate that this update strategy is applicable to Wasserstein GAN(WGAN) and other GANs that utilize WGAN-related loss functions. The proposed update strategy is based on a loss change ratio comparison between G and D. In the third part, to further enhance the quality of synthesized images, we investigate a transformer-based Uformer GAN for image restoration and propose a two-step refinement strategy. Initially, we train a Uformer model until convergence, followed by training a Uformer GAN using the restoration results obtained from the first step.In the fourth part, to generate fine-grained image from captions, we delve into the Recurrent Affine Transformation (RAT) GAN for fine-grained text-to-image synthesis. By incorporating an auxiliary classifier in the discriminator and employing a contrastive learning method, we improve the accuracy and fine-grained details of the synthesized images.Throughout this thesis, we strive to enhance the capabilities of GANs in various image synthesis applications and contribute valuable insights to the field of deep learning and image processing.
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Title: MEN, WOMEN, AND LEADERS: THE EFFECT OF GENDER-LEADER CATEGORY CONGRUENCE ON SUPERVISOR EVALUATIONS
Creator: Lauritsen, Matthew William
Date: 2020
Description: Researchers employing Schein’s (1973, 1975) paradigm, ubiquitously conclude that the greater conceptual distance between leaders and women...
Show moreResearchers employing Schein’s (1973, 1975) paradigm, ubiquitously conclude that the greater conceptual distance between leaders and women compared to leaders and men is problematic for women in leadership roles. Six hundred eighty participants were recruited from MTurk to rate men, women, and leaders on agency and communion. Using polynomial regression analysis, the category congruence hypothesis was tested using two theories as interpretive frameworks: implicit leadership theory (ILT) and role congruity theory (RCT). A strict congruence effect was not found for any of the models. The results generally supported ILT, supervisor evaluations were highest when perceived supervisor characteristics exceeded the respondents’ leader category expectations. The results did not support RCT’s hypothesis about the negative effects of incongruence of women and leader category. Supervisor evaluations were highest when respondents held traditional gender stereotypes, not when they were congruent with the leader prototype. However, a general incongruence effect was found between male communion stereotypes and leader communion stereotypes leading to lower evaluations for male supervisors. That is, for men supervisors, the highest ratings were associated with high communion ratings of both men and leader categories. The results of this study are further discussed in relation to gender-leader category congruence and leadership.
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