19 research outputs found
Resilience analysis: a mathematical formulation to model resilience of engineering systems
Get PDFResilience of engineering systems is related to their ability of absorbing both gradual and abrupt changes under exposure conditions and rapidly recover from disruptions. In this thesis, we develop a general stochastic formulation to model the recovery process and quantify system's resilience. In particular, we develop models for time-dependent capacity of a system and the imposed demand, under joint effects of recovery and shock deterioration processes. Using the developed models, a recovery curve is formulated in terms of system's reliability, functionality and work progress. Furthermore, we propose a novel approach for resilience analysis by defining measures to capture characteristics of recovery curves. The proposed approach makes a distinction in resilience of systems with different recovery patterns. A numerical example is provided to illustrate the application of the model.Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01The student, Neetesh Sharma, accepted the attached license on 2016-04-26 at 10:51.The student, Neetesh Sharma, submitted this Thesis for approval on 2016-04-26 at 10:56.This Thesis was approved for publication on 2016-04-26 at 12:38.DSpace SAF Submission Ingestion Package generated from Vireo submission #9502 on 2016-07-07 at 14:18:01Made available in DSpace on 2016-07-07T21:18:05Z (GMT). No. of bitstreams: 2
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Regional resilience analysis: Modeling, optimization, and uncertainty quantification
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Lift date: 2023-03-05T21:43:00Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemModern urban society's prosperity depends on the continuous flow of essential resources and services provided by the critical infrastructure. Ensuring the critical infrastructure's reliability and resilience is cardinal to ensure public safety and economic stability. However, past events have highlighted the infrastructure's vulnerability to disruptions caused by natural or anthropogenic hazards. Furthermore, complex interdependencies among infrastructure can cause disruptions to propagate within and across infrastructure, resulting in multi-fold catastrophic consequences on individuals, households, businesses, and communities. The consequences of past disasters have emphasized the need for hazard mitigation and recovery planning for infrastructure. Case studies of post-disaster recovery of different communities worldwide have indicated that successful recovery requires effective governance, intensive planning, community engagement, and intelligent use of resources. However, hazard mitigation and post-disaster recovery of infrastructure represent significant investments. Despite the expected economic advantage of investing in disaster preparedness, communities, businesses, and governments often struggle to budget their limited financial resources toward mitigation and recovery efforts. The uncertainty in predicting the occurrence and impacts of future hazards further increases the complexity of justifying large investments. There is a pressing need for rigorous and accurate models of infrastructure to reduce societal risk and improve regional resilience.
This dissertation develops a novel classification of infrastructure interdependencies and a general mathematical formulation for modeling interdependent infrastructure. Specifically, the developed classification partitions the space of infrastructure interdependencies based on their ontological and epistemological dimensions. Under the ontology dimension, infrastructure interdependencies are classified into chronic and episodic. Under the epistemology dimension, infrastructure interdependencies are classified according to their mathematical modeling. The proposed classification better enables us to understand and mathematically model several classes of infrastructure interdependencies. The proposed mathematical formulation models infrastructure as a set of generalized flow networks while using dynamic interfaces to model the interdependencies. Carefully chosen working and benchmark examples illustrate the implementation and the advantages of the proposed formulation in providing accuracy while tackling the computational challenges.
The dissertation then develops a rigorous mathematical formulation to model recovery, quantify resilience, and optimize large-scale infrastructure resilience. Specifically, a multi-scale recovery process model is proposed that significantly reduces the computational cost while favoring practical and easily manageable recovery schedules. The proposed resilience metrics then quantify the regional resilience by capturing the recovery process's temporal and spatial variations. A multi-objective optimization problem is then framed to improve regional resilience in terms of the proposed metrics while minimizing the recovery cost. The proposed recovery modeling is also integrated into a stochastic life-cycle formulation to account for the effects of infrastructure deterioration. The proposed approach is illustrated through large-scale examples for the post-disaster recovery modeling of infrastructure.
Engineering models for critical infrastructure and measures of the societal impact, if developed in isolation, would not be sufficient to improve community resilience. This dissertation integrates the developed engineering models with existing social science approaches to comprehensively model the impact of hazards on communities and their recovery. Specifically, in combination with a reliability-based capability approach, the developed infrastructure models are used to predict the broad societal impact of hazards in terms of changes in dimensions of individuals' well-being. Some of these concepts are then explained through an example, modeling the dynamics of physical-social systems.
Finally, the dissertation also provides an uncertainty propagation formulation for continuous improvement of the developed models and directing further research and data collection efforts. The proposed formulation quantifies the relative importance of engineering and social science models in evaluating the desired community resilience objectives. Specifically, a variable grouping using the interface function values' statistics decouples the regional resilience analysis into the constituent models, reducing the problem dimensions. The computationally intensive models are then identified, and an experimental design is developed for these models to reduce the total computation cost. The uncertainty propagation framework is performed using a global sensitivity analysis based on Sobol's indices.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2022-12-01The student, Neetesh Sharma, accepted the attached license on 2020-11-30 at 22:16.The student, Neetesh Sharma, submitted this Dissertation for approval on 2020-11-30 at 22:18.This Dissertation was approved for publication on 2020-12-02 at 08:11.DSpace SAF Submission Ingestion Package generated from Vireo submission #16003 on 2021-03-04 at 16:20:15Made available in DSpace on 2021-03-05T21:42:45Z (GMT). No. of bitstreams: 3
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Modeling of the jasmonate signaling pathway in Arabidopsis thaliana with respect to pathophysiology of Alternaria blight in Brassica
No full textAbstractThe productivity of Oilseed Brassica, one of the economically important crops of India, is seriously affected by the disease, Alternaria blight. The disease is mainly caused by two major necrotrophic fungi, Alternaria brassicae and Alternaria brassicicola which are responsible for significant yield losses. Till date, no resistant source is available against Alternaria blight, hence plant breeding methods can not be used to develop disease resistant varieties. Jasmonate mediated signalling pathway, which is known to play crucial role during defense response against necrotrophs, could be strengthened in Brassica plants to combat the disease. Since scanty information is available in Brassica-Alternaria pathosystems at molecular level therefore, in the present study efforts have been made to model jasmonic acid pathway in Arabidopsis thaliana to simulate the dynamic behaviour of molecular species in the model. Besides, the developed model was also analyzed topologically for investigation of the hubs node. COI1 is identified as one of the promising candidate genes in response to Alternaria and other linked components of plant defense mechanisms against the pathogens. The findings from present study are therefore informative for understanding the molecular basis of pathophysiology and rational management of Alternaria blight for securing food and nutritional security.</jats:p
A computational study on genetic diversity of shatterproof1 (shp1) and shatterproof2 (shp2) genes in some members of Oleraceae and its molecular implications
Get PDFAbstract Dispersal and maturation of seed is a complex event in flowering plants. The genes shatterproof1 (shp1) and shatterproof2 (shp2) are essential for fruit dehiscence in Arabidopsis. In this study, we have analyzed the diversity in these two genes and their molecular implications in some members of Oleraceae. We have studied the gene organization of these two genes and various biochemical and biophysical parameters of the proteins encoded by these two genes. Though there are some similarities, there also exist some notable differences. These differences could be exploited for creating a library of synthetic alleles (neutral or advantageous) to be used for genetic engineering, thus ensuring a wide genetic base. This diversity analysis may be significant to create diversity in the transgenic plants for shattering resistance using genetic engineered methods. This analysis explores the possible correlation of results of this study with the phenotypic data to derive functional significance of the diversity in SHP genes
An Explainable Model Using Graph-Wavelet for Predicting Biophysical Properties of Proteins and Measuring Mutational Effects
No full textProteins hold multispectral patterns of different kinds of physicochemical features of amino acids in their structures, which can help understand proteins’ behavior. Here, we propose a method based on the graph-wavelet transform of signals of features of amino acids in protein residue networks derived from their structures to achieve their abstract numerical representations. Such abstract representations of protein structures hand in hand with amino-acid features can be used for different purposes, such as modelling the biophysical property of proteins. Our method outperformed graph-Fourier and convolutional neural-network-based methods in predicting the biophysical properties of proteins. Even though our method does not predict deleterious mutations, it can summarize the effect of an amino acid based on its location and neighbourhood in protein-structure using graph-wavelet to estimate its influence on the biophysical property of proteins. Such an estimate of the influence of amino-acid has the potential to explain the mechanism of the effect of deleterious non-synonymous mutations. Thus, our approach can reveal patterns of distribution of amino-acid properties in the structure of the protein in the context of a biophysical property for better classification and more insightful understanding
Advancing Sustainable Energy : Exploring New Frontiers and Opportunities in the Green Transition
No full textThe current global scenario underlines the urgency of addressing energy consumption and its environmental implications. Contemporary international strategies aim to foster public awareness and engagement in sustainable energy initiatives. The World Environment Protection Commission aspires to qualify for an equitable transition toward energy-efficient technologies, strategic policies, and achieving net-zero carbon emissions. The principal aspiration is to enhance community understanding of energy and environmental policies. Furthermore, a root cause analysis reveals that understanding the foundational factors, both internal and external, underpinning the attainment of these objectives is of paramount importance. This study investigates the comparative advantages of renewable energy over non-renewable sources. It conducts a thorough analysis of various factors, encompassing energy sourcing, variables, challenges, technological progress, and the deployment of energy-efficient systems. Utilizing a strategic approach and conducting pre- and post-analysis data evaluations, it aims to promote the adoption of sustainable practices for a greener future. Emphasizing the importance of international cooperation and the effective implementation of policies, this research underscores the critical role of practical action in fostering energy sustainability and environmental preservation. The urgency of addressing energy consumption and environmental impacts is critical. Contemporary strategies aim to increase public awareness and engagement in sustainable energy initiatives. This study examines the benefits of renewable energy over non-renewable sources, analyzing energy sourcing, technological progress, and efficient systems. Emphasizing international cooperation, it underscores the importance of practical action for energy sustainability. image</p
Data_Sheet_1_Improving Chromatin-Interaction Prediction Using Single-Cell Open-Chromatin Profiles and Making Insight Into the Cis-Regulatory Landscape of the Human Brain.PDF
No full textSingle-cell open-chromatin profiles have the potential to reveal the pattern of chromatin-interaction in a cell type. However, currently available cis-regulatory network prediction methods using single-cell open-chromatin profiles focus more on local chromatin interactions despite the fact that long-range interactions among genomic sites play a significant role in gene regulation. Here, we propose a method that predicts both short and long-range interactions among genomic sites using single-cell open chromatin profiles. Our method, termed as single-cell epigenome based chromatin-interaction analysis (scEChIA) exploits signal imputation and refined L1 regularization. For a few single-cell open-chromatin profiles, scEChIA outperformed other tools even in terms of accuracy of prediction. Using scEChIA, we predicted almost 0.7 million interactions among genomic sites across seven cell types in the human brain. Further analysis revealed cell type for connection between genes and expression quantitative trait locus (eQTL) in the human brain and making insight about target genes of human-accelerated-elements and disease-associated mutations. Our analysis enabled by scEChIA also hints about the possible action of a few transcription factors (TFs), especially through long-range interaction in brain endothelial cells.</p
Physical Layer Security Performance of NOMA-Aided Vehicular Communications Over Nakagami-<inline-formula><tex-math notation="LaTeX"></tex-math></inline-formula> Time-Selective Fading Channels With Channel Estimation Errors
No full textThis paper considers a single-input-multiple-output non-orthogonal multiple access enabled vehicular communication system, and investigates its secrecy performance over time-selective fading and channel estimation errors. We formulate two scenarios considering the decoding ability at eavesdropper, viz., 1) Scenario I: when it has sufficient decoding capability, and 2) Scenario II: when its decoding capability can be the same as the legitimate vehicles. Under such a realistic system setup, we derive the secrecy outage probability (SOP) and ergodic secrecy capacity expressions for both legitimate vehicles and the overall system under both Scenarios I and II over Nakagami-m fading channels. We then analyze the asymptotic SOP limits of both legitimate vehicles under Scenarios I and II, by formulating cases based on the average transmit signal-to-noise ratio and average channel gains associated with the main links and wiretap link. From the asymptotic analysis under these cases, it is observed that both legitimate vehicles do not achieve any secrecy diversity order even after leveraging the benefits of fading severity parameters and multiple antennas. We also formulate several special cases of interest to reveal some more insightful information about the system's secrecy performance. Finally, we corroborate our analytical and theoretical findings via simulation results
