255 research outputs found
The sustainability zine: The value of art-based pedagogy to support education for sustainability in a creative business and management course
No full textThis chapter explores some of the opportunities, challenges and wide benefits that art-based pedagogy offers to support education for sustainability, in the Integrated Foundation in Business and Management for the Creative Industries course at the University for the Creative Arts (UCA). It shares the process and reflections of the Sustainability Zine assignment completed as part of a Materials, Models, Mindsets project that started with exploring the Zine special collection at UCA. Zines are a form of radical self-publishing, derived from the word Fanzine. Zines often challenge convention, presenting alternative personal perspectives, they generate a sense of empowerment and community among readers and creators. Intended learning outcomes included: creating a Zine using Adobe InDesign, evidence of critical analysis, evaluation and reflection, through independent research into the circular economy, greenwashing, and sustainable business case studies. In this chapter I demonstrate that using art-based pedagogy in creative business teaching, that includes authentic portfolio assessment, developing critical analysis skills and self-reflection, offers opportunities for engagement and developing knowledge in sustainability and ethics. This chapter notes the importance of mindset development at the foundation level. It recognises that developing a sustainability mindset is as important for students as skills development. I highlight the potential impact this project and mindset has for students’ future study and for creating a more sustainable world
Understanding the importance of side information in graph matching problem
No full textGraph matching algorithms rely on the availability of seed vertex pairs as side information to deanonymize users across networks. Although such algorithms work well in practice, there are other types of side information available which are potentially useful to an attacker. In this thesis, we consider the problem of matching two correlated graphs when an attacker has access to side information either in the form of community labels or an imperfect initial matching. First, we propose a naive graph matching algorithm by introducing the community degree vectors which harness the information from community labels in an e cient manner. Next, we analyze the basic percolation algorithm for graphs with community structure. Finally, we propose a novel percolation algorithm with two thresholds which uses an imperfect matching as input to match correlated graphs. We also analyze these algorithms and provide theoretical guarantees for matching graphs generated using the Stochastic Block Model.
We evaluate the proposed algorithms on synthetic as well as real world datasets using various experiments. The experimental results demonstrate the importance of communities as side information especially when the number of seeds is small and the networks are weakly correlated. These results motivate the study of other types of potential side information available to the attacker. Such studies could assist in devising mechanisms to counter the effects of side information in network deanonymization.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-12-01The student, Kushagra Singhal, accepted the attached license on 2016-11-22 at 11:10.The student, Kushagra Singhal, submitted this Thesis for approval on 2016-11-22 at 11:16.This Thesis was approved for publication on 2016-11-22 at 12:00.DSpace SAF Submission Ingestion Package generated from Vireo submission #10224 on 2017-02-28 at 14:36:15Made available in DSpace on 2017-03-01T16:36:46Z (GMT). No. of bitstreams: 2
SINGHAL-THESIS-2016.pdf: 390320 bytes, checksum: 96d12f05add1e7756426924faa9c6f2d (MD5)
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Previous issue date: 2016-11-22Embargo set by: Seth Robbins for item 98583
Lift date: 2019-03-01T16:37:19Z
Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 98583 on 2019-03-02T10:15:33Z
A new framework of optimizing keyword weights in text categorization and record querying
No full textIn text mining research, the Vector Space Model (VSM) has been commonly used to represent text documents as a vector where each component is associated with a particular word in the documents. Assigning appropriate keyword weights in VSM has been critical in Information Retrieval (IR) and Text Categorization (TC).
Traditionally keyword weighting processes are unsupervised; that is, the knowledge of document's category is not leveraged to label the documents. Typically, each keyword weight is assigned using the term frequency -- inverse document frequency (TFIDF) measure. Although the TFIDF measure has been proven effective in several text mining problems, it might not give the optimal classification power for IR and TC. In this thesis, we propose a new optimization framework to find the best keyword weights based on the proposed inter-class and intra-class similarity concept.
The optimal keyword weight can be viewed as the feature space projection where documents from the same category are best clustered together and separated from other categories. Subsequently, the category average (centroid) classification is employed to categorize text documents. The proposed approach is tested on two practical applications: record query and text categorization. The record query application is slightly different from traditional IR problems as the goal is to find correlated (duplicate and master) text records. This problem was initiated by a telecommunication company where service engineers attempt to look for associations of the current defect problem in previously recorded problems in the database. Extensive experiments demonstrate that the proposed framework significantly improves the classification accuracy and provides balanced performance as measured on all text categories when compared to the standard TFIDF search. The text categorization application is tested on the Reuters news data set which is a gold-standard benchmark data set. The results show that our framework improves performance for the two applications considered, namely Information Retrieval and Text Categorization.M.S.Includes bibliographical references (p. 80-83)
Evaluation of UML based wireless network virtualization
No full textVirtualization of wireless networks is recognized to be a difficult problem due to the fact that radios interact with their neighbors at various layers of the protocol stack, making strict isolation of virtual networks ("or slices") quite challenging. The goal of virtualization is to support concurrent experiments, both long-running services as well as short-term experiments on shared wireless network. In a wireless network, the radio resources that can be shared and hence virtualized are in time, space and frequency. Efforts have been going on to modify the ORBIT control structure to accommodate different forms of virtualization including VMAC, SDMA, FDMA and TDMA. Among different possible wireless virtualization techniques, this work is focused on allowing a node to run more than one experiment simultaneously using different frequencies i.e. Frequency Division Multiplexing (FDM). Each node in the ORBIT test bed is provided with two physical wireless cards. FDMA virtualization is achieved by running two concurrent User Level Operating Systems (ULOS) on each node and providing each operating system access to a radio card. Thus an experimental end user would view a single node as two virtual nodes, each equipped with one wireless card.
Experimental results are provided to compare the performance of a virtualized radio node with the non virtualized one for basic point-to-point experiments using TCP and UDP. Bounds on performance metrics of throughput, delay and jitter are determined and cross-coupling effects between two virtualized experiments are examined. We also look at transient behavior associated with sudden changes in traffic on one of the virtual networks. Finally, the uncertainty in performance measurements for a few typical usage scenarios is investigated, leading to guidelines for use of virtualized radio nodes for simultaneous ORBIT experiments.M.S.Includes bibliographical references (p. 44-45)
Women Empowerment through Social initiatives: An Indian Story
No full textIt is now proven that the relationship between business and society is integral in the success of any enterprise. With the growing role of business in society, organizations are becoming more “socially responsible” and engaging in various social initiatives. Organizations involve themselves in various kinds of initiatives generally targeted towards a selected underprivileged section of the society or a specific area like education, health, and environment. Women play an important role in building sustainable development. Promoting women empowerment and widening their contribution in decision-making roles are key strategies for sustainable development. The present paper is an attempt to highlight women empowerment through various social initiatives. In this regard, I attempted to review some of the exemplary sustainable social initiatives running in India through this paper
Supplemental materials for preprint: A Descriptive Study of Indian General Public’s Psychological responses during COVID-19 Pandemic Lockdown Period in India
No full textSupplemental materials for preprint: A Descriptive Study of Indian General Public’s Psychological responses during COVID-19 Pandemic Lockdown Period in India
No full textFemA based drug design for potentiation of B-lactam antibiotics against Methicillin Resistant Staphylococcus aureus
No full textThe present study was oriented to identify the resistance modifying agent (RMA) for β-lactam antibiotics, Penicillin and Oxacillin against Methicillin resistant Staphylococcus aureus (MRSA). Initially, identification of RMA’s was based on inhibitors of FemA protein’s structure, which is a novel drug target and has not been exploited so far in antibiotic drug development, using computational tools by virtual library of compounds. Catechin Gallate (CG) was selected as the inhibitor of FemA protein from in silico studies which comprised of docking, interaction, toxicity and checking of Lipinski violations. Subsequently to identifying CG as a lead molecule from in silico studies, its antibacterial property in presence and absence of antibiotics Oxacillin and Penicillin was carried out to arrive to MIC and FIC index of the antibiotic combinations. Further time kill kinetics and mucopolysaccharide content of the test and control organisms was carried out by nLC-MS in the three standard isolates of S. aureus.
Three synergistic formulations viz formulation of 62.5 μg/ml oxacillin with 7.8 μg/ml of CG, 62.5 μg/ml oxacillin with 31.25 μg/ml of CG and 125 μg/ml of oxacillin with 7.8 μg/ml of CG were found to potentiate oxacillin against three isolates of S. aureus viz. NCTC 6571, MTCC 737 and MTCC 96, while one synergistic formulation of 2000 μg/ml penicillin with 7.8 μg/ml of CG was found to be effective against
NCTC 6571
Reduction of more than 50% peak area in concentration of muropeptides of test formulations compared to their control, indicates the modulation of functional expression of FemXAB proteins by CG the selected inhibitor of FemA protein. Subsequently, β-lactam antibiotic efficiency has been increased in MRSA strains. With the use of CG, possibly due to inhibition of FemXAB protein as evident from change in muropepides content. Hence, the present study establishes the potential of Catechin Gallate as potentiator of β-lactam antibiotics against MRSA in vitro and warrants extensive studies on clinical isolates and their muropeptides composition
Single-Cell Chromatin and Transcriptional Profiles of Pediatric Brain Tumour Evolution
No full textPediatric brain tumors are one of the most common solid tumors and are the leading cause of cancer-related deaths among children. Despite significant advancements aimed at improving treatment for these tumors, the overall prognosis for affected children has improved minimally during the past decade, primarily due to a limited understanding of the underlying tumor biology. This thesis is based on the hypothesis that epigenetic dysregulation plays a key role in the development, progression, and recurrence of pediatric brain tumors. To explore this hypothesis, I analyzed the epigenetic and transcriptional profiles of three specific types of pediatric brain tumors: pediatric high-grade glioma (pHGG), posterior fossa A (PF-A) ependymoma, and supratentorial (ST) ependymoma. The first section centers on pHGG, employing single-nuclei ATAC-seq on paired diagnostic and recurrent samples. By assessing these paired samples, our data show that new chromatin states arise at relapse, enabling specific transcription factors (TFs) to dominate their chromatin accessibility landscapes and driving transcriptional divergence between diagnostic and relapse tumors. The second section explores the transcriptional and epigenetic landscape of pediatric solid tumor ependymomas, with a focus on the RELA-fused, HGNET-BCOR, and PF-A subtypes, particularly regarding the effects of azacitidine (Vidaza) treatment. I find that the response to Vidaza is highly patient-specific, with observable shifts in the epigenome and a more neuronal phenotype. Furthermore, I identify potential metabolic changes caused by azacitidine treatment. The third section presents a case study that offers valuable insights into the molecular evolution of a rare supratentorial ependymoma harboring a ZFTA-NCOA2 fusion. By utilizing a multiome strategy on paired diagnostic and relapse samples, I observe a shift from astroglial signatures to an epithelial-mesenchymal transition (EMT) state at relapse. In summary, this thesis offers comprehensive insights into the dynamic epigenetic and transcriptional dynamics in pediatric brain tumors as they progress and adapt to therapy. These findings significantly enhance our understanding of pediatric brain tumor biology and indicate promising opportunities for improving clinical outcomes in these severe childhood cancers
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