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Media from Tokini Fubara: <i>ibi minji faari </i>Exhibition
No full textMedia materials documenting ibi minji faari, an exhibition by Tokini Fubara (30 Nov 2024 to 13 December 2024, ONCA Gallery, Brighton).Exhibited materials consisted of 2D animated loops, video, and CNC fabrication.The files in this record contain a photograph of the video installation, video excerpt with colonial archive document, and stills from animated loops.Abstractibi minji faari (translated from Kalabari Ijo: “the good water is going away”) is an exploration of the riverine landscapes of the oil-producing region of Nigeria—the Niger Delta.Formed with numerous creeks and rivers leading into the Atlantic Ocean, the Niger Delta is ecologically significant as the third largest mangrove forest in the world. It also has global significance as a zone of extraction—notably for the historic extraction of humans during the transatlantic slave trade, the extraction of palm oil during British colonisation, and the current extraction of crude oil from multinational petroleum companies such as Shell-BP. Centuries of extraction from this region has contributed to loss of land and people—such as that of the Ogoniland and its people—and a loss of connection to the water.ibi minji faari is an exhibition created by ONCA’s Associate Artist Tokini Fubara in celebration of Remembrance Day for Lost Species (RDLS). ibi minji faari builds on Tokini’s previous works on race, space, and borders, turning to the creeks surrounding their place of origin in Port Harcourt city. Using time-based work, this exhibition deploys a poetics of landscape in search of good waters.This exhibition is supported by the University of Sussex through its Arts and Humanities Research Council (AHRC) Impact Acceleration Account grant. It also includes videography from the Port-Harcourt based filmmaker, Osazee Murban Irabor (@Murban_Art) and previous RDLS ONCA artists.</p
RNN‐EdgeQL: An auto‐scaling and placement approach for SFC
No full textThis paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) called gated recurrent unit (GRU) for resource demand prediction. Then, considering these predictions, we built an intuitive scale in/out algorithm. We also developed an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place these scaled-out VNFs in appropriate locations. The integrated algorithm that combines prediction, scaling, and placement are called RNN-EdgeQL. RNN-EdgeQL (v2) is further improved to achieve application agnostic group level elasticity in the chain, independent of applications installed on the VNFs. We tested our algorithm on two realistic temporal dynamic load models including Internet traffic (Abilene) and an application specific traffic (Wiki) on an OpenStack testbed. The contribution of this article is threefold. First, prediction model prepares the target SFC for the upcoming load. Second, an application agnostic characteristics of the algorithm achieves the group-level elasticity in SFC. Finally, the EdgeQL placement model minimizes the end-to-end path of an SFC in multi-access edge computing (MEC) environment. As a result, RNN-EdgeQL (v2) gives the lowest overall latency, lowest SLA violations, and lowest VNFs requirement, compared to RNN-EdgeQL (v1) and Threshold-Openstack default placement.11Nsciescopu
Aptamer-antibody hybrid ELONA that uses hybridization chain reaction to detect a urinary biomarker EN2 for bladder and prostate cancer
No full textWe report an EN2-specific (K(d) = 8.26 nM) aptamer, and a sensitive and specific enzyme-linked oligonucleotide assay (ELONA) for rapid and sensitive colorimetric detection of bladder and prostate cancer biomarker EN2 in urine. The assay relies on an aptamer-mediated hybridization chain reaction (HCR) to generate DNA nanostructures that bind to EN2 and simultaneously amplify signals. The assay can be performed within 2.5 h, and has a limit of detection of 0.34 nM in buffer and 2.69 nM in artificial urine. Moreover, this assay showed high specificity as it did not detect other urinary proteins, including biomarkers of other cancers. The proposed ELONA is inexpensive, highly reproducible, and has great chemical stability, so it may enable development of a simple, sensitive and accurate diagnostic tool to detect bladder and prostate cancers early
Gene prioritization through hybrid distance-score rank aggregation
No full textThis thesis is concerned with developing novel rank aggregation methods for gene prioritization. Gene prioritization refers to a family of computational techniques for inferring disease genes through a set of training genes and carefully chosen similarity criteria. Test genes are scored based on their average similarity to the training set, and the rankings of genes under various similarity criteria are aggregated via statistical methods. The contributions of our work are threefold: a) First, based on the realization that there is no unique way to define an optimal aggregate for rankings, we investigate the predictive quality of a number of new aggregation methods and known fusion techniques from machine learning and social choice theory. b) Second, we propose a new approach to genomic data aggregation, termed HyDRA (Hybrid Distance-score Rank Aggregation), which combines the advantages of score-based and combinatorial aggregation techniques. We also propose incorporating a new top-vs-bottom (TvB) weighting feature into the hybrid schemes. The TvB feature ensures that aggregates are more reliable at the top of the list, rather than at the bottom, since only top candidates are tested experimentally. Specifically, we combine score-based Borda and Kendall permutation distance aggregation methods with TvB weightings.
c) Third, we propose an iterative procedure for gene discovery that operates via successful augmentation of the set of training genes by genes discovered in previous rounds, checked for consistency.
We tested HyDRA on a number of gene sets, including Autism, Breast cancer, Colorectal cancer, Endometriosis, Ischaemic stroke, Leukemia, Lymphoma, and Osteoarthritis. Furthermore, we performed iterative gene discovery for Glioblastoma, Meningioma and Breast cancer, using a sequentially augmented list of training genes related to the Turcot syndrome, Li-Fraumeni condition and other diseases. The methods outperform state-of-the-art software tools such as ToppGene and Endeavour.Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2015-07-22 without embargo termsThe student, Minji Kim, accepted the attached license on 2015-04-28 at 11:48.The student, Minji Kim, submitted this Thesis for approval on 2015-04-28 at 11:53.This Thesis was approved for publication on 2015-04-30 at 10:14.DSpace SAF Submission Ingestion Package generated from Vireo submission #8152 on 2015-07-22 at 10:34:27Made available in DSpace on 2015-07-22T22:17:57Z (GMT). No. of bitstreams: 2
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Previous issue date: 2015-04-3
An approach to Handling Irregular Oversaturation in Urban Subway Stations
No full textTrain timetable, Passenger waiting time, Oversaturated condition, Genetic algorithmThis Theses presents a data-based approach for a train scheduling that aims to minimize passenger waiting time by controlling train departure time and the number of skipped trains. In contrast to existing approaches that rely on a statistical model of passenger arrival, we develop a model based on real-world automated fare collection (AFC) data from a metro line in Daegu, a Korean city. The model consists of decomposing the travel time for each passenger into waiting, riding, and walking times, clustering of passengers by trains they ride and calculating the number of passengers in each train for any given time. Based on this, for a given train schedule, the passenger waiting time of each passenger for the entire AFC data period can be calculated. The problem is formulated using the model under realistic constraints such as headway, the number of available trains, and train capacity. To find the optimal solution, we employed a genetic algorithm (GA). The results demonstrate that the average waiting time is reduced up to 56% in the highly congested situation. Moreover, letting the trains directly go to the congested station by skipping previous stations further reduces the maximum waiting time by up to 19%. The effect of the optimization varies depending on the passenger arrival pattern of highly congested stations. This approach will improve the quality of the subway services by reducing passenger waiting time.openⅠ. INTRODUCTION 1
II. RELATED WORK 4
2.1. Passenger Volume Estimation 4
2.2. Train Scheduling Optimization 5
III. PROPOSED APPROACH 6
3.1. Overview 6
3.2. Dataset 8
3.3. Scenario Analysis 9
3.3.1 Peak Hours Scenario 10
3.3.2 Congested Off-Peak Hours Scenario 10
IV. PROBLEM FORMULATION 13
4.1. Assumptions 13
4.2. Train Capacity 15
4.3. Passenger Volume Estimation 15
4.3.1. Passenger Volume on the Train 16
4.3.2. Passenger Volume on the Platform 20
4.4. Timetable Optimization Model 20
4.4.1. Train Departure Time Control 21
4.4.1.1. Passenger Waiting Time Minimization Problem 21
4.4.1.2. Oversaturation Time Minimization Problem 24
4.4.2. Train Skip Plan Control 24
4.5. Genetic Algorithm 27
V. EVALUATION 29
5.1. Peak Hours Scenario 30
5.2. Congested Off-peak Hours Scenario 32
5.2.1 Single Peak Oversaturation 32
5.2.2 Double Peak Oversaturation 36
5.2.3 Box-shaped Peak Oversaturation 40
5.3. Discussion 43
VI. CONCLUSION AND FUTURE WORK 44
REFERENCES 46
APPENDIX A. Optimization Results 48
요약문 81도시 지하철은 도로교통 상황의 영향을 크게 받지 않으며 대용량의 교통 수요를 처리할 수 있어 많은 승객들에게 이용된다. 혼잡한 지하철은 승객들에게 불편을 야기하며, 승객들의 승강장에서의 대기시간을 증가시킨다. 본 논문은 열차 출발 시간과 역들을 건너 뛴 열차 수를 조절하여 승객 대기 시간을 최소화하는 것을 목표로 한 열차 시간표 최적화 방안을 제시한다. 승객 도착 통계 모델에 의존하는 기존의 접근 방식과 달리, 이 연구는 대구의 지하철에서 수집된 교통카드 데이터들을 기반으로 하는 최적화 모델을 만든다. 모델은 각 승객의 여행 시간을 차량 대기 시간, 차량 탑승 시간 및 보행 시간으로 구분하고, 탑승한 기차에 따라 승객들을 군집화 시킨 후 각 차량마다 승객 수를 추정하는 것으로 구성된다. 이를 바탕으로 주어진 열차 스케줄에 대해 모든 승객 각각의 대기 시간들을 계산할 수 있다. 최적화 문제는 이용 가능한 열차 수, 열차가 수용 가능한 최대 승객 수, 폐색구간과 같은 현실적인 제약 조건 하에서 구성된다. 최적의 시간표를 찾기 위한 방법으로 유전자 알고리즘이 사용되었다. 그 결과 승객 평균 대기 시간은 최대 56%까지 단축되었으며, 열차 출발시간 뿐만 아니라 일부 역을 건너뛰는 열차의 수까지 최적화하면 매우 혼잡한 상황에서 승객의 차량 대기 시간을 더욱 줄일 수 있었다. 혼잡한 상황에서 기차가 일부 역을 건너뛰었을 때, 그렇지 않을 때보다 승객 최대 대기 시간은 19%, 승객 평균 대기 시간은 15% 정도 더욱 단축되었다. 또한 혼잡한 상황에서 승객 도착 패턴에 따라 최적화의 효율이 달라진다는 것을 확인하였다. 본 방안은 승객 평균 대기시간을 감소시킴으로써 지하철 서비스를 향상시킬 것이다.MasterdCollectio
Information theoretic and machine learning techniques for emerging genomic data analysis
No full text"The completion of the Human Genome Project in 2003 opened a new era for scientists. Through advanced high-throughput sequencing technologies, we now have access to a large amount of genomic data and we can use it to answer key biological questions, such as the factors contributing to the development of cancer. Large data sets and rapidly advancing sequencing technology pose challenges for processing and storing large volumes of genomic data. Moreover, the analysis of datasets may be both computationally and theoretically challenging because statistical methods have not been developed for new emerging data. In this work, I address some of these problems using tools from information theory and machine learning.
First, I focus on the data processing and storage aspect of metagenomics, the study of microbial communities in environmental samples and human organs. In particular, I introduce MetaCRAM, the first software suite specialized for metagenomic sequencing data processing and compression, and demonstrate that MetaCRAM compresses data to 2-13 percent of the original file size.
Second, I analyze a biological dataset assaying the propensity of a DNA sequence to form a four-stranded structure called ""G-quadruplex"" (GQ). GQ structures have been proposed to regulate diverse key biological processes including transcription, replication, and translation. I present main factors that lead to GQ formation, and propose highly accurate linear regression and Gaussian process regression models to predict the ability of a DNA sequence to fold into GQ.
Third, I study data structures to analyze and store three-dimensional chromatin conformation data generated from high-throughput sequencing technologies. In particular, I examine statistical properties of Hi-C contact maps and propose a few suitable formats to encode pairwise interactions between genome locations."Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2017-08-10 without embargo termsThe student, Minji Kim, accepted the attached license on 2017-04-12 at 13:41.The student, Minji Kim, submitted this Dissertation for approval on 2017-04-12 at 13:49.This Dissertation was approved for publication on 2017-04-13 at 10:38.DSpace SAF Submission Ingestion Package generated from Vireo submission #10723 on 2017-08-10 at 13:39:26Made available in DSpace on 2017-08-10T19:14:56Z (GMT). No. of bitstreams: 3
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Previous issue date: 2017-04-1
Poetry and music: Reynaldo Hahn's Le Rossignol éperdu
No full textThis study focuses on Reynaldo Hahn’s solo piano work,
Le Rossignol éperdu
(“The
Bewildered Nightingale”)
(1902-1910), the largest instrumental work by the composer.
Le
Rossignol éperdu
is a collection of fifty-three character pieces, each one with a title and
sometimes a reference to
a
poem, novel, or play that inspired the composition. The themes of the
literary quotations cover a wide range including love, nature, the pastoral, and nostalgia. This
project focuses on the theme of love and studies how Hahn describes the very popular, yet
personal, theme of human nature in his piano music. The main focus is to analyze the following
pieces: No. 3, “Douloureuse Rêverie dans un bois de sapins”;
No. 6, “Gretchen”; No. 7, “Les
Deux Écharpes”; No. 8, “Liebe! Liebe!”; No. 13,
“Nevermore”; No. 14, “Portrait”; No. 17,
“Ivresse”; No. 18, “L’Arome
suprême”; No. 27, “La Danse de l’Amour et du Danger”; No. 29,
“Chérubin tragique.”
My analysis examines the composer’s musical language and characteristics
and connects these to the background contexts and interpretations of the texts associated with the
pieces to investigate how Hahn
expresses
poetic ideas in an instrumental work. Despite its
remarkable size and the beauty of the music, little has been written about
Le Rossignol éperdu.
This study will help introduce this new piano repertoire to pianists looking for hidden gems that
are unknown, but interesting. Since this collection is
noteworthy for its integration of poetry and
music, hopefully this study will also be useful not only for performers but also for readers.U of I Only Restriction set for Item 99301 on 2017-05-23T22:10:06Z with date by [email protected] by David Butler ([email protected]) on 2017-05-23T22:20:57Z
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Production of levulinic acid from wet microalgae in a biphasic one-pot reaction processs
No full textThis work addresses the conversion of wet microalgae to levulinic acid (LA) using a one-pot reaction system. Utilizing moisture in microalgae forms a biphasic system with an organic solvent of 1, 2-dichloroethane (DCE) is formed. This system enhances the LA yield by making an acidic environment through the decomposition of DCE in a small quantity and the recovery of products in each aqueous and organic phase. With lipid-richNannochloropsis gaditanaand carbohydrate-richChlorellaspecies, the effects of reaction variables of temperature, water content, and DCE dosage on the LA production were investigated. The LA yield was 30.13 wt% and 28.15 wt% based on the mass of total hexoses (43-47 wt% of convertible hexoses) for the two types of microalgae at 160 degrees C, while the yield of free fatty acids reached 90.13 w/w% at 180 degrees C based on the esterifiable lipid. This biphasic system facilitates the forward reaction and the product recovery for concurrent reaction and separation.
Network coding for speedup in switches
No full textThesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 69-71).Network coding, which allows mixing of data at intermediate network nodes, is known to increase the throughput of networks. In particular, it is known that linear network coding in a crossbar switch can sustain traffic patterns that cannot be served if network coding were not allowed. Thus, network coding leads to a larger rate region in a multicast crossbar switch. This thesis quantities the gain in rate region in a multicast crossbar switch in terms of speedup. We present a graph theoretic upper bound on speedup needed to achieve 100% throughput in a multicast switch using network coding. By bounding speedup, we show the equivalence between network coding and speedup in multicast switches - i.e. network coding, which is usually implemented using software, can in many cases substitute speedup, which is often achieved by adding extra switch fabrics. This bound is based on an approach to network coding problems called the "enhanced conflict graph". We show that the "imperfection ratio" of the enhanced conflict graph gives an upper bound on speedup. In particular, we apply this result to K x N switches with traffic patterns consisting of unicasts and broadcasts only to obtain an upper bound of min(2K-1/K, 2N/N+1).by MinJi Kim.M.Eng
Development of an optical sandwich ELONA using a pair of DNA aptamers for yellow fever virus NS1
No full text© 2022Here, we proposed an enzyme-linked oligonucleotide assay (ELONA) for yellow fever (YF) diagnosis that uses a pair of aptamers, YFns1-4 and YFns1-31. The aptamers were selected to specifically bind to nonstructural protein 1 (NS1), which is secreted at a high concentration after YF infection. We applied the aptamers which did not interfere with each other on binding to the NS1 in a sandwich ELONA. In the assay, the best detection sensitivity was obtained when the combination of YFns1-31 as a capture aptamer and YFns1-4 as a detect aptamer was used. The sensitivity could be attributed to the results of the direct ELONA with each YFns1-4 and YFns1-31; a great absorbance intensity and a broad detectable range of NS1, respectively. The sandwich ELONA achieved a low detection limit of 0.85 nM in buffer and was highly specific to the YFV-NS1 as its detection signals were significantly distinct from those of other flavivirus-derived NS1. In addition, the assay showed a desirable sensitivity in serum-spiked condition. Our developed sandwich ELONA can be a new practical and applicable serological diagnostics in YF endemic regions where other flaviviruses coexist and facilities for complex diagnostic tests are lacking.11Nsciescopu
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