117 research outputs found
Computational Chemistry and Bioinformatics Research Core (CCBRC)
No full textDepartment/Unit poster (BioMolecular Sciences). Corresponding author: Sushil Mishra ([email protected])https://egrove.olemiss.edu/pharm_annual_posters_2022/1012/thumbnail.jp
230 - Sushil Paudyal
No full textDigital dermatitis is a major cause of lameness in dairy cows causing pain in the limbs leading to reduced animal welfare and significant economic loss. With strict antibiotic regulations and increasing organic dairies, the clinically validated non-antibiotic treatment options are of great value. The objective of the study was to evaluate the efficacy of treatment of digital dermatitis using different combinations of copper sulfate, iodine, and honey. Cows with M1 and M2 DD lesion score were identified and enrolled in the hoof-trimming chute. Cows were randomized to be treated with one of the three treatment options: Copper sulfate and Iodine (CS-I), Honey and Iodine (HO-I) and Control (CON). All 70 cows were followed up on D3, D12 and D28 and a subsample of 45 cows were followed until d120 to evaluate lesion size, lesion stage, lameness score and pain response. Tissue samples were collected on D3, D28 and D120 to investigate dynamics of microbial metagenomics. The data were analyzed in SAS using PROC MIXED and PROC GENMOD with repeated measures. The results show that 43% of the lesions were found on the left feet and 57% on the right feet. The early erosive form of lesions changes into papillomatous mature form as the lesion progresses irrespective of treatment application. The lesion size differed among treatment groups and the effect varied with different follow-up days (P< 0.05). The lesion decreased for both CS-I and HO-I group till day 12 after which the HO-I group had an increase in lesion size. In contrast to this CON group had a slower decrease in lesion size. The pain response was decreased for CS-I and less for HO-I groups. The odds of pain and the odds of getting a lame cow decreases as the time progresses. Thus, non-antibiotic treatment options are effective in controlling pain and decreasing lesion size up to 12 days. Also, clinical assessment of animals and evaluation of lesions suggest CS-I combination is superior to HO-I and CON group
Prevalence of iodine deficiency among pregnant women and factors associated with it, Kullu town, Himachal Pradesh, India.
No full textUser-independent robust statistics for computer vision
No full textThe goal of robust methods in computer vision is to extract all the information necessary to solve a given task while discarding everything that is not needed. The tasks can be very simple or very complex, but in real-life applications, a robust procedure is always required. In the end, the performance of a machine for solving a vision problem will be judged against that of human observers performing the equivalent task. Since we know that the human visual system works in a much more sophisticated manner than the present day computer vision systems, this ultimate goal is still far away. Nonetheless, the aim of robust computer vision systems has always been to emulate human vision-like behavior in the presence of noise. Furthermore, the
robust algorithms should be independent of user inputs up to quite a large extent. However, contrary to this, almost all state-of-the-art robust estimation algorithms are dependent on the user for providing some information about the underlying characteristics of the data on which the algorithm operates. Often times, it is hard for the user to supply such information to the
algorithm. The work presented here focuses on developing robust algorithms for computer vision, that
can estimate the underlying model in the data without any sort of user intervention. We present several interesting applications both in geometric computer vision and medical imaging. In the first part, we present a completely user-free robust regression algorithm called the generalized projection based M-estimator (gpbM) which can estimate multiple inlier structures present in the data also containing a lot of gross outliers without any user input. We also show how the
model estimate can be further refined by using optimization on Grassmann manifolds. In the second part, we present three important applications in medical imaging involving 3D computed tomography (CT) data – automatic detection of coronary lesions, automatic correction of coronary centerlines and automatic segmentation of coronary vessels. Finally, in the third part, we present the application of automatic and robust document image alignment and comparison.Ph. D.Includes bibliographical referencesIncludes vitaby Sushil Mitta
Public-Private Partnerships (PPPs) and the Road to Self- Reliance in Defence: a Perspective
No full textThe Industrial Policy Resolution of 1956, under Schedule A, reserved 17 industries including arms and ammunition for the public sector. Accordingly, the defence sector remained solely the domain of defence Public Sector Undertakings (PSUs), Ordnance Factory Board (OFB), and Defence Research and Development Organisation (DRDO) till 2001. However, the country had to resort to the import of ammunition for the Bofors artillery guns during the Kargil War from South Africa, amongst others, even though the country already had a large industrial base consisting of nine defence PSUs, 39 Ordnance Factories (OFs) and 52 laboratories of DRDO. The armed forces stared at the perils of dependence on imports during the war. On a positive note, post the Kargil War, the government decided to open the doors to the defence sector to the private industry. Thus, in May 2001, the government permitted 100 per cent participation by the Indian private sector, subject to licensing, with the aim to galvanise the country's defence industrial base for achieving self-reliance and indigenisation
E-Mail authorship attribution for computer forensics
No full textIn this chapter, we briefly overview the relatively new discipline of computer forensics and describe an investigation of forensic authorship attribution or identification undertaken on a corpus of multi-author and multi-topic e-mail documents. We use an extended set of e-mail document features such as structural characteristics and linguistic patterns together with a Support Vector Machine as the learning algorithm. Experiments on a number of e-mail documents generated by different authors on a set of topics gave promising results for both inter- and intratopic author categorisation
Modeling ignition and extinction in condensed phase combustion
No full textThe characteristics of ignition and extinction in thermites and intermetallics are a subject of interest in developing the latest generation of energetic materials. An experimental “striker confinement” shock compression experiment was developed in the Prof. Glumac’s research group at the University of Illinois to study ignition and reaction in composite reactive materials. These include thermitic and intermetallic reactive powders. We discuss our model for the ignition of copper oxide-aluminum thermite in the context of the striker experiment and how a Gibbs formulation model, that includes multi-components for liquid and solid phases of aluminum, copper oxide, copper and aluminum oxide, can predict the events observed at the particle scale in the experiments. Furthermore, the characteristics of a steady diffusion flame that arises at the interfaces of two condensed phase reactant (titanium-boron) and gas reactant (methane-air) streams that form an opposed counterflow are discussed. In the the gas flow scenario, the asymptotic analysis is carried on both constant and variable density formulations and compared the solutions to those obtained numerically. In the case of condensed phase reactants, several types of analyses are carried out at increasing levels of complexities: an asymptotic analysis valid in the limit of low strain rates (high residence time in the reaction zone), a constant mixture density assumption that simplifies the flow description, diffusion models with equal and unequal molecular weights for the various species, and a full numerical study for finite rate chemistry, composition-dependent density and strain rates extending from low to moderate values.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2018-12-01The student, Sushil Koundinyan, accepted the attached license on 2016-09-19 at 15:22.The student, Sushil Koundinyan, submitted this Dissertation for approval on 2016-09-19 at 15:27.This Dissertation was approved for publication on 2016-09-20 at 13:41.DSpace SAF Submission Ingestion Package generated from Vireo submission #10165 on 2017-02-28 at 14:35:59Made available in DSpace on 2017-03-01T16:36:41Z (GMT). No. of bitstreams: 3
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Implications of Environmental Differences on Strategies of Multinationals' Manufacturing Subsidiaries<sup>*</sup>
No full text In this article, Sushil Vachani develops four propositions about how multinationals' operations are affected by differences in the environments of lessdeveloped countries(LDCs), middle-income countries(MICs), and developed countries(DCs). First, LDCs will be less inclined to host multinationals that have advertisingbased assets than those which have R & D based assets. Second, the proportion of multinationals' subsidiaries formed through acquisition will be smaller in LDCs than in DCs and MICs. Third, within LDCs (and MICs and DCs), the size of the country's industrial base is a contributing factor in the formation of multinational subsidiaries by acquisition. Fourth, the size of the local market has an important bearing on the proportion of export-oriented subsidiaries in a country. Vachani examines each of these propositions using a database of overseas subsidiaries of Fortune 500 multinationals. Drawing implications for managers of multinationals, the author emphasizes the importance of taking into account the differences in the political and econpmic environment of LDCs, MICs, and DCs when formulating business strategy. </jats:p
Efficient sequential decision-making algorithms for container inspection operations
No full textSequential diagnosis is an old subject, but one that has become increasingly important recently. There exists a need for new models and algorithms as the traditional methods for making decisions sequentially do not scale. Motivated by the problem of container inspection at the U.S. ports, we investigate the problem of finding efficient algorithms for sequential diagnosis. More specifically, we formulate the port of entry inspection sequencing task as a problem of finding an optimal binary decision tree for an appropriate Boolean decision function. We provide new algorithms that are computationally more efficient than those previously presented by Stroud and Saeger [31] and Anand et al [1]. We achieve these efficiencies through a combination of specific numerical methods for finding optimal thresholds for sensor functions and two novel binary decision tree search algorithms that operate on a space of potentially acceptable binary decision trees. The improvements enable us to analyze substantially larger applications than was previously possible.
We try to solve the problem of finding an optimal inspection strategy by breaking it into two sub-problems - 1. Finding sensor threshold values that minimize the cost for a given binary decision tree and 2. ``Searching'' for the cheapest binary decision tree in a large space of trees or equivalence classes of trees. For solving the first problem, we explore various standard non-linear optimization techniques and also propose a novel algorithm by combining the gradient descent method and Newton's method in optimization to compute optimal thresholds for any given tree. We propose two novel search algorithms - A stochastic search method and a genetic algorithms based search method, as a solution to the second sub-problem. We also propose ``neighborhood'' operations to move from one tree to another in the proposed tree space and prove that the tree space is irreducible under these neighborhood operations.
We report results from numerous experiments with and without imposing restrictions on the tree space and examine how the optimal binary decision trees vary with these changes. For example, for most of the work in this thesis, we restrict the tree space to constitute only ``complete'' and ``monotonic'' binary decision trees. Later, we ``shrink'' the tree space by discovering equivalence classes of trees while we ``expand'' the tree space by removing the monotonicity constraint.M.S.Includes bibliographical references (p. 61-63)
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