On a quasi-stationary approach to bayesian computation, with application to tall data

Markov Chain Monte Carlo (MCMC) techniques have traditionally been used in a Bayesian inference to simulate from an intractable distribution of parameters. However, the current age of Big data demands more scalable and robust algorithms for the inferences to be computationally feasible. Existing MCMC-based scalable methodologies often uses discretization within their construction and hence they are inexact. A newly proposed field of the Quasi-Stationary Monte Carlo (QSMC) methodology has paved the way for a scalable Bayesian inference in a Big data setting, at the same time, its exactness remains intact. Contrary to MCMC, a QSMC method constructs a Markov process whose quasi-stationary distribution is given by the target. A recently proposed QSMC method called the Scalable Langevin Exact (ScaLE) algorithm has been constructed by suitably combining the exact method of diffusion, the Sequential Monte Carlo methodology for quasi-stationarity and sub-sampling ideas to produce a sub-linear cost in a Big data setting. This thesis uses the mathematical foundations of the ScaLE methodology as a building block and carefully combines a recently proposed regenerative mechanism for quasistationarity to produce a new class of QSMC algorithm called the Regenerating ScaLE (ReScaLE). Further, it provides various empirical results towards the sublinear scalability of ReScaLE and illustrates its application to a real world big data problem where a traditional MCMC method is likely to suffer from a huge computational cost. This work takes further inroads into some current limitations faced by ReScaLE and proposes various algorithmic modifications for targeting quasistationarity. The empirical evidences suggests that these modifications reduce the computational cost and improve the speed of convergence

Alternaria blight of oilseed brassicas: A review on management strategies through conventional, non-conventional and biotechnological approaches

Oilseed Brassicas are contributing approximately 28 percent of the India’s total oilseed production. This crop is gaining wide acceptance among t he f a rme r s b e ca u s e of adaptability for both irrigated as well as rainfed areas and suitability for sole as well as mixed cropping. Besides, it offers higher return with low cost of production and low water requirement. The production and productivity of oilseed brassicas are comparatively lower as compared to the world average due to the biotic and abiotic constraints. Among the biotic constraints, alternaria blight disease caused by Alternaria spp. has been reported from all the continents of the world, causing up to 70% yield losses in India. This disease was found on leaves, stems and siliquae and dark spots on the leaves and siliquae reduce the photosynthetic capacity and induce immature ripening, which causes reduced amount of quality seed and oil content. The severity of this disease depends upon weather conditions, varieties, age of host plants and virulence of the pathogens. Efforts are being done throughout the world for the management of alternaria blight of rapeseed-mustard. This paper comprehensively reviews the research of alternaria blight of rapeseed-mustard with special reference to management strategies through conventional, non conventional and biotechnological approaches that leads to planning the future research. The present scenario demands the traditional and modern biotechnological techniques bringing together for integrated disease management according to the need and availability at farmers level for sustainable management of alternaria blight disease of oilseed brassicas

Posterior reversible encephalopathy syndrome in a patient with underlying mixed connective tissue disease

Posterior reversible encephalopathy syndrome (PRES) is a condition which is characterized by symmetric involvement of posterior white matter on brain imaging and neurological impairments such as seizures, altered mental status, headache, and visual disturbances. This entity has been classically described with hypertension, renal failure and eclampsia but it can also been seen in cases with normal blood pressure especially in patients receiving immunosuppressive therapy, chemotherapy and in patients with underlying autoimmune disease. Although PRES has been reported with several autoimmune disorders, association of Posterior reversible encephalopathy syndrome (PRES) with mixed connective tissue disease (MCTD) is very rare, hence we report a case of Posterior reversible encephalopathy syndrome in a patient with underlying mixed connective tissue disease (MCTD)

Fuzzy Delphi and hybrid AH-MATEL integration for monitoring of paint utilization

This study investigates the unattended aspects of paint utilization selection criteria in industries. In today competitive business environment almost all companies focus towards sustainable manufacturing. The utilization evaluation and selection criteria for paint and its consumption reduction is the top priority for industry. Especially in automotive industries, paint shop stands as a centre for hazardous waste due to wastage of paint and thinner during the painting process. This research work focuses on optimizing consumption of paint by finding most important criteria affecting paint consumption and optimizing the same to achieve maximum paint yield. The study uses the routes of Delphi technique in a fuzzy environment to find out the most important criteria for paint utilization selection, so that maximize utilization and minimize consump-tion reduction of paint has been achieved. An integrated approach of AHP and DEMATEL methods has been implemented to prioritize the criteria and to familiarize the relationship within criteria. The outcomes of the study substantiate and proves that this study is the best way to select a particular paint utilization selection criteria for the paint shop and also to anticipate the optimal level of paint utilization.N/

A dynamic fluid landscape mediates the spread of bacteria

Microbial interactions regulate their spread and survival in competitive environments. It is not clear if the physical parameters of the environment regulate the outcome of these interactions. In this work, we show that the opportunistic pathogen Pseudomonas aeruginosa occupies a larger area on the substratum in the presence of yeast such as Cryptococcus neoformans , than without it. At the microscopic level, bacterial cells show an enhanced activity in the vicinity of yeast cells. We observe this behaviour even when the live yeast cells are replaced with heat-killed cells or with spherical glass beads of similar morphology, which suggests that the observed behaviour is not specific to the biology of microbes. Upon careful investigation, we find that a fluid pool is formed around yeast cells which facilitates the swimming of the flagellated P. aeruginosa , causing their enhanced motility. Using mathematical modeling we demonstrate how this local enhancement of bacterial motility leads to the enhanced spread observed at the level of the plate. We find that the dynamics of the fluid landscape around the bacteria, mediated by the growing yeast lawn, affects the spreading. For instance, when the yeast lawn grows faster, a bacterial colony prefers a lower initial loading of yeast cells for optimum enhancement in the spread. We confirm our predictions using Candida albicans and C. neoformans, at different initial compositions. In summary, our work shows the importance of considering the dynamically changing physical environment while studying bacterial motility in complex environments.Comment: 14 pages of main text, 5 figures, 4 pages of SI adde

Automatic Text Summarization for Hindi Using Real Coded Genetic Algorithm

In the present scenario, Automatic Text Summarization (ATS) is in great demand to address the ever-growing volume of text data available online to discover relevant information faster. In this research, the ATS methodology is proposed for the Hindi language using Real Coded Genetic Algorithm (RCGA) over the health corpus, available in the Kaggle dataset. The methodology comprises five phases: preprocessing, feature extraction, processing, sentence ranking, and summary generation. Rigorous experimentation on varied feature sets is performed where distinguishing features, namely- sentence similarity and named entity features are combined with others for computing the evaluation metrics. The top 14 feature combinations are evaluated through Recall-Oriented Understudy for Gisting Evaluation (ROUGE) measure. RCGA computes appropriate feature weights through strings of features, chromosomes selection, and reproduction operators: Simulating Binary Crossover and Polynomial Mutation. To extract the highest scored sentences as the corpus summary, different compression rates are tested. In comparison with existing summarization tools, the ATS extractive method gives a summary reduction of 65%

Study on low birth weight and its associated factors among newborns delivered in a tertiary care hospital of Banda district, Uttar Pradesh

Background: Low birth weight is associated with higher morbidity and mortality including impaired growth and development, malnutrition etc. Worldwide, one- fifth of newborns delivered are low birth weight. Aims & objectives: To estimate frequency of low birth weight & its associated factors among newborns delivered in a tertiary care hospital. Materials & Methods: A cross sectional analysis of 290 newborns delivered in tertiary care hospital, Banda, Uttar Pradesh during period of 15th February 2021 to 31st December 2021 was done for estimating low birth weight frequency & its associated factors including child characteristics, mother characteristics & father characteristics using data from medical record section of hospital. Frequency, percentages, mean, standard deviation, chi square test & independent t- test was used. P value <0.05 was considered significant. Results: 91 out of 290 newborns delivered were low birth weight (27.9%, 95% CI- 23.1%- 33.4%). The following factors were shown to have a significant association with low birth weight: education of mother (p=0.04), education of father (p=0.03), occupation of father (p=0.02), and duration of pregnancy (p<0.001). Conclusion: This study demonstrated that education of mother, education of father, occupation of father, and duration of pregnancy had significant association with low birth weight frequency that suggests that improving literacy status can help in decreasing burden of low birth weight apart from other factors