Changing trends in the incidence of caesarean section after a district hospital became medical college teaching hospital at Karwar, a coastal city in Karnataka-an eleven years retrospective audit

Background: Caesarean section (C-section) delivery has well established risks and adverse consequences. Every health care sector should look into the standards of maternal services it is providing. This audit look into the standards we have been providing at KRIMS Karwar with a comparison to national standards.Methods: This is a retrospective audit conducted by obstetrics and anaesthesia departments at KRIMS Karwar, Karnataka. Objectives were to look into incidence of C-sections, kind of anaesthesia used and maternal mortality. In addition, to look into any change in the obstetric care/outcome after Karwar district general hospital upgraded to a medical college teaching hospital. WHO guidelines for obstetrics care were set as standards. Retrospective data for the period between January 2007 and December 2018 has been collected and analysed.Results: Total number of pregnant women admitted for labour was 13431 with an average of 1221 per year. Proportion of caesarean delivery per hundred labouring women was 16.8 % in 2007 with significant increase to reach 51% in 2012. This has decreased in subsequent years to reach 23.5 in 2018. 99.9% of the C-sections have been done under spinal anaesthesia. There was one death of the mother subjected for C-sections in 2009.Conclusions: Incidence of C-sections was alarmingly high. Implementation of the uniform protocol has significantly reduced the C-sections rate. This reduction could be attributed to the upgrading the Karwar district hospital to a medical college teaching hospital. Protocol based practice will be continued and measures to improve overall maternity services will be implemented as per the WHO guidelines

Socio-Economic Status and Utilization of PM-KISAN Financial Assistance: An Empirical Analysis of Davanagere District Beneficiaries in Karnataka

The population which has more farmers that economy usually depends on primary sector. In such circumstances, obviously there are many obstacles for the development of the country. Due to family issues and drawbacks of the land reforms the land fragmentation was raised and simultaneously that enhanced the amount of Small and Marginal Farmers (SMFs). Those farmers have lots of issues for their usual agronomic practices, to eradicate such problems the PMKISAN scheme was launched in December 1st 2018. The study intends to analyse the utilisation pattern of scheme benefit and to evaluate the socio-economic condition of the beneficiaries in the study area. The study used both descriptive and inferential statistics research methods to interpret the gathered information and found that most of the SMFs have been utilising the scheme financial benefit in productive way and still the scheme required to enhance the socio-economic condition of Schedule Caste and Schedule Tribe (SC/ST) beneficiaries rather than the others

Abdominal Epilepsy and Foreign Body in the Abdomen - Dilemma in Diagnosis of Abdominal Pain

There are many medical causes of abdominal pain; abdominal epilepsy is one of the rarer causes. It is a form of temporal lobe epilepsy presenting with abdominal aura. Temporal lobe epilepsy is often idiopathic, however it may be associated with mesial temporal lobe sclerosis, dysembryoplastic neuroepithelial tumors and other benign tumors, arterio-venous malformations, gliomas, neuronal migration defects or gliotic damage as a result of encephalitis. When associated with anatomical abnormality, abdominal epilepsy is difficult to control with medication alone. In such cases, appropriate neurosurgery can provide a cure or, at least, make this condition easier to treat with medication

Virtual Machine Migration Triggering using Application Workload Prediction

AbstractDynamic provisioning of physical resources to Virtual Machines (VMs) in virtualized environments can be achieved by (i) vertical scaling-adding/removing attached resources from existing virtual machine and (ii) horizontal scaling-adding a new virtual machine with additional resources. The live migration of virtual machines across different Physical Machines (PMs) is a vertical scaling technique which facilitates resource hot-spot mitigation, server consolidation, load balancing and system level maintenance. It takes significant amount of resources to iteratively copy memory pages. Hence during the migration there may be too much overload which can affect the performance of applications running on the VMs on the physical server. It is better to predict the future workload of applications running on physical server for early detection of overloads and trigger the migration at an appropriate point where sufficient number of resources are available for all the applications so that there will not be performance degradation. This paper presents an intelligent decision maker to trigger the migration by predicting the future workload and combining it with predicted performance parameters of migration process. Experimental results shows that migration is triggered at an appropriate point such that there are sufficient amount of resources available (15–20% more resources than high valued threshold method) and no application performance degradation exists as compared to properly chosen threshold method for triggering the migration. Prediction with support vector regression has got decent accuracy with MSE of 0.026. Also this system helps to improve resource utilization as compared to safer threshold value for triggering migration by removing unnecessary migrations

Cloud Computing Enabled Big Multi-Omics Data Analytics.

High-throughput experiments enable researchers to explore complex multifactorial diseases through large-scale analysis of omics data. Challenges for such high-dimensional data sets include storage, analyses, and sharing. Recent innovations in computational technologies and approaches, especially in cloud computing, offer a promising, low-cost, and highly flexible solution in the bioinformatics domain. Cloud computing is rapidly proving increasingly useful in molecular modeling, omics data analytics (eg, RNA sequencing, metabolomics, or proteomics data sets), and for the integration, analysis, and interpretation of phenotypic data. We review the adoption of advanced cloud-based and big data technologies for processing and analyzing omics data and provide insights into state-of-the-art cloud bioinformatics applications

A Dual Phase Approach for Addressing Class Imbalance in Land-Use and Land-Cover Mapping From Remotely Sensed Images

Semantic segmentation of remotely sensed images for land-use and land-cover classes plays a significant role in various ecosystem management applications. State-of-the-art results in assigning land-use and land-cover classes are primarily achieved using fully convolutional encoder-decoder architectures. However, the uneven distribution of the land-use and land-cover classes becomes a major hurdle leading to performance skewness towards majority classes over minority classes. This paper proposes a novel dual-phase training, with the first phase proposing a new undersampling technique using minority class focused class normalization and the second phase that uses this learnt knowledge for ensembling to prevent overfitting and compensate for the loss of information due to undersampling. The proposed method achieved an overall performance gain of up to 2% in MIoU, Kappa, and F1 Score metrics and up to 3% in class-wise F1-score when compared to the baseline models on Wuhan Dense Labeling, Vaihingen and Potsdam datasets