Endocrinal assessment of chronic obstructive pulmonary disease patients as compared to control groups

Background: Hormones also take part in respiratory control via peripheral chemo receptors or by their local effects on the lungs and the airways. In chronic obstructive pulmonary disease patients, respiratory muscles are required to work efficiently than normal individuals to establish a sufficient respiration. Changes in serum hormone levels of COPD patients adversely affect functioning of respiratory muscles. Objective of the study was to assess endocrinal profile in COPD patient with comparable control groups.Methods: A Hospital based Case control study conducted at Department of Pulmonary Medicine, Late B.R.K.M Government Medical College, Jagdalpur, Chhattisgarh, India during July 2016 to January 2017. Study included 75 diagnosed cases of COPD in which moderate, severe, very severe COPD was 25 in each of this group (per GOLD ‘s guideline) and compared to age matched 25 healthy control.Results: In this study serum growth hormone and serum testosterone showed significant difference between COPD cases and control group and fair significant difference in serum FSH between COPD cases and control groups. There was no significant correlation between serum growth hormone, serum testosterone and serum FSH with COPD grading. There was no statistically difference observed in serum LH (p=0.425) level between COPD cases and control groups. Present study showed there was statistically difference in FT3, FT4 and TSH level between COPD cases and control groups. There was significant negative correlation between FT4 levels between COPD grading. But no correlation seen between COPD grading and control with respect to serum FT3 and TSH level.Conclusions: Endocrinal assessment in present study showed significant decrease in serum growth hormone and serum testosterone in COPD patients, which are anabolic hormones. Early detection and correction of such an anabolic hormonal abnormality may prevent skeletal and diaphragmatic muscle weakness, and improve respiratory drive of COPD patients

Retrieval of sea surface velocities using sequential Ocean Colour Monitor (OCM) data

The Indian remote sensing satellite, IRS-P4 (Oceansat-I) launched on May 26th, 1999 carried two sensors on board, i.e., the Ocean Colour Monitor (OCM) and the Multi-frequency Scanning Microwave Radiometer (MSMR) dedicated for oceanographic research. Sequential data of IRS-P4 OCM has been analysed over parts of both east and west coast of India and a methodology to retrieve sea surface current velocities has been applied. The method is based on matching suspended sediment dispersion patterns, in sequential two time lapsed images. The pattern matching is performed on a pair of atmospherically corrected and geo-referenced sequential images by Maximum Cross-Correlation (MCC) technique. The MCC technique involves computing matrices of cross-correlation coefficients and identifying correlation peaks. The movement of the pattern can be calculated knowing the displacement of windows required to match patterns in successive images. The technique provides actual flow during a specified period by integrating both tidal and wind influences. The current velocities retrieved were compared with synchronous data collected along the east coast during the GSI cruise ST-133 of R.V. Samudra Kaustubh in January 2000. The current data were measured using the ocean current meter supplied by the Environmental Measurement and CONtrol (EMCON), Kochi available with the Geological Survey of India, Marine Wing. This current meter can measure direction and magnitude with an accuracy of ±5‡ and 2% respectively. The measurement accuracies with coefficient of determination (R2 ) of 0.99, for both magnitude (cm.s-1) and direction (deg.) were achieved

Dark Web Data Classification Using Neural Network

There are several issues associated with Dark Web Structural Patterns mining (including many redundant and irrelevant information), which increases the numerous types of cybercrime like illegal trade, forums, terrorist activity, and illegal online shopping. Understanding online criminal behavior is challenging because the data is available in a vast amount. To require an approach for learning the criminal behavior to check the recent request for improving the labeled data as a user profiling, Dark Web Structural Patterns mining in the case of multidimensional data sets gives uncertain results. Uncertain classification results cause a problem of not being able to predict user behavior. Since data of multidimensional nature has feature mixes, it has an adverse influence on classification. The data associated with Dark Web inundation has restricted us from giving the appropriate solution according to the need. In the research design, a Fusion NN (Neural network)-S3VM for Criminal Network activity prediction model is proposed based on the neural network; NN- S3VM can improve the prediction

On Socially Optimal Traffic Flow in the Presence of Random Users

Traffic assignment is an integral part of urban city planning. Roads and freeways are constructed to cater to the expected demands of the commuters between different origin-destination pairs with the overall objective of minimising the travel cost. As compared to static traffic assignment problems where the traffic network is fixed over time, a dynamic traffic network is more realistic where the network's cost parameters change over time due to the presence of random congestion. In this paper, we consider a stochastic version of the traffic assignment problem where the central planner is interested in finding an optimal social flow in the presence of random users. These users are random and cannot be controlled by any central directives. We propose a Frank-Wolfe algorithm based stochastic algorithm to determine the socially optimal flow for the stochastic setting in an online manner. Further, simulation results corroborate the efficacy of the proposed algorithm

Smart scalable ML-blockchain framework for large-scale clinical information sharing

Large-scale clinical information sharing (CIS) provides significant advantages for medical treatments, including enhanced service standards and accelerated scheduling of health services. The current CIS suffers many challenges such as data privacy, data integrity, and data availability across multiple healthcare institutions. This study introduces an innovative blockchain-based electronic healthcare system that incorporates synchronous data backup and a highly encrypted data-sharing mechanism. Blockchain technology, which eliminates centralized organizations and reduces the number of fragmented patient files, could make it easier to use machine learning (ML) models for predictive diagnosis and analysis. In turn, it might lead to better medical care. The proposed model achieved an improved patient-centered CIS by personalizing the separation of information with an intelligent ”allowed list“ for clinician data access. This work introduces a hybrid ML-blockchain solution that combines traditional data storage and blockchain-based access. The experimental analysis evaluated the proposed model against the competing models in comparative and quantitative studies in large-scale CIS examples in terms of model viability, stability, protection, and robustness, with improved results

Application of IRS-P4 OCM data to study the impact of cyclone on coastal environment of Orissa

The present study emphasizes on the impact of cyclone on the coastal environment of Orissa, using the IRS-P4 (OCM) satellite data. The study includes the analysis of IRS-P4 (OCM) data to generate chlorophyll, Suspended Sediment Concentration (SSC) images for the coastal water and Normalized Difference Vegetation Index (NDVI) images for coastal vegetation in the pre and post-cyclonic stages. The effect on mangroves and change in distribution pattern of water constituents like chlorophyll and suspended sediments are brought out