Development of an Energy Efficient Stern Flap for Improved EEDI of a Typical High speed Displacement Vessel

The surge in maritime trade is leading to large scale deployment of high-speed displacement ships by all nations. Cargo vessels are designed for a voyage in pre-determined routes at consistent speeds. On the other hand, high-speed displacement vessel engines designed with a capability to cater for top speeds are under-utilised during their normal course of operation. This sub-optimal utilisation impacts efficiency and increases emissions. In this study, a most favourable stern flap is designed for reducing the energy efficiency design index of a typical high-speed displacement vessel with a slender hull. CFD simulations and experimental model testing were conducted for 12 different stern flap configurations for determining most favourable flap design in the Froude no of 0.17-0.48. Performance of the most favourable stern flap was established by calculating, energy efficiency design index (EEDI) and fuel consumption based on typical operating profile. NOx, VOC and PM emissions were estimated in with and without flap condition. Studies demonstrated that the stern flap reduced effective power demand, average fuel consumption and emissions by about 8 per cent, which when considered for the ship’s operating life cycle, are significant. The most favourable stern flap reduced EEDI by 3.74 units and 1.98 units as compared to the bare hull condition and the required EEDI respectively, thereby demonstrating that EEDI could be used as an index to indicate stern flap efficiency

QUANTITATION OF METFORMIN IN URINE BY RP-HPLC METHOD AND ITS APPLICATION IN PHARMACOKINETICS

Objective: To develop and validate an easy and sensitive HPLC method for quantitation of metformin in urine. Methods: The technique involved deproteinisation of urine sample with methanol and analysis of the supernatant the usage of Zorbax 300–SCX, 4.6 X 150 mm ID, 5 µm particle size and UV detection at a wavelength of 233 nm. Results: The assay was specific for metformin and linear from 1.25 to 50.0μg/ml. The relative standard deviation of intra-and inter-day assays was lesser than 7%. The recovery of metformin from urine ranged from 97-103%. Conclusion: An easy and sensitive HPLC approach for quantitation of metformin in urine had been developed. Due to its simplicity in sample preparation and instrumentation, this technique can be used for pharmacokinetic studies of metformin in urine samples

Exploration of Anti-HIV Phytocompounds against SARS-CoV-2 Main Protease: Structure-Based Screening, Molecular Simulation, ADME Analysis and Conceptual DFT Studies

The ever-expanding pandemic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has gained attention as COVID-19 and caused an emergency in public health to an unmatched level to date. However, the treatments used are the only options; currently, no effective and licensed medications are available to combat disease transmission, necessitating further research. In the present study, an in silico-based virtual screening of anti-HIV bioactive compounds from medicinal plants was carried out through molecular docking against the main protease (Mpro) (PDB: 6LU7) of SARS-CoV-2, which is a key enzyme responsible for virus replication. A total of 16 anti-HIV compounds were found to have a binding affinity greater than −8.9 kcal/mol out of 150 compounds screened. Pseudohypericin had a high affinity with the energy of −10.2 kcal/mol, demonstrating amino acid residual interactions with LEU141, GLU166, ARG188, and GLN192, followed by Hypericin (−10.1 kcal/mol). Moreover, the ADME (Absorption, Distribution, Metabolism and Excretion) analysis of Pseudohypericin and Hypericin recorded a low bioavailability (BA) score of 0.17 and violated Lipinski’s rule of drug-likeness. The docking and molecular simulations indicated that the quinone compound, Pseudohypericin, could be tested in vitro and in vivo as potent molecules against COVID-19 disease prior to clinical trials.This was also supported by the theoretical and computational studies conducted. The global and local descriptors, which are the underpinnings of Conceptual Density FunctionalTheory (CDFT) have beenpredicted through successful model chemistry, hoping that they could be of help in the comprehension of the chemical reactivity properties of the molecular systems considered in this study.Fil: Murali, Mahadevamurthy. University Of Mysore; IndiaFil: Gowtham, Hittanahallikoppal Gajendramurthy. Nrupathunga University; IndiaFil: Shilpa, Natarajamurthy. University Of Mysore; IndiaFil: Krishnappa, Hemanth Kumar Naguvanahalli. University Of Mysore; IndiaFil: Ledesma, Ana Estela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet Noa Sur. Centro de Investigación en Biofísica Aplicada y Alimentos. - Universidad Nacional de Santiago del Estero. Centro de Investigación en Biofísica Aplicada y Alimentos; ArgentinaFil: Jain, Anisha S.. University Of Mysore; IndiaFil: Shati, Ali A.. King Khalid University; Arabia SauditaFil: Alfaifi, Mohammad Y.. Vacsera Holding Company; EgiptoFil: Elbehairi, Serag Eldin I.. Jss Academy Of Higher Education And Research; IndiaFil: Achar, Raghu Ram. Pirogov Russian National Research Medical University; RusiaFil: Silina, Ekaterina. Universitat de Les Illesbalears; EspañaFil: Stupin, Victor. Centro de Investigaciónen Materiales Avanzados; MéxicoFil: Ortega Castro, Joaquín. Jss Academy Of Higher Education And Research; IndiaFil: Frau, Juan. Universitat de Les Illesbalears; EspañaFil: Flores Holguín, Norma. Centro de Investigaciónen Materiales Avanzados; MéxicoFil: Amruthesh, Kestur Nagaraj. University Of Mysore; IndiaFil: Shivamallu, Chandan. Jss Academy Of Higher Education And Research; IndiaFil: Kollur, Shiva Prasad. University Of Mysore; IndiaFil: Glossman Mitnik, Daniel. Centro de Investigaciónen Materiales Avanzados; Méxic

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios

White matter changes in microstructure associated with a maladaptive response to stress in rats

In today's society, every individual is subjected to stressful stimuli with different intensities and duration. This exposure can be a key trigger in several mental illnesses greatly affecting one's quality of life. Yet not all subjects respond equally to the same stimulus and some are able to better adapt to them delaying the onset of its negative consequences. The neural specificities of this adaptation can be essential to understand the true dynamics of stress as well as to design new approaches to reduce its consequences. In the current work, we employed ex vivo high field diffusion magnetic resonance imaging (MRI) to uncover the differences in white matter properties in the entire brain between Fisher 344 (F344) and Sprague-Dawley (SD) rats, known to present different responses to stress, and to examine the effects of a 2-week repeated inescapable stress paradigm. We applied a tract-based spatial statistics (TBSS) analysis approach to a total of 25 animals. After exposure to stress, SD rats were found to have lower values of corticosterone when compared with F344 rats. Overall, stress was found to lead to an overall increase in fractional anisotropy (FA), on top of a reduction in mean and radial diffusivity (MD and RD) in several white matter bundles of the brain. No effect of strain on the white matter diffusion properties was observed. The strain-by-stress interaction revealed an effect on SD rats in MD, RD and axial diffusivity (AD), with lower diffusion metric levels on stressed animals. These effects were localized on the left side of the brain on the external capsule, corpus callosum, deep cerebral white matter, anterior commissure, endopiriform nucleus, dorsal hippocampus and amygdala fibers. The results possibly reveal an adaptation of the SD strain to the stressful stimuli through synaptic and structural plasticity processes, possibly reflecting learning processes.We thank Neurospin (high field MRI center CEA Saclay) for providing its support for MRI acquisition. JB was supported by grants from Fondation pour la Recherche Médicale (FRM) and Groupe Pasteur Mutualité (GPM). This work was supported by a grant from ANR (SIGMA). This work was performed on a platform of France Life Imaging (FLI) network partly funded by the grant ANR-11-INBS-0006. This work and RM were supported by a fellowship of the project FCT-ANR/NEU-OSD/0258/2012 founded by FCT/MEC (www.fct.pt) and by Fundo Europeu de Desenvolvimento Regional (FEDER). AC was supported by a grant from the Fondation NRJ.info:eu-repo/semantics/publishedVersio

Impacts of biomedical hashtag-based Twitter campaign: #DHPSP utilization for promotion of open innovation in digital health, patient safety, and personalized medicine

The open innovation hub Digital Health and Patient Safety Platform (DHPSP) was recently established with the purpose to invigorate collaborative scientific research and the development of new digital products and personalized solutions aiming to improve human health and patient safety. In this study, we evaluated the effectiveness of a Twitter-based campaign centered on using the hashtag #DHPSP to promote the visibility of the DHPSP initiative. Thus, tweets containing #DHPSP were monitored for five weeks for the period 20.10.2020–24.11.2020 and were analyzed with Symplur Signals (social media analytics tool). In the study period, a total of 11,005 tweets containing #DHPSP were posted by 3020 Twitter users, generating 151,984,378 impressions. Analysis of the healthcare stakeholder-identity of the Twitter users who used #DHPSP revealed that the most of participating user accounts belonged to individuals or doctors, with the top three user locations being the United States (501 users), the United Kingdom (155 users), and India (121 users). Analysis of co-occurring hashtags and the full text of the posted tweets further revealed that the major themes of attention in the #DHPSP Twitter-community were related to the coronavirus disease 2019 (COVID-19), medicine and health, digital health technologies, and science communication in general. Overall, these results indicate that the #DHPSP initiative achieved high visibility and engaged a large body of Twitter users interested in the DHPSP focus area. Moreover, the conducted campaign resulted in an increase of DHPSP member enrollments and website visitors, and new scientific collaborations were formed. Thus, Twitter campaigns centered on a dedicated hashtag prove to be a highly efficient tool for visibility-promotion, which could be successfully utilized by healthcare-related open innovation platforms or initiatives

Randomized Clinical Trial of High-Dose Rifampicin With or Without Levofloxacin Versus Standard of Care for Pediatric Tuberculous Meningitis: The TBM-KIDS Trial

Background. Pediatric tuberculous meningitis (TBM) commonly causes death or disability. In adults, high-dose rifampicin may reduce mortality. The role of fluoroquinolones remains unclear. There have been no antimicrobial treatment trials for pediatric TBM. Methods. TBM-KIDS was a phase 2 open-label randomized trial among children with TBM in India and Malawi. Participants received isoniazid and pyrazinamide plus: (i) high-dose rifampicin (30 mg/kg) and ethambutol (R30HZE, arm 1); (ii) high-dose rifampicin and levofloxacin (R30HZL, arm 2); or (iii) standard-dose rifampicin and ethambutol (R15HZE, arm 3) for 8 weeks, followed by 10 months of standard treatment. Functional and neurocognitive outcomes were measured longitudinally using Modified Rankin Scale (MRS) and Mullen Scales of Early Learning (MSEL). Results. Of 2487 children prescreened, 79 were screened and 37 enrolled. Median age was 72 months; 49%, 43%, and 8% had stage I, II, and III disease, respectively. Grade 3 or higher adverse events occurred in 58%, 55%, and 36% of children in arms 1, 2, and 3, with 1 death (arm 1) and 6 early treatment discontinuations (4 in arm 1, 1 each in arms 2 and 3). By week 8, all children recovered to MRS score of 0 or 1. Average MSEL scores were significantly better in arm 1 than arm 3 in fine motor, receptive language, and expressive language domains (P < .01). Conclusions. In a pediatric TBM trial, functional outcomes were excellent overall. The trend toward higher frequency of adverse events but better neurocognitive outcomes in children receiving high-dose rifampicin requires confirmation in a larger trial. Clinical Trials Registration. NCT02958709

Statistical features learning to predict the crop yield in re-gional areas

The plethora of information presented in the form of benchmark dataset plays a significant role in analyzing and understanding the crop yield in certain regions of regional territory. The information may be presented in the form of attributes makes a prediction of crop yield in various regions of Machine Learning. The information considered for processing involves data cleaning initially followed by binning to reduce the missing data. The information collected is subjected to clustering of data items based on patterns of similarity, The data items that are similar in nature is fed to the system with similarity measure, which involves understanding the distance of data items from its related data item leading to hyper parameters for analyzing of information while calculating the crop yield. The information may be used to ascertain the patterns of data that exhibit simi-larity with nearest neighbor represented by another attribute. Thus, the research method has yielded an accuracy of 89.62% of classification for predicting the crop yield in agricultural areas of Karnataka region