A CONCEPTUAL REVIEW ON TAILA MURCHANA

Taila kalpana has widely used dosage form described in Ayurvedic formulations for both external and internal use. Taila murchana is the first step of Taila preparation. Murchana has been adopted for enhancing the potency of oil and to remove the bad odour and Amadosa. In classics Tailas like Tilataila (sesame), Eranda (Castor) taila, Katu (Mustard) Taila needs Murchanaas preliminary process of Taila paaka. It has been mentioned in detail for the first time in Bhaishajya Ratnavali. Fat/Water soluble active principles of drugs are extracted into medicated oil in this method. Taila with Murchana are having more therapeutic potency and shelf life than crude Taila. The process of Murchana has high significance in today's scenario of globalization and the urge for better therapeutic efficacy. Hence the process of Murchana must be strictly included as the prerequisites of any medicated oil preparations. In this article an attempt has been done to review concepts regarding Taila murchana in literature of Ayurvedic pharmaceutics

Climate based coconut yield model for Arsikere taluk of Hassan district in Karnataka

Coconut has a prolonged reproductive phase of 44 months from the initiation of the inflorescence primordium to full maturity of the nuts. Weather affects all stages of the long development cycle, and thus there is likely to be extended predictability based on climate variability. Arasikere taluk of Hassan district, which has a major share of coconut area of Karnataka state, is frequently experiencing deficit rainfall coupled with a decline in groundwater level. Hence, an attempt was made to relate the coconut sample survey data of Coconut Development Board with climate data of Arsikere taluk of Hassan district. Mean nut yield per palm, per year in the Arasikere taluk was 49.2. Among the villages, Gijihalli recorded significantly lower nut yield (42.2) followed by Jajur (48.8) and Aggunda (55.3). Mean maximum, and minimum temperature during 2010-2017 was 32.40C and 19.50C respectively, with an average annual rainfall of 840 mm. Annual rainfall during 2011, 2012 and 2016 was below normal compared to other years. Correlation of monthly nut yield per palm with rainfall showed a significant positive correlation with the previous three to four years rainfall. Long dry spell during primordial initiations to nut maturity during consecutive two years 2011 and 2012 has resulted in significantly low nut yield during 2014. Rainfall during 2013 and 2014 was comparatively better, resulting in significantly higher nut yield during 2016 compared to 2014. Among the different stages, the primordium initiation stage and the ovary development stages were more strongly and significantly influenced by the weather parameters during all the years. Rainfall during button development stage followed by Tmax and rainfall during the spadix emergence stage showed a significant contribution to the weather-based regression model. Future climate of Arasikere showed an increase in annual rainfall mainly during September and October but declined during November/December period. Maximum and minimum temperature showed an increase by 1-1.50C which may increase the evaporative demand and dry spell duration resulting in moisture stress thus highlighting the importance of rainwater harvesting to take advantage of increased rainfall under future climatic condition. The future climate scenario may also favour the attack of pests like eriophyid mite

RLIS: resource limited improved security beyond fifth generation networks using deep learning algorithms.

This study explores the feasibility of allocating finite resources beyond fifth generation networks for extended reality applications through the implementation of enhanced security measures via offloading analysis (RLIS). The quantification of resources is facilitated through the utilization of parameters, namely energy, capacity, and power, which are equipped with proximity constraints. These constraints are then integrated with activation functions in both multilayer perceptron and long short term memory models. Furthermore, the system model has been developed using vision-based computing, which involves managing data queues in terms of waiting periods to minimize congestion for data transmission with limited resources. The major significance of the proposed method is to utilize allocated spectrums for future generation networks by allocating necessary resources and therefore high usage of resources by all users can be avoided. In addition the advantage of the proposed method is secure the networks that operate beyond 5G where more number of users will try to share the allocated resources that needs to be provided with high security conditions

Design, development, calibration, and testing of indigenously developed strain gauge based dynamometer for cutting force measurement in the milling process

In this work, a milling dynamometer based on strain gauge with an octagonal and square ring was designed and tested. Strain gauges were attached with the mechanical rings to detect the deformation, during the machining process. Wheatstone bridge circuit was equipped with gauges to acquire the strain as voltage owing to the deformation of mechanical rings when machining takes place. The finite element analysis (FEA) was used to identify the location of maximum deformation and stress. The direction of rings and location of gauges were decided to increase the sensitivity and decrease the cross-sensitivity. Then, the cutting force was acquired through NI 6221 M series data acquisition (DAQ) card. The dynamometer had undergone a cycle of tests to verify its static and dynamic characteristics. The metrological characterization was performed according to the calibration procedure based on ISO 376 – 2011 standard. The cutting force was measured with both the dynamometers through milling experiments based on Taguchi’s L9 orthogonal array and the results were recorded. The measured cutting force varied from 300 N to 550 N. The obtained results depicted that low-cost milling dynamometer was reliable to measure the three component machining force. Overall, the square ring based dynamometer provides the better static and dynamic characteristics in terms of linearity, cross-sensitivity (4%), uncertainty (0.054%), and natural frequency (362.41 rev/s)

A multimodel-based screening framework for C-19 using deep learning-inspired data fusion.

In recent times, there has been a notable rise in the utilization of Internet of Medical Things (IoMT) frameworks particularly those based on edge computing, to enhance remote monitoring in healthcare applications. Most existing models in this field have been developed temperature screening methods using RCNN, face temperature encoder (FTE), and a combination of data from wearable sensors for predicting respiratory rate (RR) and monitoring blood pressure. These methods aim to facilitate remote screening and monitoring of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and COVID-19. However, these models require inadequate computing resources and are not suitable for lightweight environments. We propose a multimodal screening framework that leverages deep learning-inspired data fusion models to enhance screening results. A Variation Encoder (VEN) design proposes to measure skin temperature using Regions of Interest (RoI) identified by YoLo. Subsequently, the multi-data fusion model integrates electronic records features with data from wearable human sensors. To optimize computational efficiency, a data reduction mechanism is added to eliminate unnecessary features. Furthermore, we employ a contingent probability method to estimate distinct feature weights for each cluster, deepening our understanding of variations in thermal and sensory data to assess the prediction of abnormal COVID-19 instances. Simulation results using our lab dataset demonstrate a precision of 95.2%, surpassing state-of-the-art models due to the thoughtful design of the multimodal data-based feature fusion model, weight prediction factor, and feature selection model

The Race Between Stars and Quasars in Reionizing Cosmic Hydrogen

The cosmological background of ionizing radiation has been dominated by quasars once the Universe aged by ~2 billion years. At earlier times (redshifts z>3), the observed abundance of bright quasars declined sharply, implying that cosmic hydrogen was reionized by stars instead. Here, we explain the physical origin of the transition between the dominance of stars and quasars as a generic feature of structure formation in the concordance LCDM cosmology. At early times, the fraction of baryons in galaxies grows faster than the maximum (Eddington-limited) growth rate possible for quasars. As a result, quasars were not able to catch up with the rapid early growth of stellar mass in their host galaxies.Comment: 5 pages, 1 figure, Accepted for publication in JCA

Observing Supermassive Black Holes across cosmic time: from phenomenology to physics

In the last decade, a combination of high sensitivity, high spatial resolution observations and of coordinated multi-wavelength surveys has revolutionized our view of extra-galactic black hole (BH) astrophysics. We now know that supermassive black holes reside in the nuclei of almost every galaxy, grow over cosmological times by accreting matter, interact and merge with each other, and in the process liberate enormous amounts of energy that influence dramatically the evolution of the surrounding gas and stars, providing a powerful self-regulatory mechanism for galaxy formation. The different energetic phenomena associated to growing black holes and Active Galactic Nuclei (AGN), their cosmological evolution and the observational techniques used to unveil them, are the subject of this chapter. In particular, I will focus my attention on the connection between the theory of high-energy astrophysical processes giving rise to the observed emission in AGN, the observable imprints they leave at different wavelengths, and the methods used to uncover them in a statistically robust way. I will show how such a combined effort of theorists and observers have led us to unveil most of the SMBH growth over a large fraction of the age of the Universe, but that nagging uncertainties remain, preventing us from fully understating the exact role of black holes in the complex process of galaxy and large-scale structure formation, assembly and evolution.Comment: 46 pages, 21 figures. This review article appears as a chapter in the book: "Astrophysical Black Holes", Haardt, F., Gorini, V., Moschella, U and Treves A. (Eds), 2015, Springer International Publishing AG, Cha

Quantum magnetism in two dimensions: From semi-classical N\'eel order to magnetic disorder

This is a review of ground-state features of the s=1/2 Heisenberg antiferromagnet on two-dimensional lattices. A central issue is the interplay of lattice topology (e.g. coordination number, non-equivalent nearest-neighbor bonds, geometric frustration) and quantum fluctuations and their impact on possible long-range order. This article presents a unified summary of all 11 two-dimensional uniform Archimedean lattices which include e.g. the square, triangular and kagome lattice. We find that the ground state of the spin-1/2 Heisenberg antiferromagnet is likely to be semi-classically ordered in most cases. However, the interplay of geometric frustration and quantum fluctuations gives rise to a quantum paramagnetic ground state without semi-classical long-range order on two lattices which are precisely those among the 11 uniform Archimedean lattices with a highly degenerate ground state in the classical limit. The first one is the famous kagome lattice where many low-lying singlet excitations are known to arise in the spin gap. The second lattice is called star lattice and has a clear gap to all excitations. Modification of certain bonds leads to quantum phase transitions which are also discussed briefly. Furthermore, we discuss the magnetization process of the Heisenberg antiferromagnet on the 11 Archimedean lattices, focusing on anomalies like plateaus and a magnetization jump just below the saturation field. As an illustration we discuss the two-dimensional Shastry-Sutherland model which is used to describe SrCu2(BO3)2.Comment: This is now the complete 72-page preprint version of the 2004 review article. This version corrects two further typographic errors (three total with respect to the published version), see page 2 for detail

Interleukin-6 Receptor Antagonists in Critically Ill Patients with Covid-19.

BACKGROUND: The efficacy of interleukin-6 receptor antagonists in critically ill patients with coronavirus disease 2019 (Covid-19) is unclear. METHODS: We evaluated tocilizumab and sarilumab in an ongoing international, multifactorial, adaptive platform trial. Adult patients with Covid-19, within 24 hours after starting organ support in the intensive care unit (ICU), were randomly assigned to receive tocilizumab (8 mg per kilogram of body weight), sarilumab (400 mg), or standard care (control). The primary outcome was respiratory and cardiovascular organ support-free days, on an ordinal scale combining in-hospital death (assigned a value of -1) and days free of organ support to day 21. The trial uses a Bayesian statistical model with predefined criteria for superiority, efficacy, equivalence, or futility. An odds ratio greater than 1 represented improved survival, more organ support-free days, or both. RESULTS: Both tocilizumab and sarilumab met the predefined criteria for efficacy. At that time, 353 patients had been assigned to tocilizumab, 48 to sarilumab, and 402 to control. The median number of organ support-free days was 10 (interquartile range, -1 to 16) in the tocilizumab group, 11 (interquartile range, 0 to 16) in the sarilumab group, and 0 (interquartile range, -1 to 15) in the control group. The median adjusted cumulative odds ratios were 1.64 (95% credible interval, 1.25 to 2.14) for tocilizumab and 1.76 (95% credible interval, 1.17 to 2.91) for sarilumab as compared with control, yielding posterior probabilities of superiority to control of more than 99.9% and of 99.5%, respectively. An analysis of 90-day survival showed improved survival in the pooled interleukin-6 receptor antagonist groups, yielding a hazard ratio for the comparison with the control group of 1.61 (95% credible interval, 1.25 to 2.08) and a posterior probability of superiority of more than 99.9%. All secondary analyses supported efficacy of these interleukin-6 receptor antagonists. CONCLUSIONS: In critically ill patients with Covid-19 receiving organ support in ICUs, treatment with the interleukin-6 receptor antagonists tocilizumab and sarilumab improved outcomes, including survival. (REMAP-CAP ClinicalTrials.gov number, NCT02735707.)