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We propose a novel approach to visualize and compare financial markets across the globe using chaos game representation (CGR) of iterated function systems (IFS). We modified a fractal method, widely used in life sciences, and applied it to study the effect of COVID-19 on global financial markets. This modified driven IFS approach is used to generate compact fractal portraits of the financial markets in form of percentage CGR (PC) plots and subtraction percentage (SP) plots. The markets over different periods are compared and the difference is quantified through a parameter called the proximity (Pr) index. The reaction of the financial market across the globe and volatility to the current pandemic of COVID-19 is studied and modeled successfully. The imminent bearish and a surprise bullish pattern of the financial markets across the world is revealed by this fractal method and provides a new tool to study financial markets.

Accurate representation of stratospheric trace gas transport is important for ozone modeling and climate projection. Intermodel spread can arise from differences in the representation of transport by the diabatic (overturning) circulation vs. comparatively faster adiabatic mixing by breaking waves, or through numerical errors, primarily diffusion. This study investigates the impact of these processes on transport using an idealised tracer, the age-of-air. Transport is assessed in two state-of-the-art dynamical cores based on fundamentally different numerical formulations: finite volume and spectral element. Integrating the models in free-running and nudged tropical wind configurations reveals the crucial impact of tropical dynamics on stratospheric transport. Using age-budget theory, vertical and horizontal gradients of age allow comparison of the roles of the diabatic circulation, adiabatic mixing, and the numerical diffusive flux. Their respective contribution is quantified by connecting the full 3-d model to the tropical leaky pipe framework of Neu and Plumb (1999). Transport by the two cores varies significantly in the free-running integrations, with the age in the middle stratosphere differing by about 2 years primarily due to differences in adiabatic mixing. When winds in the tropics are constrained, the difference in age drops to about 0.5 years; in this configuration, more than half the difference is due to the representation of the diabatic circulation. Numerical diffusion is very sensitive to the resolution of the core, but does not play a significant role in differences between the cores when they are run at comparable resolution. It is concluded that fundamental differences rooted in dynamical core formulation can account for a substantial fraction of transport bias between climate models.

Abstract: The oral route is the most preferred delivery route for drug administration due to its advantages such as lower cost, improved patient compliance, no need for trained personnel and the drug reactions are generally less severe. The major problem with new molecules in the drug discovery pipeline is poor solubility and dissolution rate that ultimately results in low oral bioavailability. Numerous techniques are available for solubility and bioavailability (BA) enhancement, but out of all, solid dispersion (SD) is proven to be the most feasible due to the least issues in manufacturing, processing, storage, and transportation. In the past few years, SD had been extensively applied to reinforce the common issues of insoluble drugs. Currently, many hydrophobic and hydrophilic polymers are used to prepare either immediate release or controlled release SDs. Therefore, the biological behavior of the SDs is contingent upon the use of appropriate polymeric carriers and methods of preparation. The exploration of novel carriers and methodologies in SD technology leads to improved BA and therapeutic effectiveness. Moreover, the clinical applicability of SD-based formulations has been increased with the discovery of novel polymeric carriers. In this review, emphasis is laid down on the present status of recent generations of SDs (i.e., surfactant and controlled release polymer-based SD) and their application in modifying the physical properties of the drug and modulation of pharmacological response in different ailments.

The atmospheric circulation response to global warming is important for accurate prediction of climate change on regional scales. For the midlatitudes, shifts in the extratropical jet streams have important consequences for precipitation, blocking, and extreme events. It has proven to be a challenge, however, to predict. For example, the North Atlantic jet stream plays a vital role in the climate of eastern North America and Europe; in the last intercomparison of state‐of‐the‐art climate models, the models did not even agree on the sign of its wintertime response to global warming. Perhaps this should not come as a surprise, as we also lack a comprehensive theory for the impact of warming on the midlatitude circulation. In a recent study, Tan et al. (2019, https://doi.org/10.1029/2018MS001492) constructed models of simpler atmospheres to explore the response of the midlatitude jet to global warming. Their idealized atmospheres highlight the difficulty of developing a comprehensive theory for the midlatitude circulation but also provide pathways to improve models of Earth's atmosphere. Models of simpler atmospheres allow one to isolate the impact of specific atmospheric processes and connect theoretical understanding with comprehensive climate prediction systems. Such models can also be used to explore very different atmospheric regimes, from Earth's past to distant planets., Key Points The atmospheric circulation response to global warming is both a challenge to predict and to understandModels of idealized atmospheres allow a process‐oriented investigation of the circulation responseA growing number of models of simpler atmospheres are being developed and shared

In the present work, a β-Ti alloy (Ti–15V–3Sn–3Cr–3Al) was unidirectionally cold rolled to 80% thickness reduction, followed by recrystallization at two temperatures: (i) 1013 K and (ii) 1053 K. The microstructural developments were studied using light optical microscopy, scanning electron microscopy X-ray peak profile analysis, and electron backscattered diffraction. The bulk texture of deformed and fully recrystallized samples was studied using X-ray diffraction. The deformed microstructures showed the presence of high fraction of shear bands, and these bands were preferentially formed in γ-fiber grains than in the grains with other orientations. Cold rolled β-Ti alloy samples were fully recrystallized in 10 min at 1053 K and in 90 min at 1013 K. Strong α- and γ-fibers were formed after 80% cold rolling, while strong discontinuous γ-fiber (with very strong {111}〈112〉 component) was formed after complete recrystallization. Oriented nucleation was found to be the dominant mechanism for the development of recrystallization texture.

Background: The impact of left ventricular ejection fraction (LVEF) changes after sudden cardiac arrest (SCA) on implantable defibrillator (ICD) utilization and long-term survival is not known. We therefore evaluated the influence of LVEF on these parameters in SCA survivors. Methods: Data were collected on consecutive SCA survivors who had ≥1 echocardiogram after SCA and who survived to hospital discharge (n = 655). The median time from baseline to first follow-up echocardiogram was 162 days. LVEF ≥50% was defined as normal. Patients were classified into 4 groups according to baseline (LVEFb) and follow-up (LVEFf) myocardial function: normal LVEFb and LVEFf (group1, n = 261); reduced LVEFb and normal LVEFf (group 2, n = 104); normal LVEFb but reduced LVEFf (group 3, n = 41); and reduced LVEFb and LVEFf (group 4, n = 249). All-cause mortality and time to ICD implantation were examined in all groups. Results: Over a median follow up of 4.3 years, death occurred in 279 (42%) of patients. Compared with patients in group 1, patients with any reduced LVEF at any time (groups 2–4) had significantly higher mortality, even after adjusting for unbalanced covariates (HR = 1.44, 95.0% CI 1.05–1.95, p = 0.022). ICDs were most commonly implanted in patients with persistently reduced LVEF (group 4: HR = 1.72, 95% CI = 1.26–2.35, p = 0.001). Conclusion: We demonstrate that, in survivors of SCA, a reduced LVEF at or after the index event is associated with higher mortality but that patients with persistently reduced LVEF were most likely to receive ICD therapy. These findings have implications on the management of SCA survivors. Keywords: Sudden cardiac arrest, Mortality, Myocardial function, Left ventricular ejection fraction, Implantable cardioveter-defibrillator

Pulmonary complications including pulmonary hypertension (PH) and interstitial lung disease (ILD) are leading causes of mortality in systemic sclerosis/scleroderma (SSc) [1]. PH associated with SSc (SSc-PH) has been attributed to World Health Organization (WHO) group 1 disease, pulmonary arterial hypertension (SSc-PAH); WHO group 2 due to left ventricular systolic or diastolic dysfunction (SSc-PVH); or WHO group 3 related to interstitial lung disease (SSc-PH-ILD) [2, 3]. SSc-PH is found in 10–12% of SSc patients [4]. Of the 71 out of 237 patients in the PHAROS (Pulmonary Hypertension Assessment and Recognition of Outcomes in Scleroderma) cohort who had PH, 69% had SSc-PAH, 10% had SSc-PVH and 21% had SSc-PH-ILD [1]. SSc-PAH has poor prognosis and less favourable response to pulmonary vasodilator therapy compared to idiopathic pulmonary arterial hypertension (IPAH) [5, 6]. SSc-PH-ILD is a strong predictor of death compared to SSc-PAH or ILD alone [7]., There is an unexpectedly high incidence of PVOD in patients with SSc-PH-ILD. Presence of PVOD may be an unrecognised contributor to the dismal prognosis of these patients. Early transplant referral should be considered for those with SSc-PH-ILD. http://ow.ly/vPvc30neJZV

An In-situ polymerization technique was used to create an epoxy nano-alumina composite adhesive. Ultrasonication was used to achieve the distribution of nano-alumina particles in an epoxy matrix containing weight percentage 0.5,1,1.5 and 2% nanoparticles. Single lap joints with adherents of Copper-Copper (CC), Mild Steel-Mild Steel (MM) and Copper-Mild Steel (CM) were prepared following ASTM D1002 standards. Lap shear strength tests in tension of bonded joint samples (CC, MM, and CM) were conducted on UTM at 1.27 mm/min cross head travel rate. The fatigue strength of bonded specimens was tested at a stress ratio of R 0.1 and a frequency 5HZ by following ASTM D3166 standards. Stress amplitudes of fatigue test were taken at 70%, 65%, 60% and 55% of shear strength of corresponding single lap joints. Six samples were tested to calculate the average number of cycles to failure. For each combination of joints shear strength and fatigue life were increased with an increase in wt.% but maximum increment was obtained at 1.5 wt.% of nanoparticles addition. An increment of 40% for CC joints, 66% for MM joints and 47% for CM joints was obtained in nanocomposite’s shear static strength containing 1.5 wt.% nanoparticles over neat epoxy joints. Increment in fatigue life of CC, MM, and CM joints at 1.5 wt.% of spherical nanoparticle inclusions was 401%, 500%, and 409% respectively.

The circulation of the stratosphere transports important trace gases, including ozone, and can be thought of as having a fast horizontal mixing component and a slow meridional overturning component...

COVID 19 has severely affected global financial markets and it has long lasting impact on Nifty 50. The increasing daily new and total COVID 19 cases lead to anxiety among investors and there was panic selling because of lockdown and after government incentives and plan to revive the market, Nifty 50 touched it’s all time high. We can clearly imagine the amount of volatility COVID 19 created in the market. VIX touched all time high of 66.04 from 13.68 points. This research paper aims to study the magnitude of impact of COVID 19 on Nifty 50 volatility using GARCH model. &nbsp

Most working professionals as well as school and college students require very less physical activity and they are mostly seated during most part of the day. Maintaining a good posture while sitting is a step towards avoiding several lifestyle diseases that arise due to decreased physical activity. In this work, posture detection by placing multiple accelerometers on the back of user is presented. Novel use of one-class classification for detecting poor posture is proposed. One-class support vector machine and isolation forest are employed for posture detection. The classifiers are provided unlabeled data containing mostly samples of good posture. During testing, the poor postures, which we be different postures other than that considered as the good posture, are predicted as outliers. The performance of the models is compared quantitatively. The performance of one-class classifiers is also compared with commonly used unsupervised learning algorithm, k-mean clustering. Results are presented for tuning of the considered models and to study the effect of sensor placement. The isolation forest provides the highest accuracy of 99.3% and F1-score of 0.99.

The stratospheric transport circulation, or Brewer-Dobson Circulation (BDC), is often conceptually seperated into advection along the residual circulation and two-way mixing. In particular the latter part has recently been found to exert a strong influence on inter-model differences of mean age of Air (AoA), a common measure of the BDC. However, the precise reason for model differences in two-way mixing remains unknown, as many modelcomponents in multi-model projects differ. One component that likely plays an important role is model resolution, both vertically and horizontally. To analyse this aspect, we carried out a set of simulations with identical and constant year 2000 climate forcing varying the spectral horizontalresolution (T31,T42,T63,T85) and the number of vertical levels (L31,L47,L90). We find that increasing the vertical resolution leads to an increase in mean AoA. Most of this change can be attributed to aging by mixing. The mixing efficiency, defined as the ratio of isentropic mixing strength and the diabatic circulation, shows the same dependency on vertical resolution. While horizontal resolution changes do not systematically change mean AoA, we dofind a systematic decrease in the mixing efficiency with increasing horizontal resolution. Non-systematic changes in the residual circulation partly compensate the mixing efficiency changes, leading to the non-systematic mean AoA changes. The mixing efficiency changes with vertical and horizontal resolution are consistent with expectations on the effects of numerical dispersion on mean AoA. To further investigate the most relevant regions of mixing differences, we analyse height-resolved mixing efficiency differences. Overall, this work will help to shed light on the underlying reasons for the large biases of climate models in simulating stratospheric transport.

Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C) and macrophage activation syndrome (MAS) have been speculated as three distinct phenotypes of hyperinflammation seen in children during coronavirus disease (COVID-19) pandemic. KD has been reported in association with dengue, scrub typhus and leptospirosis. COVID-19 and dengue coinfection has also been described. However, MIS-C with concomitant infection has rarely been reported. We report an adolescent girl with clinical and laboratory parameters of MIS-C resembling KD with positive serology for scrub typhus at presentation. Clinical manifestations resolved and laboratory parameters improved with IVIG, azithromycin and corticosteroids. However, she developed fever recurrence with thrombocytopenia, elevated inflammatory markers, hypofibrinogenemia and hypertriglyceridemia which were consistent with MAS. With the emergence of MIS-C and increase in the number of such cases being reported throughout world, physicians should be aware of different phenotypes of hyperinflammation associated with COVID-19 and the possibility of coexistence of MIS-C with other infections. LAY SUMMARY Clinical and laboratory parameters of multisystem inflammatory syndrome in children (MIS-C) mimic Kawasaki disease (KD). KD has been described in association with dengue, scrub typhus and leptospirosis. However, MIS-C with concomitant infection has rarely been reported in literature. A 14-year-old-girl presented with fever and rash with history of redness of eyes, lips and tongue. Investigations showed anemia, lymphopenia, thrombocytosis with elevated erythrocyte sedimentation rate, C-reactive protein, pro-brain natriuretic peptide, Interleukin-6, ferritin and d-dimer. Scrub typhus immunoglobulin M was positive. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) level was also elevated. A diagnosis of MIS-C with concomitant scrub typhus was proffered. Child received azithromycin, intravenous immunoglobulin and methylprednisolone. After an afebrile period of 2.5 days, child developed unremitting fever and rash. Repeat investigations showed anemia, worsening lymphopenia, thrombocytopenia, transaminitis, hypertriglyceridemia, hyperferritinemia and hypofibrinogenemia which were consistent with a diagnosis of macrophage activation syndrome (MAS). KD, MIS-C and MAS represent three distinct phenotypes of hyperinflammation seen in children during coronavirus disease pandemic. Several tropical infections may mimic or coexist with MIS-C which can be a diagnostic challenge for the treating physician. Identification of coexistence or differentiation between the two conditions is important in countries with high incidence of tropical infections to guide appropriate investigations and treatment.

Planetary waves and gravity waves are the key drivers of middle atmospheric circulation and variability. While planetary waves are well resolved in climate models, inaccuracies in representation of gravity waves in climate models persist. Inaccuracies in representation of gravity waves limit our understanding of the planetary wave-gravity wave interactions that can be crucial during the Antarctic polar vortex breakdown. Moreover, "missing" gravity wave drag around 60oS in the upper stratosphere is considered to be responsible for the "cold-pole" bias in comprehensive climate models that employ parameterizations to appproximately represent the gravity wave drag.We illustrate the strength of the high-resolution ERA-5 reanalysis in resolving a broad spectrum of gravity waves in southern hemisphere midlatitudes and to estimate their contribution to the momentum budget around 60oS. We find that most of the resolved mountain waves excited over the Andes and Antarctic peninsula propagate away from their source and deposit momentum around 60oS over the Southern Ocean. Further, a composite analysis around 60oS during the vortex breakdown period using ERA-5 reveals considerably large fractional contribution of resolved + parameterized GWD towards the vortex deceleration. Upto 30 days prior to the breakdown, a balance between the Coriolis acceleration and the planetary wave deceleration provides a weak net deceleration of the mean winds, following which, they provide a net acceleration of the mean winds. The gravity waves, however, provide a steady deceleration of the mean winds throughout the breakdown period. The resolved drag in ERA-5 accounts for as much as one-fourth of the zonal wind deceleration at 60oS and 10 hPa, while the parameterized drag in ERA-5 accounts for more than one-half of the zonal wind deceleration. The findings establish the crucial role of gravity waves in wintertime stratospheric circulation and opens avenues for further stratospheric gravity wave analysis using ERA-5.

The fundamental design issue faced by future 5G wireless communication is to solve the growing demand of faster reliable communication with limited spectrum and lower power. MIMO diversity addresses the challenge of multipath fading. The system efficiency reduces due to limited resources and the impairments of the wireless channels like noise and ISI that must be compensated. In this work, the performance characterization of the MIMO environment is presented. BER comparative analysis is done under Rayleigh fading channel using BPSK modulation in comparison with AWGN performance. The model is implemented using MATLAB and Python programming language. Characterization of Eb/No for optimal signal-to-noise ratio and hence optimize BER and compare BER performances for BPSK (Binary phase shift keying) in MIMO (Multiple input multiple output) diversity design for 5G infrastructure.

The concept of home automation has been making rapid advancements in technology and features ever since it was introduced to the world for the first time. Automation has been serving as a wide domain for research. In the present scenario, the world is shifting towards automation, for it gives better efficiency and more features. Same is with home automation. A smart home can be termed as an automated system, which enables the user to have access over all its devices, and provides a user-friendly interface as well. For connectivity amongst devices, keeping an all-time active internet connection might not be pocket friendly for all, however, if connectivity can be ensured amongst devices without the presence of internet or any external network, it will make the system a lot more secure and cheaper, making the devices accessible to the user (for a specified range) and providing a user-friendly interface, to empower the user to connect to the device in a friendly manner. Apart from that, it will also prove to be a blessing, as it provides simple topology connection which will indeed help in easy operation and diagnosis of faults (if any). After the implementation of such ideology, it can be concluded that besides being pocket friendly to user, it can also lead to the development of revolutionary changes in the concept of automation, for same efficiency and the majority of features are being provided to the user.

Electric supply utilities invest a lot of money every year to improve the quality of power supplied to their customers. With the increase in demand day by day, an oligopoly has been created in the market to maintain a certain level of reliability to attract the customers such that they show the willingness to pay more for a more reliable system. For enhancing the customer contentment and comfort, according to their insistence, improvement of service reliability and simultaneously minimizing the capital costs is an extensive issue for the optimization of the distribution system. The reliability of the distribution system may be improved by the optimal placement of protective devices and switches but the determination of the optimal location and number of switches is a major concern from the reliability and economic outlook. In this paper, manual as well as optimal placement of reclosers have been done in a 13-bus radial distribution system to maximize the net profit to the utility by improving the system reliability. For optimal placement of reclosers, genetic algorithm (GA) technique has been utilized. Reliability indices such as- SAIFI, SAIDI, CAIDI, ASAI, ASUI & AENS before and after the placement of reclosers have been compared. The results thus obtained show that maximum profit to the utility with minimum capital and outage costs can be obtained by the optimal placement of reclosers in a radial distribution system.

The problem of predicting links has gained much attention in recent years due to its vast application in various domains such as sociology, network analysis, information science, etc. Many methods have been proposed for link prediction such as RA, AA, CCLP, etc. These methods required hand-crafted structural features to calculate the similarity scores between a pair of nodes in a network. Some methods use local structural information while others use global information of a graph. These methods do not tell which properties are better than others. With an in-depth analysis of these methods, we understand that one way to overcome this problem is to consider network structure and node attribute information to capture the discriminative features for link prediction tasks. We proposed a deep learning Autoencoder based Link Prediction (ALP) architecture for the latent representation of a graph, unified with non-negative matrix factorization to automatically determine the underlying roles in a network, after that assigning a mixed-membership of these roles to each node in the network. The idea is to transfer these roles as a feature vector for the link prediction task in the network. Further, cosine similarity is applied after getting the required features to compute the pairwise similarity score between the nodes. We present the performance of the algorithm on the real-world datasets, where it gives the competitive result compared to other algorithms.

Meta-Learning, the ability of learning to learn, helps to train a model to learn very quickly on a variety of learning tasks; adapting to any new environment with a minimal number of examples allows us to speed up the performance and training of the model. It solves the traditional machine learning paradigm problem, where it needed a vast dataset to learn any task to train the model from scratch. Much work has already been done on meta-learning in various learning environments, including reinforcement learning, regression task, classification task with image, and other datasets, but it is yet to be explored with the time-series domain. In this work, we aimed to understand the effectiveness of meta-learning algorithms in time series classification task with multivariate time-series datasets. We present the algorithm’s performance on the time series archive, where the result shows that using meta-learning algorithms leads to faster convergence with fewer iteration over the non-meta-learning equivalent.

Cryptocurrencies are becoming a major moneymaker because of their high availability and abundance of easy investment platforms. In this paper, we have attempted to predict bitcoin value by taking into consideration various features that may affect its price. The amount of cryptocurrency in circulation, the volume of cryptocurrency exchanged in a day and the demand for cryptocurrency are a few of the factors that influence its cost. The forecasting is done using different time series analysis techniques like moving average, ARIMA and machine learning algorithms including SVM, linear regression, LSTM and GRU. Our goal is to compare all these models based on their observed accuracy. The dataset has been recorded daily over the course of three years.

Image and video-based content has become ever present in a variety of domains like news, entertainment and education. Users typically discover and engage with content via search and recommendation systems. It is also important to serve high quality data to users by filtering out irrelevant or harmful content. Thus, there is an increasing need to leverage the rich information in image and video content in order to power systems for search and recommendation. At the same time, the effectiveness and efficiency of these systems has been accelerated by the availability of large-scale labeled datasets and sophisticated deep learning-based models. This tutorial is aimed at providing an overview of image and video understanding, and their practical applications in the industry. We focus on deep learning-based state of the art techniques for image and video understanding. This includes tasks like image classification and segmentation, image-based content retrieval and video classification. We also focus on applications of these technologies to large-scale recommendation and low quality content detection systems. We present concrete examples from various LinkedIn production systems, and also discuss associated practical challenges.

There have been recent reports of children presenting with severe multi-system hyperinflammatory syndrome resembling Kawasaki disease (KD) during current COVID-19 pandemic. Exact pathophysiology is unknown, however, most of the children have multi-organ dysfunction and respiratory system involvement is less common compared to adults. These patients have certain characteristic laboratory parameters different from those seen in children with KD. However, only limited literature is available at present for identification and management of such patients. We report a young girl who presented with fever, rash and other manifestations mimicking classic KD and fulfilling the case definitions for pediatric multi-system inflammatory syndrome. She had lymphopenia, thrombocytopenia and hyponatremia in the absence of macrophage activation syndrome, similar to that seen in patients reported from UK and Italy. Clinical manifestations resolved and laboratory parameters improved with intravenous immunoglobulin and corticosteroids. Early recognition is important to administer immunomodulatory therapy which may be life saving for these patients.Several cases of a severe multi-system inflammatory syndrome have been reported in children during the current COVID-19 pandemic. Clinical manifestations may resemble Kawasaki disease (KD) which is the most common childhood vasculitis. A 7-year-old-girl presented with fever, rash and abdominal pain. Examination showed maculopapular rash over lower limbs, back, right ear, trunk and abdomen; erythema and swelling over bilateral upper eyelids; conjunctival injection; reddened lips and erythema over palms and soles. She had lymphopenia, thrombocytopenia, with elevated erythrocyte sedimentation rate, C-reactive protein (CRP), pro-brain natriuretic peptide and interleukin-6 (IL-6). Real-time polymerase chain reaction for severe acute respiratory syndrome coronavirus 2 was negative. Serology, however, could not be performed due to unavailability. Macrophage activation syndrome was ruled out with normal ferritin and triglyceride with raised fibrinogen level. Echocardiography showed normal coronary diameters. Child received meropenem, intravenous immunoglobulin, aspirin and methylprednisolone. She improved with resolution of fever, decrease in CRP, increase in platelet and lymphocyte count. Index child had features similar to those reported from UK and Italy: features of KD, abdominal pain, lymphopenia, thrombocytopenia, elevated IL-6 and myocardial dysfunction with no significant respiratory involvement. Pediatricians should be aware of such uncommon presentation in children to initiate early treatment with immunomodulatory therapy.

This paper deals with a novel approach to visualize and compare financial markets across the globe using chaos game representation of iterated function systems. We modified a widely used fractal method to study genome sequences and applied it to study the effect of COVID-19 on global financial markets. We investigate the financial market reaction and volatility to the current pandemic by comparing its behavior before and after the onset of COVID-19. Our method clearly demonstrates the imminent bearish and a surprise bullish pattern of the financial markets across the world.

We developed a compact and computationally inexpensive method for in-silico comparison of nucleotide sequences at a macro level using subtraction-percentage plots (SP-plots) of a modified chaos game representation (CGR). Analyzing these plots, we defined the k-mer proximity index quantifying the differences between SARS-CoV-2 and other pathogens’ genome sequences. We categorized 31 pathogens, on the basis of their proximity to SARS-CoV-2, in four groups to possibly plan a treatment strategy for Covid-19.

Accurate representation of tracer transport --- the movement of trace constituents by the atmospheric flow --- continues to be a challenge for climate models. Differences in the resolved circulation, biases due to physical parameterizations, and differences in the numerical representation of trace gases result in large variations in transport, even among state-of-the-art climate models. These differences result in disagreement among model projections of the evolution of stratospheric ozone throughout the 21st century particularly in the recovery of the Antarctic ozone hole. In addition to transport, the delicate momentum balance in the upper-troposphere and lower-stratosphere (UTLS) also presents a stiff challenge for model numerics, exposing the impacts of numerical dissipation, the resolution of waves, and the consequences of imperfect momentum conservation. Biases in this region impact the global circulation, e.g., influencing the extratropics jets and stratospheric polar vortices, and alter the transport and exchange of trace gases between and through the troposphere and stratosphere. In this study, we compare 2 modern dynamical cores (dycores) that employ very different numerics: the cubed sphere finite volume (CSFV) core from GFDL and the spectral element (SE) core from NCAR-CAM5. We force these dycores using identical Held-Suarez diabatic forcing in the troposphere and Polvani-Kushner diabatic forcing in the stratosphere, varying the horizontal and vertical resolution. We observe significant differences in circulation, between the two models at high vertical resolution in the lower and middle tropical stratosphere. While the finite volume core is relatively insensitive to any changes in vertical resolution, the PS and SE dycores resolve considerably different tropical stratospheric dynamics at high vertical resolution (80 levels). These models develop QBO-like westerly winds in the tropics and induce a secondary meridional circulation in the tropical stratosphere, which sets of transport between the models. Using the theoretical leaky pipe transport model we analyze and separate out the transport differences due to differences is diabatic circulation and isentropic mixing and infer that this secondary circulation strikingly modulates stratospheric tracer transport (age of air) by altering the tropical-extratropical mixing, and impacts the extratropical circulation through the subtropical jets. Implications for comprehensive atmospheric modeling are discussed.

Today we have enormous amount of data available in every sector, with the advent of technology available, it is possible to provide solutions to many problems. In this paper we are going to provide solutions to the problems related to healthcare data management using Machine Learning and Blockchain. Extracting only the relevant information from the data is possible with the use of Machine Learning. This is done using trained algorithms. Once this data is stored, the next problem is Data sharing and its reliability. This is where Blockchain comes into picture. The consensus in Blockchain technology makes sure that data is legitimate and transactions are secure. Blockchain technology can potentially change health care management for the better by placing patient at the epicentre of the healthcare system and increasing the privacy and interoperability of health data. This paper focuses primarily on solving healthcare data management problems by using Blockchain technology and including some indispensable features using Machine Learning.

Cryptography is a scientific art which deals with the methods for changing over messages, data and information in some dynamically and haphazardly planned language of characters which are mixed up and not understandable for people or notwithstanding for a machine. While doing this, it is guaranteed that authorized frameworks and humans can recuperate the original message by validating their authentication. There are different calculations and strategies are proposed for cryptography purposes. This paper focuses on examination of two individual cryptographic calculation RSA and Blowfish, how they work and what will be the impact on security and speed if these two are converged into one hybrid calculation with appropriate modification. This paper presents the comparative analysis in terms of encryption and decryption time for individual as well as hybrid algorithm.

Background Smokers are highly susceptible to lung and cardiovascular disease that can reduce their survival. Tumor necrosis factor (TNF)-related apoptosis-inducing ligand (TRAIL) is a protein in the circulation that may suppress vascular and pulmonary inflammation. Therefore, we hypothesized that diminished circulating TRAIL levels would be associated with poor survival in smokers with lung and cardiovascular disease. Methods Serum TRAIL level was measured by immunoassay in 474 smokers. Coronary atherosclerosis was assessed by coronary artery calcium scoring along with emphysema, lung function, and survival. Results The 474 smokers were 65.7 ± 6.3 years old and 52.2% male with 55.3 ± 31.5 pack-years of tobacco-exposure. 83 of them died during 3588.2 person-years of follow up. At baseline, lower TRAIL level was associated with more coronary artery calcium (OR = 1.2 per SD, 95%CI 1.1–1.5, p = 0.02), and with history of myocardial infarction (OR = 2.3 per SD, 95%CI 1.2–4.5, p = 0.02), angina (OR = 1.6 per SD, 95%CI 1.1–2.6, p = 0.03), and angioplasty (OR = 1.8 per SD, 95%CI 1.0–3.1, p = 0.04) in models adjusted for cardiovascular risk-factors, FEV1, and emphysema. Also, lower TRAIL level was associated with emphysema severity independent of demographics and tobacco exposure (β = 0.11 sq. root units, 95% CI 0.01–0.22, p = 0.03). Further, TRAIL level was lowest in smokers with comorbid emphysema and coronary artery calcification rather than either condition alone. Finally, lower TRAIL level was independently associated with increased mortality in smokers particularly in those with comorbid emphysema and coronary artery calcification (HR = 1.38, 95% CI 1.01–1.90). Conclusions TRAIL level is reduced in smokers with comorbid emphysema and coronary artery disease, and is associated with reduced survival.

In the present work, the effect of cooling rate on the evolution of the microstructure and mechanical properties of an α + β titanium alloy has been systematically investigated. Titanium alloy samples were heated to 1066 °C (above the β transus), 930 °C (just below the β transus), and 850 °C (well below the β transus) followed by oil quenching, air cooling, and furnace cooling, respectively. Primary alpha (αp), lamellar alpha (αL), and martensite (α′) were the dominant features of the microstructures for all the samples heated below the β transus. Furnace-cooled samples showed variation in the size and shape of the αp and fraction of αL according to the heating temperature. At slower cooling rates, the thickness of the αL increased with the increase in temperature. Transmission electron microscopy and X-ray diffraction confirmed the presence of α′ in all the quenched samples. The volume fraction and size of the αp decreased with the increase in temperature but was independent of the cooling rate. The microhardness was relatively unaffected by the cooling rate for heating just below the β transus, i.e., 930 °C. The modulus of elasticity was found to be extremely sensitive to the microstructure.

In the present work, microstructure and texture development during deformation and recrystallisation of a twin roll cast (TRC) AA8011 aluminum alloy was investigated. TRC AA8011 alloy with initial thickness 7.2 mm and Fe:Si ratio ∼0.93 was cold rolled in a laboratory rolling mill to 2 mm final thickness. The cold rolled sheet was recrystallized at 375 °C and 450 °C for different times. The hardness decreased with increase in recrystallisation time for both temperatures, but there was no effect on centre line segregation (consisting predominantly of Al-Fe-Si intermetallics). XRD confirmed the presence of β-AlFeSi particles at all the stages of recrystallisation. The initial TRC sheet had a very weak texture close to brass ({110} ). After 72% cold rolling (final thickness, 2 mm), strong brass, copper ({112} ) and S ({123} ) texture i.e. metal type texture developed. Low temperature annealing (4 h at 375 °C) was found to give optimum combination of deformation texture and cube recrystallisation texture ({100} ) expected to improve the forming behavior of cold rolled and annealed sheet.

In the present work, UNS S32750 super duplex stainless steel sheets were welded by shielded metal arc welding process with E2595 electrode using two different heat inputs, 0.54 and 1.10 kJ/mm. Microstructural investigations (optical and scanning electron microscopy) showed very small differences in the heat affected zone for both the heat inputs. The weld metals showed presence of three different morphologies of austenite—Widmanstatten, intra-granular and grain boundary austenite along with ferrite. Ferrite content in the weld region was also nearly same and did not change significantly with the increase in heat input. Both the weldments showed similar mechanical properties (ultimate tensile strength, impact strength and hardness) and failed in a ductile manner. Electrochemical studies in 3.5% NaCl solution showed the degree of sensitization to less than 1% and nearly same pitting potential for both heat inputs. Since the effect of heat input on the weld behavior was negligible, low heat input may be preffered for welding UNS S32750 super duplex stainless steel.

The aim of this thesis is to address capabilities in the prediction of compressive strength of concrete to affect quality control in construction. To comprehend this, a compressive strength predicting model using the principles of fuzzy logic set theory had been employed. The model put into use ‘fuzzy logic’ as a tool to predict the compressive strength of concrete on a given day. Data collected from previous researches and laboratory work had been put into use in the model construction and testing. The input variables of water/binder ratio, cement content, water content, and fly ash percentage and the output variable of 28-day cement compressive strength were fuzzified by the use of triangular membership functions and Gaussian membership functions which were deployed for the fuzzy subsets. The prediction of the 28-day cement strength data by the developed fuzzy model proved to be quite satisfactory. The training and testing of 4 different models were done. The Minimum average percentage error levels in the fuzzy model were seen to be as low as (3%) in the case of Model 3. A comparative study of the different models (all 3 Triangular and 1 Gaussian) had been done. The results indicated that the application of the fuzzy logic algorithm was quite satisfactory when a triangular membership function with decreased subset range was used. The outputs of the Triangular and Gaussian models were almost similar.

Texture development during multi-step cross rolling of a dual-phase Fe–Cr–Ni alloy has been investigated. X-ray diffraction was used to investigate changes in crystallographic texture of both the constituent phases (austenite and ferrite) through changes in orientation distribution function. After deformation, rotated brass (rotated along φ1, i.e. the sample normal direction ND), along with a weak cube texture was observed in austenite, while a strong rotated cube texture was obtained in ferrite. Texture was also simulated for various strains using a co-deformation model by self-consistent visco-plastic (VPSC) formulation. Simulations showed strong rotated brass texture in austenite and a strongly rotated cube, α-fibre (sample rolling direction RD // ) and γ-fibre (ND // ) in ferrite after highest strain (et = 1.6). VPSC models could not effectively capture the change in crystallographic texture during cross rolling. In ferrite, simulations showed an overestimation of γ-fibre component...

Injuries to tendons and ligaments are a common problem limiting daily activities and athletic participation across all age groups. Conventional magnetic resonance imaging (MRI) is reliable for detecting complete tears in tendons and ligaments, but it has difficulty identifying low-grade injuries due to poor contrast and low intensity signal. We describe recent MRI advances using ultra-high magnetic fields and very short time echoes which overcome many of the limitations of the low signal and the short T2 of connective tissues. Using diffusion and relaxometry measurements at 11.7 T, we measured the high field relaxation times, mean diffusivity, and the fractional anisotropy of rabbit semitendinosus tendons and medial collateral ligaments. We found that ultra-high field diffusion tensor imaging and tractography provide repeatable and quantitative maps of fiber organization, which could improve injury diagnosis and therapeutic treatments and assist in presurgical planning.

A meshfree based numerical tool is developed for stochastic eigenvalue analysis of columns.Statistics of critical buckling loads are calculated using perturbation method and compared with those obtained from Monte Carlo Simulation.The solution and the computational time are compared with that of stochastic FEM.Robustness of the proposed stochastic meshfree eigenvalue analysis is shown. This paper proposes a probabilistic approach for the solution of elastic buckling of columns, involving uncertainties, using stochastic element free Galerkin method. In the present work, modulus of elasticity is modeled as a homogeneous random field. Karhunen-Loeve expansion and shape function method are used to represent random field and their effectiveness is compared in modeling the same in a computationally viable manner. Both Gaussian and non-Gaussian field are considered for the present study. The stochastic eigenvalue problem is solved for first and second moment characteristics of buckling load, using perturbation analysis. Numerical examples of columns with different boundary conditions are solved. Monte Carlo simulation is used as a validation tool. The obtained results are found in good agreement with those obtained by Monte Carlo simulation.