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Corona disease outbreak occurred in 2019 from Wuhan, China and the virus was identified to be a member of the Coronaviridae family. The disease was named as COVID-19 by WHO. It started as an epidemic and later turned into pandemic affecting world wide. The present study was aimed to conduct a systematic review on most prevalent symptoms associated with COVID-19 and to perform a Meta analysis on Comorbidities associated with COVID-19 and assess its severity in underlying conditions. A literature search was conducted using search engines like Pubmed, Web of Science, Elsevier COVID-19 and LitCovid databases between January 2020 and August 2020. Forest plot analysis was done using Random effects model to estimate odds ratio and 95% confidence interval estimate. 83 papers with a sample size of 19,454 were analysed statistically after adopting inclusion and exclusion criteria. Our results showed the most prevalent symptoms to be Cough, fever, dyspnoea and myalgia in interval plot analysis. When assessed for comorbidities, Hypertension (27.02%), Diabetes (16.61%), Cardiovascular disease (12.75%) and Chronic pulmonary obstructive diseases(19.9%) are found to be the most risk factors in comparison with others. However, a finer experimental research needs to be confirmed with the findings.

Corona disease outbreak occurred in 2019 from Wuhan, China and the virus was identified to be a member of the Coronaviridae family. the disease was named as COVID-19 by WHO. It started as an epidemic and later turned into pandemic affecting world wide. the present study was aimed to conduct a systematic review on most prevalent symptoms associated with COVID-19 and to perform a Meta analysis on Comorbidities associated with COVID-19 and assess its severity in underlying conditions. A literature search was conducted using search engines like Pubmed, Web of Science, elsevier COVID-19 and litCovid databases between January 2020 and August 2020. Forest plot analysis was done using Random effects model to estimate odds ratio and 95% confidence interval estimate. 83 papers with a sample size of 19,454 were analysed statistically after adopting inclusion and exclusion criteria. Our results showed the most prevalent symptoms to be Cough, fever, dyspnoea and myalgia in interval plot analysis. When assessed for comorbidities, Hypertension (27.02%), Diabetes (16.61%), Cardiovascular disease (12.75%) and Chronic pulmonary obstructive diseases(19.9%) are found to be the most risk factors in comparison with others. However a finer experimental research needs to be confirmed with the findings. [ABSTRACT FROM AUTHOR]

FSW joints are promising because they have a minimal heat input in welding and can limit the amount of intermetallic compound formation in dissimilar metals. Solid-state welding processes, particularly friction stir welding (FSW), are preferred for welding aluminium alloys due to their low heat input and ability to minimize intermetallic compound formation. This study conducted FSW trials on AA7075 and AA2024 alloys, each 4 mm thick. Parameters such as axial force, rotational speed, and traverse feed were adjusted while keeping other factors constant. The study analyzed weld geometry characteristics, including bead width and penetration depth. Angle distortions during FSW of thin plates in a butt joint were investigated. Additionally, the study examined the effects of FSW parameters on mechanical properties such as tensile strength and hardness immediately after welding. Microstructures of the welds were observed using optical microscopy (OM). The optimal mechanical properties are achieved at a rotational speed of 1000 rpm, a traverse feed of 30 mm/min, and an axial force of 10 kN. This optimal condition facilitates material flow around the pin at an ideal speed, ensuring adequate material filling and preventing tunnel formation. The stir zone's microstructure under these parameters exhibits a finely recrystallized structure with a significantly smaller grain size than the base material. Consequently, this enhances the joint's microhardness and tensile strength.

Background/objectives: GAITRite is often considered as the gold standard for gait assessment in the clinical settings. However, the utility of GAITRite is limited in the community settings where technology is not feasible. The study identifies the agreement between GAITRite and other conventional gait assessment tools, so as to explore their use in the community settings. Methods: 80 right dominant young adults were recruited and were assessed in pre-designed lab settings for GAITRite analysis, 10 m walk test (10 MWT), Timed up and go test (TUG test), and video analysis of gait using Kinovea software. Results: The Pearson correlation analysis reported significant correlation of 10 MWT, TUG test, and video analysis with the parameters assessed using GAITRite, except for the FAP score. The study identified good agreement between the gait speed assessed using 10 MWT and the GAITRite software (ICC = 0.730; 95%CI: 0.433 to 0.872). Excellent agreement was observed between the right step length assessed using Kinovea software and GAITRite analysis. (ICC = 0.777; 95%CI: 0.532 to 0.894). TUG test also identified to have significant negative correlation with the spatial and temporal parameters assessed using GAITRite software. Conclusion: The conventional gait analysis tools can be used as alternatives for GAITRite analysis where technology is not feasible.

Diabetic retinopathy (DR) is a retinal disorder that may lead to blindness in people all over the world. The major cause of DR is diabetes for a longer period and early detection is the only solution to prevent the vision. This paper focuses on the classes of Normal eye (No DR), Mild NPDR (Non-Proliferative Diabetic Retinopathy), Moderate NPDR, Severe NPDR, and PDR. On retinal fundus images, an effective method for identifying diabetic retinopathy (DR) is proposed by combining the U-Net architecture with the K-nearest neighbours (KNN) algorithm. The U-Net architecture is used for segmenting exudates in retinal pictures, and the KNN algorithm is used for final classification. The combination of U-Net and KNN enables accurate feature extraction and efficient classification, effectively overcoming the computational challenges common to deep learning models. The experiments are carried out utilizing a publicly available dataset of retinal fundus images from Kaggle to assess the effectiveness of our suggested strategy. The proposed architecture provides precise output when compared to other models GoogleNet, ResNet18, and VGG16. The proposed model provides a training accuracy of 82.96% and detection of PDR with high accuracy in the short period which prevents loss of vision in early stage.

Background: The COVID-19 pandemic has brought attention to student psychological wellness. Because of isolation, lack of socialisation and intellectual and physical development from excessive media use, primary and secondary school students are at high risk for health problems. Aim: This study aimed to identify the most effective machine learning model for predicting the offline and online instructional strategies students would choose during a pandemic. Setting: The study was carried out at a number of primary and middle schools in Hyderabad, India. Methods: We evaluated the data using machine learning methods such as logistic regression, K-nearest neighbour (KNN), decision trees, bagging and boosting using the Python programming language. Results: In this study, 414 instances were collected from different schools. Exploratory data analysis showed that few students chose online courses. According to the research, very few students choose online classes, and the majority of students favoured offline classes over online because of physical and mental health difficulties; online education effects include a lack of social and peer relationships that affects young children psychologically, and they may not be disciplined enough to resist internet diversions. Smartphones, laptops, etc., affect their vision, causing headaches and impaired eyesight. Conclusion: The KNN was the most accurate machine learning algorithm, with 92.13% accuracy to fits the data to identify the preferences of online education. Contribution: This article examined the perspectives of primary and middle-school children on online education. Most students in this survey also reported experiencing mental or physical health issues that made online education difficult for them. Machine learning algorithms were applied to identify the most effective model for predicting students’ online and offline study preferences. This machine learning method will help schools improve their course delivery methods, allowing students to continue their studies without interruption.

Solar water pumps are bringing environmental and socio-economic benefits for remote areas where agriculture plays a vital role in the livelihoods of people. Maximum power point tracking (MPPT) solar charge controller known as smart DC-DC converter is necessary for all solar photovoltaic (PV) power system to extract maximum available power from PV module MPPT controller forces PV module to operate at voltage close to a maximum power point which improves the efficiency of the solar PV system. The prior knowledge of MPP is necessary to start any conventional MPP algorithm. As the MPPT algorithms have no prior knowledge of the MPP at the start of the perturbation, these algorithms take a long time to reach the MPP. In this work, a deep learning-based long short term memory (LSTM) network is used to provide the prior knowledge on MPP which minimizes the tracking time and maximizes the efficiency of the PV system. The cascaded buck-boost converter is utilized to minimize the input and output side ripples. The dataset needed to train the LSTM is collected from a 33 kWp PV plant installed in PSR Engineering College, India. The PV system along with an Arduino UNO based MPP controller is simulated using Proteus software. The proposed algorithm is found to be far better by its statistical analysis and also this initial value makes the MPP algorithm superior in both accuracy and response, i.e., 0.05 seconds prior. To validate the proposed algorithm hardware prototype with proportional parameters is developed.

In this study, a Mg3Zn alloy and a Mg3Zn1HA nanocomposite were fabricated through disintegrated melt deposition, followed by the hot extrusion process. The specimens were subjected to fiber laser surface modification in air and demineralized water (DM) medium to determine the influence of the laser treatment medium on the microstructure, microhardness, grain size, and surface topography. During the laser treatment, the samples under the water medium produced a lower surface roughness when compared with the air medium. The atomic force microscope (AFM) surface topography scan showed the presence of surface modifications caused by the presence of nanohydroxyapatite (nHA) in the matrix, resulting in a 367.4% and 632.4% increase in the surface roughness (Ra) in the Mg3Zn1HA nanocomposite when compared with the Mg3Zn alloy in water and air media, respectively.

In recent days, additive manufacturing (AM) plays a vital role in manufacturing a component compared to subtractive manufacturing. AM has a wide advantage in producing complex parts and revolutionizing logistics panorama worldwide. Many researchers compared this emerging manufacturing methodology with the conventional methodology and found that it helps in meeting the demand, designing highly complex components, and reducing wastage of materials, and there are a wide variety of AM processes. The process of making the components in full use of technology with several manufacturing applications to meet the above is studied along with the properties of AM, and subsequently, the advantages of AM over the subtractive methods are described. In this paper, the achievements in this manner with considerable gains are studied and are concluded as a paradigm shift to fulfil the AM potential.

Controlling the defect in the perovskite absorber layer is a very crucial issue for developing highly efficient and stable perovskite solar cells (PSCs) as it exhibits the existence of unavoidable defects even after the careful fabrication process. In this study, the presence of defects in the perovskite layer has been evaluated through the analysis of its structural and optical properties. Then the investigations on the impact of defect density on perovskite absorber layer and its associated solar cell parameters have been carried out by numerical simulation utilizing SCAPS-1D software. Besides the defect density, the thickness of the absorber layer has also been varied to find optimum values of cell parameters. It has been found that when the thickness of absorber and shallow defect density is increased from 200 nm to 800 nm and 1 × 1013 cm-3 to 1 × 1018 cm-3 respectively, power conversion efficiency (PCE) is varied from 26.7% to 0.90%. However, when the thickness and deep defect density are raised from 200 nm to 800 nm and 1 × 1013 cm-3 to 1 × 1016 cm-3, respectively, the PCE is varied from 19.3% to 6.15%. It is revealed that optimum absorber thickness is 550 nm and the tolerances of shallow level and deep level defect density are 1 × 1017 cm-3 and 1 × 1015 cm-3, respectively.

India’s rapid economic growth has been accompanied by slower improvements in population health. Given the need to reconcile the ambitious goal of achieving Universal Coverage with limited resources, a robust priority-setting mechanism is required to ensure that the right trade-offs are made and the impact on health is maximised. Health Technology Assessment (HTA) is endorsed by the World Health Assembly as the gold standard approach to synthesizing evidence systematically for evidence-informed priority setting (EIPS). India is formally committed to institutionalising HTA as an integral component of the EIPS process. The effective conduct and uptake of HTA depends on a well-functioning ecosystem of stakeholders adept at commissioning and generating policy-relevant HTA research, developing and utilising rigorous technical, transparent, and inclusive methods and processes, and a strong multisectoral and transnational appetite for the use of evidence to inform policy. These all require myriad complex and complementary capacities to be built at each level of the health system . In this paper we describe how a framework for targeted and locally-tailored capacity building for EIPS, and specifically HTA, was collaboratively developed and implemented by an international network of priority-setting expertise, and the Government of India.

Abstract In the present study, well-defined one-dimensional (1D) rod-like strontium vanadate (SrV2O6) was prepared by simple hydrothermal method without using any other surfactants/templates. The successful formation of rod-like SrV2O6 was confirmed by various analytical and spectroscopic techniques. Interestingly, for the first time the dual role of as-prepared rod-like SrV2O6 were employed as an electrochemical sensor for the detection of caffeic acid (CA) as well as visible light active photocatalyst for the degradation of metronidazole (MNZ) antibiotic drug. As an electrochemical sensor, the SrV2O6 modified glassy carbon electrode (GCE) demonstrated a superior electrocatalytic activity for the detection of CA by chronoamperometry and cyclic voltammetry (CVs). In addition, the electrochemical sensor exhibited a good current response for CA with excellent selectivity, wide linear response range, lower detection limit and sensitivity of 0.01–207 µM, 4 nM and 2.064 μA μM−1cm−2, respectively. On the other hand, as-synthesized rod-like SrV2O6 showed highly efficient and versatile photocatalytic performances for the degradation of MNZ, which degrades above 98% of MNZ solution under visible light irradiation within 60 min. The obtained results evidenced that the improvement of rod-like SrV2O6 might be a resourceful electrocatalyst and photocatalyst material in the probable applications of environmental and biomedical applications.

Abstract A main challenge that significantly impedes REBa2Cu3Ox (RE = rare earth) coated conductor applications is the low engineering critical current density J e because of the low superconductor fill factor in a complicated layered structure that is crucial for REBa2Cu3Ox to carry supercurrent. Recently, we have successfully achieved engineering critical current density beyond 2.0 kA/mm2 at 4.2 K and 16 T, by growing thick REBa2Cu3Ox layer, from ∼1.0 μm up to ∼3.2 μm, as well as controlling the pinning microstructure. Such high engineering critical current density, the highest value ever observed so far, establishes the essential role of REBa2Cu3Ox coated conductors for very high field magnet applications. We attribute such excellent performance to the dense c-axis self-assembled BaZrO3 nanorods, the elimination of large misoriented grains, and the suppression of big second phase particles in this ~3.2 μm thick REBa2Cu3Ox film.

With the advancement of nano-technology and push toward flexible electronics, the opportunity to generate electricity using solid-state devices has ushered tremendous research interest in improving the thermoelectric (TE) properties of flexible semiconducting materials. The majority of research done so far was focused on finding suitable doping schemes for all-organic flexible substrates or mixing organic and inorganic components to obtain flexible substrates with an optimized thermoelectric performance. Unfortunately, their performance is limited by their low power factor (PF) values and often suffers from degradation issues due to the organic component that limits them to low temperature applications. Here, through coupled microstructural and thermoelectric analysis, we show how to overcome these limitations by introducing a new inorganic GaAs flexible substrate with enhanced TE performance. We show that these flexible thin films are single-crystal-like biaxially textured with low angle grain boundary misalignment; and charge transport is dominated by multi- valley electron conduction. This results in a PF ∼1300 µW/mK2, the highest value for non-toxic inorganic flexible thin films and an estimated 3-fold enhancement in the figure of merit compared with bulk GaAs. We present the temperature-dependent experimental PF, mobility, and carrier concentration data coupled with the theoretical models to elucidate the charge transport characteristics of this new class of films. Moreover, these unique charge transport characteristics are material growth dependent, and thus, such novel thermoelectric properties are expected in different material systems.

Introduction: We aim to determine the prevalence of silent liver diseases and to correlate it with age, sex, life style and its other risk factors. Histopathology is an important and most useful way to find out the conditions of internal visceral organs and the unique method for diagnosis of liver diseases because the liver is the site of many diseases, of which become symptomatic while some are diagnosed only on autopsy. Histopathology study is conducted in 120 cases in VMKV. Medical College Hospital, in the department of Pathology Salem, Tamil Nadu, India. Methods: The study was conducted over a period of two years (2009-2011) as elective and prospective study. We collected samples from 120 cases for histopathology study. Samples from the right and left lobe and one in the centre of the liver as well as other morphological findings was observed and recorded. Tissue sections were made and stained with Haematoxylin and eosin were evaluated. Results: Of the 120 cases were studied, 12 cases were excluded due to autolysis. 108 cases have analysed for study, the cases ranging from 6 years to 80years. Among 108 cases, Males were 82 & Females were 26, with the mean age of 46+/-9.52 years. Fatty changes were found in 26.9% followed by normal 25.9%, congestion 16.7%, hepatitis 13.9%, cirrhosis & abscess 7.4% and malignancy 1.9%. Causes of death were RTA -51, poisoning-15, hanging-15, suspecious-11, myocardial infarction-5, drowning-4, burns and electrocution each 3 and 1 in railway. Conclusion: From this study, the most common findings were fatty changes were more prone in the age of 50 – 70 years and starts with 40 years of age due to chronic consumption of alcohol in the Population of Salem district, Tamil Nadu.

Applications of REBCO coated conductors are now being developed for a very wide range of temperatures and magnetic fields and it is not yet clear whether vortex pinning strategies aimed for high temperature, low field operation are equally valid at lower temperatures and higher fields. A detailed characterization of the superconducting properties of a 15 mol. % Zr-added REBCO thin film made by metal organic chemical vapor deposition, from 4.2 to 77 K under magnetic fields up to 31 T is presented in this article. Even at a such high level of Zr addition, Tc depression has been avoided (Tc = 91 K), while at the same time an exceptionally high irreversibility field Hirr ≈ 14.8 T at 77 K and a remarkably high vortex pinning force density Fp ≈ 1.7 TN/m3 at 4.2 K have been achieved. We ascribe the excellent pinning performance at high temperatures to the high density (equivalent vortex matching field ∼7 T) of self-assembled BZO nanorods, while the low temperature pinning force is enhanced by large additional pinning which we ascribe to strain-induced point defects induced in the REBCO matrix by the BZO nanorods. Our results suggest even more room for further performance enhancement of commercial REBCO coated conductors and point the way to REBCO coil applications at liquid nitrogen temperatures since the critical current density Jc(H//c) characteristic at 77 K are now almost identical to those of fully optimized Nb-Ti at 4 K.

In the title compound, C11H10O3, there is an intramolecular O—H...O hydrogen bond generating an S(6) ring motif. The O atom of the hydroxy group deviates by 0.0200 (1) Å from the benzene ring to which it is attached. The propyne group is almost linear, the C—C[triple-bond]C angle being 177.83 (15)°, and is almost coplanar with the benzene ring; the C—C—O—C torsion angle being only −1.1 (2)°. In the crystal, molecules are linked via C—H...O hydrogen bonds, forming infinite C(11) chains running parallel to [103]. These chains are linked by a pair of C—H...O hydrogen bonds, enclosing R22(8) inversion dimers, forming a corrugated two-dimensional network lying parallel to (103).

The asymmetric unit of the title compound, C10H8O3, contains two independent molecules, both of which are almost planar (r.m.s deviations for all non-H atoms of 0.044 and 0.053 Å). The dihedral angles between the benzene ring and the prop-1-yne group are 3.47 (1) and 3.07 (1)° in the two molecules, and the prop-1-yne groups adopt extended conformations. In each molecule, an intramolecular O—H...O hydrogen bond involving the OH and aldehyde substituents forms an S(6) ring. In the crystal, molecules are linked into cyclic centrosymmetric dimers via C—H...O hydrogen bonds, generating R22(14) ring motifs. The crystal structure is further stabilized by aromatic π–π stacking interactions between the benzene rings [centroid–centroid distances = 3.813 (2) and 3.843 (2) Å]

In the title compound, C13H10O3, two prop-2-ynyloxy groups are attached to the benzaldehyde ring at positions 2 and 6. The crystal packing features C—H...O interactions.