Chemistry research in India in a global perspective : a scientometrics profile

We measure India’s contribution to chemistry research in a global perspective. In the five years 2011-2015 Indian researchers have published 62,448 papers in 557 journals. In terms of % share, India (with 6.9% of the world’s publications) is behind only China (25%) and USA (17%). But only 0.86% of papers from India are among the top 1% of the most highly cited papers of the world, compared to 4.86% of papers from Singapore, 2.65% of papers from USA, 2.09% of papers from China, 1.87% of papers from the UK, 1.71% of papers from South Korea and 1.6% of papers from Germany. Papers from India are cited 14.68 times on average compared to cites per paper of 45.34 for Singapore, 30.47 for USA, 23.12 for China, 26.51 for the UK, 21.77 for South Korea and 24.77 for Germany. Less than 39% of papers from India are found in quartile 1 (high impact factor) journals, compared to 53.6% for China and 53.8% for South Korea. Percent share of papers in quartile 1 journals from India is lower than that for the world for all of chemistry and for each one of the eight categories, viz. analytical, applied, inorganic & nuclear, medicinal, multidisciplinary, organic, physical and electrochemistry whether one considers data for the entire five-year period or for 2015 alone. About 20% of Indian chemistry papers are in collaboration with international coauthors. Researchers from only 160 Indian institutions have published at least 100 papers (compared to 362 in USA and 399 in China) and these include 67 state, 14 central and 11 private universities, 27 institutions under the Ministry of Human Resource Development, 20 CSIR laboratories, seven Department of Atomic Energy institutions, and seven Department of Science & Technology institutions. About 40% of all Indian chemistry papers have come from public universities. Only three Indian institutions, viz Bhabha Atomic Research Centre, Indian Institute of Science and Indian Institute of Chemical Technology, have published more than 2,000 papers. None of the Indian universities has performed as well as leading Asian universities. Amrita Vishwa Vidyapeetham, a small institution with less than 200 papers, has performed reasonably well

Backward whirl in a simple rotor supported on hydrodynamic bearings

The asymmetric nature of the fluid film stiffness and damping properties in rotors supported on fluid film bearings causes a forward or a backward whirl depending on the bearing parameters and the speed of the rotor. A rotor was designed to exhibit backward synchronous whirl. The rotor-bearing system exhibited split criticals, and a backward whirl was observed between the split criticals. The orbital diagrams show the whirl pattern

Demographic, clinico-pathological features and management pattern of primary bone tumors in a tertiary care hospital of South India

Background: Primary bone tumors are very rare tumors. The true incidence of bone tumors is not well established and is under reported due to rarity and lack of accurate registries. Hence it is essential to study about the demographic, clinico-pathological features and the pattern of surgical management of bone tumors. The aim of this study is to analyze the demographic and clinico-pathological features of primary bone tumors that were managed by surgery.Methods: A retrospective analysis of all patients with primary bone tumor who were treated by surgery from 2012 to 2019 was done. The age, sex distribution, histopathology, location of the tumor and surgical procedure done were analyzed.Results: Among 103 patients analyzed, 66 (64%) were men and 37 (36%) were women. Primary bone tumors most commonly presented in 11 to 20 years of age with 35 (33.9%) patients occurring in this age group. Osteosarcoma was the most common primary bone tumor and it occurred in 49 (47.6%) patients, out of which 34 (69.3%) patients were below 20 years of age. Giant cell tumor was the most common benign bone tumor and it occurred in 22 patients, out of which nine (40.9%) patients were of age 21 to 30 years. Distal femur was the most common site with 39 (37.9%) patients. The limb preservation rate for malignant appendicular bone tumors was 69.0%.Conclusions: The diagnosis of bone tumor depends not only on histopathological features but also needs correlation with age, clinical features, tumor location and radiological features for confirmation of diagnosis

Investigation of 316L Stainless Steel by Flame Hardening Process

Austenitic stainless steel offer great imperviousness to general erosion because of the development of a detached surface film. They are broadly utilized as a part of the sustenance and concoction preparing ventures and in addition in biomaterial applications. In any case, they can experience the ill effects of setting erosion in chloride particle containing arrangements. All things considered, in the meantime they have discovered little use in mechanical building applications in view of their low hardness and poor wear resistance. In this examination work, to enhance the previously mentioned reasons, surface solidifying by Flame hardening procedure is done. It has for some time been an outstanding a warm treatment for enhancing the surface properties of austenitic stainless steel. The examples were fire solidified for 5 minutes, 10 minutes and 15 minutes separately. Wear test for every one of the examples were completed by stick on plate testing process. The outcomes were contrasted and an untreated specimen and finished up with metallographic tests like optical tiny tests and examining electron magnifying lens tests

Spontaneous cervical epidural haematoma: role of antiplatelets

Cervical epidural haematomas should be considered in any patient presenting with neck pain and suspected stroke particularly if taking antiplatelets

Effect of multiwalled carbon nanotubes on improvement of fracture toughness of spark-plasma-sintered yttria-stabilized zirconia nanocomposites

Highly dense yttria-stabilized zirconia (YSZ) nano-ceramics reinforced with TC-CVD-synthesized multiwall carbon nanotubes (MWCNTs) were fabricated using spark plasma sintering at a temperature of 1350°C, the heating rate of 100 °C/min and pressure of 50MPa with a dwell time of 10 minutes. The identical parameters were utilized for fabricating composites with a varying weight ratio of YSZ and MWNCTs. The samples were characterized for their phase transformation, microstructure and elemental composition using x-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS). The physical and mechanical properties such as density, porosity, hardness, fracture toughness and wear were also investigated. The increase in the MWCNTs concentration has resulted in the deterioration of the hardness due to CNT agglomerations. The wear resistance of the composites revealed MWNCTs enhanced wear resistance of YSZ nanocomposite by undergoing MWNCTs pull-out and crack branching mechanisms. Further indentation method and single-beam V-notch beam (SEVNB) methods were utilized to study the effect of MWCNTs on the fracture toughness of the nanocomposites. The fracture toughness of YC1 (6.58 ± 0.3 MPa m1/2) was 21% higher than the YSZ (5.21 ± 0.2 MPa m1/2) due to the toughening mechanisms attributable to crack deflection, branching and bridging of MWCNTs

White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification.

Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques