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There are many renewable energy sources available in the earth like the sun, wind, biomass, tides, heat of the earth etc. Wind energy is one of the important energy sources, which can be used for generating electricity, water pumping, grinding of grains with the use of wind energy conversion system. Wind is generated on the earth surface by uneven heating and it is intermittent in nature. Wind speed is a very important factor and its prediction is very useful for wind power generation and many other purposes. In this study, artificial neural network (ANN) and multiple linear regressions (MLR) techniques are utilized to predict wind speed. For this aim, one year’s data has been taken which is a total of 365 data sets. In both techniques, five parameters were taken which are relative humidity, wind direction, ambient temperature, ambient pressure and perceptible water. In the first ANN technique, an MLP model was developed using these parameters in input layer, with wind speed as output variable. The 5-20-1 neural model has been trained by LM learning algorithm and predicted minimum RMSE and MRE values as 0.4558 and 0.1589 respectively and acceptable value of coefficient of correlation (R) as 0.90162. In the second technique of MLR, same parameters have been used as independent variables and dependent variable. The regression model predicted with coefficient of correlation value as 0.77852. The comparative analysis of performance of both models shows that the MLP model is better than MLR model. Additionally, to find the most sensitive variable, sensitivity analysis has also been done. Perceptible water was found to be the most sensitive variable and the sensitivity sequence of the parameters is: Prw> Ta> WD> Pa> RH.

Increase of greenhouse gases and pollution of environment due to use of conventional sources of energy has made the world aware of the need to increase the use of renewable energy sources like solar power, wind power and hydropower. The scope of the solar power is vast and proper optimization of solar power plants can fulfill varying load demands. This paper studies an optimization technique for such a purpose. Estimation of ideal solar power plant sizes is done for fulfilling the load requirements of selected four districts of Madhya Pradesh, a state in the central part of India. The districts are chosen on the basis of solar irradiance and land availability. In this paper, installation of solar power plants of required sizes is recommended at each district to meet their power demands locally as well as to supply the nearby districts when needed. This will reduce the reliance on grid for energy supply and help in making the system more decentralized and distributed. It also reduces significantly the losses incurred during transmission and distribution. This paper presents the problem of power plant size estimation as a multi objective optimization problem. The first objective is to minimize the gap between power demand and generation in each district on a monthly basis. The second objective minimizes the cost of each unit of electricity generated. The third objective deals with minimizing the transmission and distribution losses on supplying power from one district to another. The genetic algorithm is used for solving this multi objective problem. The selected plant installation sites have the minimum capacity utilization factor of 18%. The simulation of the proposed optimization technique shows that the plant size obtained by the algorithm closely follows the objectives set.

Melatonin has been studied to have anxiolytic, sedative, and analgesic effects. However, there is limited data on the effect of melatonin in the attenuation of hemodynamic response to intubation. We aimed to study whether preanesthetic oral melatonin attenuates hemodynamic responses to intubation and anesthetic requirements.

Wind energy plays a major role in meeting the world’s growing power demand, due to which wind speed forecasting is essential for power system management, energy trading and maintaining the balance between consumption and generation for a stable electricity market. In this article, three different types of predicting techniques have been implemented for estimating wind speed by means of different meteorological parameters. Group method of data handling (GMDH), multi linear regression (MLR) and artificial neural network (ANN) models have been developed. For these models, data sets of 05 years (12 datasets from each year) were collected from the National Renewable Energy Laboratory (NREL). Five different types of input variables, which are ambient temperature (Ta), atmospheric pressure (Pr), wind direction (WD), relative humidity (RH) and precipitation (Pc) were selected as independent variables in all models. The collected wind speed (Wv) is selected as output or dependent variable. In this study, 48 sets of data were picked for training process and 12 datasets were selected for testing. The performances of models were examined using statistical parameters such as RMSE, MAPE and R2. MLR, GMDH and ANN techniques accurately performed with values of correlation coefficient (R) being obtained as 0.90552, 0.95542 and 0.97617 respectively. Comparative study of all models reveals that out of these three techniques, ANN performs the best. In the ANN model, the values of RMSE, MAE and R2obtained were 0.17476, 0.12984 and 0.95210 respectively, which are optimal results when compared to those of other models. After ANN, GMDH performed better than MLR. Above analysis reveals that the wind speed was predicted with the highest accuracy by the neural technique.

Purpose: The purpose of this study is to assess the value of altmetrics or other indicators, showcasing the impact of academic output, which is seen too often correlated with the citation count. Design/methodology/approach: This study considered three reputed journals of Library and Information Science (LIS) published by Elsevier. A total of 1,164 articles were found in these journals from 2016 to 2020 and the relationships between altmetric attention scores (AAS) and citations were examined. The analysis was extended to compare the grouped data set based on percentile ranks of AAS like top 50%, top 25%, top 10% and top 1%. Findings: Using Spearman correlation analysis, the findings reveal a positive correlation between AAS and citations with different significant levels for all articles, and articles with AAS, as well as for normalized AAS in the top 50%, top 25%, top 10% and top 1% data set. For the three journals International Journal of Information Management(IJIM), Journal of Informetrics(JIF) and Library and Information Science Research(LISR), a significant positive correlation is observed across all data sets. But an unexpected result was observed: in the case of the top 50% of articles for the IJIM and JIF showed no significant correlation but the LISR journal showed a positive correlation for the whole data set. This journal though has fewer articles in comparison to the other two. Research limitations/implications: A source item that is highly cited may not be having high social media attention as reflected in the findings. This demarcates AAS with citations implying various factors on which these measurements are dependent. The study distinguishes these metrics lucidly. There is not a single guideline or uniformity in assessing the correlation found. But the problem is that the interpretation of the correlation strength affects the conclusion of the study. Moreover, this study will be a role model as a draft for librarians to select relevant journals for their libraries and will facilitate authors in the choice of the publication outlets for their papers, particularly concerning the journals that have both visibility and research impact. Originality/value: The study reported devising a comprehensive tool to validate AAS as a measure of scholarly impact to include appropriate social media sources and verify its relationship with other metrics. To the best of the authors’ knowledge, this paper is the first attempt to discover the correlation between AAS and citations for the highly impactful LIS journal published by Elsevier. The empirical evidence lies in the citation and altmetric data extracted from the dimension database.

Angiotensin Receptor Neprilysin Inhibitor (ARNI) has not shown to be promising in Heart failure with preserved LVEF. Role of ARNI in Right ventricular systolic dysfunction with preserved LVEF needs to be evaluated. We took 50 patients with isolated RV systolic dysfunction and divided them into two groups of 25 patients each- Conventional treatment and ARNI in addition to conventional treatment. We found that the ARNI group showed a significant improvement in NYHA class, all the echo parameters of RV size, RV function and NT-pro BNP levels at 4 weeks and 12 weeks.

Purpose: This paper aims to determine the gender composition of journal editorial boards in the field of library and information science and to identify trends in the gender composition of different editorial roles and the country of affiliation and occupation of people fulfilling these roles. Design/methodology/approach: In an analysis of 13 selected Library Information Science journals published by the Emerald Publishing group, data relating to 549 editors and editorial board members were obtained from the Open Editors online database. Data were assessed by role, gender, country and continent of their affiliation, and occupation. Findings: Women were found to be under-represented as editors and editorial board members in 10 of the 13 journals. This was most evident in the highest-ranking role of editor or editor-in-chief. The majority of editors and editorial board members were from English-speaking countries located in Europe and the Americas, followed by Asia. The vast majority of editorial personnel belonged to the teaching and learning profession, with relatively few support staff, or researchers taking on these roles. Originality/value: The findings of this study highlight the gender inequality in prestigious and career-advancing academic roles across multiple research areas. To the best of the authors’ knowledge, no such research has yet been conducted in the field of library and information science.

Recent mouse knockout studies identified adapter protein-2 associated kinase 1 (AAK1) as a viable target for treating neuropathic pain. Potent small-molecule inhibitors of AAK1 have been identified and show efficacy in various rodent pain models. (S)-1-((2′,6-Bis(difluoromethyl)-[2,4′-bipyridin]-5-yl)oxy)-2,4-dimethylpentan-2-amine (BMS-986176/LX-9211) (34) was identified as a highly selective, CNS penetrant, potent AAK1 inhibitor from a novel class of bi(hetero)aryl ethers. BMS-986176/LX9211 (34) showed excellent efficacy in two rodent neuropathic pain models and excellent central nervous system (CNS) penetration and target engagement at the spinal cord with an average brain to plasma ratio of 20 in rat. The compound exhibited favorable physicochemical and pharmacokinetic properties, had an acceptable preclinical toxicity profile, and was chosen for clinical trials. BMS-986176/LX9211 (34) completed phase I trials with good human pharmacokinetics and minimum adverse events and is currently in phase II clinical trials for diabetic peripheral neuropathic pain (ClinicalTrials.gov identifier: NCT04455633) and postherpetic neuralgia (ClinicalTrials.gov identifier: NCT04662281).

Purpose: This study aims to present a scientometric analysis of publications related to “Augmented Reality.” In today’s Information Technology-driven era, augmented reality (A.R.) has evolved as a new immersive data source for developing knowledge combining authentic and digital images. Consequently, extensive research is going on “Augmented Reality” to discover its potential in knowledge development. Methodology: The paper analyses and emphasizes the bibliographic data of Scopus articles with a suitable search query. The study was done concerning the chronological growth of research publications, highly cited publications, productive countries, prominent journals, prolific authors and institutions, author and country co-authorship network analysis and keywords analysis. The analysis was conducted by using open-source tools such as VOSViewer, Biblioshiny and Gephi. Findings: The study reveals that a maximum number of publications on research in “Augmented Reality” are in the form of conference proceedings and articles. A.R., Virtual reality and A.R. application are the keywords with maximum number of occurrences. A significant number of publications are done in the USA, followed by Germany in the year 2020. Originality/value: This study provides a precise idea of work done in different countries, a network of co-authorship between authors and countries, publication and citation impact of authors, journals, institutions and countries, year-wise progression and trending “augmented reality” topics research. This investigation will be advantageous for researchers and stakeholders to obtain rigorous bibliographic knowledge on literature related to the topic and work accordingly for R&D activities.

This paper discusses the theoretical aspects of Bradford’s law, its implications in different subject areas, various implementation forms, and its relevance in the published Information Science literature. The study attempts to apply Bradford’s law and Leimkuhler model on Information Science literature to a total of 213 source items listed in the Scopus database during the study period of 2001 to 2020. A ranking table of journals and articles has been prepared to contain the cumulative count. In the first phase, the verbal formulation or the laws’ algebraic form is employed in the journal distribution pattern that depicts a very high percentage of error in the actual observation that does not comply with Bradford’s distribution. The Leimkuhler model’s application follows this after finding the Bradford multiplier (k) value, thereby estimating the number of journals in the core and the successive two zones (r0, r1, r2). A minor percentage error of 0.0092357% has been observed, followed bythe graphical depiction of the data, i.e., “Bradford’s Bibliograph.” “Scientometrics” (39), “Bulletin of the Medical Library Association” (33), “Canadian Journal of Information and Library Science”(26 ) were found as the most preferred journals with the highest frequency.

Oral Cancer (OC) is a serious and growing problem which constitutes a huge burden on people in more and less economically developed countries alike. The scenario is clearly depicted from the increase in the expected number of new cases in the US diagnosed with OC from 49,670 people in 2016, to 49,750 cases in 2017. The situation is even more alarming in India, with 75,000 to 80,000 new cases being reported every year, thus making it the OC capital of the world. Leukoplakia, erythroplakia, oral lichen planus, oral submucous fibrosis, discoid lupus erythmatosus, hereditary disorders such as dyskeratosis congenital and epidermolisys bullosa are highlighted by WHO expert working group as the predisposing factors increasing the risk of OC. Consumption of tobacco and alcohol, genetic factors, and human papilloma virus are assigned as the factors contributing to the aetiology of OC. On the other hand, pathogenesis of OC involves not only apoptosis but also pain, inflammation and oxidative stress. Inspite of current treatment options (surgery, radiotherapy, and chemotherapy), OC is often associated with recurrence and formation of secondary primary tumours resulting in poor overall survival rates (∼50%). The intervention of nano technology-based drug delivery systems as therapeutics for cancers is often viewed as a cutting edge for technologists. Though ample literature on the usefulness of nano-coutured cancer therapeutics, rarely any product is in pipeline. Yet, despite all the hype about nanotechnology, there are few ongoing trials. This review discusses the current and future trends of nano-based drug delivery for the treatment of OC.

Latent Tuberculosis infection (LTBI) care cascade involves identification of high-risk individuals, testing, identification of LTBI, initiation of Tuberculosis Preventive therapy (TPT) and documentation of outcomes. There is a dearth of such evidence in Indian setting which needs to be further explored. Hence, this feasibility study with public private engagement was conducted with the primary objective of estimating the prevalence of LTBI among household contacts (HHCs) of pulmonary tuberculosis patients using Interferon gamma release assay (IGRA). Secondary objective was to determine the treatment outcomes among the patients of LTBI initiated on TPT.

The present study aims to evaluate and analyse the web contents of Indian Institutes of Technology (IITs) library website/web pages. Survey and observation method was used to collect the primary data from the respective library websites of 19 IITs library websites/web pages where 128 criteria have been designed and data were collected during the month of November, 2016 that was further verified between the periods of 1stMarch – 15thMarch, 2017.The study reveals, only four IITs have a library webpage and one has a dedicated page for library. And very less IIT library website/web page have provided the information about the collections, services, value – added services, and Web 2.0 tools link. The evaluation of the websites will help to upgrade the IITs library website which will be useful for other institutions too.

The websites play a vital role in disseminating the information widely to their end users. The university websites are the gateway to their students/users through online medium which helps in communicating the information published by the authorities relating to their respective universities. This article is an attempt to evaluate and compare the web contents of the North-Eastern Hill University (NEHU) and Mizoram University (MZU). A structured checklist was prepared for the present study based on the previous evaluation of websites conducted by different authors. For analysing the contents of the university websites, a list of 73 criteria was prepared under the 14 headings. A quantitative three point rating scale was designed for the present study to determine a meaningful numerical rating for the individual features of the NEHU and MZU websites. It is observed from the present study that almost all the criteria listed for evaluation of the websites has been fulfilled by both the universities and both the websites are found to be very simple and easily accessible by the users. The study reveals that NEHU scored the highest point, that is, 59 out of 69 to that of MZU (47). Based on this study, it has been suggested that both the university websites have to be evaluated frequently and the current information needs to be made available to their end users.

Recent mouse knockout studies identified adapter protein-2-associated kinase 1 (AAK1) as a viable target for treating neuropathic pain. BMS-986176/LX-9211 (4), as a highly selective, CNS-penetrable, and potent AAK1 inhibitor, has advanced into phase II human trials. On exploring the structure–activity relationship (SAR) around this biaryl alkyl ether chemotype, several additional compounds were found to be highly selective and potent AAK1 inhibitors with good druglike properties. Among these, compounds 43and 58showed very good efficacy in two neuropathic pain rat models and had excellent CNS penetration and spinal cord target engagement. Both compounds also exhibited favorable physicochemical and oral pharmacokinetic (PK) properties. Compound 58, a central pyridine isomer of BMS-986176/LX-9211 (4), was 4-fold more potent than 4in vitro and showed lower plasma exposure needed to achieve similar efficacy compared to 4in the CCI rat model. However, both 43and 58showed an inferior preclinical toxicity profile compared to 4.

The aim of this research paper was to look into the high-stress abrasive or sliding wear of the LM13 and ADC12 alloy and its composites as LM13-7% SiC, LM13-7% TiB2, ADC12-7% SiC and ADC12-7% TiB2reinforced with SiC and TiB2particles. The high-stress abrasive test was examined at various emery grit meshes as 180, 220, 320 and 400 correspond to 88, 60, 44 and 37 micron, and a load of 20 N and 30 N was applied for scratching distance of 200 m and 300 m at a sliding speed of 5.2 m/s. Smooth wear track or channel was shown in case of alloys. Wider and discontinuous wear tracks were depicted in case of composites due to inclusion of strengthen particles. Alloys showed less frictional heating as compared to their composites and among composites titanium diboride amalgam showed more frictional heating as compared to silicon carbide strengthen composites. There was rapid increase in frictional heating in composites as compared to alloys due to presence of hard particles. Frictional heating increased on inclusion of ceramic particles. Experiment result showed that titanium diboride composites in aluminium base alloy considered excellent material for tribological and general purpose engineering application.

The reactions of naphthoquinone derivative (I) with different amines and thiols and intramolecular nucleophilic addition—elimination and cyclization reactions in the presence or absence of a base using water as solvent are studied.