In vitro and in vivo MRI imaging and photothermal therapeutic properties of Hematite (α-FeO) Nanorods.

Herein we report synthesis of hematite (α-FeO) nanorods by calcinating hydrothermally synthesized goethite nanorods at 5000C. The structural, optical and MRI imaging guided cancer therapeutic properties of fabricated nanorods have been discussed in this manscript. FESEM and TEM imaging techniques were used to confirm the nanorod like morphology of as prepared materials.

Suitably Band-aligned MOF-Derived Ni P/MnO Heterostructure with Ni(I) Coordination Surface Sites for Self-Coupling of Aryl Halides to Biaryls.

An efficient photo-redox strategy for the aryl-aryl self-coupling of aryl halides through a heterogeneous catalysis route has been demonstrated. A coordinatively unsaturated Ni P surface with the enhanced photochemical property upon hetero-structuring with δ-MnO affects the organic transformation to biaryls with impressive yield and photo-conversion efficiency. The dual-role of the Ni P catalyst with its participation as the catalytic active surface and the photo-redox center distinguishes the organic transformation achieved herein with other catalytic and photocatalytic aryl-aryl self-coupling.

Simultaneous Removal of Heavy Metals and Ciprofloxacin Micropollutants from Wastewater Using Ethylenediaminetetraacetic Acid-Functionalized β-Cyclodextrin-Chitosan Adsorbent.

The current study pertains to the synthesis of an EDTA-functionalized β-cyclodextrin-chitosan (β-CD-CS-EDTA) composite via a two-step process for the adsorptive removal of toxic heavy metallic ions (i.e., Pb(II), Cu(II), and Ni(II)) and antibiotic micropollutant, i.

Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences.

Background And Objective: The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters.

Risk assessment of COVID-19 pandemic using deep learning model for J&K in India: a district level analysis.

The coronavirus disease 2019 (COVID-19) is an ongoing pandemic with high morbidity and mortality rates. Current epidemiological studies urge the need of implementing sophisticated methods to appraise the evolution of COVID-19. In this study, we analysed the data for 228 days (1 May to 15 December 2020) of daily incidence of COVID-19 cases for a district level analysis in the region of Jammu and Kashmir in the northern Himalayan belt of India.

Artificial Intelligence against COVID-19 Pandemic: A Comprehensive Insight.

COVID-19 is a pandemic initially identified in Wuhan, China, which is caused by a novel coronavirus, also recognized as the Severe Acute Respiratory Syndrome (SARS-nCoV-2). Unlike other coronaviruses, this novel pathogen may cause unusual contagious pain, which results in viral pneumonia, serious heart problems, and even death. Researchers worldwide are continuously striving to develop a cure for this highly infectious disease, yet there are no well-defined absolute treatments available at present.

ECG denoising and feature extraction techniques - a review.

The electrocardiogram (ECG) is a non-invasive approach for the recording of bioelectric signals generated by the heart which is used for the examination of the electro physical state, the function of the heart, and many cardiac diseases. However, various artefacts and measurement noise usually hinder providing accurate feature extraction such as power line interference, baseline wander, electromyographic noise (EMG) and electrode motion artefact. Therefore, for better analysis and interpretation ECG signals must be noise-free.

A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment.

Heavy metal is released from many industries into water. Before the industrial wastewater is discharged, the contamination level should be reduced to meet the recommended level as prescribed by the local laws of a country. They may be poisonous or cancerous in origin.

Recent changes in heatwaves and maximum temperatures over a complex terrain in the Himalayas.

The temperature response to anthropogenic global warming and forest cover changes is dependent on regional climatic characteristics. It is challenging to segregate the impacts of the two anthropogenic changes on local temperatures and heatwaves over complex mountainous regions. Here we present estimates of regional and local heat stress responses to the recent global climate change and local forest cover loss in complex terrain in the Himalayas using a satellite-based high-resolution land-surface temperature dataset.

Pyrolysis of almond (Prunus amygdalus) shells: Kinetic analysis, modelling, energy assessment and technical feasibility studies.

The aim of this work was to study the thermogravimetric analysis through the pyrolysis of almond (Prunus amygdalus) shells for evaluating its potential for bioenergy at different heating rates (10, 25, and 50 K min). The activation energy values for the process were of the range of 153.0, 152.

Investigating the potential of Morris algorithm for improving the computational constraints of global sensitivity analysis.

Sensitivity analysis (SA) is widely acknowledged as advantageous and worthwhile in recognizing parameters for model calibration and optimization, especially in complex hydrological models. Although Sobol global SA is an efficient way to evaluate the sensitivity indices, the computational cost is a constraint. This study analyzes the potential of Morris global SA to achieve results tantamount to Sobol SA, at a much cheaper computational expense, using a new approach of increasing the number of replications for the Morris algorithm.

Deep insight: Convolutional neural network and its applications for COVID-19 prognosis.

Background And Objective: SARS-CoV-2, a novel strain of coronavirus' also called coronavirus disease 19 (COVID-19), a highly contagious pathogenic respiratory viral infection emerged in December 2019 in Wuhan, a city in China's Hubei province without an obvious cause. Very rapidly it spread across the globe (over 200 countries and territories) and finally on 11 March 2020 World Health Organisation characterized it as a "pandemic". Although it has low mortality of around 3% as of 18 May 2021 it has already infected 164,316,270 humans with 3,406,027 unfortunate deaths.

Gas Chromatographic-Mass Spectrometric Analysis, Antibacterial, Antioxidant and Antiproliferative Activities of the Needle Essential Oil of Abies pindrow growing wild in Kashmir, India.

The essential oil composition of the leaves of Abies pindrow, growing in Kashmir, India, along with its antioxidant, antibacterial and anticancer activity is reported for the first time. Gas chromatography coupled with mass spectrometry (GC-MS) revealed the presence of 12 constituents, representing 99.9% of the total oil.

Mathematical modeling and a month ahead forecast of the coronavirus disease 2019 (COVID-19) pandemic: an Indian scenario.

India, the second-most populous country in the world is witnessing a daily surge in the COVID-19 infected cases. India is currently among the worst-hit nations worldwide due to the COVID-19 pandemic and ranks just behind Brazil and the USA. The prediction of the future course of the pandemic is thus of utmost importance in order to prevent further worsening of the situation.

Maintaining efficient logistics and supply chain management operations during and after coronavirus (COVID-19) pandemic: learning from the past experiences.

The outbreak of the novel coronavirus (COVID-19) forced the governing bodies across the world to ban all kinds of travel involving the movement of people. However, the policymakers have been working hard to mobilize the movement of essential goods and services considering its importance in containing the pandemic. It signifies how important the establishment and maintenance of logistics and supply chain management (LSCM) operations are, both during the containment and the successive periods.

Performance evaluation of artificial intelligence paradigms-artificial neural networks, fuzzy logic, and adaptive neuro-fuzzy inference system for flood prediction.

Flood prediction has gained prominence world over due to the calamitous socio-economic impacts this hazard has and the anticipated increase of its incidence in the near future. Artificial intelligence (AI) models have contributed significantly over the last few decades by providing improved accuracy and economical solutions to simulate physical flood processes. This study explores the potential of the AI computing paradigm to model the stream flow.

Temporary reduction in air pollution due to anthropogenic activity switch-off during COVID-19 lockdown in northern parts of India.

Due to fast and deadly spread of corona virus (COVID-19), the Government of India implemented lockdown in the entire country from 25 April 2020. So, we studied the differences in the air quality index (AQI) of Delhi (DTU, Okhla and Patparganj), Haryana (Jind, Palwal and Hisar) and Uttar Pradesh (Agra, Kanpur and Greater Noida) from 17 February 2020 to 4 May 2020. The AQI was calculated by combination of individual sub-indices of seven pollutants, namely PM, PM, NO, NH, SO, CO and O, collected from the Central Pollution Control Board website.

A novel adaptive deep learning model of Covid-19 with focus on mortality reduction strategies.

We employ deep learning to propose an Artificial Neural Network (ANN) based and data stream guided real-time incremental learning algorithm for parameter estimation of a non-intrusive, intelligent, adaptive and online analytical model of Covid-19 disease. Modeling and simulation of such problems pose an additional challenge of continuously evolving training data in which the model parameters change over time depending upon external factors. Our main contribution is that in a scenario of continuously evolving training data, unlike typical deep learning techniques, this non-intrusive algorithm eliminates the need to retrain or rebuild the model from scratch every time a new training data set is received.

UV-visible and fluorescence spectroscopic assessment of meso‑tetrakis‑(4‑halophenyl) porphyrin; HTXPP (X = F, Cl, Br, I) in THF and THF-water system: Effect of pH and aggregation behaviour.

The current study determines optical and fluorescence response of halogen substituted series of meso‑tetrakis‑(4‑halophenyl) porphyrin; HTXPP (Halo = F, Cl, Br, I) dye in tetrahydrofuran; THF and THF-water system at changing pH in relationship with changing medium of allure. Effects produced by varying the pH and medium, over spectral and aggregation were discussed in detail. Results show sequential protonation and deprotonation of HTXPP series in acidic (pH = 4) and (pH =10) basic medium.

Analyzing barriers for implementation of public health and social measures to prevent the transmission of COVID-19 disease using DEMATEL method.

Background And Aims: COVID-19 is a contagious disease caused by a new coronavirus known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Till now there are no vaccines and explicit medicines available for the treatment of COVID-19 disease, therefore the initiatives like public health and social measures are helpful in limiting the quantity of contaminations and saving lives. Keeping this in view, an attempt has been made in the present work to identify the barriers which hinder the implementation of these public health and social measures METHODS: The barriers to implement public health and social measures for preventing transmission of COVID-19 are identified using systematic literature review and these barriers are then categorized using Decision-making trial and evaluation laboratory (DEMATEL) method.

Agricultural soils a trigger to nitrous oxide: a persuasive greenhouse gas and its management.

Agricultural soils form the backbone of the country's economic development. The increased population has not only reduced this treasure but also has affected the global climate at an alarming rate. Among the GHGs, emission of NO due to agricultural activities is nowadays a global concern.

Neem gum based pH responsive hydrogel matrix: A new pharmaceutical excipient for the sustained release of anticancer drug.

The present research work was aimed to synthesize neem gum-based site-specific drug delivery device for anticancer drug methotrexate at different pH condition. The hydrogel-based drug delivery device was synthesized by optimizing reaction parameters using a factorial design approach response surface method. This model comprised of various sets of reactions with varying concentrations of solvent, crosslinker, initiator and monomer under microwave radiation.

Prioritization of lower back pain risk factors among industrial workers using the best-worst method.

Although lower back pain (LBP) is reported to be the most prevalent musculoskeletal disorder related to work amongst workers, little systematic effort has been made to identify the factors and to determine their priority for the onset of LBP. In this article, an attempt has been made to determine the priority of LBP risk factors using a relatively new, simple and effective multicriteria decision-making method, i.e.

Microwave synthesis of nanoparticles and their antifungal activities.

Microwave assisted approach was adopted for obtaining copper (II) oxide and Iron (III) oxide nanoparticles using leaves extract of Euphorbia helioscopia. Tanins component extracted from Euphorbia helioscopia were responsible for the formation of stable nanoscopic particles. Nanoparticles of iron and copper oxide was characterized using powder X-ray Diffractometer (PXRD), Scanning electron microscopy (SEM), Energy Dispersive X-ray Analysis (EDAX) and Transmission Electron Microscopy (TEM).