Determinants of research productivity

Earlier researchers like Turkeli, suggested that ‘the factors which determine the productivity of scientists are admittedly complex and perhaps not amenable to real scientific analysis′. The present investigation was designed with the sole purpose of confronting such a complex problem. Nearly 200 variables influencing research productivity were collected through relevant literature, analysis of biographies of great scientists, and discussion with eminent scientists. Finally, through a critical examination, 80 variables were selected for the use of Q-sort technique. The sample for the study consisted of a cross section of scientists ranging from Fellows of Indian National Science Academy to young agricultural scientists. Mailed questionnaires and personal interview methods were used for collecting data. Out of a total of 912 respondents, reply was obtained from 325. On the basis of Q-sorted data, 26 variables were selected for further analysis and they were subjected to principal component factor analysis. The results indicated eleven factors affecting research productivity of scientists. They were: persistence, resource adequacy, access to literature, initiative, intelligence, creativity, learning capability, stimulative leadership, concern for advancement, external orientation, and professional commitment.     

Mobilization of lead by calcium versenate and dimercaptosuccinate in the rat

1. Calcium disodium ethylenediaminetetraacetate (CaNa2 EDTA) and meso-2,3-dimercaptosuccinic acid (DMSA) individually and in permutation-combination in various doses (0.1, 0.2 and 0.4 mmol/kg bodyweight) were investigated for their efficacy to mobilize lead from vital tissues into urine and faeces and to restore the lead-sensitive biochemical parameters in lead pre-exposed rats with a view to develop the most acceptable treatment regimen for lead poisoning with a minimal loss of endogenous essential elements. 2. The combined therapy was more effective than a single chelator treatment. 3. The combination of 0.2 mmol/kg CaNa2EDTA + 0.4 mmol/kg DMSA caused a lower depletion of zinc, calcium and iron but possessed almost equal capability to that of 0.4 mmol/kg CaNa2EDTA + 0.4 mmol/kg DMSA to produce urinary as well as faecal excretion of lead, to reduce the tissue burden of lead, including that of the brain, and to reverse lead-induced biochemical alterations. 4. The combination of 0.2 mmol/kg CaNa2EDTA + 0.4 mmol/kg DMSA has shown a definite improvement over previously reported combinations in terms of removal of lead from tissues, particularly the brain, restoration of urinary delta-aminolevulinic acid levels and a decrease in the loss of body zinc and is, therefore, recommended for the treatment of lead intoxication.     

Differential proteomics of human seminal plasma: A potential target for searching male infertility marker proteins

The clinical fertility tests, available in the market, fail to define the exact cause of male infertility in almost half of the cases and point toward a crucial need of developing better ways of infertility investigations. The protein biomarkers may help us toward better understanding of unknown cases of male infertility that, in turn, can guide us to find better therapeutic solutions. Many clinical attempts have been made to identify biomarkers of male infertility in sperm proteome but only few studies have targeted seminal plasma. Human seminal plasma is a rich source of proteins that are essentially required for development of sperm and successful fertilization. This viewpoint article highlights the importance of human seminal plasma proteome in reproductive physiology and suggests that differential proteomics integrated with functional analysis may help us in searching potential biomarkers of male infertility.     

Influence of thermomechanical ageing on tensile properties of 2014 aluminium alloy

2014 aluminium alloy was subjected to various thermomechanical ageing (TMA) treatments which included partial peak ageing (25% and 50%), warm rolling (10% and 20%) and further ageing to peak hardness level at 160 ° C. The tensile tests reveal that TMA treatments cause a substantial improvement in tensile properties and thermal stability. The electron microscopic studies reveal that the TMA treatments affect substantially the ageing characteristics. The TMA Ib treatment yields the finest needles having longitudinal dimensions of 40nm. The TMA treatments also lead to precipitate-dislocation networks of different densities. It is observed that TMA IIb treatment results in the densest precipitate-dislocation tangles of all the TMA treatments. As a result, a significant improvement in the tensile properties of 2014 aluminium alloy has been observed.     

Hexagonal (superlattice) form of Ga2Te3

Gallium telluride (Ga₂Te₃) was synthesized at different temperatures (850 to 460° C) using different cooling rates. Materials synthesized at higher temperatures (including quenched materials from the melt) always yielded zinc-blende lattice with well resolved _{1 2} doublet X-ray powder diffraction lines. In the material synthesized at lower temperature ( 460°C), we obtained additional (superlattice) lines as reported by Newman and Cundall [4]. It was possible to index these reflections not only on an orthorhombic unit cell (a=0.417, b=2.360, c=1.252 nm) but also on cubic (a=1.7678 nm) and hexagonal (a=0.832, c=3.065 nm) unit cells. To us, the hexagonal cell appears to be more realistic. If sufficient time is given to reach equilibrium, the whole of the zinc-blende form of Ga₂Te₃ is transformed to the hexagonal form. It has been further observed that conversion of the hexagonal into the cubic form and vice versa can be brought about by heating the material at temperatures greater or less than 460° C, respectively. Lastly, the zinc-blende phase of Ga₂Te₃ is metastable and slowly transforms to hexagonal form at room temperature.     

An intelligent hybrid approach for hepatitis disease diagnosis: Combining enhanced k‐means clustering and improved ensemble learning

In real world, the automatic detection of liver disease is a challenging problem among medical practitioners. The intent of this work is to propose an intelligent hybrid approach for the diagnosis of hepatitis disease. The diagnosis is performed with the combination of k‐means clustering and improved ensemble‐driven learning. To avoid clinical experience and to reduce the evaluation time, ensemble learning is deployed, which constructs a set of hypotheses by using multiple learners to solve a liver disease problem. The performance analysis of the proposed integrated hybrid system is compared in terms of accuracy, true positive rate, precision, f‐measure, kappa statistic, mean absolute error, and root mean squared error. Simulation results showed that the enhanced k‐means clustering and improved ensemble learning with enhanced adaptive boosting, bagged decision tree, and J48 decision tree‐based intelligent hybrid approach achieved better prediction outcomes than other existing individual and integrated methods.     

Densely connected convolutional networks-based COVID-19 screening model

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.

     

Hierarchical clustering and routing protocol to ensure scalability and reliability in large-scale wireless sensor networks

The Journal of Supercomputing - Cluster-based routing protocols have been proven efficient in prolonging the life cycle of wireless sensor networks (WSNs). Periodic and multi-hop clustering are the...     

Predator–prey optimization based clustering algorithm for wireless sensor networks

Neural Computing and Applications - Grouping the sensor nodes into clusters is an effective way to organize wireless sensor networks and to prolong the networks’ lifetime. This paper presents...     

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Neural Computing and Applications - COVID-19 has emerged as a global crisis with unprecedented socio-economic challenges, jeopardizing our lives and livelihoods for years to come. The...