55 research outputs found
Unsupervised machine learning based CD-SEM image segregator for OPC and process window estimation
No full textBohr radius and its asymptotic value for holomorphic functions in higher dimensions
No full textWe establish sharp Bohr phenomena for holomorphic functions defined on a bounded balanced domain in a complex Banach space , which map into a simply connected domain or a convex domain in the complex plane . Taking as the -dimensional complex plane and as the open unit polydisk, we consider a version of the Bohr inequality stronger than the above mentioned one and study the exact asymptotic behaviour of the Bohr radius. Explicit lower bounds on the Bohr radii of this type are also provided. Extending a recent result of Liu and Ponnusamy, we further record a refined form of the Bohr inequality for the particular case , i.e. the open unit disk in
Preparation of graphene oxide from coal
No full textNanomaterial synthesis from low-cost precursors is a highly desirable approach for bulk application in material science and technology. Among the various nanomaterials, graphene is a single layer two-dimensional honeycomb carbon nanomaterial. Graphene /or graphene oxide is widely utilized in material science, bio-medicine technology as a sensor, cellular imaging, and many more due to its surface area, nanoscale size, and electrical charge properties, etc. Coal is the most abundant combustible energy source. Although, coal possesses a very complex structure, however, it consists significant amount of polyaromatic structure. Due to the presence of an inherent polyaromatic structure, coal
becomes a promising candidate to replace graphite as a precursor material for the production of graphene / or graphene oxide. Herein, a facile cost-effective approach is reported to synthesize graphene oxide from low-grade coal
Identifying Terrorist Index (T+) for Ranking Homogeneous Twitter Users and Groups by Employing Citation Parameters and Vulnerability Lexicon
No full textTomato leaf disease detection using Taguchi-based Pareto optimized lightweight CNN
No full textThe prospect of food security becoming a global danger by 2050 due to the exponential growth of the world population. An increase in production is indispensable to satisfy the escalating demand for food. Considering the scarcity of arable land, safeguarding crops against disease is the best alternative to maximize agricultural output. The conventional method of visually detecting agricultural diseases by skilled farmers is time-consuming and vulnerable to inaccuracies. Technology-driven agriculture is an integral strategy for effectively addressing this matter. However, orthodox lightweight convolutional neural network (CNN) models for early crop disease detection require fine-tuning to enhance the precision and robustness of the models. Discovering the optimal combination of several hyperparameters might be an exhaustive process. Most researchers use trial and error to set hyperparameters in deep learning (DL) networks. This study introduces a new systematic approach for developing a less sensitive CNN for crop leaf disease detection by hyperparameter tuning in DL networks. Hyperparameter tuning using a Taguchi-based orthogonal array (OA) emphasizes the S/N ratio as a performance metric primarily dependent on the model’s accuracy. The multi-objective Pareto optimization technique accomplished the selection of a robust model. The experimental results demonstrated that the suggested approach achieved a high level of accuracy of 99.846% for tomato leaf disease detection. This approach can generate a set of optimal CNN models’ configurations to classify leaf disease with limited resources accurately
Investigation of the possible association of LRP5 gene polymorphisms with osteoporosis in an Indian subpopulation of Malda, West Bengal: a case-control study
No full textBackground: The involvement of low-density lipoprotein receptor-related protein 5 (LRP5) in bone-related diseases with low bone mineral density like osteoporosis is scientifically well established. This study aims to explore the relationship between two LRP5 gene polymorphisms viz. rs3736228 (A1330V) and rs41494349 (Q89R) and the risk of osteoporosis in an Indian subpopulation.
Methods: This case-control study included 61 patients with osteoporosis, and 30 healthy controls from Malda Medical College and Hospital. The SNP analysis was performed by PCR-RFLP method with DraIII and AvaII enzymes for rs3736228 (A1330V) and rs41494349 (Q89R) respectively. The data is validated with DNA sequencing. The results are statistically evaluated.
Results: The distribution of the A1330V and Q89R genotypes in this population was as follows: AA 81.97%, AV 18.03%, and VV 0.00%; QQ 100%, QR 0.00%, and RR 0.0 0%. No homozygous mutant for A1330V and heterozygous or homozygous mutant for Q89Rare detected in this population. Both the polymorphisms in this population are in Hardy-Weinberg equilibrium. The genotype distributions of rs3736228 showed difference between the osteoporotic patients and control groups [odds ratio (OR):1.98, 95% confidence interval (CI): 0.51 to 7.71, p=0.374]. DNA sequencing of exon 18 not only confirms the presence of A1330V in Indian population but also identifies a novel mutation.
Conclusions: The odds ratio (OR) suggests a positive trend toward an association between the A1330V variant and osteoporosis risk. Exon 18 of LRP5 demands special scientific attention. No variation is detected for rs41494349 in the study population
Characterization and leaching of rare earth elements from Indian coal ash
No full textConsumption of critical elements is increasing day by day with the advancement of modern science and technology. Many of the rare earth elements belongs to the class of critical elements and play a pivotal role in modern society. Neodymium (Nd), Europium (Eu), Terbium (Tb), Dysprosium (Dy), and Yttrium (Y) are considered as critical rare earth elements (CREE) for both the short and long term perspective. Lanthanum (La) and Cerium (Ce) are considered as near critical rare earths. Rare Earth Elements (REEs) are consumed in the high-tech industry for the manufacture of various consumer goods such as cell phones, computers, permanent magnets, catalysts, medical devices, etc. Extensive use of REEs is also seen in the manufacturing of wind turbines. To meet the demand in light of restricted distribution of these elements worldwide, it is necessary to recover critical elements from secondary resources. Coal ash is an abundant industrial byproduct of coal combustion due to its large-scale reserves and low cost for energy production. A few ventures have been reported to recover rare earth elements (REEs) from coal-related ash materials in recent years. Coal ash is a very good source for critical elements, as it contains valuable rare earth elements. In this work, a leaching study on two ash samples, formed by the combustion of carbonaceous coal is carried out. The coal samples were taken from the coal mines of eastern region, India. One ash sample contains around 500 ppm REEs, named as Low Total REEs Ash (LTRA) and the other contains around 1000 ppm REEs, named High Total REEs Ash (HTRA). The leaching study was carried out in presence of two different types of acid, i.e. nitric acid (mineral acid), acetic acid (monocarboxylic acid). The leaching efficiency was measured by Inductively Coupled Plasma Mass Spectrometry (ICP-MS) analysis of the leached solution. For both types of ash samples (LTRA and HTRA), REEs leaching efficiency was observed around 70% in nitric acid. In the case of organic acid i.e. acetic acid, around 30% REEs leaching efficiency was observed for Low Total REEs Ash sample. However, it is observed that, only 40-45% leaching efficiency was there for the High Total REE Ash sample in organic acid
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