Electrostatic characteristics of a high-k stacked gate-all-around heterojunction tunnel field-effect transistor using the superposition principle

Journal of Computational Electronics - We use the superposition method to model the electrostatic characteristics of a high-k stacked gate-all-around heterojunction tunneling field-effect...     

Phase Modifications of WO3 Nanoparticles with Green Capping Agents for Effective Removal of Copper Ions from Waste Water

Journal of Inorganic and Organometallic Polymers and Materials - Different phases of tungsten trioxide (WO3) nanoparticles were synthesised by hydrothermal method involving the extracts of...     

The adverse consequences of not using sunscreens

The adverse effects of solar ultraviolet radiation (UVR) on normal skin are well established, especially in those with poorly melanized skin. Clinically, these effects may be classified as acute, such as erythema or chronic such as keratinocyte and melanocyte skin cancers. Apart from skin type genetics, clinical responses to solar UVR are dependent on geophysical (e.g., solar intensity) and behavioural factors. The latter are especially important because they may result in ‘solar overload’ with unwanted clinical consequences and ever greater burdens to healthcare systems. Correctly used, sunscreens can mitigate the acute and chronic effects of solar UVR exposure. Laboratory studies also show that sunscreens can inhibit the initial molecular and cellular events that are responsible for clinical outcomes. Despite public health campaigns, global trends continue to show increasing incidence of all types of skin cancer. Large-scale epidemiological studies have shown the benefits of sunscreen use in preventing skin cancer, though it is likely that sunscreen use has not been optimal in such studies. It is evident that without substantial changes in sun-seeking behaviour, sunscreen use is a very important part of the defence against the acute and chronic effects of solar exposure. Ideally, sunscreens should be able to provide the level of protection that reduces the risk of skin cancer in susceptible skin types to that observed in heavily melanized skin.     

Habitat suitability of golden mahseer young ones (Tor putitora,Hamilton 1822) in Himalayan waters,India

Habitat suitability refers to the mechanism that enables organisms to choose suitable habitats to survive. In the present study, we studied the habitat suitability of young golden mahseer (fingerlings and juveniles) in the streams of the Ramganga River, one of the prominent rivers of the western Himalaya. Different habitat types and microhabitat features were documented. Habitat suitability was evaluated by generating habitat suitability criteria curves in response to varying habitat availability and use by young golden mahseer during different seasons. Generalised linear modelling (GLM) was used to analyse environmental characteristics responsible for selecting golden mahseer habitats. We studied mahseer based on body size into two main classes, that is, fingerlings (1.5–10 cm) and juveniles (10–30 cm). Golden mahseer fingerlings preferred mean depth (0.1–0.6 m) and mean stream velocity (0–0.2 m/s) at stream reach dominated by diverse substrate compositions such as sand, gravel, and small cobbles. Similarly, juveniles preferred mean depth (0.1–1.5 m) and mean water velocity (0.1–0.5 m/s), with cobbles, bedrock, and sand dominating their habitats. The GLM results indicated that dissolved oxygen, temperature, and water velocity were the significant factors influencing habitat suitability. High dissolved oxygen positively influenced fingerlings, whereas moderate to high water velocity affected juveniles the most. Therefore, to understand the flow requirement for threatened species like golden mahseer, it is essential to characterise critical habitats and develop criteria based on habitat suitability curves.     

Relevancy contemplation in medical data analytics and ranking of feature selection algorithms

This article performs a detailed data scrutiny on a chronic kidney disease (CKD) dataset to select efficient instances and relevant features. Data relevancy is investigated using feature extraction, hybrid outlier detection, and handling of missing values. Data instances that do not influence the target are removed using data envelopment analysis to enable reduction of rows. Column reduction is achieved by ranking the attributes through feature selection methodologies, namely, extra-trees classifier, recursive feature elimination, chi-squared test, analysis of variance, and mutual information. These methodologies are ranked via Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) using weight optimization to identify the optimal features for model building from the CKD dataset to facilitate better prediction while diagnosing the severity of the disease. An efficient hybrid ensemble and novel similarity-based classifiers are built using the pruned dataset, and the results are thereafter compared with random forest, AdaBoost, naive Bayes, k-nearest neighbors, and support vector machines. The hybrid ensemble classifier yields a better prediction accuracy of 98.31% for the features selected by extra tree classifier (ETC), which is ranked as the best by TOPSIS.