Effect of Silicon Segregation in the Argon Arc Welded Incoloy 20 Superalloy

Silicon - Incoloy 20 is prone to hot cracking due to micro-segregation of alloying elements. The aim of the work is to investigate the chances of curtailing the micro-segregation of elements using...     

Classification analysis of burnout people's brain images using ontology-based speculative sense model

Burnout is a state of exhaustion that results from prolonged, excessive workplace stress. This can be examined with the biological explications of burnout and physical consequences and classified against prolonged vigorous activities. The research aims to classify burnout people's brain images against prolonged emotional activities using ontology analysis of treatment and prevention and intermediate layers formation based on a speculative sense model. In this segment, the Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model is employed for burnout people's classification analysis. The methodology is performed in the platform of ontology creation and performs the classification analysis. The calculation analysis found the result, and the brain images were classified. The classification analysis of burnout people's brain images, separation of prolonged vigorous activities, and the ontology creation for treatment and prevention against burnout people's brain images were obtained. The analysis received the result, and the results of the precision, recall, storage, computation time, specificity, and classification of burnout people's brain images were obtained. Furthermore, all these Ontology analysis of Treatment and prevention and intermediate layers formation based on a hypothetical sense model had the prediction sensitivity (SN) over 50% and specificity (SP) over 90%. The Classification of Burnout People's Brain performance comparison shows that the proposed system is much more successful than existing methods, especially on a scoring accuracy of 98%.     

Metallic Metastable Hybrid 1T′/1T Phase Triggered Co,P?SnS2 Nanosheets for High Efficiency Trifunctional Electrocatalyst

The development of trifunctional electrocatalyst for oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) with deeply understanding the mechanism to enhance the electrochemical performance is still a challenging task. In this work, the distorted metastable hybrid-phase induced 1T′/1T Co,P SnS₂ nanosheets on carbon cloth (1T′/1T Co,P SnS₂@CC) is prepared and examined. The density functional theoretical (DFT) calculation suggests that the distorted 1T′/1T Co,P SnS₂ can provide excellent conductivity and strong hydrogen adsorption ability. The electronic structure tuning and enhancement mechanism of electrochemical performance are investigated and discussed. The optimal 1T′/1T Co,P SnS₂@CC catalyst exhibits low overpotential of ≈94 and 219.7 mV at 10 mA cm⁻² for HER and OER, respectively. Remarkably, the catalyst exhibits exceptional ORR activity with small onset potential value (≈0.94 V) and half-wave potential (≈0.87 V). Most significantly, the 1T′/1T Co,P SnS₂||Co,P SnS₂ electrolyzer required small cell voltages of ≈1.53, 1.70, and 1.82 V at 10, 100, and 400 mA cm⁻², respectively, which are better than those of state-of-the-art Pt-C||RuO₂ (≈1.56 and 1.84 V at 10 and 100 mA cm⁻²). The present study suggests a new approach for the preparation of large-scalable, high performance hierarchical 3D next-generation trifunctional electrocatalysts.     

A Facile Microwave Assisted Synthesis of La@PbS Nanoparticles and Their Characterizations for Optoelectronics

Journal of Inorganic and Organometallic Polymers and Materials - In this report, we studied the optical and dielectric properties of lanthanum doped lead sulphide (La@PbS) nanoparticles prepared by...     

Big data feature selection using fish and frog optimization

Big data denotes a large amount of data which includes a wide range of such methodologies like big data collection, storage, analysis, and managing the data. Every data collected in this process (homogeneous or heterogeneous considered as data), we called as big data. In this article, fish colony and their social behavior are used recently for developing an algorithm, we called as novel represented as fish swarm optimization algorithm (FSOA), which is based on the fish swarm and its behavior while search for food. The shuffled frog leaping algorithm (SFLA) is one which we introduced recently for finding near optimal solutions. The technique of Hybrid FSO-SFLA is used here for evaluating performance in big data queries.