Hollow MgNi1.4Zn0.6/CaCu2.79Fe4.21O12 nanocomposite synthesis via ultrasonic high‐temperature spray pyrolysis

In this study, a distinctive hollow MgNi_1.4Zn_0.6/CaCu_2.79Fe_4.21O₁₂ nanocomposite was synthesized for the first time using ultrasonic high‐temperature spray pyrolysis method controlled at 1200°C. Effect of various concentrations (0.01, 0.1, and 1 mol L^?1) of the precursor solution on particle size and crystalline phase of nanocomposites was also analyzed. XRD and SEM results confirmed the difference in the particle size and crystalline pattern of the synthesized nanocomposite arisen due to the difference in concentrations. The results of antibacterial and antioxidant studies showed that the nanocomposites possessed remarkable antibacterial and antioxidant activities. Thus, the prepared hollow MgNi_1.4Zn_0.6/CaCu_2.79Fe_4.21O₁₂ metal oxide nanocomposite via ultrasonic high‐temperature spray pyrolysis can be an excellent material in various biomedical applications.     

Synthetic hydrogels: Synthesis,novel trends,and applications

Hydrogels have been generated and explored for use in various applications. The main objective of this comprehensive review is to collate the fundamental concepts of hydrogels, and elaborate on knowledge gaps, and to provide a perspective on the future directions. This review includes details of constituent molecules (monomers, cross-linkers, composite materials, etc.) and the methods used to prepare polymer networks. Moreover, the review highlights modifications of hydrogels that introduce new properties or enhance the existing features to suit the desired applications and challenges of synthetic polymer hydrogels. The other important topics covered in this review are the synthesis and applications of 3D printed hydrogels, nanocomposite hydrogels, injectable hydrogels, and self-healing hydrogels.     

Microwave assisted polymerisation of phenylacetylenes

Summary. Methathesis Polymerisation of phenylacetylenes using in situ generated (arene)M(CO)₃ complexes under unconventional microwave energy condition was achieved successfully. The reaction time was reduced to 1 hour in contrast to refluxing conditions of 24 hours. Received: 2 May 1997/Revised: 7 July 1997/Accepted: 9 July 1997     

Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome (PCOS). Consequently, timely screening of polycystic ovarian syndrome can help in the process of recovery. Finding a method to aid doctors in this procedure was crucial due to the difficulties in detecting this condition. This research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and methodologies. Additionally, feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of classifiers. In this research, the tri-stage wrapper method is used because it reduces the computation time. The proposed study for the Automatic diagnosis of PCOS contains preprocessing, data normalization, feature selection, and classification. A dataset with 39 characteristics, including metabolism, neuroimaging, hormones, and biochemical information for 541 subjects, was employed in this scenario. To start, this research pre-processed the information. Next for feature selection, a tri-stage wrapper method such as Mutual Information, ReliefF, Chi-Square, and Xvariance is used. Then, various classification methods are tested and trained. Deep learning techniques including convolutional neural network (CNN), multi-layer perceptron (MLP), Recurrent neural network (RNN), and Bi long short-term memory (Bi-LSTM) are utilized for categorization. The experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method + CNN delivers the highest precision (97%), high accuracy (98.67%), and recall (89%) when compared with other machine learning algorithms.     

Pressure analysis for green supply chain management implementation in Indian industries using analytic hierarchy process

Due to growing awareness about environment protection, firms are forced to implement environmental practices to enhance their green image. In recent times, academicians and practitioners have shown interest in green marketing and green supply chain management (GSCM). Fields of green branding and sustainability have seen special interest from different business disciplines including information management, marketing, supply chain management, etc. Due to economical and ecological impact, there is a growing concern for the environment and related critical issues. Pressure on the environment is dynamic and diverse, and demands new levels of accountability, financial commitment and supply chain capabilities. Indian manufacturing industries have started adopting green concepts in their supply chain management giving special attention to environmental issues based on pressures from different directions, e.g. customer pressure, government regulations etc. Yet, industries struggle hard to identify essential pressures for implementation of GSCM. This work focuses mainly on identifying such pressures for implementation of GSCM. Initially 65 pressures were identified through detailed literature and categorised into six groups. Then common acceptable pressures were identified through a questionnaire survey from different industrial sectors in Phase 1. Finally, essential pressures are prioritised with the help of analytic hierarchy process in Phase 2.     

Development of a mathematical model for multihearth roasters

A simultaneous heat and mass transfer model has been developed for the multiheart roasters, considering dead roasting of chalcopyrite as a typical roasting reaction. Various mass and energy balances have been worked out during the development of this model yielding coupled nonlinear partial differential equations with highly complex boundary conditions. These equations have been solved numerically using a line-by-line finite difference approach to obtain profiles of gas temperature, solid temperature, oxygen concentration, and solid fraction reacted in the roaster. The trend of the computed results appears to be realistic and can be easily explained from simple physical considerations. The effects of gas preheating and the heat transfer coefficient between the solid and the gas upon the roasting process are examined. The results show that gas preheating is beneficial for the roasting process, and the process parameters, such as particle size, gas flow rate,etc., must be adjusted so as to give the desirable value of the heat transfer coefficient needed for proper roasting.     

Fast generalized cross-validation algorithm for sparse model learning

We propose a fast, incremental algorithm for designing linear regression models. The proposed algorithm generates a sparse model by optimizing multiple smoothing parameters using the generalized cross-validation approach. The performances on synthetic and real-world data sets are compared with other incremental algorithms such as Tipping and Faul's fast relevance vector machine, Chen et al.'s orthogonal least squares, and Orr's regularized forward selection. The results demonstrate that the proposed algorithm is competitive.     

Erosion efficiency-a new parameter to characterize the dominant erosion micromechanism

A new parameter called erosion efficiency, capable of identifying the dominant micromechanism leading to solid particle erosion, has been introduced in this paper. The objective of this paper is to demonstrate the usefulness of this parameter by considering the large body of data in the literature pertaining to the solid particle erosion of metallic materials, ceramics, cermets and coatings under normal impact conditions.     

Effects of compressibility and transition to turbulence on flow through microchannels

A detailed experimental study of flow through long microchannels of hydraulic diameter ranging from 60.5 to 211 μm has been carried out. The internal pressure distribution along the length of the channel has been measured to analyze the local flow behaviour. The effects of compressibility and transition to turbulence occurring in the microchannel flow were investigated in detail. In addition, the resulting flow has been analyzed numerically using a commercially available CFD code, FLUENT. It has been shown that there are no special micro-scale effects, including early transition to turbulence at least in the present range of hydraulic diameters after the significant effects of compressibility are accounted appropriately.