A Novel TiO2–TiC–TiC0.3N0.7–C–SiCN Multiphase Ceramic Nanocomposite from Preceramic Polymer Pyrolysis

Journal of Inorganic and Organometallic Polymers and Materials - The current investigation describes the synthesis of SiTiCNO ceramics derived from the mixture of a preceramic polymer...     

The Defining Role of Local Shear on the Development of As-Rolled Microstructure and Crystallographic Texture in Steel

Metallurgical and Materials Transactions A - This study involved laboratory unidirectional (UDR) and reverse (RR) cold rolling of steel, and corresponding direct (and indirect) observations of...     

Identification and spatio-temporal analysis of earthquake clusters using SOM–DBSCAN model

Neural Computing and Applications - Seismic catalogs are vital to understanding and analyzing the progress of active fault systems. The background seismicity rate in a seismic catalog, strongly...     

Recrystallization Kinetics,Precipitate Evolution and Grain Refinement of Nb Stabilized Ferritic Stainless Steel for Producing Thicker Plate/Strip Industrially

Metallurgical and Materials Transactions A - The study focuses on the processing of thick plate (≥ 12 mm) and strip (3 to 4 mm) where hot-rolling reduction is...     

Emergence of a Non-Van der Waals Magnetic Phase in a Van der Waals Ferromagnet

Manipulation of long-range order in 2D van der Waals (vdW) magnetic materials (e.g., CrI₃, CrSiTe₃ ,etc.), exfoliated in few-atomic layer, can be achieved via application of electric field, mechanical-constraint, interface engineering, or even by chemical substitution/doping. Usually, active surface oxidation due to the exposure in the ambient condition and hydrolysis in the presence of water/moisture causes degradation in magnetic nanosheets that, in turn, affects the nanoelectronic /spintronic device performance. Counterintuitively, the current study reveals that exposure to the air at ambient atmosphere results in advent of a stable nonlayered secondary ferromagnetic phase in the form of Cr₂Te₃ (T_C2 ≈160 K) in the parent vdW magnetic semiconductor Cr₂Ge₂Te₆ (T_C1 ≈69 K). The coexistence of the two ferromagnetic phases in the time elapsed bulk crystal is confirmed through systematic investigation of crystal structure along with detailed dc/ac magnetic susceptibility, specific heat, and magneto-transport measurement. To capture the concurrence of the two ferromagnetic phases in a single material, Ginzburg-Landau theory with two independent order parameters (as magnetization) with a coupling term can be introduced. In contrast to the rather common poor environmental stability of the vdW magnets, the results open possibilities of finding air-stable novel materials having multiple magnetic phases.     

AI-enabled farm-friendly automatic machine for washing,image-based sorting,and weight grading of citrus fruits: Design optimization,performance evaluation,and ergonomic assessment

The modernization of postharvest operations and penetration of emerging technologies in horticultural processing have provided intelligent solutions for reducing postharvest losses. Work environmental and occupational health issues require immediate attention as the awkward posture and continuous drudgery-prone on-farm sorting and grading activities may lead to musculoskeletal disorders. The main objective of this study was to develop an automatic farm-friendly machine for real-time citrus fruit washing, image-based sorting, and weight grading; designed optimally and equipped with an embedded system comprising a lightweight convolutional neural network (CNN) model. Also included in this study was a thorough ergonomic assessment of the developed machine in a real work environment. The parametric choice of the fruit washing and singulation system was performed by employing computational fluid dynamics modeling and response surface methodology designed optimization. It was observed that under steady-state conditions, the water jet would arrive at a velocity of 11.36 m/s which would eventually suit a singulation conveyor with a slope of 25°. A noninvasive grading and sorting approach for citrus fruits is presented in this paper that leverages deep learning to classify the fruits into “accept” and “reject” classes. The custom lightweight CNN model “SortNet” has shown excellent classification results with an overall accuracy of 97.6%. The ergonomic evaluation shows that the average body part discomfort score in case of operating an automatic fruit grading machine was much lower (12.3 ± 2.0) than the traditional method (30.9 ± 3.3). Further, in the case of machine operation, the percentage load on the muscles ranged from 28.67 to 34.31 reflecting that subjects can work for longer duration on the machine without fatigue as compared with the traditional manual operation.     

Non-linear steady-state data reconciliation: Theoretical perspective and practical scenarios

Data reconciliation (DR) is one of the primary error handling methods to reduce measurement errors in industries that may otherwise cause misleading information about the plant. In this article, the mathematical aspects of measurement errors and their treatment by DR are discussed in detail. The flaws in the existing DR methods have been identified and re-investigated. More importantly, the feasibility and health check-up of the DR problem have been discussed. The primary objective of the work is to develop a DR code based on the observations made in the present study, which involves DR solutions by both successive linearization (SL) and sequential quadratic programming (SQP) schemes. Benchmarking of the code with standard cases showed its wider suitability in solving DR problems. The algebraic SL method was found suitable for proper data health check-ups and reliable solutions, whereas SQP was robust. The developed code was tested successfully for a chemical plant as well.