Chemical toughening of epoxies. I. Structural modification of epoxy resins by reaction with hydroxy-terminated poly(butadiene-co-acrylonitrile)

In an attempt to toughen the epoxy resin matrix for fiber-reinforced composite applications, a chemical modification procedure of a commercially available bisphenol-A-based epoxy resin using reactive liquid rubber HTBN [hydroxy-terminated poly(butadiene-co-acrylonitrile)] and TDI (tolylene diisocyanate) is described. The progress of the reaction and the structural changes during modification process are studied using IR spectroscopy, viscosity data, and chemical analysis (epoxy value determination). The studies support the proposition that TDI acts as a coupling agent between the epoxy and HTBN, forming a urethane linkage with the former and an oxazolidone ring with the latter. The chemical reactions that possibly take place during the modification are discussed.     

An International Collaborative Study on Trypsin Inhibitor Assay for Legumes,Cereals, and Related Products

For determining trypsin inhibitor activity (TIA) in soy products, the American Oil Chemists' Society (AOCS) Method Ba 12-75 has been used. It measures differences in absorbance at 410 nm of bovine trypsin activity toward a synthetic substrate (Nα-benzoyl-DL-arginine-p-nitroanilide) in the absence and presence of an inhibitor. Recently, a significantly improved method was developed (JAOCS, 2019, 96:635–645), featuring 5 mL of total assay volume, enzyme-last sequence, and single inhibitor level in duplicate. It is proposed as the AOCS Method Ba 12a-2020. As a part of the AOCS method approval process, a collaborative study involving 12 international laboratories was conducted to evaluate the performance of the proposed method. The study involved measuring TIA in 10 selected test samples plus a blind duplicate. They included soybeans, pulses, cereals, and their processed products (flours, concentrates, and isolates). After rigorous statistical treatment of the data, only three outliers were removed from the data of two samples. Repeatability relative standard deviations (RSDr) for the 11 samples ranged from 0.99% to 5.52%. Reproducibility RSD (RSD_R) ranged from 7.07% to 22.92%, with seven samples having RSD_R around 10% or less. The remaining four samples had very low TIA, and their RSD_R values ranged from 13.34% to 22.92%. The study has demonstrated reliable performance of the proposed AOCS method. Several collaborators carried out additional experiments addressing some aspects of the method, leading to further refinements. The proposed method is undergoing evaluation by the AOCS Uniform Methods Committee for adoption as an Official Method for measuring TIA in various legume and grain products.     

Multi-item inventory model of breakable items with stock-dependent demand under stock and time dependent breakability rate

Items made of glass, ceramic, etc. are normally stored in stacks and get damaged during the storage due to the accumulated stress of heaped stock. These items are known as breakable items. Here a multi-item inventory model of breakable items is developed, where demands of the items are stock dependent, breakability rates increase linearly with stock and nonlinearly with time. Due to non-linearity and complexity of the problem, the model is solved numerically and final decisions are made using Genetic Algorithm (GA). In a particular case, model is solved analytically as well as numerically and results are compared. Models are developed with both crisp and uncertain inventory costs. For uncertain inventory costs both fuzzy and stochastic parameters are considered. A chance constrained approach is followed to deal with simultaneous presence of stochastic and fuzzy parameters. Different numerical examples are used to illustrate the problem for different cases.     

A particle swarm approach for multi-objective optimization of electrical discharge machining process

This paper proposes an experimental investigation and optimization of various machining parameters for the die-sinking electrical discharge machining (EDM) process using a multi-objective particle swarm (MOPSO) algorithm. A Box–Behnken design of response surface methodology has been adopted to estimate the effect of machining parameters on the responses. The responses used in the analysis are material removal rate, electrode wear ratio, surface roughness and radial overcut. The machining parameters considered in the study are open circuit voltage, discharge current, pulse-on-time, duty factor, flushing pressure and tool material. Fifty four experimental runs are conducted using Inconel 718 super alloy as work piece material and the influence of parameters on each response is analysed. It is observed that tool material, discharge current and pulse-on-time have significant effect on machinability characteristics of Inconel 718. Finally, a novel MOPSO algorithm has been proposed for simultaneous optimization of multiple responses. Mutation operator, predominantly used in genetic algorithm, has been introduced in the MOPSO algorithm to avoid premature convergence. The Pareto-optimal solutions obtained through MOPSO have been ranked by the composite scores obtained through maximum deviation theory to avoid subjectiveness and impreciseness in the decision making. The analysis offers useful information for controlling the machining parameters to improve the accuracy of the EDMed components.     

3D computer vision based on machine learning with deep neural networks: A review

Recent advances in the field of computer vision can be attributed to the emergence of deep learning techniques, in particular convolutional neural networks. Neural networks, partially inspired by the brain's visual cortex, enable a computer to “learn” the most important features of the images it is shown in relation to a specific, specified task. Given sufficient data and time, (deep) convolutional neural networks offer more easily designed, more generalizable, and significantly more accurate end‐to‐end systems than is possible with previously employed computer vision techniques. This review paper seeks to provide an overview of deep learning in the field of computer vision with an emphasis on recent progress in tasks involving 3D visual data. Through a backdrop of the mammalian visual processing system, we hope to also provide inspiration for future advances in automated visual processing.     

33.7 MHz heavy-ion radio frequency quadrupole linac at VECC Kolkata

A 33.7 MHz heavy-ion radio frequency quadrupole (RFQ) linear accelerator has been designed, built, and tested. It is a four-rod-type RFQ designed for acceleration of 1.38 keVu, qA> or =116 ions to about 29 keVu. Transmission efficiencies of about 85% and 80% have been measured for the unanalyzed and analyzed beams, respectively, of oxygen ((16)O(2+), (16)O(3+), (16)O(4+)), nitrogen ((14)N(3+), (14)N(4+)), and argon ((40)Ar(4+)). The system design and measurements along with results of beam acceleration test will be presented.     

Sensorless current control for active rectifiers

A novel model-based sensorless-input current controller for three-phase active rectifiers is presented. The proposed algorithm is based upon a load-conductance rectifier controller, which was previously implemented using current sensors. The paper presents analytical studies and simulation results and discusses a simple implementation using a low cost integrated microcontroller. Experimental verification utilizes an active input/output insulated gate bipolar transistor (IGBT) converter. Simulation and experimental results for the proposed controller are compared to that of the sensor-based version to validate the efficacy of this novel algorithm     

A general compiler framework for speculative multithreaded processors

Speculative multithreading (SpMT) promises to be an effective mechanism for parallelizing nonnumeric programs, which tend to have irregular and pointer-intensive data structures and complex flows of control. Proper thread formation is crucial for obtaining good speedup in an SpMT system. This paper presents a compiler framework for partitioning a sequential program into multiple threads for parallel execution in an SpMT system. This framework is very general and supports speculative threads, nonspeculative threads, loop-centric threads, and out-of-order thread spawning. It is therefore useful for compiling for a wide variety of SpMT architectures. For effective partitioning of programs, the compiler uses profiling, interprocedural pointer analysis, data dependence information, and control dependence information. The compiler is implemented on the SUIF-MachSUIF platform. A simulation-based evaluation of the generated threads shows that the use of nonspeculative threads and nonloop speculative threads provides a significant increase in speedup for nonnumeric programs.     

Electrical conductivity of a novel cast 6351 Al–Al4SiC4 in situ composite

In this research work, electrical conductivity of a novel cast 6351 Al–Al₄SiC₄ composite has been studied for varying Al₄SiC₄ particle content (2–7 vol%). Analytical models were developed on the basis of free electron theory and local resistance approach in order to predict electrical conductivity of metal matrix composites. The electrical conductivity of cast 6351 Al–Al₄SiC₄ composite was found to decrease with increasing Al₄SiC₄ content. Overprediction of results and more % deviation of the predicted electrical conductivity from experimental value were observed for local-resistance-approach-based model. However, the model developed on the basis of electron theory considering scattering of electrons at particle–matrix interface accurately predicted the experimental electrical conductivity data.