Role of sample size in the nucleation kinetics of phase transformations in steel alloys

Certain commercial steel alloys exhibit a two-step phase transformation process during solidification when substantial undercooling of the liquid allows access to the metastable phase. This two-step transformation leads to a desirable microstructure under certain conditions. Electrostatic Levitation (ESL) and Electromagnetic Levitation (EML) are two methods of containerless processing used to study how nucleation and growth kinetics influence the transformation delay between phases. Because the two facilities show substantially different delay results, the test environment differences have been analyzed to determine the root cause of this deviation. In particular, the difference in sample size between ESL and EML is examined and modeling shows that this difference is not the controlling factor in determining transformation delay behavior.     

An investigation of mating and population maintenance strategies in hybrid genetic heuristics for product line designs

This research builds on prior work on developing near optimal solutions to the product line design problems within the conjoint analysis framework. In this research, we investigate and compare different genetic algorithm operators; in particular, we examine systematically the impact of employing alternative population maintenance strategies and mutation techniques within our problem context. Two alternative population maintenance methods, that we term “Emigration” and “Malthusian” strategies, are deployed to govern how individual product lines in one generation are carried over to the next generation. We also allow for two different types of reproduction methods termed “Equal Opportunity” in which the parents to be paired for mating are selected with equal opportunity and a second based on always choosing the best string in the current generation as one of the parents which is referred to as the “Queen bee”, while the other parent is randomly selected from the set of parent strings. We also look at the impact of integrating the artificial intelligence approach with a traditional optimization approach by seeding the GA with solutions obtained from a Dynamic Programming heuristic proposed by others. A detailed statistical analysis is also carried out to determine the impact of various problem and technique aspects on multiple measures of performance through means of a Monte Carlo simulation study. Our results indicate that such proposed procedures are able to provide multiple “good” solutions. This provides more flexibility for the decision makers as they now have the opportunity to select from a number of very good product lines. The results obtained using our approaches are encouraging, with statistically significant improvements averaging 5% or more, when compared to the traditional benchmark of the heuristic dynamic programming technique.     

A Library-Based Approach to Task Parallelism in a Data-Parallel Language

Pure data-parallel languages such as High Performance Fortran version 1 (HPF) do not allow efficient expression of mixed task/data-parallel computations or the coupling of separately compiled data-parallel modules. In this paper, we show how these common parallel program structures can be represented, with only minor extensions to the HPF model, by using a coordination library based on the Message Passing Interface (MPI). This library allows data-parallel tasks to exchange distributed data structures using calls to simple communication functions. We present microbenchmark results that characterize the performance of this library and that quantify the impact of optimizations that allow reuse of communication schedules in common situations. In addition, results from two-dimensional FFT, convolution, and multiblock programs demonstrate that the HPF/MPI library can provide performance superior to that of pure HPF. We conclude that this synergistic combination of two parallel programming standards represents a useful approach to task parallelism in a data-parallel framework, increasing the range of problems addressable in HPF without requiring complex compiler technology.     

Stochastic analysis of a multi-unit cold standby system working in orbit form

This paper considers the analysis of a single server n-similar unit system. Initially k(<n) units form an orbit which functions if one unit functions at a time and the remaining n − k units work as cold standbys. When a unit fails in the orbit it is instantaneously replaced by one of the standbys with the help of a perfect transfer switch. The system is said to fail when n − k + 1 units have failed. The distribution of time to failure and time to repair of a unit are negative exponential. Using the regenerative point technique several reliability characteristics are obtained to carry out the cost-benefit analysis.     

Computationally efficient variational Bayesian method for PAPR reduction in multiuser MIMO‐OFDM systems

This paper investigates the use of the inverse‐free sparse Bayesian learning (SBL) approach for peak‐to‐average power ratio (PAPR) reduction in orthogonal frequency‐division multiplexing (OFDM)‐based multiuser massive multiple‐input multiple‐output (MIMO) systems. The Bayesian inference method employs a truncated Gaussian mixture prior for the sought‐after low‐PAPR signal. To learn the prior signal, associated hyperparameters and underlying statistical parameters, we use the variational expectation‐maximization (EM) iterative algorithm. The matrix inversion involved in the expectation step (E‐step) is averted by invoking a relaxed evidence lower bound (relaxed‐ELBO). The resulting inverse‐free SBL algorithm has a much lower complexity than the standard SBL algorithm. Numerical experiments confirm the substantial improvement over existing methods in terms of PAPR reduction for different MIMO configurations.     

Tunable Crystallinity in Regioregular Poly(3‐Hexylthiophene) Thin Films and Its Impact on Field Effect Mobility

The properties of poly(alkylthiophenes) in solution are found to have a profound impact on the self assembly process and thus the microstructural and electrical properties of the resultant thin films. Ordered supramolecular precursors can be formed in regioregular poly(3‐hexylthiophene) (P3HT) solutions through the application of low intensity ultrasound. These precursors survive the casting process, resulting in a dramatic increase in the degree of crystallinity of the thin films obtained by spin coating. The crystallinity of the films is tunable, with a continuous evolution of mesoscale structures observed as a function of ultrasonic irradiation time. The photophysical properties of P3HT in solution as well in the solid state suggest that the application of ultrasound leads to a π stacking induced molecular aggregation resulting in field effect mobilities as high as 0.03 cm² V^?1 s^?1. A multiphase morphology, comprising short quasi‐ordered and larger, ordered nanofibrils embedded in a disordered amorphous phase is formed as a result of irradiation for at least 1 min. Two distinct regions of charge transport are identified, characterized by an initial sharp increase in the field effect mobility by two orders of magnitude due to an increase in crystallinity up to the percolation limit, followed by a gradual saturation where the mobility becomes independent of the thin film microstructure.     

Copper@ZIF-8 Core-Shell Nanowires for Reusable Antimicrobial Face Masks

SARS-CoV-2 and other respiratory viruses spread via aerosols generated by infected people. Face masks can limit transmission. However, widespread use of disposable masks consumes tremendous resources and generates waste. Here, a novel material for treating blown polypropylene filtration media used in medical-grade masks to impart antimicrobial activity is reported. To produce thin copper@ZIF-8 core-shell nanowires (Cu@ZIF-8 NWs), Cu NWs are stabilized using a pluronic F-127 block copolymer, followed by growth of ZIF-8 to obtain uniform core-shell structures. The Cu@ZIF-8 NWs are applied to filtration media by dip coating. Aerosol filtration efficiency decreases upon exposure to ethanol (solvent for dip-coating), but increases with addition of Cu@ZIF-8 NWs. Cu@ZIF-8 NWs shows enhanced antibacterial activity, compared to Cu NWs or ZIF-8 alone, against Streptococcus mutans and Escherichia coli. Antiviral activity against SARS-CoV-2 is assayed using virus-infected Vero E6 cells, demonstrating 55% inhibition of virus replication after 48 h by 1 µg of Cu@ZIF-8 NWs per well. Cu@ZIF-8 NWs’ cytotoxicity is tested against four cell lines, and their effect on inflammatory response in A549 cells is examined, demonstrating good biocompatibility. This low-cost, scalable synthesis and straightforward deposition of Cu@ZIF-8 NWs onto filter media has great potential to reduce disease transmission, resource consumption, and environmental impact of waste.     

Comparison of two stochastic models for two-unit series system with cold standbys

Reliability characteristics are compared for two stochastic models of a system that has two non-identical units, arranged in series, each unit with its identical cold standby. The same set of assumptions is used for both models, except that in model 2 both of the standby units replace the failed operative unit instantaneously whereas in model 1 an operative failed unit is replaced by its corresponding standby unit (i.e. only one unit is replaced in this case). A single repair facility is available to repair the failed unit. Failure and repair time distributions are assumed to be negative exponential.     

A design optimization tool of earth-to-air heat exchanger using a genetic algorithm

Advancement in genetic algorithm (GA) optimization tools for design applications, coupled with techniques of soft computing, have led to new possibilities in the way computers interact with the optimization process. In this paper, the concept of goal-oriented GA has been used to design a tool for evaluating and optimizing various aspects of earth-to-air heat exchanger behavior. A new optimization method based on GA is applied as a generative and search procedure to optimize the design of earth-to-air heat exchanger. The GA is used to generate possible design solutions, which are evaluated in terms of passive heating and cooling of building, using a detailed thermal analysis of non air-condition building environment The results from the simulations are subsequently used to further guide the GA search to find the high-energy solutions for optimized design parameters. The specific problem addressed in this study is the sizing of earth-to-air heat exchanger in a non air-conditioned residential building. The developed algorithm is suitable for the calculation of the outlet air temperature and therefore of the heating and cooling potential of the earth-to-air heat exchanger system. This methodology is applicable to a wide range of design optimization problems like choice of building such as green house, solar house, or heating and cooling of buildings by mechanical system.     

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false‐positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network.