Identifying the time of step change in binary profiles

Control charts are intended to aid quality practitioners in monitoring whether a change has occurred in a process. When a control chart indicates an out-of-control signal, it means that the process has changed. However, control chart signals do not indicate the real time of process changes; so estimators are applied to indicate the time when a change in the process takes place, which is referred to as the change point. This paper provides a maximum likelihood estimator to identify the real time of a step change in phase II monitoring of binary profiles, in which the quality of a process is characterized by a logistic regression between the response and predictor variables. Simulation studies are provided to evaluate the effectiveness of the change point estimator.     

Buffer allocation in unreliable production lines based on design of experiments, simulation, and genetic algorithm

This paper presents a multiobjective formulation of the buffer allocation problem in unreliable production lines. Majority of the solution methods for buffer allocation problems assume that the process times, time between failures, and repair times are deterministic or exponentially distributed. This paper relaxes these restrictions by proposing a simulation-based methodology which can consider general function distributions for all parameters of production lines. Factorial design has been used to build a meta-model for estimating production rate based on a detailed, discrete event simulation model. We use genetic algorithm combined to line search method to solve the multiobjective model and determining the optimal (or near optimal) size of each buffer storage.     

On the Relationship Between Wear and Thermal Response in Sliding Systems

Experimental results reveal that the temperature rise of two contacting bodies in relative sliding motion is related to the wear rate. Experimental wear tests pertain to a ring-on-ring configuration for two sets of contacting materials: Bronze SAE 40 on Steel 4140 and 70-30 Brass on Steel 4140. Temperature variation within the contacting bodies during the tests is measured using a thermocouple. It is shown that the temperature of the interface can effectively characterize the steady-state wear. The results of the present approach are verified by calculating Archard’s wear coefficient using the relationships derived in this paper and compared to published values in literature.     

Simultaneous minimization of total tardiness and waiting time variance on a single machine by genetic algorithms

This paper considers a single machine scheduling problem, with the objective of minimizing a linear combination of total tardiness and waiting time variance in which the idle time is not allowed. Minimizing total tardiness is always regarded as one of the most significant performance criteria in practical systems to avoid penalty costs of tardiness, and waiting time variance is an important criterion in establishing quality of service (QoS) in many systems. Each of these criteria is known to be non-deterministic polynomial-time hard (NP-hard); therefore, the linear combination of them is NP-hard too. For this problem, we developed a genetic algorithm (GA) by applying its general structure that further improves the initial population, utilizing some of heuristic algorithms. The GA is shown experimentally to perform well by testing on various instances.     

Topology optimization and seismic collapse assessment of shape memory alloy (SMA)-braced frames: Effectiveness of Fe-based SMAs

This paper presents a seismic topology optimization study of steel braced frames with shape memory alloy (SMA) braces. Optimal SMA-braced frames (SMA-BFs) with either Fe-based SMA or NiTi braces are determined in a performance-based seismic design context. The topology optimization is performed on 5- and 10-story SMA-BFs considering the placement, length, and cross-sectional area of SMA bracing members. Geometric, strength, and performance-based design constraints are considered in the optimization. The seismic response and collapse safety of topologically optimal SMA-BFs are assessed according to the FEMA P695 methodology. A comparative study on the optimal SMA-BFs is also presented in terms of total relative cost, collapse capacity, and peak and residual story drift. The results demonstrate that Fe-based SMA-BFs exhibit higher collapse capacity and more uniform distribution of lateral displacement over the frame height while being more cost-effective than NiTi braced frames. In addition to a lower unit price compared to NiTi, Fe-based SMAs reduce SMA material usage. In frames with Fe-based SMA braces, the SMA usage is reduced by up to 80%. The results highlight the need for using SMAs with larger recoverable strains.     

Study of Cu-doping effects on magnetic properties of Fe-doped ZnO by first principle calculations

Using ab initio calculations on Zn_{0.975? x} Fe_0.025Cu _x O (x = 0, 0.01, 0.02, 0.05), we study the variations of magnetic moments vs Cu concentration. The electronic structure is calculated by using the Korringa-Kohn-Rostoker (KKR) method combined with coherent potential approximation (CPA). We show that the total magnetic moment and magnetic moment of Fe increase on increasing Cu content. From the density of state (DOS) analysis, we show that Cu-induced impurity bands can assure, by two mechanisms, the enhancement of Fe magnetic moment in Zn_{0.975? x} Fe_0.025Cu _x O.     

Induced structural modifications in ZnS nanowires via physical state of catalyst:Highlights of 15R crystal phase

Peculiar and unique growth mechanisms involved in semiconductor nanowires(NWs)pave the way to the achievement of new crystallographic phases and remarkable material properties,and hence,studying polytypism in semiconductor NWs arouses a strong interest for the next generation of electronic and photonic applications.In this context,the growth of ZnS nanowires has been investigated,as bulk ZnS compound exhibits numerous unstable polytypes at high temperatures,but their stable occurrence is highly anticipated in a nanowire due to its special quasi-dimensional shape and growth modes.In this work,the idea is to provide a change in the growth mechanism via the physical state of catalyst droplet(liquid or solid)and hence,study the induced structural modifications in ZnS nanowires.The HRTEM images of VLS(via liquid alloyed catalyst)grown ZnS NWs show periodic stacking faults,which is precisely identified as a stacking sequence of cubic or hexagonal individual planes leading to an astonishing 15R crystal polymorph.This crystallographic phase is observed for the first time in nanowires.Contrastingly,NWs grown with VSS(via solid catalyst)show crystal polytypes of zinc blende and wurtzite.We calculate and discuss the role of cohesive energies in the formation of such ZnS polytypes.Further,we present the selection rules for the crystallization of such 15R structure in NWs and discuss the involved VLS and VSS growth mechanisms leading to the formation of different crystal phases.     

A constraint consensus memetic algorithm for solving constrained optimization problems

Constraint handling is an important aspect of evolutionary constrained optimization. Currently, the mechanism used for constraint handling with evolutionary algorithms mainly assists the selection process, but not the actual search process. In this article, first a genetic algorithm is combined with a class of search methods, known as constraint consensus methods, that assist infeasible individuals to move towards the feasible region. This approach is also integrated with a memetic algorithm. The proposed algorithm is tested and analysed by solving two sets of standard benchmark problems, and the results are compared with other state-of-the-art algorithms. The comparisons show that the proposed algorithm outperforms other similar algorithms. The algorithm has also been applied to solve a practical economic load dispatch problem, where it also shows superior performance over other algorithms.     

Can silver nanoparticles be useful as potential biological labels?

Silver (Ag) nanoparticles have unique plasmon-resonant optical scattering properties that are finding use in nanomedical applications such as signal enhancers, optical sensors, and biomarkers. In this study, we examined the chemical and biological properties of Ag nanoparticles of similar sizes, but that differed primarily in their surface chemistry (hydrocarbon versus polysaccharide), in neuroblastoma cells for their potential use as biological labels. We observed strong optical labeling of the cells in a high illumination light microscopy system after 24?h of incubation due to the excitation of plasmon resonance by both types of Ag nanoparticle. Surface binding of both types of Ag nanoparticle to the plasma membrane of the cells was verified with scanning electron microscopy as well as the internalization and localization of the Ag nanoparticles into intracellular vacuoles in thin cell sections with transmission electron microscopy. However, the induction of reactive oxygen species (ROS), degradation of mitochondrial membrane integrity, disruption of the actin cytoskeleton, and reduction in proliferation after stimulation with nerve growth factor were found after incubation with Ag nanoparticles at concentrations of 25?μg?ml(-1) or greater, with a more pronounced effect produced by the hydrocarbon-based Ag nanoparticles in most cases. Therefore, the use of Ag nanoparticles as potential biological labels, even if the surface is chemically modified with a biocompatible material, should be approached with caution.     

AMBO: All Members-Based Optimizer for Solving Optimization Problems

There are many optimization problems in different branches of science that should be solved using an appropriate methodology. Population-based optimization algorithms are one of the most efficient approaches to solve this type of problems. In this paper, a new optimization algorithm called All Members-Based Optimizer (AMBO) is introduced to solve various optimization problems. The main idea in designing the proposed AMBO algorithm is to use more information from the population members of the algorithm instead of just a few specific members (such as best member and worst member) to update the population matrix. Therefore, in AMBO, any member of the population can play a role in updating the population matrix. The theory of AMBO is described and then mathematically modeled for implementation on optimization problems. The performance of the proposed algorithm is evaluated on a set of twenty-three standard objective functions, which belong to three different categories: unimodal, high-dimensional multimodal, and fixed-dimensional multimodal functions. In order to analyze and compare the optimization results for the mentioned objective functions obtained by AMBO, eight other well-known algorithms have been also implemented. The optimization results demonstrate the ability of AMBO to solve various optimization problems. Also, comparison and analysis of the results show that AMBO is superior and more competitive than the other mentioned algorithms in providing suitable solution.