The effect of methyl-tri-n-butylammonium methylsulfate and graphite nanoplates on production of antistatic acrylic polymer

Methyl-tri-n-butylammonium methylsulfate (BIL) was incorporated into acrylic resin to improve antistatic property of acrylic polymer (AP). In order to avoid reduction in the mechanical properties of acrylic film and to reach higher electrical conductivity values, the combination of graphite nanoplates (Gr) and BIL was used. The effects of incorporation of BIL and Gr into AP on UV-blocking properties and UV transmittance data of acrylic films were measured. After 120 days, AP containing 5 wt% BIL and 0.01 wt% Gr, and AP+15 wt% BIL exhibited antistatic property. While BIL incorporation into acrylic resin deteriorated the mechanical properties, 0.01 wt% Gr incorporation increased the tensile strength by 83%.     

Thermal buffering effect of a packaging design with microencapsulated phase change material

Temperature fluctuations during storage and transportation are the most important factors affecting quality and shelf life of food products. Phase change materials (PCM) with their isothermal characteristics are used to control temperature in various thermal operations. In this study, octanoic acid as PCM candidate was used in a packaging material design for thermal control of a food product. The PCM candidate was microencapsulated in different shell materials in our laboratory. Among the synthesized microcapsules, microencapsulated PCM (mPCM) (ΔHm = 42.9 J/g) with styrene polymer as the shell material was selected based on its properties of being cost effective and compatibility with human health. Thermal buffering effect of PCM in bulk and microencapsulated forms was tested in a packaging design with special PCM pockets. Results showed that packages with mPCM and bulk PCM provided 8.8 and 6 hours of thermal buffering effect for 160 g of chocolate compared with the package without PCM (reference package).     

Robust Adaptive Control of Pulse-width Modulated Rectifiers Based on Adaptive Super-twisting Sliding-mode and State Feedback Controllers

Abstract—This article proposes a new robust adaptive hybrid controller for three-phase pulse-width modulated rectifiers to improve control performance. This hybridization is implemented by combining the second-order sliding mode with the adaptive gain super-twisting control law in the DC bus voltage control loop and state feedback adaptive control in the d-q-axis current control loops. The control objectives are fourfold: (1) driving the DC bus voltage to a reference signal without using a priori knowledge of the plant parameters, (2) forcing the d-q-axis current errors to zero, (3) assuring a satisfactory power factor correction in relation to the AC source, and (4) eliminating the chattering effect. In the closed-loop control system, three-phase source currents and the DC bus voltage are supposed to be available for feedback. As a result, no a priori knowledge of the plant parameters is necessary in the design of the proposed controller. In addition, the proposed controller does not have the disadvantages of singularity, over-parameterization, and chattering phenomenon. Results of experimental studies prove that the proposed control system guarantees the tracking of reference signals with high performance despite all the parameter and external disturbance uncertainties.     

Dirichlet Priors for MAP Inference of Protein Conformation Abundances from SAXS

Estimation of mixture coefficients of protein conformations in solution find applications in understanding protein behavior. We describe a method for maximum a posteriori (MAP) estimation of the mixture coefficients of ensemble of conformations in a protein mixture solution using measured small angle X-ray scattering (SAXS) intensities. The proposed method builds upon a model for the measurements of crystallographically determined conformations. Assuming that a priori information on the protein mixture is available, and that priori information follows a Dirichlet distribution, we develop a method to estimate the relative abundances with MAP estimator. The Dirichlet distribution depends on concentration parameters which may not be known in practice and thus need to be estimated. To estimate these unknown concentration parameters we developed an expectation-maximization (EM) method. Adenylate kinase (ADK) protein was selected as the test bed due to its known conformations Beckstein et al. (Journal of Molecular Biology, 394(1), 160 1). Known conformations are assumed to form the full vector bases that span the measurement space. In Monte Carlo simulations, mixture coefficient estimation performances of MAP and maximum likelihood (ML) (which assumes a uniform prior on the mixture coefficients) estimators are compared. MAP estimators using known and unknown concentration parameters are also compared in terms of estimation performances. The results show that prior knowledge improves estimation accuracy, but performance is sensitive to perturbations in the Dirichlet distribution’s concentration parameters. Moreover, the estimation method based on EM algorithm shows comparable results to approximately known prior parameters.     

Language of Thought: The Connectionist Contribution

Fodor and Pylyshyn's critique of connectionism has posed a challenge to connectionists: Adequately explain such nomological regularities as systematicity and productivity without postulating a "language of thought" (LOT). Some connectionists like Smolensky took the challenge very seriously, and attempted to meet it by developing models that were supposed to be non-classical. At the core of these attempts lies the claim that connectionist models can provide a representational system with a combinatorial syntax and processes sensitive to syntactic structure. They are not implementation models because, it is claimed, the way they obtain syntax and structure sensitivity is not "concatenative," hence "radically different" from the way classicists handle them. In this paper, I offer an analysis of what it is to physically satisfy/realize a formal system. In this context, I examine the minimal truth-conditions of LOT Hypothesis. From my analysis it will follow that concatenative realization of formal systems is irrelevant to LOTH since the very notion of LOT is indifferent to such an implementation level issue as concatenation. I will conclude that to the extent to which they can explain the law-like cognitive regularities, a certain class of connectionist models proposed as radical alternatives to the classical LOT paradigm will in fact turn out to be LOT models, even though new and potentially very exciting ones.     

Effectiveness of phase I applications for identifying randomly scattered out‐of‐control observations and estimating control chart parameters

Estimation of unknown process parameters with fixed‐size samples are studied in the following. The standard textbook approach for phase I control chart implementation with a Shewhart control chart is evaluated for the case of normally distributed independent observations with random sampling. The charts are simultaneously implemented by generating observations that have a given percentage of randomly scattered out‐of‐control observations. Simulating the phase I steps, where out‐of‐control samples are detected iteratively by determining trial control limits, identifying samples exceeding these limits, and revising the control limits, the standard practice is evaluated in terms of both detection performance and quality of parameter estimates. It is shown that standard phase I control chart implementations with 3‐σ‐limits may perform very poorly in identifying true out‐of‐control observations and providing a reference set of in‐control observations for estimation in some practical settings. A chart design with 2‐σ‐limits is recommended for a successful phase I analysis.     

Increasing production rate in U-type assembly lines with sequence-dependent set-up times

U-shaped assembly lines are commonly used in just-in-time production systems as they have some advantages over straight lines. Although maximizing production rates on these lines by assigning tasks to stations is common practice in industrial environments, studies on the stated assembly line balancing problem are limited. This article deals with maximizing the production rate on U-shaped assembly lines under sequence-dependent set-up times. Sequence-dependent set-up times mean that after a task is performed, a set-up time, the duration of which depends on adjacent tasks, is required to start the next task operation. These set-ups are considered by dividing them into two groups, named forward and backward set-ups, to make the problem more practical. Two heuristics based on simulated annealing and genetic algorithms are improved beside the mathematical model. Experimental results show that solving the stated problem using the mathematical model is nearly impossible, while heuristics may obtain solutions that have acceptable deviations from the lower bounds.     

EEG source localization using a sparsity prior based on Brodmann areas

Localizing the sources of electrical activity in the brain from electroencephalographic (EEG) data is an important tool for noninvasive study of brain dynamics. Generally, the source localization process involves a high‐dimensional inverse problem that has an infinite number of solutions and thus requires additional constraints to be considered to have a unique solution. In this article, we propose a novel method for EEG source localization. The proposed method is based on dividing the cerebral cortex of the brain into a finite number of “functional zones” which correspond to unitary functional areas in the brain. To specify the sparsity profile of human brain activity more concisely, the proposed approach considers grouping of the electrical current dipoles inside each of the functional zones. In this article, we investigate the use of Brodmann's areas as the functional zones while sparse Bayesian learning is used to perform sparse approximation. Numerical experiments are conducted on a realistic head model obtained from segmentation of MRI images of the head and includes four major compartments namely scalp, skull, cerebrospinal fluid (CSF), and brain with relative conductivity values. Three different electrode setups are tested in the numerical experiments. The results demonstrate that the proposed approach is quite promising in solving the EEG source localization problem. In a noiseless environment with 71 electrodes, the proposed method was found to accurately locate up to 6 simultaneously active sources with accuracy >70%.     

A grey wolf optimizer approach for optimal weight design problem of the spur gear

The weight of gears is an important issue for high performance of the machine elements. Thus, the design of the gear pairs with respect to the minimum weight is an important and attractive subject to study. Various analytical and heuristic approaches were utilized to optimize the weight of gears in previous studies. In this study, the problem of gear optimization as per the minimum weight was solved with Grey Wolf Optimizer (GWO). GWO is a meta-heuristic optimization method that stands out with its successful performance in engineering applications. In this study, the performance of GWO was investigated in the case of constituting a spur gear with respect to minimum weight for the first time. Compared with the previous studies, the results obtained in the study show that it is possible to design a lighter gear with GWO.