Sampling-based Methodology Research Articles

Determination of Equilibrium Adsorbed Morphologies of Surfactants at Metal-Water Interfaces Using a Modified Umbrella Sampling-Based Methodology.

Surfactants adsorb to metal-water interfaces in various morphologies, including self-assembled monolayers (SAMs), cylindrical and spherical micelles, or hemimicelles. Current molecular simulation methods are unable to efficiently sample the formation of these morphologies because of the large diffusive/energetic barriers. We introduce a modified umbrella sampling-based methodology that allows sampling of these morphologies from any initial configuration and provides free energy differences between them. Using this methodology, we have studied adsorption behavior of cationic [quaternary ammonium (quat) of 4 and 12 carbon long alkyl tails], uncharged [decanethiol], and anionic [phosphate monoester] surfactants and their mixtures at a gold-water interface. We find that while Coulombic repulsion between the charged head groups of quat-4 limits their adsorption to a sparse layer, stronger hydrophobic interactions between the alkyl tails of quat-12 promote adsorption resulting in a morphology with adsorbed hemispherical micelles sitting atop a monolayer. Decanethiol molecules adsorb in a densely packed bilayer with the molecules standing-up on the surface in the first layer and lying parallel to the surface in the second layer. Cationic and anionic surfactant mixtures display a synergistic adsorption behavior. These results elucidate the role of molecular characteristics in dictating the nature of adsorbed morphologies of surfactants at metal-water interfaces.

Goods and services related brand image and B2B customer loyalty: effects of construal level

PurposeThough extant literature has identified goods-based brand image (GBBI) and services-based brand image (SBBI) as drivers of business-to-business (B2B) loyalty, their relative importance has remained unexplored. This study aims to bridge this gap.Design/methodology/approachThe authors have used a retrospective sampling-based methodology to collect data from B2B customers via an offline survey with a sample size of 125 purchase managers.FindingsThe authors found that both GBBI and SBBI have positive relationships with B2B loyalty, with customer satisfaction being the mediator. Using the construal level theory (CLT), the authors argue that the B2B purchase term, vendor–customer relationship strength and physical accessibility of the vendor are associated with the construal level of the purchase context. Further, the authors show that B2B customers give higher importance to GBBI in lower construal level and higher importance to SBBI in higher construal level. The authors have also found the moderated mediation effect of customer satisfaction in GBBI–loyalty and SBBI–loyalty relationships with construal level as moderator.Research limitations/implicationsThis study contributes to extant literature of B2B branding and purchase decision-making by bringing in concepts of CLT. It also extends the literature of the GBBI–SBBI–loyalty relationship by bringing in newer results, which reassure the coexistence of goods-dominant and service-dominant logic in the B2B marketplace.Practical implicationsImportant managerial implications have been discussed to help B2B managers in brand building, product–service design and relationship management.Originality/valueThis paper is a pioneer in using the CLT in the B2B purchase contexts. It also provides a theoretical and psychological underpinning of goods–service dilemmas in the B2B context, which is also noble.

Microstructure optimization with constrained design objectives using machine learning-based feedback-aware data-generation

Microstructure sensitive design has a critical impact on the performance of engineering materials. The safety and performance requirements of critical components, as well as the cost of material and machining of Titanium components, make dovetailing of the microstructure imperative. This paper addresses the optimization of several microstructure design problems for Titanium components under specific design constraints using a feedback-aware data-driven solution methodology. In this study, the microstructure is modeled with an orientation distribution function (ODF) that measures the volumes of different crystallographic orientations. Two algorithms are used to sample the entire microstructure space followed by machine learning-aided identification of a minimal subset of ODF dimensions which is subsequently explored by targeted sampling.Conventional optimization methods lead to a unique microstructure rather than yielding a comprehensive space of optimal or near-optimal microstructures. Multiple solutions are crucial for the deployment of materials design for manufacturing as traditional manufacturing processes can only generate a limited set of microstructures. Our data sampling-based methodology not only outperforms or is on par with other optimization techniques in terms of the optimal property value, but also provides numerous near-optimal solutions, 3–4 orders of magnitude more than previous methods. Consequently, the proposed framework delivers a spectrum of optimal solutions in the microstructure space which can accelerate materials development and reduce manufacturing costs.

Data Sampling Schemes for Microstructure Design with Vibrational Tuning Constraints

Microstructures significantly impact the performance of sensitively engineered components, such as wireless impact detectors used in military vehicles or sensors used in aircrafts. These components can operate safely only within a certain range of frequencies, and frequencies outside that range can lead to instability because of resonance. This paper addresses optimization of the microstructure design to maximize the yield stress of a galfenol beam under vibration tuning constraints defined for the first torsional and bending natural frequencies by using a data-driven solution scheme. In this study, two carefully designed algorithms are used to sample the entire microstructure space. Classical optimization techniques often lead to a unique microstructural solution rather than yielding the complete space of optimal microstructures. Multiple optimal solutions are imperative for the practicality of design because conventional low-cost manufacturing processes can generate only a limited set of microstructures. The current data sampling-based methodology outperforms or is on par with other optimization techniques but also provides numerous near-optimal solutions, which is two to three orders of magnitude more than previous methods. Consequently, the proposed framework delivers a spectrum of optimal solutions in the microstructure space that can accelerate material development and reduce manufacturing costs.

Using confidence distribution sampling to visualize confidence sets

This paper presents a new sampling-based methodology designed to fa- cilitate the visual analysis of the confidence sets generated by an inference function such as the likelihood. This methodology generates a sample of parameters from a confidence distribution. This distribution is designed so that its probabilities on the parameter space are equal to the asymptotic coverage probabilities of the targeted confidence sets. Plotting these samples provides a picture of the inference function surface around the point estimator optimizing the inference function. Once the sample is created, one can also picture the profile inference function confidence sets for various functions of the parameters, all without further numerical optimization. The result is similar to a Bayesian analysis based on samples from the posterior. One distinction is that we can target the samples to obtain a clearer picture of the confidence set boundary. We illustrate the methodology with four different inference functions. Although this methodology is related to Fisher's concept of fiducial inference, the fiducial-like confidence distribution we create here is chosen for its ability to recover the confidence sets generated by the inference function and for its ease in computation, nothing more. Unlike resampling methods such as the parametric bootstrap, our method uses only the original data set, as in Bayesian inference. We use illustrative examples to compare our sampling-based confidence sets with those based on numerical optimization, and to compare the confidence regions generated by different inference functions.

Collaboration Pursuing Method for Multidisciplinary Design Optimization Problems

Multidisciplinary design optimization problems are dominated by couplings among subsystems formulated from different disciplines. Effective and efficient collaboration between subsystems is always desirable when solving multidisciplinary design optimization problems. This work proposes a new sampling-based methodology, named the collaboration pursuing method, for multidisciplinary design optimization problems. In the collaboration pursuing method, a new collaboration model, reflecting both physical and mathematical characteristics of couplings in multidisciplinary design optimization problems, is formulated to guide the search of feasible design solutions. The interdisciplinary consistency among coupled state parameters in multidisciplinary design optimization problems is reflected and maintained by the collaboration model. An adaptive sampling strategy is also developed to speed up the search of local optimal solutions. The new method is implemented using MATLAB® 6.0 and successfully applied to four test problems, including an engineering design application.

Yield prediction by sampling IC layout

This paper reports a survey sampling-based methodology for critical area and other property estimates of IC layout. A software implementation of the method, Edinburgh yield estimator sampling (EYES) is presented. The EYES tool implements the survey sampling-based methodology for critical area estimation enabling the yield prediction of ULSI chips. The method requires an analysis of only a small fraction of the chip layout. As a result the practical application of the technique is not limited by the size of the chip, or the design hierarchy. The EYES system is able to process non-Manhattan layout. This enables yield predictions in a reasonable time for even the largest state-of-the-art chips, using modest computing resources.