Measuring the disparity among scientific disciplines using Library of Congress Subject Headings

Scientometrics - Examining the relationships among scientific disciplines is important today, but existing methods are limited by the contents and structure of their bibliographic databases. We...     

A microfluidic approach for development of hybrid collagen-chitosan extracellular matrix-like membranes for on-chip cell cultures

To advance organ-on-a-chip development and other areas befitting from physiologically-relevant biomembranes,a microfluidic platform is presented for synthesis of biomembranes during gelation and investigation into their role as extracellular matrix supports.In this work,high-throughput studies of collagen,chitosan,and collagen-chitosan hybrid biomembranes were carried out to characterize and compare key properties as a function of the applied hydrodynamic conditions during gelation.Specifically,depending on the biopolymer material used,varying flow conditions during biomembrane gelation caused width,uniformity,and swelling ratio to be differently affected and controllable.Finally,cell viability studies of seeded fibroblasts were conducted,thus showing the potential for biological applications.     

Untapped Knowledge about Water Reuse: the Roles of Direct and Indirect Educational Messaging

Water Resources Management - Public awareness of water quality concerns has increased since the Flint, MI crisis, but knowledge about water resources management remains low. Consequently,...     

Direct comparison of Eulerian–Eulerian and Eulerian–Lagrangian simulations for particle-laden vertical channel flow

Particle-laden flows in a vertical channel were simulated using an Eulerian–Eulerian, Anisotropic Gaussian (EE-AG) model. Two sets of cases varying the overall mass loading were done using particle sizes corresponding to either a large or small Stokes number. Primary and turbulent statistics were extracted from these results and compared with counterparts collected from Eulerian–Lagrangian (EL) simulations. The statistics collected from the small Stokes number particle cases correspond well between the two models, with the EE-AG model replicating the transition observed using the EL model from shear-induced turbulence to relaminarization to cluster-induced turbulence as the mass loading increased. The EE-AG model was able to capture the behavior of the EL simulations only at the largest particle concentrations using the large Stokes particles. This is due to the limitations involved with employing a particle-phase Eulerian model (as opposed to a Lagrangian representation) for a spatially intermittent system that has a low particle number concentration.     

Learning from evolving data streams through ensembles of random patches

Knowledge and Information Systems - Ensemble methods represent an effective way to solve supervised learning problems. Such methods are prevalent for learning from evolving data streams. One of the...     

From sustainability to adaptation: Goldman Sachs’ corporate real estate strategy

This paper is premised on a conceptual framework that attempts to draw theoretical and practical connections between sustainability, resilience and adaptation. The framework is explored through a case study of the corporate real estate (property) strategies of Goldman Sachs (a multinational investment banking and investment management company) developed over the course of the consolidation and development of its corporate headquarters. This case seeks to identify the existence and nature of the relationships by and between sustainable corporate real estate strategies, resilient operations planning and the firm's adaptive capacity. A secondary proposition seeks to evaluate whether the capacity of the firm to adapt and be resilient to changing conditions has been positively advanced by the firm's sustainable corporate real estate strategies. The findings support the proposition that these connections do exist, as well as the proposition that sustainability was promoting adaptive capacity and operational resilience. However, it remains an open question to what extent these practices and capacities are deterministic of one another. This paper sets the stage for future research that seeks to measure and model organizational adaptive capacity and to understand the potential co-benefits that may serve the interests of firms who struggle to rationalize the costs of sustainability.     

Discrete-time contraction constrained nonlinear model predictive control using graph-based geodesic computation

Modern chemical processes need to operate around time-varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time-varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.