Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis

This article addresses the optimal design and planning of cellulosic ethanol supply chains under economic, environmental, and social objectives. The economic objective is measured by the total annualized cost, the environmental objective is measured by the life cycle greenhouse gas emissions, and the social objective is measured by the number of accrued local jobs. A multiobjective mixed‐integer linear programming (mo‐MILP) model is developed that accounts for major characteristics of cellulosic ethanol supply chains, including supply seasonality and geographical diversity, biomass degradation, feedstock density, diverse conversion pathways and byproducts, infrastructure compatibility, demand distribution, regional economy, and government incentives. Aspen Plus models for biorefineries with different feedstocks and conversion pathways are built to provide detailed techno‐economic and emission analysis results for the mo‐MILP model, which simultaneously predicts the optimal network design, facility location, technology selection, capital investment, production planning, inventory control, and logistics management decisions. The mo‐MILP problem is solved with an ε‐constraint method; and the resulting Pareto‐optimal curves reveal the tradeoff between the economic, environmental, and social dimensions of the sustainable biofuel supply chains. The proposed approach is illustrated through two case studies for the state of Illinois. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Sourcing the Arab Spring: A Case Study of Andy Carvin's Sources on Twitter During the Tunisian and Egyptian Revolutions

News sourcing practices are critical as they shape from whom journalists get their information and what information they obtain, mostly from elite sources. This study evaluates whether social media platforms expand the range of actors involved in the news through a quantitative content analysis of the sources cited by NPR's Andy Carvin on Twitter during the Arab Spring. Results show that, on balance, nonelite sources had a greater representation in the content than elite sources. Alternative actors accounted for nearly half of the messages. The study points to the innovative forms of production that can emerge with new communication technologies, with the journalist as a central node trusted to authenticate and interpret news flows on social awareness streams.     

Gender differences in peer relations of children with neurodevelopmental conditions.

Objective: To examine gender differences in the peer relations of children with congenital neurodevelopmental conditions (NDC). Participants: Thirty-four children with cerebral palsy or spina bifida and 41 typically developing (TD) children, ages 6-12. Measures: Personality Inventory for Children-Second Edition (PIC-2), Social Network Inventory for Children-Child Version, and the Friendship Quality Questionnaire-Revised (FQQ-R). Results: Children with NDC report smaller social networks and lower quality of friendships than TD peers. Group × Gender interaction effects were not significant. Conclusion: Prior to adolescence, the social differences and risks associated with NDC do not appear to be gender specific. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Identification of FIR Wiener systems with unknown,non-invertible,polynomial non-linearities

Wiener systems consist of a linear dynamic system whose output is measured through a static non-linearity. In this paper we study the identification of single-input single-output Wiener systems with finite impulse response dynamics and polynomial output non-linearities. Using multi-index notation, we solve a least squares problem to estimate products of the coefficients of the non-linearity and the impulse response of the linear system. We then consider four methods for extracting the coefficients of the non-linearity and impulse response: direct algebraic solution, singular value decomposition, multi-dimensional singular value decomposition and prediction error optimization.     

Strictly positive-definite spike train kernels for point-process divergences

Exploratory tools that are sensitive to arbitrary statistical variations in spike train observations open up the possibility of novel neuroscientific discoveries. Developing such tools, however, is difficult due to the lack of Euclidean structure of the spike train space, and an experimenter usually prefers simpler tools that capture only limited statistical features of the spike train, such as mean spike count or mean firing rate. We explore strictly positive-definite kernels on the space of spike trains to offer both a structural representation of this space and a platform for developing statistical measures that explore features beyond count or rate. We apply these kernels to construct measures of divergence between two point processes and use them for hypothesis testing, that is, to observe if two sets of spike trains originate from the same underlying probability law. Although there exist positive-definite spike train kernels in the literature, we establish that these kernels are not strictly definite and thus do not induce measures of divergence. We discuss the properties of both of these existing nonstrict kernels and the novel strict kernels in terms of their computational complexity, choice of free parameters, and performance on both synthetic and real data through kernel principal component analysis and hypothesis testing.     

Planar-Based Visual Inertial Navigation: Observability Analysis and Motion Estimation

In this paper, we address the problem of ego-motion estimation by fusing visual and inertial information. The hardware consists of an inertial measurement unit (IMU) and a monocular camera. The camera provides visual observations in the form of features on a horizontal plane. Exploiting the geometric constraint of features on the plane into visual and inertial data, we propose a novel closed form measurement model for this system. Our first contribution in this paper is an observability analysis of the proposed planar-based visual inertial navigation system (VINS). In particular, we prove that the system has only three unobservable states corresponding to global translations parallel to the plane, and rotation around the gravity vector. Hence, compared to general VINS, an advantage of using features on the horizontal plane is that the vertical translation along the normal of the plane becomes observable. As the second contribution, we present a state-space formulation for the pose estimation in the analyzed system and solve it via a modified unscented Kalman filter (UKF). Finally, the findings of the theoretical analysis and 6-DoF motion estimation are validated by simulations as well as using experimental data.     

A system for recognizing a large class of engineering drawings

We present a system for recognizing a large class of engineering drawings characterized by alternating instances of symbols and connection lines. The class includes domains such as flowcharts, logic and electrical circuits, and chemical plant diagrams. The output of the system, a netlist identifying the symbol types and interconnections, may be used for design simulation or as a compact portable representation of the drawing. The automatic recognition task is divided into two stages: 1) domain-independent rules are used to segment symbols from connection lines in the drawing image that has been thinned, vectorized, and preprocessed in routine ways; 2) a drawing understanding subsystem works in concert with a set of domain-specific matchers to classify symbols and correct errors automatically. A graphical user interface is provided to correct residual errors interactively and to log data for reporting errors objectively. The system has been tested on a database of 64 printed images drawn from text books and handbooks in different domains and scanned at 150 and 300 dpi resolution     

Reconfigurable distributed storage for dynamic networks

This paper presents a new algorithm for implementing a reconfigurable distributed shared memory in an asynchronous dynamic network. The algorithm guarantees atomic consistency (linearizability) in all executions in the presence of arbitrary crash failures of the processing nodes, message delays, and message loss. The algorithm incorporates a classic quorum-based algorithm for read/write operations, and an optimized consensus protocol, based on Fast Paxos for reconfiguration, and achieves the design goals of: (i) allowing read and write operations to complete rapidly and (ii) providing long-term fault-tolerance through reconfiguration, a process that evolves the quorum configurations used by the read and write operations. The resulting algorithm tolerates dynamism. We formally prove our algorithm to be correct, we present its performance and compare it to existing reconfigurable memories, and we evaluate experimentally the cost of its reconfiguration mechanism.