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Dynamic mechanoelectrochemistry of polypyrrole membranes via shear-force tracking

PHYSICAL CHEMISTRY CHEMICAL PHYSICS 2016年第26期18卷 17366-17372页

作者： Northcutt, Robert G. Heinemann, Christian Sundaresan, Vishnu Baba Ohio State Univ Scott Lab W363 Columbus OH 43210 USA HEKA Elekt Dr Schulze GmbH Weisenstr 71 D-67466 Lambrecht Pfalz Germany Ohio State Univ Scott Lab E528 Columbus OH 43210 USA

Mechanoelectrochemistry is the study of elastic and plastic deformation of materials during reversible reduction and oxidation processes. In this article, we introduce shear-force tracking as a method to dynamically measure mechanical (strain), chemical (ion transport), and electrical (applied redox potentials) responses of the conducting polymer polypyrrole (PPy) during redox reactions. This tracking technique uses a control algorithm to maintain a set distance between a ultramicroelectrode (UME) tip and a surface via shear-force regulation. Due to the sensitivity of shear-force signals in the near field of substrate surfaces, a significantly improved signal to noise ratio (20 : 1) is possible and allows for nanoscale measurement of redox events. Chemomechanical coupling (the ratio of ion transport to resultant extensional actuation) is calculated for PPy-based membranes of various thicknesses based on a mechanistic interpretation of charge storage in redox active conducting polymers. The measured dynamic response demonstrates that chemomechanical coupling is not a constant, as assumed in literature, but is dependent on the polymers state of charge and the direction (ingress/egress) of ion transport.

关键词： polypyrrole signal to noise ratios Materials deformation SHEAR FORCE Redox processes elastoplastic deformation oxidation reduction potential ion transport computer algorithms

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Computational discovery of pathway-level genetic vulnerabilities in non-small-cell lung cancer

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BIOINFORMATICS 2016年第9期32卷 1373-1379页

作者： Young, Jonathan H. Peyton, Michael Kim, Hyun Seok McMillan, Elizabeth Minna, John D. White, Michael A. Marcotte, Edward M. Univ Texas Austin Inst Computat Engn & Sci Austin TX 78712 USA Univ Texas Austin Ctr Syst & Synthet Biol Austin TX 78712 USA Univ Texas Austin Dept Mol Biosci Austin TX 78712 USA Univ Texas SW Med Ctr Dallas Hamon Ctr Therapeut Oncol Res Dallas TX 75390 USA Yonsei Univ Coll Med Severance Biomed Sci Inst Seoul South Korea Univ Texas SW Med Ctr Dallas Dept Cell Biol Dallas TX 75390 USA

Motivation: Novel approaches are needed for discovery of targeted therapies for non-small-cell lung cancer (NSCLC) that are specific to certain patients. Whole genome RNAi screening of lung cancer cell lines provides an ideal source for determining candidate drug targets. Results: Unsupervised learning algorithms uncovered patterns of differential vulnerability across lung cancer cell lines to loss of functionally related genes. Such genetic vulnerabilities represent candidate targets for therapy and are found to be involved in splicing, translation and protein folding. In particular, many NSCLC cell lines were especially sensitive to the loss of components of the LSm2-8 protein complex or the CCT/TRiC chaperonin. Different vulnerabilities were also found for different cell line subgroups. Furthermore, the predicted vulnerability of a single adenocarcinoma cell line to loss of the Wnt pathway was experimentally validated with screening of small-molecule Wnt inhibitors against an extensive cell line panel.

关键词： Lungs -- Cancer Cancer genetics Cancer patients Cell lines computer algorithms

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Towards Minimum-Information Adaptive Controllers for Robot Manipulators

Towards Minimum-Information Adaptive Controllers for Robot M...

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American Control Conference

作者： Tobia Marcucci Cosimo Della Santina Marco Gabiccini Antonio Bicchi Research Center "Enrico Piaggio" University of Pisa Largo Lucio Lazzarino 1 56122 Pisa Italy

ISBN: (纸本)9781509045839

The aim of this paper is to move a step in the direction of determining the minimum amount of information needed to control a robot manipulator within the framework of adaptive control. Recent innovations in the state of the art show how global asymptotic trajectory tracking can be achieved despite the presence of uncertainties in the kinematic and dynamic models of the robot. However, a clear distinction between which parameters can be included among the uncertainties, and which parameters can not, has not been drawn yet. Since most of the adaptive control algorithms are built on linearly parameterized models, we propose to reformulate the problem as finding a procedure to determine whether and how a given dynamical system can be linearly parameterized with respect to a specific set of parameters. Within this framework, we show how the trajectory tracking problem of a manipulator can be accomplished with the only knowledge of the number of joints of the manipulator. As an illustrative example, we present the end-effector trajectory tracking control of a robot initialized with the kinematic model of a different robot.

关键词： Controllers Robot Manipulators Robot Manipulators Robot Manipulator Controllers kinematics trajectory tracking adaptive controller Uncertainty DYNAMICAL SYSTEMS Frameworks computer algorithms dynamic models

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Improving microRNA target prediction by modeling with unambiguously identified microRNA-target pairs from CLIP-ligation studies

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BIOINFORMATICS 2016年第9期32卷 1316-1322页

作者： Wang, Xiaowei Washington Univ Sch Med Dept Radiat Oncol St Louis MO 63108 USA

Motivation: MicroRNAs (miRNAs) are small non-coding RNAs that are extensively involved in many physiological and disease processes. One major challenge in miRNA studies is the identification of genes targeted by miRNAs. Currently, most researchers rely on computational programs to initially identify target candidates for subsequent validation. Although considerable progress has been made in recent years for computational target prediction, there is still significant room for algorithmic improvement. Results: Here, we present an improved target prediction algorithm, which was developed by modeling high-throughput profiling data from recent CLIPL (crosslinking and immunoprecipitation followed by RNA ligation) sequencing studies. In these CLIPL-seq studies, the RNA sequences in each miRNA-target pair were covalently linked and unambiguously determined experimentally. By analyzing the CLIPL data, many known and novel features relevant to target recognition were identified and then used to build a computational model for target prediction. Comparative analysis showed that the new algorithm had improved performance over existing algorithms when applied to independent experimental data.

关键词： MICRORNA computer simulation computer algorithms COMPARATIVE studies FORECASTING

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APPAGATO: an APproximate PArallel and stochastic GrAph querying TOol for biological networks

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BIOINFORMATICS 2016年第14期32卷 2159-2166页

作者： Bonnici, Vincenzo Busato, Federico Micale, Giovanni Bombieri, Nicola Pulvirenti, Alfredo Giugno, Rosalba Univ Verona Dept Comp Sci Str Le Grazie 15 I-37134 Verona Italy Univ Catania Dept Math & Comp Sci Viale Doria 6 I-95125 Catania Italy Univ Catania Dept Clin & Expt Med Via Palermo 636 I-95122 Catania Italy

Motivation: Biological network querying is a problem requiring a considerable computational effort to be solved. Given a target and a query network, it aims to find occurrences of the query in the target by considering topological and node similarities (i.e. mismatches between nodes, edges, or node labels). Querying tools that deal with similarities are crucial in biological network analysis because they provide meaningful results also in case of noisy data. In addition, as the size of available networks increases steadily, existing algorithms and tools are becoming unsuitable. This is rising new challenges for the design of more efficient and accurate solutions. Results: This paper presents APPAGATO, a stochastic and parallel algorithm to find approximate occurrences of a query network in biological networks. APPAGATO handles node, edge and node label mismatches. Thanks to its randomic and parallel nature, it applies to large networks and, compared with existing tools, it provides higher performance as well as statistically significant more accurate results. Tests have been performed on protein-protein interaction networks annotated with synthetic and real gene ontology terms. Case studies have been done by querying protein complexes among different species and tissues. Availability and implementation: APPAGATO has been developed on top of CUDA-C++ Toolkit 7.0 framework. The software is available online http://***/similar to bombieri/APPAGATO.

关键词： Biological networks Computational biology Graph algorithms computer algorithms Parallel algorithms

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Disturbance rejection of real power flow by grid-tied inverter

Disturbance rejection of real power flow by grid-tied invert...

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American Control Conference

作者： Xin Zhao Benjamin T. Gwynn Raymond A. de Callafon William Torre Dept. of Mechanical and Aerospace Engineering and the Sychrophasor Grid Monitoring and Automation (SyGMA) laboratory at the University of California San Diego Center for Energy Research University of California San Diego

ISBN: (纸本)9781509045839

High penetration of distributed and renewable power generation systems challenges the conventional paradigm of stabilizing power systems with the standard rotational inertia used in power generation. To improve the stability of a power system, advanced disturbance rejection control techniques can be used to provide damping and power disturbance mitigation. An approach for three-phase (real) power disturbance mitigation using real-time feedback control is outlined in this paper. The control algorithm for power disturbance rejection is formulated via the internal model principle, computed via minimum variance control and designed to operate with a commercial grid-tied inverter. The end result is a real-time controlled inverter that is able to reduce power disturbances created by unanticipated load changes in an electrical grid. An experimental setup is used to validate the proposed control algorithm through on/off switching of a dynamic load and the results illustrate the feasibility of the proposed controller for real-time mitigation of power flow oscillations.

关键词： Power Disturbances Disturbance rejection Power flow computer algorithms Inventors power systems Power system stability real-time feedback control Internal model principle Variance Power production dynamic loading

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The Minimum Step Linkage Algorithm and Cumulative Cost Model for Time-Dependent Shortest Paths

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TRANSACTIONS IN GIS 2016年第6期20卷 962-975页

作者： Indriasari, Vini Dean, Denis Univ Texas Dallas Geog & Geospatial Informat Sci Dallas TX USA

Real-word road networks are typically time-dependent, but research on time-dependent shortest paths (TDSPs) is not as rich as that on static shortest paths (SSPs). For large networks, some speed-up techniques are usually applied to compute SSPs. Unfortunately, many of these techniques are impractical for TDSPs, especially the techniques requiring a precomputation because the number of time instances may be incredibly large or even infinite. Therefore, this study introduced a new TDSP technique that is able to overcome the issue of infinite time instances by utilizing network geometry alone for the pre-computation, namely the Minimum Step Linkage (MSL) algorithm. It works with a data model called Cumulative Cost Model (CCM). The theoretical concepts of the new data model and algorithm are described, and its performances were tested and compared with existing algorithms through a series of simulations.

关键词： NAVIGATION MATHEMATICAL models computer algorithms DATA modeling computer simulation MATRICES REAL-time computing

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Resilient Consensus for Time-Varying Networks of Dynamic Agents

Resilient Consensus for Time-Varying Networks of Dynamic Age...

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American Control Conference

作者： David Saldana Amanda Prorok Shreyas Sundaram Mario F. M. Campos Vijay Kumar University of Pennsylvania Purdue University Universidade Federal de Minas Gerais

ISBN: (纸本)9781509045839

We consider networks of dynamic agents that execute cooperative, distributed control algorithms in order to coordinate themselves and to collectively achieve goals. The agents rely on consensus algorithms that are based on local interactions with their nearest neighbors in the communication graph. However, such systems are not robust to one or more malicious agents and there are no performance guarantees when one or more agents do not cooperate. Recent results in network science deal with this problem by requiring specific graph topological properties. Nevertheless, the required network topologies imply high connectivity levels, which may be difficult to achieve in systems that exhibit time-varying communication graphs. In this paper, we propose an approach that provides resilience for networks of dynamic agents whose communication graphs are time-varying. We show that in the case where the required connectivity constraints cannot be satisfied at all times, we can resort to a consensus protocol that guarantees resilience when the union of communication graphs over a bounded period of time satisfies certain robustness properties. We propose a control policy to attain resilient behavior in the context of perimeter surveillance with a team of robots. We provide simulations that support our theoretical analyses.

关键词： Time varying networks Agents control strategy Resilience computer algorithms Communication Research Network topology Nearest neighbor Network

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Reducing Controller Updates Via Event-Triggered Model Predictive Control In An Embedded Artificial Pancreas

Reducing Controller Updates Via Event-Triggered Model Predic...

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American Control Conference

作者： Ankush Chakrabarty Stamatina Zavitsanou Francis J. Doyle Eyal Dassau Harvard John A. Paulson School of Engineering and Applied Sciences Harvard University Cambridge MA 02138 USA

ISBN: (纸本)9781509045839

An essential component in the design of a wearable or implantable artificial pancreas is a low-power, embedded control algorithm capable of tight glucose regulation in people with Type 1 Diabetes Mellitus (T1DM). In this paper, physiological insights into glucose management are leveraged to formulate a safety-constrained, event-triggered model predictive control (MPC) algorithm customized to reduce the number of controller updates, leading to reduced processor runtime and lowered energy expenditure. The proposed event-triggered MPC is deployed on a wearable (65 mm × 30 mm × 5 mm) embedded prototyping platform running a single core 1 GHz 32-bit ARM11 processor. The efficiency of the proposed method is verified in a hardware-in-the-loop simulation study with ten subjects simulated in-silico using the United States Food and Drug Administration (US FDA) accepted UVA/Padova Metabolic Simulator. The controller is challenged with large, unannounced meals and sudden hypoglycemia. Empirical studies reveal that the proposed event-triggered MPC remains idle for 50% of the time in closed-loop, while maintaining 32 (out of 53) hours in the clinically accepted safe glucose region (70-180 mg/dL), compared to 35.5 hours for its time-triggered counterpart, with reduced time in hypoglycemia. Thus, the proposed strategy significantly reduces the number of controller updates required, without compromising safety in glycemic regulation.

关键词： Event trigger component design Artificial Pancreas Wearable Controllers Model predictive control Predictive control computer algorithms In Silico

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Remaining useful life estimation of Lithium-ion batteries based on thermal dynamics

Remaining useful life estimation of Lithium-ion batteries ba...

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American Control Conference

作者： Dong Zhang Satadru Dey Hector E. Perez Scott J. Moura Energy Controls and Applications Lab (eCAL) in Civil and Environmental Engineering University of California Berkeley 94720 USA

ISBN: (纸本)9781509045839

Longevity remains one of the key issues for Lithium-ion (Li-ion) battery technology. On-board Intelligent Battery Management Systems (BMS) implement health-conscious control algorithms in order to increase battery lifetime while maintaining the performance. For such algorithms, the information on Remaining Useful Life (RUL) of the battery is crucial for optimizing the battery performance and ensuring minimal degradation. However, accurate prediction of RUL remains one of the most challenging tasks until this date. In this paper, we present an online RUL estimation scheme for Li-ion batteries, which is designed from a thermal perspective. The key novelty lies in (i) leveraging thermal dynamics to predict RUL and, (ii) developing a hierarchical estimation algorithm with provable convergence properties. The algorithm consists of three stages working in cascade. The first two stages estimate the core temperature, State-of-Charge (SOC) and battery capacity based on a combination of thermal and Coulombic SOC model. The third stage receives this capacity information and in turn identifies a capacity fade aging model. Finally, we estimate the RUL by predicting the battery capacity fade over the cycles utilizing the identified aging model. A combination of sliding mode observers and nonlinear least-squares algorithm is utilized for designing the estimators. Simulation results illustrate the performance of the proposed RUL estimation scheme.

关键词： lithium ion battery Life estimation remaining service life Battery Capacity computer algorithms Batteries Thermal double-stage Battery Life algorithms

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