Control of high precision roll-to-roll manufacturing systems

The flexible electronic industry has been growing rapidly over the past decade. One of the barriers to commercialization is the high cost of manufacturing micro- and nano-scale printed electronics using traditional methods. Roll-to-roll manufacturing has been identified as a method of achieving low cost and high throughput. A dynamic model of a roll-to-roll system is presented. In all roll-to-roll applications, tension and velocity must be accurately controlled to desired reference trajectories to ensure a quality finished product. Additionally, a registration error model is presented for the control design. Minimization of the registration is the primary objective for flexible electronics, but web tension and velocity cannot be neglected. The model is needed in order to formulate a methodology that can simultaneously control tension, velocity, and registration error in the presence of disturbances. Micro and nano-scale features are susceptible to damage from friction between the web and the roller. Therefore, tension estimation techniques is highly desired to eliminate load cells from the system. The reduced order observer, extended Kalman filter, and an unknown input observer is presented. Development of tension and velocity control strategies have historically revolved around decentralized SISO control schemes. In order to achieve higher precision, a centralized MIMO strategy is proposed and compared to decentralized SISO. The advantage of the MIMO controller improved handling of the tension velocity coupling in roll-to-roll systems. The tension observer is introduced to the control design and evaluated for overall effectiveness. In simulation, the centralized MIMO control with the unknown input observer demonstrated superior tension and velocity tracking as well as minimal registration error. Development of the proposed MIMO control strategy can enable flexible electronic fabrication using roll-to-roll manufacturing.Mechanical Engineerin

Numerical and physical simulation of rapid microstructural evolution of gas atomised Ni superalloy powders

The rapid microstructural evolution of gas atomised Ni superalloy powder compacts over timescales of a few seconds was studied using a Gleeble 3500 thermomechanical simulator, finite element based numerical model and electron microscopy. The study found that the microstructural changes were governed by the characteristic temperatures of the alloy. At a temperature below the γ' solvus, the powders maintained dendritic structures. Above the γ' solvus temperature but in the solid-state, rapid grain spheroidisation and coarsening occurred, although the fine-scale microstructures were largely retained. Once the incipient melting temperature of the alloy was exceeded, microstructural change was rapid, and when the temperature was increased into the solid + liquid state, the powder compact partially melted and then re-solidified with no trace of the original structures, despite the fast timescales. The study reveals the relationship between short, severe thermal excursions and microstructural evolution in powder processed components, and gives guidance on the upper limit of temperature and time for powder-based processes if desirable fine-scale features of powders are to be preserved

Enhancing Chinese SME performance through innovative HR practices

Purpose &ndash; The purpose of this paper is to show how understanding of human resource (HR) management practices which have been adopted in the emerging markets such as that in China is particularly interesting to academia and management practitioners. The purpose of this study is to shed some light on the implementation of innovative HR practices among 74 Chinese small and medium-sized enterprises (SMEs) and to explain how the HR practices influence their firm performance. Design/methodology/approach &ndash; Cluster analysis is used to group Chinese SMEs according to their adoption of innovative human resource (HR) practices and examine how the practices are associated with HR outcomes and firm performance. Findings &ndash; It is found that the membership of clusters is influenced by several factors, including ownership, age and size of firms. These characteristics have influenced the motivation, capacity and ability of firms in the sample to adopt high performance human resource practices. The extent to which firms have adopted innovative human resource practices is shown to be closely associated with human resource outcomes and firm performance. Originality/value &ndash; The key implication is that SMEs, especially those rapidly developing domestic and collectively owned small firms, as well as those state-owned enterprises in China, may see clearly the benefits of devoting greater attention to HR practices to achieve their future growth potential.<br /

Squid (Loligo pealei) giant fiber system : a model for studying neurodegeneration and dementia?

Author Posting. © Marine Biological Laboratory, 2006. This article is posted here by permission of Marine Biological Laboratory for personal use, not for redistribution. The definitive version was published in Biological Bulletin 210 (2006): 318-333.In many neurodegenerative disorders that lead to memory loss and dementia, the brain pathology responsible for neuronal loss is marked by accumulations of proteins in the form of extracellular plaques and intracellular filamentous tangles, containing hyperphosphorylated cytoskeletal proteins. These are assumed to arise as a consequence of deregulation of a normal pattern of topographic phosphorylation—that is, an abnormal shift of cytoskeletal protein phosphorylation from the normal axonal compartment to cell bodies. Although decades of studies have been directed to this problem, biochemical approaches in mammalian systems are limited: neurons are too small to permit separation of cell body and axon compartments. Since the pioneering studies of Hodgkin and Huxley on the giant fiber system of the squid, however, the stellate ganglion and its giant axons have been the focus of a large literature on the physiology and biochemistry of neuron function. This review concentrates on a host of studies in our laboratory and others on the factors regulating compartment-specific patterns of cytoskeletal protein phosphorylation (primarily neurofilaments) in an effort to establish a normal baseline of information for further studies on neurodegeneration. On the basis of these data, a model of topographic regulation is proposed that offers several possibilities for further studies on potential sites of deregulation that may lead to pathologies resembling those seen in mammalian and human brains showing neurodegeneration, dementia, and neuronal cell death.This research was supported by the Intramural Research Programs of the NIH, National Institute of Neurological Disorders and Stroke

Robustness and Distance Discrimination of Adaptive Near Field Beamformers

A robust adaptive beamformer is proposed using the near field regionally constrained adaptive approach that designs a set of linear constraints by filtering on a low rank subspace of the near field signal over a spatial region and a wide frequency band. This method can accurately control the beam-former response over the designed spatial-temporal region using a small number of linear constraint vectors and improve the robustness against target location errors. Meanwhile, this method enhances the capability of the near field beamformer in distance discrimination without additional constraints so that interference impinging at the same direction as the desired signal but at a different distance can be effectively suppressed

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task

Minimising bulk lifetime degradation during the processing of interdigitated back contact silicon solar cell

In this work, we develop a fabrication process for an interdigitated back contact solar cell using BBr3 diffusion to form the p+ region and POCl3 diffusion to form the n+ regions. We use the industry standard technology computer-aided design modelling package, Synopsys Sentaurus, to optimize the geometry of the device using doping profiles derived from electrochemical capacitance voltage measurements. Cells are fabricated using n-type float-zone silicon substrates with an emitter fraction of 60%, with localized back surface field and contact holes. Key factors affecting cell performance are identified including the impact of e-beam evaporation, dry etch damage, and bulk defects in the float zone silicon substrate. It is shown that a preoxidation treatment of the wafer can lead to a 2 ms improvement in bulk minority carrier lifetime at the cell level, resulting in a 4% absolute efficiency boost

DNA Polymerase Conformational Dynamics and the Role of Fidelity-Conferring Residues: Insights from Computational Simulations

Herein we investigate the molecular bases of DNA polymerase I conformational dynamics that underlie the replication fidelity of the enzyme. Such fidelity is determined by conformational changes that promote the rejection of incorrect nucleotides before the chemical ligation step. We report a comprehensive atomic resolution study of wild type and mutant enzymes in different bound states and starting from different crystal structures, using extensive molecular dynamics (MD) simulations that cover a total timespan of ~5 ms. The resulting trajectories are examined via a combination of novel methods of internal dynamics and energetics analysis, aimed to reveal the principal molecular determinants for the (de)stabilization of a certain conformational state. Our results show that the presence of fidelity-decreasing mutations or the binding of incorrect nucleotides in ternary complexes tend to favor transitions from closed toward open structures, passing through an ensemble of semi-closed intermediates. The latter ensemble includes the experimentally observed ajar conformation which, consistent with previous experimental observations, emerges as a molecular checkpoint for the selection of the correct nucleotide to incorporate. We discuss the implications of our results for the understanding of the relationships between the structure, dynamics, and function of DNA polymerase I at the atomistic level