Stability of systems with power-law non-linearities

It is shown that a sufficient condition for the asymptotic stability-in-the-large of an autonomous system containing a linear part with transfer function G(jω) and a non-linearity belonging to a class of power-law non-linearities with slope restriction [0, K] in cascade in a negative feedback loop is ReZ(jω)[G(jω) + 1 K] ≥ 0 for all ω where the multiplier is given by, Z(jω) = 1 + αjω + Y(jω) - Y(-jω) with a real, y(t) = 0 for t < 0 and ∫ 0 ∞ |y(t)|dt < 1 2c2, c2 being a constant associated with the class of non-linearity. Any allowable multiplier can be converted to the above form and this form leads to lesser restrictions on the parameters in many cases. Criteria for the case of odd monotonic non-linearities and of linear gains are obtained as limiting cases of the criterion developed. A striking feature of the present result is that in the linear case it reduces to the necessary and sufficient conditions corresponding to the Nyquist criterion. An inequality of the type |R(T) - R(- T)| ≤ 2c2R(0) where R(T) is the input-output cross-correlation function of the non-linearity, is used in deriving the results.

Board Effectiveness in Small Firms

Purpose: This study investigates boards of directors in small firms and explores the link between board effectiveness and the composition, roles and working styles of the boards. Design/methodology/approach: The study analyses data from a telephone survey of boards in 45 small firms. The survey included both the CEO and the chairperson of the board. Findings: The study identifies three groups of small firms: ‘paperboards’, ‘professional boards’, and ‘management lead’ boards. Results show that board composition, board roles and board working style influence board effectiveness in small firms. Research limitations/implications: Although the present study has found a link between board effectiveness and the role, composition and working style of boards of small firms, other potentially influential factors are also worthy of investigation; for example, the personal characteristics of the individuals involved, generational factors in family firms, and the situational circumstances of various firms. Practical implications: The study reveals that, in practice, the management team and the board are substantially intertwined in small firms. Originality/value: The main contributions are that the study explores how boards in small firms actually function and gives a detailed account of their composition and roles.More insight into this issue is important given the overemphasis within the governance literature on input-output studies using samples of large publiclylisted firms.

Applications of reverse saturable absorbers in laser science

A variety of applications exist for reverse saturable absorbers (RSAs) in the area of optical pulse processing and computing. An RSA can be used as power limiter/pulse smoother and energy limiter/pulse shortner of laser pulses. A combination of RSA and saturable absorber (SA) can be used for mode locking and pulse shaping between high power laser amplifiers in oscillator amplifier chain. Also, an RSA can be used for the construction of a molecular spatial light modulator (SLM) which acts as an input/output device in optical computers. A detailed review of the theoretical studies of these processes is presented. Current efforts to find RSAs at desired wavelength for testing these theoretical predictions are also discussed.

Local systems, global impacts : Using life cycle assessment to analyse the potential and constraints of industrial symbioses

Human activities extract and displace different substances and materials from the earth s crust, thus causing various environmental problems, such as climate change, acidification and eutrophication. As problems have become more complicated, more holistic measures that consider the origins and sources of pollutants have been called for. Industrial ecology is a field of science that forms a comprehensive framework for studying the interactions between the modern technological society and the environment. Industrial ecology considers humans and their technologies to be part of the natural environment, not separate from it. Industrial operations form natural systems that must also function as such within the constraints set by the biosphere. Industrial symbiosis (IS) is a central concept of industrial ecology. Industrial symbiosis studies look at the physical flows of materials and energy in local industrial systems. In an ideal IS, waste material and energy are exchanged by the actors of the system, thereby reducing the consumption of virgin material and energy inputs and the generation of waste and emissions. Companies are seen as part of the chains of suppliers and consumers that resemble those of natural ecosystems. The aim of this study was to analyse the environmental performance of an industrial symbiosis based on pulp and paper production, taking into account life cycle impacts as well. Life Cycle Assessment (LCA) is a tool for quantitatively and systematically evaluating the environmental aspects of a product, technology or service throughout its whole life cycle. Moreover, the Natural Step Sustainability Principles formed a conceptual framework for assessing the environmental performance of the case study symbiosis (Paper I). The environmental performance of the case study symbiosis was compared to four counterfactual reference scenarios in which the actors of the symbiosis operated on their own. The research methods used were process-based life cycle assessment (LCA) (Papers II and III) and hybrid LCA, which combines both process and input-output LCA (Paper IV). The results showed that the environmental impacts caused by the extraction and processing of the materials and the energy used by the symbiosis were considerable. If only the direct emissions and resource use of the symbiosis had been considered, less than half of the total environmental impacts of the system would have been taken into account. When the results were compared with the counterfactual reference scenarios, the net environmental impacts of the symbiosis were smaller than those of the reference scenarios. The reduction in environmental impacts was mainly due to changes in the way energy was produced. However, the results are sensitive to the way the reference scenarios are defined. LCA is a useful tool for assessing the overall environmental performance of industrial symbioses. It is recommended that in addition to the direct effects, the upstream impacts should be taken into account as well when assessing the environmental performance of industrial symbioses. Industrial symbiosis should be seen as part of the process of improving the environmental performance of a system. In some cases, it may be more efficient, from an environmental point of view, to focus on supply chain management instead.  

Model Based Target Tracking in a Wireless Network of Passive Infrared Sensor Nodes

We consider the problem of tracking an intruder in a plane region by using a wireless sensor network comprising motes equipped with passive infrared (PIR) sensors deployed over the region. An input-output model for the PIR sensor and a method to estimate the angular speed of the target from the sensor output are proposed. With the measurement model so obtained, we study the centralized and decentralized tracking performance using the extended Kalman filter.

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

Diffuse optical tomographic imager using a single light source

Near-infrared diffuse optical tomography (DOT) technique has the capability of providing good quantitative reconstruction of tissue absorption and scattering properties with additional inputs such as input and output modulation depths and correction for the photon leakage. We have calculated the two-dimensional (2D) input modulation depth from three-dimensional (3D) diffusion to model the 2D diffusion of photons. The photon leakage when light traverses from phantom to the fiber tip is estimated using a solid angle model. The experiments are carried for single (5 and 6 mm) as well as multiple inhomogeneities (6 and 8 mm) with higher absorption coefficient in a homogeneous phantom. Diffusion equation for photon transport is solved using finite element method and Jacobian is modeled for reconstructing the optical parameters. We study the development and performance of DOT system using modulated single light source and multiple detectors. The dual source methods are reported to have better reconstruction capabilities to resolve and localize single as well as multiple inhomogeneities because of its superior noise rejection capability. However, an experimental setup with dual sources is much more difficult to implement because of adjustment of two out of phase identical light probes symmetrically on either side of the detector during scanning time. Our work shows that with a relatively simpler system with a single source, the results are better in terms of resolution and localization. The experiments are carried out with 5 and 6 mm inhomogeneities separately and 6 and 8 mm inhomogeneities both together with absorption coefficient almost three times as that of the background. The results show that our experimental single source system with additional inputs such as 2D input/output modulation depth and air fiber interface correction is capable of detecting 5 and 6 mm inhomogeneities separately and can identify the size difference of multiple inhomogeneities such as 6 and 8 mm. The localization error is zero. The recovered absorption coefficient is 93% of inhomogeneity that we have embedded in experimental phantom.

Novel algorithm for online voltage stability assessment based on feed forward neural network

This paper presents an artificial feed forward neural network (FFNN) approach for the assessment of power system voltage stability. A novel approach based on the input-output relation between real and reactive power, as well as voltage vectors for generators and load buses is used to train the neural net (NN). The input properties of the feed forward network are generated from offline training data with various simulated loading conditions using a conventional voltage stability algorithm based on the L-index. The neural network is trained for the L-index output as the target vector for each of the system loads. Two separate trained NN, corresponding to normal loading and contingency, are investigated on the 367 node practical power system network. The performance of the trained artificial neural network (ANN) is also investigated on the system under various voltage stability assessment conditions. As compared to the computationally intensive benchmark conventional software, near accurate results in the value of L-index and thus the voltage profile were obtained. Proposed algorithm is fast, robust and accurate and can be used online for predicting the L-indices of all the power system buses. The proposed ANN approach is also shown to be effective and computationally feasible in voltage stability assessment as well as potential enhancements within an overall energy management system in order to determining local and global stability indices

Physical realizations: transforming into physical embodiments of concepts in the design of mechanical movements

Conceptual design involves identification of required functions of the intended design, generation of concepts to fulfill these functions, and evaluation of these concepts to select the most promising ones for further development. The focus of this paper is the second phase-concept generation, in which a challenge has been to develop possible physical embodiments to offer designers for exploration and evaluation. This paper investigates the issue of how to transform and thus synthesise possible generic physical embodiments and reports an implemented method that could automatically generate these embodiments. In this paper, a method is proposed to transform a variety of possible initial solutions to a design problem into a set of physical solutions that are described in terms of abstraction of mechanical movements. The underlying principle of this method is to make it possible to link common attributes between a specific abstract representation and its possible physical objects. For a given input, this method can produce a set of concepts in terms of their generic physical embodiments. The method can be used to support designers to start with a given input-output function and systematically search for physical objects for design consideration in terms of simplified functional, spatial, and mechanical movement requirements.

Synthesis of Instruction Extensions on HyperCell, a Reconfigurable Datapath

In this paper we present HyperCell as a reconfigurable datapath for Instruction Extensions (IEs). HyperCell comprises an array of compute units laid over a switch network. We present an IE synthesis methodology that enables post-silicon realization of IE datapaths on HyperCell. The synthesis methodology optimally exploits hardware resources in HyperCell to enable software pipelined execution of IEs. Exploitation of temporal reuse of data in HyperCell results in significant reduction of input/output bandwidth requirements of HyperCell.

Part II: Design of Well Doping Profile for Improved Breakdown and Mixed-Signal Performance of STI-Type DePMOS Device

Shallow-trench isolation drain extended pMOS (STI-DePMOS) devices show a distinct two-stage breakdown. The impact of p-well and deep-n-well doping profile on breakdown characteristics is investigated based on TCAD simulations. Design guidelines for p-well and deep-n-well doping profile are developed to shift the onset of the first-stage breakdown to a higher drain voltage and to avoid vertical punch-through leading to early breakdown. An optimal ratio between the OFF-state breakdown voltage and the ON-state resistance could be obtained. Furthermore, the impact of p-well/deep-n-well doping profile on the figure of merits of analog and digital performance is studied. This paper aids in the design of STI drain extended MOSFET devices for widest safe operating area and optimal mixed-signal performance in advanced system-on-chip input-output process technologies.

DANTE: Un MEGA dinámico para el control del cambio climático en España

El estudio de los impactos económicos de las políticas de control del cambio climático requiere del uso de modelos adecuados. Este artículo presenta un Modelo Dinámico de Equilibrio General Aplicado tipo Ramsey. El modelo implementa un mercado de permisos de emisión perfecto que garantiza una reducción de emisiones eficiente y efectiva, permitiéndonos calcular los costes económicos mínimos asociados al control de las emisiones de efecto invernadero. Además aprovecha al máximo la disponibilidad de datos existentes en España 1) utilizando una matriz de contabilidad social (o SAM) energética mediante la integración de la información económica de la Tablas Input-Output y la información energética de los Balances Energéticos y 2) considerando todas la emisiones sujetas a control además del CO2. Los MEGAs dinámicos son inéditos en cuanto a su elaboración y aplicación en España y permiten investigar ex-ante los efectos de políticas públicas en el medio y en largo plazo.

The economic importance of marine angler expenditures in the United States.

In 1998, the National Marine Fisheries Service (NMFS) began a series of marine angler expenditure surveys in the coastal regions of the United States (U.S.) to evaluate marine recreational fishing expenditures and the financial impacts of these expenditures in each region and the U.S. as a whole. In this report, we use the previously estimated expenditure estimates to assess the total financial impact of anglers’ saltwater expenditures. Estimates are provided for sales, income, employment, and tax impacts for each coastal state in the U.S. Aggregate estimates are also provided for the entire U.S., excluding Alaska, Hawaii, and Texas. Direct, indirect, and induced effects associated with resident and non-resident angler expenditures were estimated using a regional input-output modeling system called IMPLAN Pro. Nationwide, recreational saltwater fishing generated over $30.5 billion in sales in 2000, nearly $12.0 billion in income, and supported nearly 350,000 jobs. Approximately 89 cents of every dollar spent by saltwater anglers was estimated to remain within the U.S. economy. At the state level, many of the goods anglers purchased were imports, and, as such, as little as 44 cents of every dollar stayed in Rhode Island and as much as 80 cents of every dollar stayed in Georgia. In the Northeast, the highest impacts were generated in New Jersey, even though recreational fishing expenditures in Massachusetts and Maryland were considerably higher. In the Southeast, the highest impacts were generated in Florida, and on the Pacific Coast, the highest impacts were generated in California. Expenditures on boat maintenance/expenses generated more impacts than any other expenditure category in the U.S. Expenditures on rods and reels was the single most important expense category in terms of generating impacts in most of the Northeast states. Expenditures on boat expenses generated the highest in most Southeast states, and expenditures for boat accessories produced the highest impacts in most Pacific Coast states.(PDF file contains 184 pages.)

An observer-based vaccination control law for an SEIR epidemic model based on feedback linearization techniques for nonlinear systems

This paper presents a vaccination strategy for fighting against the propagation of epidemic diseases. The disease propagation is described by an SEIR (susceptible plus infected plus infectious plus removed populations) epidemic model. The model takes into account the total population amounts as a refrain for the illness transmission since its increase makes the contacts among susceptible and infected more difficult. The vaccination strategy is based on a continuous-time nonlinear control law synthesised via an exact feedback input-output linearization approach. An observer is incorporated into the control scheme to provide online estimates for the susceptible and infected populations in the case when their values are not available from online measurement but they are necessary to implement the control law. The vaccination control is generated based on the information provided by the observer. The control objective is to asymptotically eradicate the infection from the population so that the removed-by-immunity population asymptotically tracks the whole one without precise knowledge of the partial populations. The model positivity, the eradication of the infection under feedback vaccination laws and the stability properties as well as the asymptotic convergence of the estimation errors to zero as time tends to infinity are investigated.

Experimental and theoretical studies of Notch signaling-mediated spatial pattern

Notch signaling acts in many diverse developmental spatial patterning processes. To better understand why this particular pathway is employed where it is and how downstream feedbacks interact with the signaling system to drive patterning, we have pursued three aims: (i) to quantitatively measure the Notch system's signal input/output (I/O) relationship in cell culture, (ii) to use the quantitative I/O relationship to computationally predict patterning outcomes of downstream feedbacks, and (iii) to reconstitute a Notch-mediated lateral induction feedback (in which Notch signaling upregulates the expression of Delta) in cell culture. The quantitative Notch I/O relationship revealed that in addition to the trans-activation between Notch and Delta on neighboring cells there is also a strong, mutual cis-inactivation between Notch and Delta on the same cell. This feature tends to amplify small differences between cells. Incorporating our improved understanding of the signaling system into simulations of different types of downstream feedbacks and boundary conditions lent us several insights into their function. The Notch system converts a shallow gradient of Delta expression into a sharp band of Notch signaling without any sort of feedback at all, in a system motivated by the Drosophila wing vein. It also improves the robustness of lateral inhibition patterning, where signal downregulates ligand expression, by removing the requirement for explicit cooperativity in the feedback and permitting an exceptionally simple mechanism for the pattern. When coupled to a downstream lateral induction feedback, the Notch system supports the propagation of a signaling front across a tissue to convert a large area from one state to another with only a local source of initial stimulation. It is also capable of converting a slowly-varying gradient in parameters into a sharp delineation between high- and low-ligand populations of cells, a pattern reminiscent of smooth muscle specification around artery walls. Finally, by implementing a version of the lateral induction feedback architecture modified with the addition of an autoregulatory positive feedback loop, we were able to generate cells that produce enough cis ligand when stimulated by trans ligand to themselves transmit signal to neighboring cells, which is the hallmark of lateral induction.