Geochemistry and isotopes at DSDP Site 76-533

Concentrations and d34S and d13C values were determined on SO4, HCO3, CO2, and CH4 in interstitial water and gas samples from the uppermost 400 m of sediment on the Blake Outer Ridge. These measurements provide the basis for detailed interpretation of diagenetic processes associated with anaerobic respiration of electrons generated by organic- matter decomposition. The sediments are anaerobic at very shallow depths (<1 m) below the seafloor. Sulfate reduction is confined to the uppermost 15 m of sediment and results in a significant outflux of oxidized carbon from the sediments. At the base of the sulfate reduction zone, upward-diffusing CH4 is being oxidized, apparently in conjunction with SO4 reduction. CH4 generation by CO2 reduction is the most important metabolic process below the 15-m depth. CO2 removal is more rapid than CO2 input over the depth interval from 15 to 100 m, and results in a slight decrease in HCO3 concentration accompanied by a 40 per mil positive shift in d13C. The differences among coexisting CH4, CO2, and HCO3 are consistent with kinetic fractionation between CH4 and dissolved CO2, and equilibrium fractionation between CO2 and HCO3. At depths greater than 100 m, the rate of input of CO2 (d13C = -25 per mil) exceeds by 2 times the rate of removal of CO2 by conversion to CH4 (d13C of -60 to -65 per mil). This results in an increase of dissolved HCO3 concentration while maintaining d13C of HCO3 relatively constant at +10 per mil. Non-steady-state deposition has resulted in significantly higher organic carbon contents and unusually high (70 meq/l) pore-water alkalinities below 150 m. These high alkalinities are believed to be related more to spontaneous decarboxylation reactions than to biological processes. The general decrease in HCO3 concentration with constant d13C over the depth interval of 200 to 400 m probably reflects increased precipitation of authigenic carbonate. Input-output carbon isotope-mass balance calculations, and carbonate system equilibria in conjunction with observed CO2-CH4 ratios in the gas phase, independently suggest that CH4 concentrations on the order of 100 mmol/kg are present in the pore waters of Blake Outer Ridge sediments. This quantity of CH4 is believed to be insufficient to saturate pore waters and stabilize the CH4*6H2O gas hydrate. Results of these calculations are in conflict with the physical recovery of gas hydrate from 238 m, and with the indirect evidence (seismic reflectors, sediment frothing, slightly decreasing salinity and chlorinity with depth, and pressure core barrel observations) of gas-hydrate occurrence in these sediments. Resolution of this apparent conflict would be possible if CH4 generation were restricted to relatively thin (1-10 m) depth intervals, and did not occur uniformly at all depths throughout the sediment column, or if another methanogenic process (e.g., acetate fermentation) were a major contributor of gas.

Interindustrial Structure in the Asia-Pacific Region: Growth and Integration, by Using 2000 AIO Table

Over the past 20 years Asian countries have achieved a certain degree of economic growth and at the same time deepened spatial interdependence. In January 2006, IDE completed the 2000 Asian International Input-Output Table, which covers eight major East Asian countries/regions as well as Japan and the United States. Given the dynamic changes in the economies of East Asia, this paper attempts to summarize the characteristics and their patterns of change in industrial structures and trade structures of the countries/regions in the Asia-Pacific region from the three viewpoints of time, space, and industry, by using the AIO table for 1985, 1990, 1995, and 2000.

Application of the Extended Kalman filter to fuzzy modeling: Algorithms and practical implementation

Modeling phase is fundamental both in the analysis process of a dynamic system and the design of a control system. If this phase is in-line is even more critical and the only information of the system comes from input/output data. Some adaptation algorithms for fuzzy system based on extended Kalman filter are presented in this paper, which allows obtaining accurate models without renounce the computational efficiency that characterizes the Kalman filter, and allows its implementation in-line with the process

A slide scanner input for paramatrix,

"Supported in part by the Office of Naval Research. Contract no.N00014-67-A-0305-0007."

Subcellular localization determines MAP kinase signal output

The Raf-MEK-ERK MAP kinase cascade transmits signals from activated receptors into the cell to regulate proliferation and differentiation. The cascade is controlled by the Ras GTPase, which recruits Raf from the cytosol to the plasma membrane for activation. In turn, MEK, ERK, and scaffold proteins translocate to the plasma membrane for activation. Here, we examine the input-output properties of the Raf-MEK-ERK MAP kinase module in mammalian cells activated in different cellular contexts. We show that the MAP kinase module operates as a molecular switch in vivo but that the input sensitivity of the module is determined by subcellular location. Signal output from the module is sensitive to low-level input only when it is activated at the plasma membrane. This is because the threshold for activation is low at the plasma membrane, whereas the threshold for activation is high in the cytosol. Thus, the circuit configuration of the module at the plasma membrane generates maximal outputs from low-level analog inputs, allowing cells to process and respond appropriately to physiological stimuli. These results reveal the engineering logic behind the recruitment of elements of the module from the cytosol to the membrane for activation.

Conditional distribution modeling for estimating and exploiting uncertainty in control systems

This paper presents a general methodology for estimating and incorporating uncertainty in the controller and forward models for noisy nonlinear control problems. Conditional distribution modeling in a neural network context is used to estimate uncertainty around the prediction of neural network outputs. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localize the possible control solutions to consider. A nonlinear multivariable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non Gaussian distributions of control signal as well as processes with hysteresis.

Probability distribution modelling to improve stability in nonlinear MIMO control

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.

Mathematical modelling and control of agitated extraction columns

Liquid-liquid extraction has long been known as a unit operation that plays an important role in industry. This process is well known for its complexity and sensitivity to operation conditions. This thesis presents an attempt to explore the dynamics and control of this process using a systematic approach and state of the art control system design techniques. The process was studied first experimentally under carefully selected. operation conditions, which resembles the ranges employed practically under stable and efficient conditions. Data were collected at steady state conditions using adequate sampling techniques for the dispersed and continuous phases as well as during the transients of the column with the aid of a computer-based online data logging system and online concentration analysis. A stagewise single stage backflow model was improved to mimic the dynamic operation of the column. The developed model accounts for the variation in hydrodynamics, mass transfer, and physical properties throughout the length of the column. End effects were treated by addition of stages at the column entrances. Two parameters were incorporated in the model namely; mass transfer weight factor to correct for the assumption of no mass transfer in the. settling zones at each stage and the backmixing coefficients to handle the axial dispersion phenomena encountered in the course of column operation. The parameters were estimated by minimizing the differences between the experimental and the model predicted concentration profiles at steady state conditions using non-linear optimisation technique. The estimated values were then correlated as functions of operating parameters and were incorporated in·the model equations. The model equations comprise a stiff differential~algebraic system. This system was solved using the GEAR ODE solver. The calculated concentration profiles were compared to those experimentally measured. A very good agreement of the two profiles was achieved within a percent relative error of ±2.S%. The developed rigorous dynamic model of the extraction column was used to derive linear time-invariant reduced-order models that relate the input variables (agitator speed, solvent feed flowrate and concentration, feed concentration and flowrate) to the output variables (raffinate concentration and extract concentration) using the asymptotic method of system identification. The reduced-order models were shown to be accurate in capturing the dynamic behaviour of the process with a maximum modelling prediction error of I %. The simplicity and accuracy of the derived reduced-order models allow for control system design and analysis of such complicated processes. The extraction column is a typical multivariable process with agitator speed and solvent feed flowrate considered as manipulative variables; raffinate concentration and extract concentration as controlled variables and the feeds concentration and feed flowrate as disturbance variables. The control system design of the extraction process was tackled as multi-loop decentralised SISO (Single Input Single Output) as well as centralised MIMO (Multi-Input Multi-Output) system using both conventional and model-based control techniques such as IMC (Internal Model Control) and MPC (Model Predictive Control). Control performance of each control scheme was. studied in terms of stability, speed of response, sensitivity to modelling errors (robustness), setpoint tracking capabilities and load rejection. For decentralised control, multiple loops were assigned to pair.each manipulated variable with each controlled variable according to the interaction analysis and other pairing criteria such as relative gain array (RGA), singular value analysis (SVD). Loops namely Rotor speed-Raffinate concentration and Solvent flowrate Extract concentration showed weak interaction. Multivariable MPC has shown more effective performance compared to other conventional techniques since it accounts for loops interaction, time delays, and input-output variables constraints.

Exploiting uncertainty in nonlinear stochastic control problem

This work introduces a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. Convergence of the output error for the proposed control method is verified by using a Lyapunov function. Several simulation examples are provided to demonstrate the efficiency of the developed control method. The manner in which such a method is extended to nonlinear multi-variable systems with different delays between the input-output pairs is considered and demonstrated through simulation examples.

A mixture density network approach to modelling and exploiting uncertainty in nonlinear control problems

In nonlinear and stochastic control problems, learning an efficient feed-forward controller is not amenable to conventional neurocontrol methods. For these approaches, estimating and then incorporating uncertainty in the controller and feed-forward models can produce more robust control results. Here, we introduce a novel inversion-based neurocontroller for solving control problems involving uncertain nonlinear systems which could also compensate for multi-valued systems. The approach uses recent developments in neural networks, especially in the context of modelling statistical distributions, which are applied to forward and inverse plant models. Provided that certain conditions are met, an estimate of the intrinsic uncertainty for the outputs of neural networks can be obtained using the statistical properties of networks. More generally, multicomponent distributions can be modelled by the mixture density network. Based on importance sampling from these distributions a novel robust inverse control approach is obtained. This importance sampling provides a structured and principled approach to constrain the complexity of the search space for the ideal control law. The developed methodology circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider. A nonlinear multi-variable system with different delays between the input-output pairs is used to demonstrate the successful application of the developed control algorithm. The proposed method is suitable for redundant control systems and allows us to model strongly non-Gaussian distributions of control signal as well as processes with hysteresis. © 2004 Elsevier Ltd. All rights reserved.

Carbon emissions locked - in trade

In recent years there has been a growing concern about the emission trade balance of countries. It is due to the fact that countries with an open economy are active players in the international trade, though trade is not only a major factor in forging a country’s economic structure anymore, but it does contribute to the movement of embodied emissions beyond the country borders. This issue is especially relevant from the carbon accounting policy’s point of view, as it is known that the production-based principle is in effect now in the Kyoto agreement. The study aims at revealing the interdependence of countries on international trade and its environmental impacts, and how the carbon accounting method plays a crucial role in evaluating a country’s environmental performance and its role in the climate mitigation processes. The input-output models are used in the methodology, as they provide an appropriate framework for this kind of environmental accounting; the analysis shows an international comparison of four European countries (Germany, the United Kingdom, the Netherlands, and Hungary) with extended trading activities and carbon emissions. Moving from the production-based approach in the climate policy, to the consumptionperspective principle and allocation [15], it would also help increasing the efficiency of emission reduction targets and the evaluation of the sustainability dimension and its impacts of international trade. The results of the study have shown that there is an importance of distinction between the two emission allocation approaches, both from global and local level point of view.

Measuring the sustainability of a national economy. The application of three measures of sustainability to Ireland

An assessment of the sustainability of the Irish economy has been carried out using three methodologies, enabling comparison and evaluation of the advantages and disadvantages of each, and potential synergies among them. The three measures chosen were economy-wide Material Flow Analysis (MFA), environmentally extended input-output (EE-IO) analysis and the Ecological Footprint (EF). The research aims to assess the sustainability of the Irish economy using these methods and to draw conclusions on their effectiveness in policy making both individually and in combination. A theoretical description discusses the methods and their respective advantages and disadvantages and sets out a rationale for their combined application. The application of the methods in combination has provided insights into measuring the sustainability of a national economy and generated new knowledge on the collective application of these methods. The limitations of the research are acknowledged and opportunities to address these and build on and extend the research are identified. Building on previous research, it is concluded that a complete picture of sustainability cannot be provided by a single method and/or indicator.

SEQUENTIAL DECISIONS AND PREDICTIONS IN NATURAL LANGUAGE PROCESSING

Natural language processing has achieved great success in a wide range of ap- plications, producing both commercial language services and open-source language tools. However, most methods take a static or batch approach, assuming that the model has all information it needs and makes a one-time prediction. In this disser- tation, we study dynamic problems where the input comes in a sequence instead of all at once, and the output must be produced while the input is arriving. In these problems, predictions are often made based only on partial information. We see this dynamic setting in many real-time, interactive applications. These problems usually involve a trade-off between the amount of input received (cost) and the quality of the output prediction (accuracy). Therefore, the evaluation considers both objectives (e.g., plotting a Pareto curve). Our goal is to develop a formal understanding of sequential prediction and decision-making problems in natural language processing and to propose efficient solutions. Toward this end, we present meta-algorithms that take an existent batch model and produce a dynamic model to handle sequential inputs and outputs. Webuild our framework upon theories of Markov Decision Process (MDP), which allows learning to trade off competing objectives in a principled way. The main machine learning techniques we use are from imitation learning and reinforcement learning, and we advance current techniques to tackle problems arising in our settings. We evaluate our algorithm on a variety of applications, including dependency parsing, machine translation, and question answering. We show that our approach achieves a better cost-accuracy trade-off than the batch approach and heuristic-based decision- making approaches. We first propose a general framework for cost-sensitive prediction, where dif- ferent parts of the input come at different costs. We formulate a decision-making process that selects pieces of the input sequentially, and the selection is adaptive to each instance. Our approach is evaluated on both standard classification tasks and a structured prediction task (dependency parsing). We show that it achieves similar prediction quality to methods that use all input, while inducing a much smaller cost. Next, we extend the framework to problems where the input is revealed incremen- tally in a fixed order. We study two applications: simultaneous machine translation and quiz bowl (incremental text classification). We discuss challenges in this set- ting and show that adding domain knowledge eases the decision-making problem. A central theme throughout the chapters is an MDP formulation of a challenging problem with sequential input/output and trade-off decisions, accompanied by a learning algorithm that solves the MDP.

Self-defeating austerity? Assessing the impact of fiscal consolidations on unemployment

The great recession of 2008/2009 has had a huge impact on unemployment and public finances in most advanced countries, and these impacts were magnified in the southern Euro area countries by the sovereign debt crisis of 2010/2011. The fiscal consolidation imposed by the European Union on highly indebted countries was based on the assumptions of the so-called expansionary austerity. However, the reality so far shows proof to the contrary, and the results of this paper support the opposing view of a self- defeating austerity. Based on the input-output relations of the productive system, an unemployment rate/budget balance trade-off equation is derived, as well as the impact of a strong fiscal consolidation based on social transfers and the notion of neutral budget balance. An application to the Portuguese case confirms the huge costs of a strong fiscal consolidation, both in terms of unemployment and social policy regress, and it allows one to conclude that too much consolidation in one year makes consolidation more difficult in the following year.

Undermined climate policies : a study on the impact of regulatory and financial discrimination across heterogeneous firms in China

Firms in China within the same industry but with different ownership and size have very different production functions and can face very different emission regulations and financial conditions. This fact has largely been ignored in most of the existing literature on climate change. Using a newly augmented Chinese input–output table in which information about firm size and ownership are explicitly reported, this paper employs a dynamic computable general equilibrium (CGE) model to analyze the impact of alternative climate policy designs with respect to regulation and financial conditions on heterogeneous firms. The simulation results indicate that with a business-as-usual regulatory structure, the effectiveness and economic efficiency of climate policies is significantly undermined. Expanding regulation to cover additional firms has a first-order effect of improving efficiency. However, over-investment in energy technologies in certain firms may decrease the overall efficiency of investments and dampen long-term economic growth by competing with other fixed-capital investments for financial resources. Therefore, a market-oriented arrangement for sharing emission reduction burden and a mechanism for allocating green investment is crucial for China to achieve a more ambitious emission target in the long run.