928 resultados para E16 - Aggregate Input-Output Analysis


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Induced mild hypothermia after cardiac arrest interferes with clinical assessment of the cardiovascular status of patients. In this situation, non-invasive cardiac output measurement could be useful. Unfortunately, arterial pulse contour is altered by temperature, and the performance of devices using arterial blood pressure contour analysis to derive cardiac output may be insufficient.

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One of the most influential statements in the anomie theory tradition has been Merton’s argument that the volume of instrumental property crime should be higher where there is a greater imbalance between the degree of commitment to monetary success goals and the degree of commitment to legitimate means of pursing such goals. Contemporary anomie theories stimulated by Merton’s perspective, most notably Messner and Rosenfeld’s institutional anomie theory, have expanded the scope conditions by emphasizing lethal criminal violence as an outcome to which anomie theory is highly relevant, and virtually all contemporary empirical studies have focused on applying the perspective to explaining spatial variation in homicide rates. In the present paper, we argue that current explications of Merton’s theory and IAT have not adequately conveyed the relevance of the core features of the anomie perspective to lethal violence. We propose an expanded anomie model in which an unbalanced pecuniary value system – the core causal variable in Merton’s theory and IAT – translates into higher levels of homicide primarily in indirect ways by increasing levels of firearm prevalence, drug market activity, and property crime, and by enhancing the degree to which these factors stimulate lethal outcomes. Using aggregate-level data collected during the mid-to-late 1970s for a sample of relatively large social aggregates within the U.S., we find a significant effect on homicide rates of an interaction term reflecting high levels of commitment to monetary success goals and low levels of commitment to legitimate means. Virtually all of this effect is accounted for by higher levels of property crime and drug market activity that occur in areas with an unbalanced pecuniary value system. Our analysis also reveals that property crime is more apt to lead to homicide under conditions of high levels of structural disadvantage. These and other findings underscore the potential value of elaborating the anomie perspective to explicitly account for lethal violence.

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This article refines Lipsky’s (1980) assertion that lacking resources negatively affect output performance. It uses fuzzy-set Qualitative Comparative Analysis to analyse the nuanced interplay of contextual and individual determinants of the output performance of veterinary inspectors as street-level bureaucrats in Switzerland. Moving ‘beyond Lipsky’, the study builds on recent theoretical contributions and a systematic comparison across organizational contexts. Against a widespread assumption, output performance is not all about the resources. The impact of perceived available resources hinges on caseloads, which prove to be more decisive. These contextual factors interact with individual attitudes emerging from diverse public accountabilities. The results contextualize the often-emphasized importance of worker-client interaction. In a setting where clients cannot escape the interaction, street-level bureaucrats are not primarily held accountable by them. Studies of output performance should thus sensibly consider gaps between what is being demanded of and offered to street-level bureaucrats, and the latter’s multiple embeddedness.

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Ray (1998) developed measures of input- and output-oriented scale efficiency that can be directly computed from an estimated Translog frontier production function. This note extends the earlier results from Ray (1998) to the multiple-output multiple input case.

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We measure the capacity output of a firm as the maximum amount producible by a firm given a specific quantity of the quasi-fixed input and an overall expenditure constraint for its choice of variable inputs. We compute this indirect capacity utilization measure for the total manufacturing sector in the US as well as for a number of disaggregated industries, for the period 1970-2001. We find considerable variation in capacity utilization rates both across industries and over years within industries. Our results suggest that the expenditure constraint was binding, especially in periods of high interest rates.

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This paper evaluates inflation targeting and assesses its merits by comparing alternative targets in a macroeconomic model. We use European aggregate data to evaluate the performance of alternative policy rules under alternative inflation targets in terms of output losses. We employ two major alternative policy rules, forward-looking and spontaneous adjustment, and three alternative inflation targets, zero percent, two percent, and four percent inflation rates. The simulation findings suggest that forward-looking rules contributed to macroeconomic stability and increase monetary policy credibility. The superiority of a positive inflation target, in terms of output losses, emerges for the aggregate data. The same methodology, when applied to individual countries, however, suggests that country-specific flexible inflation targeting can improve employment prospects in Europe.

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We propose a nonparametric model for global cost minimization as a framework for optimal allocation of a firm's output target across multiple locations, taking account of differences in input prices and technologies across locations. This should be useful for firms planning production sites within a country and for foreign direct investment decisions by multi-national firms. Two illustrative examples are included. The first example considers the production location decision of a manufacturing firm across a number of adjacent states of the US. In the other example, we consider the optimal allocation of US and Canadian automobile manufacturers across the two countries.

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A discussion of nonlinear dynamics, demonstrated by the familiar automobile, is followed by the development of a systematic method of analysis of a possibly nonlinear time series using difference equations in the general state-space format. This format allows recursive state-dependent parameter estimation after each observation thereby revealing the dynamics inherent in the system in combination with random external perturbations.^ The one-step ahead prediction errors at each time period, transformed to have constant variance, and the estimated parametric sequences provide the information to (1) formally test whether time series observations y(,t) are some linear function of random errors (ELEM)(,s), for some t and s, or whether the series would more appropriately be described by a nonlinear model such as bilinear, exponential, threshold, etc., (2) formally test whether a statistically significant change has occurred in structure/level either historically or as it occurs, (3) forecast nonlinear system with a new and innovative (but very old numerical) technique utilizing rational functions to extrapolate individual parameters as smooth functions of time which are then combined to obtain the forecast of y and (4) suggest a measure of resilience, i.e. how much perturbation a structure/level can tolerate, whether internal or external to the system, and remain statistically unchanged. Although similar to one-step control, this provides a less rigid way to think about changes affecting social systems.^ Applications consisting of the analysis of some familiar and some simulated series demonstrate the procedure. Empirical results suggest that this state-space or modified augmented Kalman filter may provide interesting ways to identify particular kinds of nonlinearities as they occur in structural change via the state trajectory.^ A computational flow-chart detailing computations and software input and output is provided in the body of the text. IBM Advanced BASIC program listings to accomplish most of the analysis are provided in the appendix. ^

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The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

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The purpose of this work is to propose a structure for simulating power systems using behavioral models of nonlinear DC to DC converters implemented through a look-up table of gains. This structure is specially designed for converters whose output impedance depends on the load current level, e.g. quasi-resonant converters. The proposed model is a generic one whose parameters can be obtained by direct measuring the transient response at different operating points. It also includes optional functionalities for modeling converters with current limitation and current sharing in paralleling characteristics. The pusposed structured also allows including aditional characteristics of the DC to DC converter as the efficency as a function of the input voltage and the output current or overvoltage and undervoltage protections. In addition, this proposed model is valid for overdamped and underdamped situations.

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The selection of predefined analytic grids (partitions of the numeric ranges) to represent input and output functions as histograms has been proposed as a mechanism of approximation in order to control the tradeoff between accuracy and computation times in several áreas ranging from simulation to constraint solving. In particular, the application of interval methods for probabilistic function characterization has been shown to have advantages over other methods based on the simulation of random samples. However, standard interval arithmetic has always been used for the computation steps. In this paper, we introduce an alternative approximate arithmetic aimed at controlling the cost of the interval operations. Its distinctive feature is that grids are taken into account by the operators. We apply the technique in the context of probability density functions in order to improve the accuracy of the probability estimates. Results show that this approach has advantages over existing approaches in some particular situations, although computation times tend to increase significantly when analyzing large functions.

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There are many the requirements that modern power converters should fulfill. Most of the applications where these converters are used, demand smaller converters with high efficiency, improved power density and a fast dynamic response. For instance, loads like microprocessors demand aggressive current steps with very high slew rates (100A/mus and higher); besides, during these load steps, the supply voltage of the microprocessor should be kept within tight limits in order to ensure its correct performance. The accomplishment of these requirements is not an easy task; complex solutions like advanced topologies - such as multiphase converters- as well as advanced control strategies are often needed. Besides, it is also necessary to operate the converter at high switching frequencies and to use capacitors with high capacitance and low ESR. Improving the dynamic response of power converters does not rely only on the control strategy but also the power topology should be suited to enable a fast dynamic response. Moreover, in later years, a fast dynamic response does not only mean accomplishing fast load steps but output voltage steps are gaining importance as well. At least, two applications that require fast voltage changes can be named: Low power microprocessors. In these devices, the voltage supply is changed according to the workload and the operating frequency of the microprocessor is changed at the same time. An important reduction in voltage dependent losses can be achieved with such changes. This technique is known as Dynamic Voltage Scaling (DVS). Another application where important energy savings can be achieved by means of changing the supply voltage are Radio Frequency Power Amplifiers. For example, RF architectures based on ‘Envelope Tracking’ and ‘Envelope Elimination and Restoration’ techniques can take advantage of voltage supply modulation and accomplish important energy savings in the power amplifier. However, in order to achieve these efficiency improvements, a power converter with high efficiency and high enough bandwidth (hundreds of kHz or even tens of MHz) is necessary in order to ensure an adequate supply voltage. The main objective of this Thesis is to improve the dynamic response of DC-DC converters from the point of view of the power topology. And the term dynamic response refers both to the load steps and the voltage steps; it is also interesting to modulate the output voltage of the converter with a specific bandwidth. In order to accomplish this, the question of what is it that limits the dynamic response of power converters should be answered. Analyzing this question leads to the conclusion that the dynamic response is limited by the power topology and specifically, by the filter inductance of the converter which is found in series between the input and the output of the converter. The series inductance is the one that determines the gain of the converter and provides the regulation capability. Although the energy stored in the filter inductance enables the regulation and the capability of filtering the output voltage, it imposes a limitation which is the concern of this Thesis. The series inductance stores energy and prevents the current from changing in a fast way, limiting the slew rate of the current through this inductor. Different solutions are proposed in the literature in order to reduce the limit imposed by the filter inductor. Many publications proposing new topologies and improvements to known topologies can be found in the literature. Also, complex control strategies are proposed with the objective of improving the dynamic response in power converters. In the proposed topologies, the energy stored in the series inductor is reduced; examples of these topologies are Multiphase converters, Buck converter operating at very high frequency or adding a low impedance path in parallel with the series inductance. Control techniques proposed in the literature, focus on adjusting the output voltage as fast as allowed by the power stage; examples of these control techniques are: hysteresis control, V 2 control, and minimum time control. In some of the proposed topologies, a reduction in the value of the series inductance is achieved and with this, the energy stored in this magnetic element is reduced; less stored energy means a faster dynamic response. However, in some cases (as in the high frequency Buck converter), the dynamic response is improved at the cost of worsening the efficiency. In this Thesis, a drastic solution is proposed: to completely eliminate the series inductance of the converter. This is a more radical solution when compared to those proposed in the literature. If the series inductance is eliminated, the regulation capability of the converter is limited which can make it difficult to use the topology in one-converter solutions; however, this topology is suitable for power architectures where the energy conversion is done by more than one converter. When the series inductor is eliminated from the converter, the current slew rate is no longer limited and it can be said that the dynamic response of the converter is independent from the switching frequency. This is the main advantage of eliminating the series inductor. The main objective, is to propose an energy conversion strategy that is done without series inductance. Without series inductance, no energy is stored between the input and the output of the converter and the dynamic response would be instantaneous if all the devices were ideal. If the energy transfer from the input to the output of the converter is done instantaneously when a load step occurs, conceptually it would not be necessary to store energy at the output of the converter (no output capacitor COUT would be needed) and if the input source is ideal, the input capacitor CIN would not be necessary. This last feature (no CIN with ideal VIN) is common to all power converters. However, when the concept is actually implemented, parasitic inductances such as leakage inductance of the transformer and the parasitic inductance of the PCB, cannot be avoided because they are inherent to the implementation of the converter. These parasitic elements do not affect significantly to the proposed concept. In this Thesis, it is proposed to operate the converter without series inductance in order to improve the dynamic response of the converter; however, on the other side, the continuous regulation capability of the converter is lost. It is said continuous because, as it will be explained throughout the Thesis, it is indeed possible to achieve discrete regulation; a converter without filter inductance and without energy stored in the magnetic element, is capable to achieve a limited number of output voltages. The changes between these output voltage levels are achieved in a fast way. The proposed energy conversion strategy is implemented by means of a multiphase converter where the coupling of the phases is done by discrete two-winding transformers instead of coupledinductors since transformers are, ideally, no energy storing elements. This idea is the main contribution of this Thesis. The feasibility of this energy conversion strategy is first analyzed and then verified by simulation and by the implementation of experimental prototypes. Once the strategy is proved valid, different options to implement the magnetic structure are analyzed. Three different discrete transformer arrangements are studied and implemented. A converter based on this energy conversion strategy would be designed with a different approach than the one used to design classic converters since an additional design degree of freedom is available. The switching frequency can be chosen according to the design specifications without penalizing the dynamic response or the efficiency. Low operating frequencies can be chosen in order to favor the efficiency; on the other hand, high operating frequencies (MHz) can be chosen in order to favor the size of the converter. For this reason, a particular design procedure is proposed for the ‘inductorless’ conversion strategy. Finally, applications where the features of the proposed conversion strategy (high efficiency with fast dynamic response) are advantageus, are proposed. For example, in two-stage power architectures where a high efficiency converter is needed as the first stage and there is a second stage that provides the fine regulation. Another example are RF power amplifiers where the voltage is modulated following an envelope reference in order to save power; in this application, a high efficiency converter, capable of achieving fast voltage steps is required. The main contributions of this Thesis are the following: The proposal of a conversion strategy that is done, ideally, without storing energy in the magnetic element. The validation and the implementation of the proposed energy conversion strategy. The study of different magnetic structures based on discrete transformers for the implementation of the proposed energy conversion strategy. To elaborate and validate a design procedure. To identify and validate applications for the proposed energy conversion strategy. It is important to remark that this work is done in collaboration with Intel. The particular features of the proposed conversion strategy enable the possibility of solving the problems related to microprocessor powering in a different way. For example, the high efficiency achieved with the proposed conversion strategy enables it as a good candidate to be used for power conditioning, as a first stage in a two-stage power architecture for powering microprocessors.

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The paper presents a method to analyze robust stability and transient performance of a distributed power system consisting of commercial converter modules interconnected through a common input filter. The method is based on the use of four transfer functions, which are measurable from the converter input and output terminals. It is shown that these parameters provide important information on the power module sensitivity to the interactions caused by the external impedances. Practical characterization for the described system structure is performed introducing special transfer functions utilized for the interactions assessment. Experimental results are provided to support the presented analysis procedure.