360 research outputs found
R&D, Production Structure, and Productivity Growth in the U.S., Japaneseand German Manufacturing Sectors
The paper analyzes the production structure and the demand for inputs in three major industrialized countries, the U.S., Japan and Germany. A dynamic factor demand model with two variable inputs (labor and energy)and two quasi-fixed inputs (capital and R&D) is derived directly from an intertemporal cost-minimization problem formulated in discrete time. Adjustment costs are explicitly specified. The model is estimated for the manufacturing sector of the three countries using annual data from 1965 to 1977. Particular attention is given to the role of R&D. For all countries the rate of return on R&D is found to be higher than that on capital. Their respective magnitudes are similar across countries.We find considerable differences in factor demand schedules; we also find that for all countries the speed of adjustment for capital is higher than that of R&D. Adjustment costs are of importance in the demand equations for capital and R&D, but play a minor role in the decomposition of total factor productivity growth.
Supervised intelligent committee machine method for hydraulic conductivity estimation
Hydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers
Elaboration and characterization by XRD and SEM of the glass-ceramics in BaTiO3 – NaPO3 system
There is a strong need for low cost glass-ceramic materials whose high chemical stability and dielectric properties can be engineered flexibly in bulk and fiber forms. Therefore we have manufactured transparent ferroelectric glass-ceramics consisting of barium titanate (BT) crystallites in a chain metaphosphate glass matrix. Several glass compositions, in the series (1-x) NaPO3 – x BaTiO3 have been prepared by a conventional melt quenching method. The compositions have been selected on the basis of chemical stability. X-ray diffraction (XRD) and scanning electron microscopy (SEM) indicate the formation of small particle size of BaTiO3 in the glass network when its amount is above 4 mol%.There is a strong need for low cost glass-ceramic materials whose high chemical stability and dielectric properties can be engineered flexibly in bulk and fiber forms. Therefore we have manufactured transparent ferroelectric glass-ceramics consisting of barium titanate (BT) crystallites in a chain metaphosphate glass matrix. Several glass compositions, in the series (1-x) NaPO3 – x BaTiO3 have been prepared by a conventional melt quenching method. The compositions have been selected on the basis of chemical stability. X-ray diffraction (XRD) and scanning electron microscopy (SEM) indicate the formation of small particle size of BaTiO3 in the glass network when its amount is above 4 mol%
The Structure of Production, Technological Change and the Rate of Growth of Total Factor Productivity in the Bell System
A distinct cohort of the TGFβ superfamily members expressed in human endometrium regulate decidualization
NLRP3 Inflammasome: Key Mediator of Neuroinflammation in Murine Japanese Encephalitis
Background: Japanese Encephalitis virus (JEV) is a common cause of acute and epidemic viral encephalitis. JEV infection is associated with microglial activation resulting in the production of pro-inflammatory cytokines including Interleukin-1 b (IL-1b) and Interleukin-18 (IL-18). The Pattern Recognition Receptors (PRRs) and the underlying mechanism by which microglia identify the viral particle leading to the production of these cytokines is unknown. Methodology/Principal Findings: For our studies, we have used murine model of JEV infection as well as BV-2 mouse microglia cell line. In this study, we have identified a signalling pathway which leads to the activation of caspase-1 as the key enzyme responsible for the maturation of both IL-1b and IL-18 in NACHT, LRR and PYD domains-containing protein-3 (NLRP3) dependent manner. Depletion of NLRP3 results in the reduction of caspase-1 activity and subsequent production of these cytokines. Conclusion/Significance: Our results identify a mechanism mediated by Reactive Oxygen Species (ROS) production and potassium efflux as the two danger signals that link JEV infection to caspase-1 activation resulting in subsequent IL-1b an
Efficiency and productivity growth comparisons of European and U.S. Air carriers: A first look at the data
In this paper we carry out technical efficiency and productivity growth comparisons among the four largest European carriers and eight of their American counterparts. The time period of our comparisons is 1976 through 1986. This is a particularly interesting period since it begins just after the informal steps toward deregulation in the United States and ends just prior to the introduction of the first wave of reforms by the Council of Ministers in Europe. We also identify the potential efficiency gains of the European liberalization by comparing efficiency differences between the two carrier groups. The reductions in inefficiency describe the amount that inputs can be decreased without altering output.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/47558/1/11123_2005_Article_BF01073469.pd
Active learning and optimal climate policy
This paper develops a climate-economy model with uncertainty, irreversibility, and active learning. Whereas previous papers assume learning from one observation per period, or experiment with control variables to gain additional information, this paper considers active learning from investment in monitoring, specifically in improved observations of the global mean temperature. We find that the decision maker invests a significant amount of money in climate research, far more than the current level, in order to increase the rate of learning about climate change. This helps the decision maker make improved decisions. The level of uncertainty decreases more rapidly in the active learning model than in the passive learning model with only temperature observations. As the uncertainty about climate change is smaller, active learning reduces the optimal carbon tax. The greater the risk, the larger is the effect of learning. The method proposed here is applicable to any dynamic control problem where the quality of monitoring is a choice variable, for instance, the precision at which we observe GDP, unemployment, or the quality of education
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