Stochastic Trade Networks

This paper develops a simple network model to describe the dynamic of the intensive and extensive margin of international trade flows. The result is achieved by means of the combination of two mechanisms of proportional growth: the first (discrete) determines the formation of trade links, the second (continuous) governs trade intensity. We show that our setup is able to simultaneously match a large number of empirical regularities, such as the fraction of zero trade flows across pairs of countries or the high concentration of trade with respect to both products and destinations. Our findings suggest that stylized facts are strongly interconnected across different levels of aggregation of trade data , so that a unifying explanation is called for. By incorporating stochastic elements into standard trade models we can improve their ability to explain relevant facts about world trade

Hospital Ownership Mix Efficiency in the US: An Exploratory Study

This paper offers an empirical test of ownership mix efficiency in the U.S. hospital services industry. The test compares the benefits of quality assurance with the costs from the attenuation of property rights that result from an increased presence of nonprofit organizations. The empirical results suggest that too many not-for-profit and public hospitals may exist in the typical market area of the U.S. The policy implication is that more quality of care per dollar might be obtained by attracting a greater percentage of for-profit hospitals into some market areas. This conclusion, however, is tempered with several caveats. We discuss these and also make recommendations for further research.

Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing

Glass bottle-manufacturing companies produce bottles of different colors, shapes and sizes. One identified problem is that seeds appear in the bottle mainly due to the temperature and parameters of the oven. This paper presents a new system capable of detecting seeds of 0.1 mm2 in size in glass bottles as they are being manufactured, 24 h per day and 7 days per week. The bottles move along the conveyor belt at 50 m/min, at a production rate of 250 bottles/min. This new proposed method includes deep learning-based artificial intelligence techniques and classical image processing on images acquired with a high-speed line camera. The algorithm comprises three stages. First, the bottle is identified in the input image. Next, an algorithm based in thresholding and morphological operations is applied on this bottle region to locate potential candidates for seeds. Finally, a deep learning-based model can classify whether the proposed candidates are real seeds or not. This method manages to filter out most of false positives due to stains in the glass surface, while no real seeds are lost. The F1 achieved is 0.97. This method reveals the advantages of deep learning techniques for problems where classical image processing algorithms are not sufficient.This work was partially supported by OPENZDM project. This is a project from the European Union’s Horizon Europe research and innovation programme under Grant Agreement No. 101058673 in the call HORIZON-CL4-2021-TWIN-TRANSITION-0

Experimental verification of vapor deposition model in Mach 0.3 burner rigs

A comprehensive theoretical framework of deposition from combustion gases was developed covering the spectrum of various mass delivery mechanisms including vapor, thermophoretically enhanced small particle, and inertially impacting large particle deposition. Rational yet simple correlations were provided to facilitate engineering surface arrival rate predictions. Experimental verification of the deposition theory was validated using burner rigs. Toward this end, a Mach 0.3 burner rig apparatus was designed to measure deposition rates from salt-seeded combustion gases on an internally cooled cylindrical collector

Optical signal processing with a network of semiconductor optical amplifiers in the context of photonic reservoir computing

Photonic reservoir computing is a hardware implementation of the concept of reservoir computing which comes from the field of machine learning and artificial neural networks. This concept is very useful for solving all kinds of classification and recognition problems. Examples are time series prediction, speech and image recognition. Reservoir computing often competes with the state-of-the-art. Dedicated photonic hardware would offer advantages in speed and power consumption. We show that a network of coupled semiconductor optical amplifiers can be used as a reservoir by using it on a benchmark isolated words recognition task. The results are comparable to existing software implementations and fabrication tolerances can actually improve the robustness

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 359)

This bibliography lists 164 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Jan. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

A Performance Analysis of Movement Patterns

This study investigates the differences in movement patterns followed by users navigating within a virtual environment. The analysis has been carried out between two groups of users, identified on the basis of their performance on a search task. Results indicate significant differences between efficient and inefficient navigators’ trajectories. They are related to rotational, translational and localised-landmarks behaviour. These findings are discussed in the light of theoretical outcomes provided by environmental psychology