THE DEMAND FOR WHOLESALE BEEF CUTS BY SEASON AND TREND

This study estimates demand during the 1980-90 period for wholesale beef cuts by season and by trend. A data set containing monthly nominal prices for wholesale cuts and average choice boxed beef from January 1980 to December 1990 was collected from multiple sources. The approach expressed the change in demand for wholesale cuts as the change in the price ratio of individual cuts relative to the price of boxed beef. This approach shows changes in amount by season and over time relative to the average wholesale cut. Brisket, Armbone Chuck, Bottom Gooseneck, and Knuckle showed the strongest demand in winter and lowest in summer. Top (Inside) Round had a clear downward trend in demand, but the seasonal pattern was less pronounced and more erratic than the lower-priced cuts. Top Sirloin Butt had its highest demand in spring and summer with November-December being the lowest period. Strip Loin had the strongest warm season demand during the period which contains Memorial Day. Ribeye experienced a seasonal demand highest in November-December and lowest in January to April. Full Tenderloin was the most expensive wholesale beef cut analyzed in the study, and its demand was highest in November-December. The study clearly showed that a change in seasonal demand was responsible for the major part of price ratio fluctuations for individual wholesale cuts.Demand and Price Analysis,

More on Estimating Elasticities using Lagged Endogenous Variables

Demand and Price Analysis,

DETERMINANTS OF WHOLESALE BEEF-CUT PRICES

Key determinants of monthly wholesale prices for 12 beef cuts include the quantity of the specific cut, stickiness in prices, marketing costs, quantities of pork and chicken, and seasonality. Seasonal patterns across the respective cuts are very different. Relative to the price in December, prices at the wholesale level in other months can be as much as 6 percent lower to as much as 21 percent higher.Wholesale prices, Beef cuts, Seasonality, Demand and Price Analysis, Livestock Production/Industries,

Biological Correlates of Empathy

Empathy can be defined as the capacity to know emotionally what another is experiencing from within the frame of reference of that other person and the capacity to sample the feelings of another or it can be metaphorized as to put oneself in another’s shoes. Although the concept of empathy was firstly described in psychological theories, researches studying the biological correlates of psychological theories have been increasing recently. Not suprisingly, dinamically oriented psychotherapists Freud, Kohut, Basch and Fenichel had suggested theories about the biological correlates of empathy concept and established the basis of this modality decades ago. Some other theorists emphasized the importance of empathy in the early years of lifetime regarding mother-child attachment in terms of developmental psychology and investigated its role in explanation of psychopathology. The data coming from some of the recent brain imaging and animal model studies also seem to support these theories. Although increased activity in different brain regions was shown in many of the brain imaging studies, the role of cingulate cortex for understanding mother-child relationship was constantly emphasized in nearly all of the studies. In addition to these studies, a group of Italian scientists has defined a group of neurons as “mirror neurons” in their studies observing rhesus macaque monkeys. Later, they also defined mirror neurons in human studies, and suggested them as “empathy neurons”. After the discovery of mirror neurons, the hopes of finding the missing part of the puzzle for understanding the biological correlates of empathy raised again. Although the roles of different biological parameters such as skin conductance and pupil diameter for defining empathy have not been certain yet, they are going to give us the opportunity to revise the inconsistent basis of structural validity in psychiatry and to stabilize descriptive validity. In this review, the possible neurobiological background of empathy will be discussed in the light of the recent brain imaging and animal studies

Probing Disordered Substrates by Imaging the Adsorbate in its Fluid Phase

Several recent imaging experiments access the equilibrium density profiles of interacting particles confined to a two-dimensional substrate. When these particles are in a fluid phase, we show that such data yields precise information regarding substrate disorder as reflected in one-point functions and two-point correlations of the fluid. Using Monte Carlo simulations and replica generalizations of liquid state theories, we extract unusual two-point correlations of time-averaged density inhomogeneities induced by disorder. Distribution functions such as these have not hitherto been measured but should be experimentally accessible.Comment: 10 pages revtex 4 figure

Sheila Widnall interview transcript, 1977 March

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Vibrational Analysis of Engine Components Using Neural-Net Processing and Electronic Holography

The use of computational-model trained artificial neural networks to acquire damage specific information from electronic holograms is discussed. A neural network is trained to transform two time-average holograms into a pattern related to the bending-induced-strain distribution of the vibrating component. The bending distribution is very sensitive to component damage unlike the characteristic fringe pattern or the displacement amplitude distribution. The neural network processor is fast for real-time visualization of damage. The two-hologram limit makes the processor more robust to speckle pattern decorrelation. Undamaged and cracked cantilever plates serve as effective objects for testing the combination of electronic holography and neural-net processing. The requirements are discussed for using finite-element-model trained neural networks for field inspections of engine components. The paper specifically discusses neural-network fringe pattern analysis in the presence of the laser speckle effect and the performances of two limiting cases of the neural-net architecture

Interview with Leon S. Forman

For transcript, click the Download button above. For video index, click the link below. Leon S. Forman ((L\u2739) was an authority on bankruptcy and creditors\u27 rights. He practiced law for more than sixty years and served as chairman of the Philadelphia Bar Association\u27s corporation, banking and business law section, and as chairman of the Pennsylvania Bar Association\u27s bankruptcy committee. He was a member of the American Law Institute. He taught bankruptcy and creditors\u27 rights at the Law School of the University of Pennsylvania and at Temple University School of Law. He died in 2006

Flow Induced Organization and Memory of a Vortex Lattice

We report on experiments probing the evolution of a vortex state in response to a driving current in 2H-NbSe$_2$ crystals. By following the vortex motion with fast transport measurements we find that the current enables the system to reorganize and access new configurations. During this process the system exhibits a long-term memory: if the current is turned off the vortices freeze in place remembering their prior motion. When the current is restored the motion resumes where it stopped. The experiments provide evidence for a dynamically driven structural change of the vortex lattice and a corresponding dynamic phase diagram that contains a previously unknown regime where the critical current can be either $increased$ or $decreased$ by applying an appropriate driving current.Comment: 5 pages, 4figure