Vapor pressures of Y,Ba, Cu precursors for the growth of YBa2Cu3O7 by MOVPE

The vapor pressures of theβ-diketonates Y(THD), Ba(THD), and Cu(THD), commonly used as precursors for MOVPE of YBa₂Cu₃O₇, and Y(MCP) were measured at different temperatures. A time-resolved static method recording the pressure vs. time at constant temperature was used, permitting us to deduce the vapor pressure even if the materials tend to decompose. The values of the constants of the Clausius-Clapeyron equation log₁₀p(T)/p ₀=A-B/T are,A=11.7, 8.7, 8.27, 16.6 andB=4359, 2654, 3602, and 6453 K for Y(THD), Y(MCP), Ba(THD), and Cu(THD), respectively withp ₀= 1 Pa and temperatureT in K. The thermal stability of the sources was measured and are discussed.     

The effect of Gaussian error in object recognition

In model based recognition, the goal is to locate an instance of one or more known objects in an image. The problem is compounded in real images by the presence of clutter, occlusion, and sensor error, which can lead to “false negatives”, failures to recognize the presence of the object, and “false positives”, in which the algorithm incorrectly identifies an occurrence of the object. The probability of either event is affected by parameters within the recognition algorithm, which are almost always chosen in an ad-hoc fashion. The effect of the parameter values on the likelihood that the recognition algorithm will make a mistake are usually not understood explicitly. To address the problem, we explicitly model the noise that occurs in the image. In a typical recognition algorithm, hypotheses about the position of the object are tested against the evidence in the image, and an overall score is assigned to each hypothesis. We use a statistical model to determine what score a correct or incorrect hypothesis is likely to have, and use standard binary hypothesis testing techniques to distinguish correct from incorrect hypotheses. Using this approach, we can compare algorithms and noise models, and automatically choose values for internal system thresholds to minimize the probability of making a mistake     

Is it possible to differentiate between choledochal cyst and congenital biliary atresia (type I cyst) by antenatal ultrasonography?

Both choledochal cyst and congenital biliary atresia (type I cyst) may share the same ultrasonographic pattern. We report 2 cases which were shown to have cystic structures on the upper abdomen by antenatal ultrasonography. The size of the choledochal cyst found at 24 weeks' gestation increased steadily as gestational age advanced. But the size of the cyst in congenital biliary atresia found at 29 weeks' gestation remained unchanged throughout the remaining pregnancy. Cyst enlargement, therefore, may suggest the possibility of a choledochal cyst.     

Nursing care of acute stroke patients after receiving rt-PA therapy. The NINDS rt-PA Stroke Study Group

Treatment with tissue plasminogen activator (rt-PA) for acute stroke requires intensive care of the patient. The risk of thrombolytic therapy and the need for rapid interventions make it clear that the nursing role during this time is crucial. Nurses should be familiar with safe dosage and administration of rt-PA for stroke, which is clearly different than administration of rt-PA for myocardial infarction. Furthermore, thrombolytic stroke treatment must be accompanied by intensive neurological monitoring to observe for complications. Intracerebral hemorrhage is usually accompanied by an acute change in neurological status and vital sign instability. Intensive monitoring of neurologic condition, vital signs, cardiac status and other standard critical care practices must be initiated immediately to optimize patient outcome.     

Reliability modeling and performance of variable link-capacitynetworks

The discussion by R. Schanzer (see ibid., vol.44, p.620-1, 1995) tries to correct the algorithms of P.K. Varshney, et al. (see ibid., vol.43, p.378-82, 1994) and K.K Aggarwal (see ibid., vol.37, p.65-9, 1988). But Schanzer doesn't explain why those algorithms have difficulty in calculating the maximum flow. This paper explains the problem and gives a correction of the algorithm so that the basic problem can be solved