Physical Properties of Pecans Relating to Shelling Efficiency

Inshell pecans of 21 cultivars and numbered trial selections were evaluated for selected physical properties and for their adaptability to be efficiently shelled by existing commercial technology. Processing efficiency was based on yield of unbroken half kernels during shelling. There were highly significant differences among varieties in both selected physical properties and shelling efficiency. Correlations between shelling efficiency and physical properties were determined. Results showed that percent total kernel, inshell length/diameter ratio and shell thickness were the physical properties that appeared to have the greatest effect on shelling efficiency.     

Cotyledon Structure of Resting Peanut (Arachis Hypogaea L. cv. Florunner) Seed Before and After Hydraulic Pressing

The cotyledon structure of resting peanut (Arachis hypogaea L. cv. Florunner) seed before and after hydraulic pressing was investigated with light and scanning electron microscopy. Observations were made of the appearance of cell walls and the major subcellular components: spherosomes (oil reserve bodies), aleurone grains and starch grains. Major findings include previously unreported cell wall damage and surface fissures that may be important to processors who express the oil from peanuts.     

Polyphenol Oxidase System in Red Delicious Apples

The polyphenol oxidase system (E.C. 1.14.18.1) in‘Red Delicious’apples (Malus domestica) was investigated. Polyacrylamide gel electrophoresis (PAGE) revealed two active isoenzymes as detected with pyrocatechol substrate. The crude enzyme extract (acetone powder) was found to have one optimum pH of 6.2 and one optimum temperature for the browning reaction of 30°C with the experimental conditions described. The Michaelis constant (Km) as an average of the two active enzyme forms was 2.2 × 10^?lM pyrocatechol and the average maximum velocity (Vmax) was 4.8 × 10^?l AA390 per minute.     

Establishing Thermal Processes for Products with Straight-Line Heating Curves from Data Taken at Other Retort and Initial Temperatures

Heat penetration data were obtained at high and low retort temperatures (RTs) and at high and low product initial temperatures in two can sizes for food products displaying straight-line heating curves in an agitating retort. The extrapolation of data to other RTs was found to be a safe practice for products heating with conduction or significant induced convection. Extrapolating to lower RTs for intermediate viscosity products slightly underpredicted required processing times. The effect of initial temperature was consistent with that found in a previous study for broken-heating products in a still retort, in that heat penetration tests conducted at different initial temperatures can have a significant influence on process establishment, depending on the product.     

Physicochemical Properties of Ovalbumin and Lysozyme Treated with Oleic Acid

Results from polyacrylamide gel electrophoresis confirmed that proteins were more negatively charged in the presence of oleic acid. The electrophoretic mobility of unmodified and modified lysozyme was of opposite but similar magnitude. Critical micelle concentration (50.0 μ M) of oleic acid suggested that association between the fatty acid and proteins was mostly hydrophobic. Muramidase activity of modified lysozyme and difference spectra of modified ovalbumin suggested that oleic acid was partially or totally surrounding the proteins. Improved freeze-thaw characteristics of treated egg white were attributed to greater negative charge and instability of proteins produced by oleic acid.     

Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials

We present a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial neural network (ANN) structure. Our method uses ensembled electroencephalogram (EEG) data just as used in conventional averaging, however without the need for a priori data subgrouping into distinct categories (e.g., stimulus- or event-related), and thus avoids conventional assumptions on response invariability. The competitive ANN, often described as a winner takes all neural structure, is based on dynamic competition among the net neurons where learning takes place only with the winning neuron. Using a simple single-layered structure, the proposed scheme results in convergence of the actual neural weights to the embedded ERP patterns. The method is applied to real event-related potential data recorded during a common odd-ball type paradigm. For the first time, within-session variable signal patterns are automatically identified, dismissing the strong and limiting requirement of a priori stimulus-related selective grouping of the recorded data. The results present new possibilities in ERP research.     

Gain control in L-band EDFAs by monitoring backward travelingC-band ASE

A novel and simple technique for gain flatness control is reported for gain shifted, long wavelength band (L-band) erbium-doped fiber amplifiers (EDFAs). Utilization of the backward traveling amplified spontaneous emission (ASE) in the C-band is analyzed with respect to controlling the gain tilt observed in the L-band when the total input power of the EDFA is changed. It is shown that a gain flatness of 0.6 dB/30 nm can be achieved over a dynamic range greater than 10 dB by using the backward traveling ASE power in the C-band as a monitor to adjust the copropagating pump power of the EDFA. The proposed technique eliminates the need to extract the output signals from the monitored ASE signal, demonstrating the suitability and simplicity of the proposed technique for wavelength division multiplexed applications     

Implicit admission control

Internet protocols currently use packet-level mechanisms to detect and react to congestion. Although these controls are essential to ensure fair sharing of the available resource between multiple flows, in some cases they are insufficient to ensure overall network stability. We believe that it is also necessary to take account of higher level concepts, such as connections, flows, and sessions when controlling network congestion. This becomes of increasing importance as more real-time traffic is carried on the Internet, since this traffic is less elastic in nature than traditional Web traffic. We argue that, in order to achieve better utility of the network as a whole, higher level congestion controls are required. By way of example, we present a simple connection admission control (CAC) scheme which can significantly improve the overall performance. This paper discusses our motivation for the use of admission control in the Internet, focusing specifically on control for TCP flows. The technique is not TCP specific, and can be applied to any type of flow in a modern IP infrastructure. Simulation results are used to show that it can drastically improve the performance of TCP over bottleneck links. We go on to describe an implementation of our algorithm for a router running the Linux 2.2.9 operating system. We show that by giving routers at bottlenecks the ability to intelligently deny admission to TCP connections, the goodput of existing connections can be significantly increased. Furthermore, the fairness of the resource allocation achieved by TCP is improved     

How Multirobot Systems Research will Accelerate our Understanding of Social Animal Behavior

Our understanding of social insect behavior has significantly influenced artificial intelligence (AI) and multirobot systems' research (e.g., ant algorithms and swarm robotics). In this work, however, we focus on the opposite question: "How can multirobot systems research contribute to the understanding of social animal behavior?" As we show, we are able to contribute at several levels. First, using algorithms that originated in the robotics community, we can track animals under observation to provide essential quantitative data for animal behavior research. Second, by developing and applying algorithms originating in speech recognition and computer vision, we can automatically label the behavior of animals under observation. In some cases the automatic labeling is more accurate and consistent than manual behavior identification. Our ultimate goal, however, is to automatically create, from observation, executable models of behavior. An executable model is a control program for an agent that can run in simulation (or on a robot). The representation for these executable models is drawn from research in multirobot systems programming. In this paper we present the algorithms we have developed for tracking, recognizing, and learning models of social animal behavior, details of their implementation, and quantitative experimental results using them to study social insects.     

KINETIC ANALYLSIS OF THE FORMATION OF NITROSYLMYOGLOBIN

The formation of nitric oxide myoglobin (nitrosylmyoglobin) was followed in buffered solutions in which the concentrations of ascorbate, nitrite, chloride, myoglobin and hydrogen ion were varied systematically to determine their effect on the rate constants. The rate of formation of nitrosylmyoglobin was zero order with respect to the pigment. The orders for the other reactants were determined by plotting the zero order rate constants as functions of varying orders of each reagent to determine which order gave a linear plot. The results were used to develop a mechanism and a mathematical expression for the reaction. Two reaction sequences involving different nitrosating species were involved; 1, direct action of nitrous acid and 2, the formation of nitrosyl chloride. Both species then nitrosated ascorbate and ascorbic acid, by different mechanisms. The nitric oxide for nitrosylmyoglobin formation came from the nitrosated ascorbate.