Rotary atomizer drop size distribution database

Wind tunnel measurements of drop Size distributions from Micronair A U4000 and A U5000 rotary atomizers were collected to develop a database for model use. The measurements varied tank mix, flow rate, air speed, and blade angle conditions, which were correlated by multiple regressions (average R-2 = 0.995 for A U4000 and 0.988 for AU5000). This database replaces an outdated set of rotary atomizer data measured in the 1980s by the USDA Forest Service and fills in a gap in data measured in the 1990s by the Spray Drift Task Force. Since current USDA Forest Service spray projects rely on rotary atomizers, the creation of the database (and its multiple regression interpolation) satisfies a need seen for ten years.

Zeolite from alkali modified kaolin increases NH4+ retention by sandy soil : column experiments

Sandy soils have low water and nutrient retention capabilities so that zeolite soil amendments are used for high value land uses including turf and horticulture to reduce leaching losses of NH4+ fertilisers. MesoLite is a zeolitic material made by caustic treatment of kaolin at 80-95oC. It has a moderately low surface area (9-12m2/g) and very high cation exchange capacity (494 cmol(+)/kg). Laboratory column experiments showed that an addition of 0.4% MesoLite to a sandy soil greatly (90%) reduced leaching of added NH4+ compared to an unamended soil and MesoLite is 11 times more efficient in retaining NH4+ than natural zeolite. Furthermore, NH4+-MesoLite slowly releases NH4+ to soil solution and is likely to be an effective slow release fertiliser.

Increasing NH4+ retention efficiency of sandy soils by adding a modified kaolin material: Mesolite

Water and ammonium retention by sandy soils may be low and result in leaching of applied fertiliser. To increase water and nutrient retention, zeolite is sometimes applied as a soil ameliorant for high value land uses including turf and horticulture. We have used a new modified kaolin material (MesoLite) as a soil amendment to test the efficiency of NH4+ retention and compared the results with natural zeolite. MesoLite is made by caustic reaction of kaolin at temperature between 80-95°C; although it has a moderate surface area, its cation exchange capacity is very high;(SA=13m2/g,CEC=500meq/100g). A 13cm tall sand column filled with ~450g of sandy soil homogeneously mixed with 1, 2, 4, and 8g of MesoLite or natural zeolite per 1kg of soil was prepared. After saturation with local bore water, concentrated ammonium sulfate solution was injected at the base. Then, bore water was passed from bottom to top through the column at amounts up to 6 pore volumes and at a constant flow rate of 10ml/min using a peristaltic pump. Concentrations of leached NH4+ were determined using an AutoAnalyser. The concentration of NH4+ leached from the column with 0.4% MesoLite was greatly (90%) reduced relative to unamended soil. Under these conditions NH4+ retention by the soil-MesoLite mixture was 11.5 times more efficient than the equivalent soil-natural zeolite mixture. Glasshouse experiments conducted in a separate study show that NH4+ adsorbed by MesoLite is available to plants.

Piggery pond sludge as a nitrogen source for crops. 1. Mineral N supply estimated from laboratory incubations and field application of stockpiled and wet sludge

Piggery pond sludge (PPS) was applied, as-collected (Wet PPS) and following stockpiling for 12 months (Stockpiled PPS), to a sandy Sodosol and clay Vertosol at sites on the Darling Downs of Queensland. Laboratory measures of N availability were carried out on unamended and PPS-amended soils to investigate their value in estimating supplementary N needs of crops in Australia's northern grains region. Cumulative net N mineralised from the long-term (30 weeks) leached aerobic incubation was described by a first-order single exponential model. The mineralisation rate constant (0.057/week) was not significantly different between Control and PPS treatments or across soil types, when the amounts of initial mineral N applied in PPS treatments were excluded. Potentially mineralisable N (No) was significantly increased by the application of Wet PPS, and increased with increasing rate of application. Application of Wet PPS significantly increased the total amount of inorganic N leached compared with the Control treatments. Mineral N applied in Wet PPS contributed as much to the total mineral N status of the soil as did that which mineralised over time from organic N. Rates of C02 evolution during 30 weeks of aerobic leached incubation indicated that the Stockpiled PPS was more stabilised (19-28% of applied organic C mineralised) than the WetPPS (35-58% of applied organic C mineralised), due to higher lignin content in the former. Net nitrate-N produced following 12 weeks of aerobic non-leached incubation was highly correlated with net nitrate-N leached during 12 weeks of aerobic incubation (R^2 = 0.96), although it was <60% of the latter in both sandy and clayey soils. Anaerobically mineralisable N determined by waterlogged incubation of laboratory PPS-amended soil samples increased with increasing application rate of Wet PPS. Anaerobically minemlisable N from field-moist soil was well correlated with net N mineralised during 30 weeks of aerobic leached incubation (R^2 =0.90 sandy soil; R^2=0.93 clay soil). In the clay soil, the amount of mineral N produced from all the laboratory incubations was significantly correlated with field-measured nitrate-N in the soil profile (0-1.5 m depth) after 9 months of weed-free fallow following PPS application. In contrast, only anaerobic mineralisable N was significantly correlated with field nitrate-N in the sandy soil. Anaerobic incubation would, therefore, be suitable as a rapid practical test to estimate potentially mineralisable N following applications of different PPS materials in the field.

A game theoretic approach for feature clustering and its application to feature selection

In this paper, we develop a game theoretic approach for clustering features in a learning problem. Feature clustering can serve as an important preprocessing step in many problems such as feature selection, dimensionality reduction, etc. In this approach, we view features as rational players of a coalitional game where they form coalitions (or clusters) among themselves in order to maximize their individual payoffs. We show how Nash Stable Partition (NSP), a well known concept in the coalitional game theory, provides a natural way of clustering features. Through this approach, one can obtain some desirable properties of the clusters by choosing appropriate payoff functions. For a small number of features, the NSP based clustering can be found by solving an integer linear program (ILP). However, for large number of features, the ILP based approach does not scale well and hence we propose a hierarchical approach. Interestingly, a key result that we prove on the equivalence between a k-size NSP of a coalitional game and minimum k-cut of an appropriately constructed graph comes in handy for large scale problems. In this paper, we use feature selection problem (in a classification setting) as a running example to illustrate our approach. We conduct experiments to illustrate the efficacy of our approach.

Component Assembling Model and Its Application to Quasi-Brittle Damage

Potential energy can be approximated by ‘‘pair-functional’’ potentials which is composed of pair potentials and embedding energy. Pair potentials are grouped according to discrete directions of atomic bonds such that each group is represented by an orientational component. Meanwhile, another kind of component, the volumetric one is derived from embedding energy. Damage and fracture are the changing and breaking of atomic bonds at the most fundamental level and have been reflected by the changing of these components’ properties. Therefore, material is treated as a component assembly, and its constitutive equations are formed by means of assembling these two kinds of components’ response functions. This material model is referred to as the component assembling model. Theoretical analysis and numerical computing indicate that the proposed model has the capacity of reproducing some results satisfactorily, with the advantages of physical explicitness and intrinsic induced anisotropy, etc.

Adaptive agent-based manufacturing control and its application to flow shop routing control

The manufacturing industry is currently facing unprecedented challenges from changes and disturbances. The sources of these changes and disturbances are of different scope and magnitude. They can be of a commercial nature, or linked to fast product development and design, or purely operational (e.g. rush order, machine breakdown, material shortage etc.). In order to meet these requirements it is increasingly important that a production operation be flexible and is able to adapt to new and more suitable ways of operating. This paper focuses on a new strategy for enabling manufacturing control systems to adapt to changing conditions both in terms of product variation and production system upgrades. The approach proposed is based on two key concepts: (1) An autonomous and distributed approach to manufacturing control based on multi-agent methods in which so called operational agents represent the key physical and logical elements in the production environment to be controlled - for example, products and machines and the control strategies that drive them and (2) An adaptation mechanism based around the evolutionary concept of replicator dynamics which updates the behaviour of newly formed operational agents based on historical performance records in order to be better suited to the production environment. An application of this approach for route selection of similar products in manufacturing flow shops is developed and is illustrated in this paper using an example based on the control of an automobile paint shop.

Double synaptic weight neuron theory and its application

In this paper, a novel mathematical model of neuron-Double Synaptic Weight Neuron (DSWN)(l) is presented. The DSWN can simulate many kinds of neuron architectures, including Radial-Basis-Function (RBF), Hyper Sausage and Hyper Ellipsoid models, etc. Moreover, this new model has been implemented in the new CASSANN-II neurocomputer that can be used to form various types of neural networks with multiple mathematical models of neurons. The flexibility of the DSWN has also been described in constructing neural networks. Based on the theory of Biomimetic Pattern Recognition (BPR) and high-dimensional space covering, a recognition system of omni directionally oriented rigid objects on the horizontal surface and a face recognition system had been implemented on CASSANN-II neurocomputer. In these two special cases, the result showed DSWN neural network had great potential in pattern recognition.