Adaptive agents and multiagent systems

New information systems and recent applications (grid computing, Web Services, and so on) are often distributed, large-scale, open, heterogeneous, and characterized by a dynamic environment. To model these complex systems, researchers have spent much effort during the last few years on multiagent systems. The aim is to model complex distributed systems as a set of (possibly organized) software agents that interact in a common environment. The decomposition of a system into a number of agents lets the system react and adapt better in a changing environment. Moreover, organized structures ("social" structures) can emerge from interactions between agents, which in turn constrain and coordinate the agents' behavior. A multiagent system takes its metaphors of interaction from social systems rather than using the metaphor of the isolated thinker that early artificial intelligence researchers preferred. An important issue when dealing with this increasing complexity is to build adaptive agents and multiagent systems. Agents and multiagent systems must be aware of their own capabilities and of changes to other agents and their environment. To remain effective, agents must be able to adapt their structures and knowledge while they execute.     

Synthesis of Rice Processing Plants. I. Development of Simplified Models

This article, the first of three articles on the synthesis of rice processing plants, focuses on the development of simplified mathematical models necessary for use in optimizing rice processing plants. The second concentrates on the optimal synthesis of a rice plant and the third on the sensitivity of the optimization to uncertainty in model parameters. Existing models for rice processing unit operations are not suitable for flowsheet optimization and new models need to be developed to overcome numerical difficulties that occur in optimization applications, specifically in mixed integer nonlinear programming (MINLP) applications. Simplified models of the drying, cooling, and tempering units are developed. In addition head rice yield models, used as a quality indicator, energy consumption, and economic models were also developed. Naturally, the new models exhibit some mismatch with respect to the existing models from which they were developed. However, a sensitivity analysis, presented in Part III, has shown that the optimal flowsheet structure was not sensitive to a lack of fit between the simplified and complex models. The simplified models were found adequate to be appropriate for use at the synthesis stage.     

Effect of Sb implantation on copper silicides formation and morphology after annealing of Cu/Si structures

In this work, the solid state reaction between a thin film of copper and silicon has been studied using Rutherford backscattering spectroscopy, X-ray diffraction, scanning electron microscopy and microprobe analysis. Cu films of 400 and 900 Å thicknesses are thermally evaporated on Si(1 1 1) substrates, part of them had previously been implanted with antimony ions of 5×10¹⁴ or 5×10¹⁵ at. cm⁻² doses. The samples are heat-treated in vacuum at temperatures in the range 200–700 °C for various times. The results show the growth and formation of Cu₃Si and Cu₄Si silicides under crystallites shape dispatched on the sample surface, independently of the implantation dose. On the other hand, it is established that the copper layer is less and less consumed as the antimony dose increases, resulting in the accumulation of Sb⁺ ions at silicide/Si interface and in the silicide layer close to surface. The exposure of samples to air at room temperature shows the stability of Cu₄Si phase whereas the Cu₃Si silicide disappears to the benefit of the silicon dioxide formation. The observed phenomena are discussed.     

量子密码技术开辟通信安全新时代

量子密码技术被认为是绝对安全的加密技术。近年来,在美国、德国、日本和中国,相关研究都取得了明显进展。2004年6月,世界上第一个量子密码通信网络在美国马萨诸塞州剑桥城正式投入运行,标志着这一技术迈上了新台阶。据相关机构估算,量子保密通信系统一旦商用,将形成高达10亿美元的市场。     

The Intellectual Development of Science and Engineering Students. Part 2: Teaching to Promote Growth

As college students experience the challenges of their classes and extracurricular activities, they undergo a developmental progression in which they gradually relinquish their belief in the certainty of knowledge and the omniscience of authorities and take increasing responsibility for their own learning. At the highest developmental level normally seen in college students (which few attain before graduation), they display attitudes and thinking patterns resembling those of expert scientists and engineers, including habitually and skillfully gathering and analyzing evidence to support their judgments. This paper proposes an instructional model designed to provide a suitable balance of challenge and support to advance students to that level. The model components are (1) variety and choice of learning tasks; (2) explicit communication and explanation of expectations; (3) modeling, practice, and constructive feedback on high‐level tasks; (4) a student‐centered instructional environment; and (5) respect for students at all levels of development.     

Evolution and structure of drying material bridges of pharmaceutical excipients: studies on a microscope slide

An experimental procedure was developed to study directly the process by which liquid bridges between small particles in a granule form and solidify. The evolution of saturated solutions of such pharmaceutical excipients as lactose and mannitol in a liquid bridge was studied on a system situated on a microscope slide. Solidification and crystallization kinetics and phase composition during and immediately following bridge formation were observed directly. It was shown that bridges on the microscope slide and in the granule behave very much the same regardless of the different length and diffusion-scales of the two systems.We found that solid bridge formation takes place in several consecutive but distinct steps. In the case of lactose, considerable shrinkage of the initial liquid bridge takes place prior to the onset of crystallization. Further bridge solidification at ambient conditions occurs via simultaneous crystallization and vitrification within minutes. As a result, a “solid” bridge usually contains both a crystalline and a non-crystalline phase, the crystalline phase being predominately α-lactose monohydrate. Most of the non-crystalline phase eventually converts to crystalline β-lactose but the process may take many hours or even days. Results for this process are compared for samples obtained from different manufacturers of commercially available lactose. In the case of mannitol, different polymorphic forms crystallize as the drying/crystallization process progresses. A formed “solid” bridge usually contains several polymorphs of mannitol. The relevance of the behavior of the two model systems (pure lactose and pure mannitol) to a real granulation and tabletting process is discussed.     

Evaluation and optimization of package processing and design through solder joint profile prediction

Solder joints are generated using a variety of methods to provide both mechanical and electrical connection for applications such as flip-chip, wafer level packaging, fine pitch, ball-grid array, and chip scale packages. Solder joint shape prediction has been incorporated as a key tool to aid in process development, wafer level and package level design and development, assembly, and reliability enhancement. This work demonstrates the application of an analytical model and the Surface Evolver software in analyzing a variety of solder processing methods and package types. Bump and joint shape prediction was conducted for the design of wafer level bumping, flip-chip assembly, and wafer level packaging. The results from the prediction methodologies are validated with experimentally measured geometries at each level of design.     

Training and optimization of an artificial neural network controlling a hybrid power filter

A hybrid power compensator (HPC) consisting of a static VAr compensator and a dynamic compensator needs to be optimally controlled during the compensation of nonlinear loads. The HPC must be controlled to meet minimum requirements in terms of power factor and harmonic distortion, while at the same time minimizing its total cost. An artificial neural network (ANN) is used to control the HPC amidst a very dynamic power system environment. The performance of a reference ANN is evaluated while controlling an HPC connected to a typical nonlinear industrial load. The training and performance of the ANN is then optimized in terms of training set size, training set packing and ANN topology and the performance compared to the reference ANN. This paper highlights the importance of optimising the mentioned ANN parameters to achieve optimum ANN training and modeling accuracy. The results obtained reveals that the application of an ANN in controlling an HPC is feasible given that the ANN parameters are chosen appropriately.