Bioactive Chemical Constituents from the Brown Alga Homoeostrichus formosana

A new chromene derivative, 2-(4'',8''-dimethylnona-3''E,7''-dienyl)-8-hydroxy-2,6-dimethyl-2H-chromene (1) together with four known natural products, methylfarnesylquinone (2), isololiolide (3), pheophytin a (4), and β-carotene (5) were isolated from the brown alga Homoeostrichus formosana. The structure of 1 was determined by extensive 1D and 2D spectroscopic analyses. Acetylation of 1 yielded the monoacetylated derivative 2-(4'',8''-dimethylnona-3''E,7''-dienyl)-8-acetyl-2,6-dimethyl-2H-chromene (6). Compounds 1–6 exhibited various levels of cytotoxic, antibacterial, and anti-inflammatory activities. Compound 2 was found to display potent in vitro anti-inflammatory activity by inhibiting the generation of superoxide anion (IC₅₀ 0.22 ± 0.03 μg/mL) and elastase release (IC₅₀ 0.48 ± 0.11 μg/mL) in FMLP/CB-induced human neutrophils.     

一种互联网海量情报规则优化方法

现在很多部门都需要通过规则处理海量情报数据,规则的数量一旦达到海量级规模,对规则的处理技术的要求将会变得越来越高,海量规则的优化将是一个十分重要的问题.文中展示了一种海量规则的优化方法.若两个规则网最后的计算结果是一样的,则可以认为这两个规则网是等价的.基于该等价规则模块的变换可以将代价小的规则模块替换掉代价大的规则模块,从而以后可以选择一种代价最小的规则模块.本文提出规则网模块等价替换的5种优化方法,并对其做了相应的证明.通过优化,可以使得海量规则网处于计算代价尽量最小状态,以此减少处理机的计算工作量.分析表明,通过等价规则替换,可以大大提高规则处理效率.     

Modified Hopfield neural networks for retrieving the optimalsolution

Due to the rugged energy function of the original Hopfield networks, the output is usually one local minimum in the energy function. An analysis on the locations of local minima in Hopfield networks is presented, and a modified network architecture to eliminate such local minima is described. In particular, another amplifier is introduced at the processor nodes to give correction terms. This modified Hopfield network has been successfully applied to the construction of analog-to-digital converters with optimal solutions. Experimental results on the voltage transfer characteristics of data converters are presented.     

Partitioning and mapping nested loops on multiprocessor systems

A method for executing nested loops with constant loop-carried dependencies in parallel on message-passing multiprocessor systems to reduce communication overhead is presented. In the partitioning phase, the nested loop is divided into blocks that reduce the interblock communication, without regard to the machine topology. The execution ordering of the iterations is defined by a given time function based on L. Lamport's (1974) hyperplane method. The iterations are then partitioned into blocks so that the execution ordering is not disturbed, and the amount of interblock communication is minimized. In the mapping phase, the partitioned blocks are mapped onto a fixed-size multiprocessor system in such a manner that the blocks that have to exchange data frequently are allocated to the same processor or neighboring processors. A heuristic mapping algorithm for hypercube machines is proposed     

A programmable analog VLSI neural network processor forcommunication receivers

An analog VLSI neural network processor was designed and fabricated for communication receiver applications. It does not require prior estimation of the channel characteristics. A powerful channel equalizer was implemented with this processor chip configured as a four-layered perceptron network. The compact synapse cell is realized with an enhanced wide-range Gilbert multiplier circuit. The output neuron consists of a linear current-to-voltage converter and a sigmoid function generator with a controllable voltage gain. Network training is performed by the modified Kalman neuro-filtering algorithm to speed up the convergence process for intersymbol interference and white Gaussian noise communication channels. The learning process is done in the companion DSP board which also keeps the synapse weight for later use of the chip. The VLSI neural network processor chip occupies a silicon area of 4.6 mmx6.8 mm and was fabricated in a 2-mum double-polysilicon CMOS technology. System analysis and experimental results are presented.     

Evaluation and optimization of query programs in an object-orientedand symbolic information system

OASIS, a database programming environment that extends UNIX with the concept of persistent objects, is discussed. The OASIS query languages extend conventional database query languages with procedural methods and general control statements. As the complexity of the languages makes it difficult to device a query optimizer based on a universally applicable algorithm, each query in OASIS is optimized based on a collection of basic patterns for which each pattern is associated with a separation query optimization algorithm. The optimization techniques for a set of basic patterns consisting of iterative statements and a set of nested statements is described. The optimization techniques discussed include an extended decomposition algorithm, evaluation of multiple conditions, data dependence analysis, and optimization of queries with arbitrary nesting     

Using Cpk index with fuzzy numbers to evaluate service quality

Service quality is measured by customers' satisfaction. Traditionally, the degree of satisfaction is calculated from the data obtained from questionnaires that have been filled by customers directly. The percentile of each different level of a customer's satisfaction is employed to summarize and compare the quality of service provided by different enterprises. This approach does not consider the consistency of the customers' perceptions, thus making comparison difficult. This paper introduces the concept of a process capability index that considers both the average and the consistency of the data simultaneously. Evaluations of service quality are usually vague and linguistic. We use the fuzzy numbers of linguistic variables developed in fuzzy set theory to modify the process capability index, and then apply it to evaluate the quality of a service. The average and consistency of the data obtained from a service quality evaluation are thus considered simultaneously, making the comparison of the performance of service quality easier. Moreover, the value of the index can be applied to help to point out the direction for improving the performance of service quality whenever it is lower than some default value.     

Dynamics of a microchain of superparamagnetic beads in an oscillating field

The dynamics of microchains containing superparamagnetic particles in an oscillating field are studied experimentally. The chains are first formed by a static directional field, and then manipulated by an additional dynamical perpendicular field. The present methodology represents a simple reversible chaining process, whose particles can be re-dispersed after removal of the field. The motion of superparamagnetic chains is dominated by magnetic torque and induced hydrodynamic drag. The effects of key parameters, such as field strengths and the lengths of particle chains, are thoroughly analyzed. Distinct behaviors, from rigid body oscillations and bending distortions to rupture failures, are observed by increasing the amplitudes of oscillating fields or chains’ lengths. Because of lower induced drag, a shorter chain follows the field trajectory closely and oscillates more synchronically with the external field. On the other hand, the influences of field strengths are not consistent. Even the overall oscillating phase trajectory in a stronger external field deviates less significantly from the corresponding field trajectory, a stronger dynamical component of the external field results in larger phase angle lags at certain points. The experimental results confirm the criterion of ruptures can be effectively determined by the value of (N*Mn ^1/2), where Mn is the Mason number defined as the ratio of induced drag to dipolar attraction, and N represents the number of particles contained in a chain.     

Scalable continuous object detection and tracking in sensor networks

With the advancement of MEMS technologies, sensor networks have opened up broad application prospects. An important issue in wireless sensor networks is object detection and tracking, which typically involves two basic components, collaborative data processing and object location reporting. The former aims to have sensors collaborating in determining a concise digest of object location information, while the latter aims to transport a concise digest to sink in a timely manner. This issue has been intensively studied in individual objects, such as intruders. However, the characteristic of continuous objects has posed new challenges to this issue. Continuous objects can diffuse, increase in size, or split into multiple continuous objects, such as a noxious gas. In this paper, a scalable, topology-control-based approach for continuous object detection and tracking is proposed. Extensive simulations are conducted, which show a significant improvement over existing solutions.