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本文研究了北京翠湖湿地公园夏、秋季水华优势藻血红裸藻（Euglena sanguinea）从水华发生到衰亡过程的细胞密度、形态特征及其与环境因子的关系.结果发现，2016年8月下旬至9月中旬血红裸藻密度稳定增加，9月20日藻密度达到峰值5.13×106 cells/L；9月下旬至11月底藻密度显著下降；血红裸藻在衰亡过程中呈现细胞形态由大变小、由细长变为圆形，细胞颜色由深变浅，运动速率由快变慢的趋势，衰亡后形成具透明胶被孢子的休眠体；血红裸藻群落生长的主控因子为水温和pH，其适宜的环境阈值范围为水温23~27℃，pH 7.3~8.0；其受光照强度影响存在分层现象，弱光强区域藻密度相对较低；血红裸藻的衰亡过程与铵态氮浓度呈显著正相关，低浓度时加速衰亡；与总氮浓度呈负相关，衰亡后总氮浓度显著升高；受总磷浓度影响相对较小，与溶解氧浓度无显著相关关系.

Qualitative models are more suitable than classical quantitative models in many tasks like Model-based Diagnosis (MBD), explaining system behavior, and designing novel devices from first principles. Monotonicity is an important feature to leverage when constructing qualitative models. Detecting monotone pieces robustly and efficiently from sensor or simulation data remains an open problem. This paper introduces an approach based on scale-dependent monotonicity: the notion that monotonicity can be defined relative to a scale. Real-valued functions defined on a finite set of reals e.g. the sensor data the simulation results, can be partitioned into quasi-monotone segments, i.e. segments monotone with respect to nonzero scale. We can identify the extrema of the quasi-monotone segments. This paper then uses this method to abstract qualitative models from simulation models for the purpose of diagnosis. It shows that using monotone analysis, the abstracted qualitative model is not only sound, but also parsimonious because it generates few landmarks.

The concurrent design of a product involves the cooperation of many functions such as manufacturing, logistics, marketing, and sales. It can be represented as a multiagent system with each agent representing a kind of expertise. Each agent has its own evaluation criteria and egoistic goals. Decision-making leads to problems typical for concurrent engineering, in which all related processes are simultaneously taken into consideration. In this paper, we discuss some characteristics of conflict, conflict type identification, as well as conflict resolution. A system architecture and an agent structure are proposed. We also propose a novel scheme named distributed dynamic conceptual tree to overcome the difficulty of understanding between agents during the problem solving process and reduce the amount of communications between agents. An demonstration example validating our work is presented.

The current design of a product involves the cooperation of many functions such as manufacturing, logistics, marketing and sales. It can be represented as a multiagent system with each agent representing a kind of expertise. Each agent has its own evaluation criteria and egoistic goals. The design process can be viewed as an optimization process of multiple participants and multiple criteria. We use GA as a heuristic technique to search in a large feasible solution space, and a tool to synthesize different counter-proposals from different agents. These counter-proposals are used in GA operations to speed up the evolutionary process. We also provide an experiment, in which the process of designing a gear pump is simulated. The system structure and agent structure are based on our former work (Hu Yongtong et al., 1995).

Multiagent systems have been widely used in concurrent engineering. However, most multi-agent tasks have high complexity. Optimal solutions of them are nearly intractable due to the heavy load of communication and large complexity of knowledge exchanges. In this paper, we extend the agent structure in our previous work. Rather than using common multi-agent diagram, we use stochastic search techniques such as genetic algorithms (GAs) to explore the solutions. Moreover, we also add counter proposals to speed up the searching process. Experimental results about designing a gear pump are satisfying, which turn out that the combination of GAs (modified to adapt to counter proposals) and Multiagent Systems can considerably speed up the convergence process, and at the same time, guarantee the best globe solution.