**5. Conclusion and future researches**

When we design an emergency rescue system, we need to coordinate the manpower with the financial, material resources. It is a complicated process to optimally allocate various elements within a system. It involves a wide range of contents. Repeated researches should be made on several theories and methods. Designing of an emergency rescue system covers the following four aspects which have been cross-linked each other essentially, that are 1) Demand Forecasting of Emergency Resources;2) Optimal Site Selection for the Base Station of Emergency Resources;3) Appropriate Allocation of Emergency Resources;4) Optimal Dispatching of Emergency Resources. Here, it proposed the overall and detailed methods to fulfill these four aspects.

In the future it is necessary to develop a computer system, so that these methods can adapt to the dynamic optimization process of emergency resource scheduling scheme under complex conditions such as many times of derivation and many kinds of resources etc., and it can more greatly satisfy the actual need.

#### **6. Acknowledgments**

The authors appreciate the support of No.47 China's Postdoc Foundation(NO:20100470305) and the national science foundation for the youth (NO:41105099).

#### **7. References**

152 Novel Approaches and Their Applications in Risk Assessment

 5.1 0.3 2.69 0.7 0 2.69 6.99 11.8 0.8 2.69 0.2 0 2.69 12.388 

Select the strategy <sup>1</sup> u(1) . Scheme I is used for emergency resource scheduling under the state

 8.6 0.6 2.69 0.4 0 0 6.99 4.5 0.5 2.69 0.5 0 3.15 

Select the strategy <sup>2</sup> u(2) . Scheme IV is used for emergency resource scheduling under the

computing results. The strategy 1 is different from <sup>0</sup> , so no optimized strategy is

 (1) (1) (1) (1) 1 12 (1) (1) (1) (1) 2 12 v f 5.1 0.3f 0.7f v f 4.5 0.5f 0.5f 

(1)

 5.1 0.3 ( 0.5) 0.7 0 0.5 4.75 11.8 0.8 ( 0.5) 0.2 0 0.5 11.7 

 8.6 0.6 ( 0.5) 0.4 0 0 8.9 4.5 0.5 ( 0.5) 0.5 0 0 4.75 

iteration results, so the optimized strategy is obtained as <sup>1</sup> . That is, take the scheduling scheme I when the sudden event is under the state of *S1*, and take the scheduling scheme IV

1 (2) (2) u is obtained, which is exactly the same as the previous

The fourth step is fixed value operation for the purpose of obtaining (1) v , (1)

(1) 1 f 0.5 , 1 (1) (1) u , <sup>2</sup>

2f 0 is obtained through solving the equations

1 (2) (2) u from the above

1f , (1) 2f

For the state *S1*, select a strategy (k) u1 , so as to maximize kk k (0) (0) q pf Pf 1 11 12 1 1 , that is

For the state *S2*, select a strategy (k) u2 , so as to maximize kk k (0) (0) q pf pf 2 21 22 1 2 , that is

The improvement strategy is obtained as <sup>1</sup>

obtained and it is necessary to go on iteration.

In the fifth step, seek the improvement strategy <sup>2</sup> .

2f 0 , then (1) v 4.75 ,

So the strategy <sup>1</sup> u(1) is still taken.

So the strategy <sup>2</sup> u(2) is still taken.

1 (1) (1) u , <sup>2</sup>

when the sudden event is under the state of *S2*.

For the state *S1*, there is

For the state *S2*, there is

As a result, <sup>1</sup>

of *S1*.

state of *S2*.

Let (1)

set.


Theories and Methods for the Emergency Rescue System 155

[26] Oxman, R.E., (1993b). Case-based design support: Supporting architectural composition through precedent libraries. Journal of Architectural Planning Research. [27] Venkatamaran, S., Krishnan, R. & Rao, K.K. (1993). A rule-case based system for image

[28] Kitano, H. (1993). Challenges for massive parallelism. In, Proc. *13th. Int. Conference on* 

[29] Roger. C. S,David. B. L.*Creativity and Learning in a Case-based Explainer*.AI,1989,

[30] Sun Mingxi, Wu Junqing, Ai Guoqiang, et.,al. *Practical Prediction Method and Instance* 

[31] Liu Mao, Wu Zongzhi, *Introduction to Emergency Rescue - Emergency Rescue System and* 

[33] Wayne L. Winston, *Operations Research Application and Algorithms*. BeiJixianng:

[34] Donald Waters. *Logistics: An Introduction to Supply Chain Management*. BeiJixianng:

[35] He Jixiananmin, Liu Chunlin, Cao Jixianedeng et al., *Emergency Management and* 

[36] Zhu Tan, Liu Mao and Zhao Guomin. *Study of points of urban public security planning and* 

[37] Liu Mao, Zhu Tan and Zhao Guomin. *Study of urban public security emergency systems*.

[38] Chen Zhizong and You Jixiananxin. *Modeling of hierarchy location for urban disaster* 

[39] Sun Ying, Chi Hong, Jixiana Chuanliang. *Non-linear mixed integer planning model of* 

[40] Zhou Xiaomeng, Jixianang Lizhen, Zhang Yunlong. *Study of emergency resource optimal* 

[41] Larson R E, Carstea J L. *Dynamic programming principle* [M]. Chen Weiji, Wang Yongxian, Yang Jiaben. Trans, Beijing:Tsinghua University Press, 1984. [42] Bertsekas D P. *Dynamic programming: deterministic and stochastic models*[M].Trans. Xi'an:

[43] Feinberg E A, Shwartzz A. *Markov decision models with weighted discounted criteria* [J].

[44] Bouakiz M, Kebir Y. *Target-level criterion in Markov decision processes*[J]. J Optim Theory

*Emergency Response System - Location, Scheduling and Algorithms*. BeiJixianng: Science

*prevention and mitigation facilities*. Journal of Natural Disasters, 2005 Volume 14 (2)

*multipath emergency resource scheduling*. in Operations Research and Management,

*allocation quantitative model for unforeseen accidents*. Journal of Safety and

*Analysis*. Beijing: Science and Technology Document Press, 1993.

[32] Xiong wei, *Operational Research*. BeiJixianng: Mechanical Industry Press, 2005

*Programs*. BeiJixianng: Chemical Industry Press, 2004

*developing*. China's Development 2003 (4) pp.10-12

China's development 2003 (4) pp.13-16

*Presentations*, 2: pp.410-15.

40:353~385

pp.131-135

2007, pp.16.

Environment, 2007, pp.6.

Math O R, 1994, 19:152-168.

Appl, 1995, 86:1-15.

Xi'an Jiaotong University Press, 1990.

*Artificial Intelligence*, IJCAI-93: pp813-34.

Tsinghua University Press, 2006

Electronic Industry Press, 2006

and Technology Press, 2007

analysis. In, Proc. *1st. European Workshop on Case-Based Reasoning, Posters &* 


[12] Porter, B.W. & Bareiss, E.R. (1986). PROTOS: An experiment in knowledge acquisition

[13] Bareiss, E. R.,(1988). *PROTOS: A Unified Approach to Concept Representation,* 

[14] Ashley, K.D. (1988). Arguing by Analogy in Law: A Case-Based Model. In D.H. Helman

[15] Rissland, E.L., & Skala , D.B. (1989). Combining case-based and rule-based reasoning: A

[16] Sharma, S. & Sleeman, D. (1988). REFINER: A Case-Based Differential Diagnosis Aide

[17] Keane, M., (1988). Where's the Beef? The absence of pragmatic factors in theories of

[18] Althoff, K.D. (1989). Knowledge acquisition in the domain of CBC machine centres:

[19] Richter, A.M. & Weiss, S. (1991). Similarity, uncertainty and case-based reasoning in

[20] Aamodt, A., (1989). Towards robust expert systems that learn from experience - an

[21] Aamodt, A. (1991). A Knowledge intensive approach to problem solving and sustained

[22] Watson, I.D., & Abdullah, S. (1994). Developing Case-Based Reasoning Systems: A Case

[24] Moore, C.J., Lehane, M.S. & Proce, C.J. (1994). Case-Based Reasoning for Decision

[25] Oxman, R.E., (1993a). PRECEDENTS: Memory structure in design case libraries. In

*Reasoning: Prospects for Applications*, Digest No: 1994/057, pp.1/1-1/3. [23] Yang, S., & Robertson, D. (1994). A case-based reasoning system for regulatory

Technology, May 1991. University Microfilms PUB 92-08460.

*Prospects for Applications*, Digest No: 1994/057, pp.4/1-4/4.

*Applications*, Digest No: 1994/057, pp.3/1-3/3.

CAAD Futures 93, Elsevier Science Publishers.

*Advances in Learning (IMAL)*, Les Arcs, France, pp.159-74.

University of Texas.

*Philosophy*. D. Reidel.

1989.

(ed.): pp249-265.

pp.311-326. Paris, July 1989.

IJCAI-89: pp. 524-30, Detroit, Michigan.

analogy. In, *ECAI-88*: pp.327-32.

*European Working Session on Learning*: pp201-10.

for heuristic classification tasks. *In Proceedings of the First International Meeting on* 

*Classification, and learning*. Ph.D. thesis, Department. of Computer Science,

(Ed.), *Analogical Reasoning: Perspectives of Artificial Intelligence, Cognitive Science, and* 

heurestic approach. In, *Eleventh International Joint Conference on Artificial Intelligence*,

for Knowledge Acquisition and Knowledge Refinement. In, *EWSL 88; Proc.* 

the MOLTKE approach. In*, EKAW-89, Third European Workshop on Knowledge-Based Systems*, Boos, J., Gaines, B. & Ganascia, J.G. (eds.), pp.180-95. Paris, July

PATDEX. In, *Automated reasoning, essays in honor of Woody Bledsoe. Kluwer* R.S. Boyer

architectural framework. In, EKAW-89: Third European Knowledge Acquisition for Knowledge-Based Systems Workshop, Boose, J., Gaines, B. & Ganascia J.-G. (eds.),

learning, PhD. dissertation, University of Trondheim, Norwegian Institute of

Study in Diagnosing Building Defects. In, Proc. *IEE Colloquium on Case-Based* 

information. In, Proc. *IEE Colloquium on Case-Based Reasoning: Prospects for* 

Support in Engineering Design. In, Proc. *IEE Colloquium on Case-Based Reasoning:* 


**Absorption and Accumulation of Heavy Metal** 

Soil - plant system is the biosphere and pedosphere whose the basic structural unit of soil is the main target. Soil - plant systems enable human productivity but suffer from pollution damage caused by humans. Currently, the annual loadings of harmful metals in soil are (104 t / a): Hg 0.83, Cd 2.2, Cr 89.6, Pb 79.6, Ni 32.5, Cu 95.4, Zn 137.1, As 8.1, Se 4.1 [1,2]. Contaminated soil will directly, or by amplifying through the food chain, affect the normal function and growth of plant and even human health. At the same time, the ecosystem, through a series of physical, chemical and biological processes in the environment, provides a purification of pollutants. Beyond the loading capacity of the environmental pollution load capacity and super-threshold, its biological production will be affected, resulting in severe loss of productivity and may even directly or indirectly endanger human life and health. Phytoremediation is considered a green technology for the removal of heavy metal pollution in the ascendant [3]. With the rapid development of the national economy and the subsequent traffic pollution, negative environmental effects are becoming increasingly apparent, especially for roadside soil - plant systems. The the evidence is apparent in Shanxi Province where 5,000 km of roadside farmland was polluted by coal dust, reducing food productivity by 2 800 × 104kg [4]. In recent years, car exhaustion and road dust caused heavy metal pollution on the soil-plant systems on both sides of the roads, and consequently, the heavy metal content has brought stress on the structure and function of ecosystem, which increasingly exposed agricultural issues. Currently, research on the domestic and international distribution of heavy metals in soil

**1. Introduction** 

\* Corresponding Author

**Pollutants in Roadside Soil-Plant Systems –** 

**A Case Study for Western Inner Mongolia** 

Hua Yupeng2, Hong Ge2, Emmy Camada5 and Yao Yiping1

Lu Zhanyuan1, Zhi Yingbiao2,3\*, Wang Zai-lan4,

*2Ordos College, Inner Mongolia University, Ordos, 3College of Life Science, Nanjing University, Nanjing, 4School of Environment and Natural Resources,* 

*5Chinese Culture Center of San Francisco, CA,* 

*Renmin University of China, Beijing,* 

*1,2,3,4China 5USA* 

*1Inner Mongolia Academy of Agricultural Science, Hohhot,* 

[45] Chen M, Filar J A, Liu K. *Semi-infinite Markov decision processes*[J]. Mathematical Methods of Operations Research, 2000, 51:115-117. **8** 
