Spherical Functions on Fuzzy Lie Group

Let G be a locally compact Lie group and its Lie algebra. We consider a fuzzy analogue of G, denoted by called a fuzzy Lie group. Spherical functions on are constructed and a version of the existence result of the Helgason-spherical function on G is then established on .

Elements’important ranking of China drug safety management system:applying the non-structural fuzzy group decision method

Drug safety management is an important issue in China drug management system and attracts great attentions from the whole society.In order to reduce drug incident,this study discusses some important elements associated with China drug safety management system and analyzes the data collected by questionnaires.Besides,a methodology for rating the important elements is described and applied.The non-structural fuzzy group decision method not only considers the insufficient precise information but also combines the opinions of different kinds of respondents in China’s four municipalities.The results indicate that the sample systems are the most important in these important elements,and the order of importance is sampling systems,licensing systems,traceability systems,transaction models,pharmacovigilance and emergence management.This study not only points out the important ranking of the pivotal elements in China drug safety management but also gives some specific proposals about how to enhance drug safety management in China.

Internet of Things Enabled Intelligent Energy Management and Control System for Heavy Equipment Industrial Park and Fuzzy Assessment of Its Schemes

In order to solve the problems of poor informationflow,low energy utilization rate and energy consumption data reuse in the heavy equipment industrial park,the Internet of Things(IoT)technology is applied to construct the intelligent energy management and control system(IEMCS).The application architecture and function module planning are analyzed and designed.Furthermore,the IEMCS scheme is not unique due to the fuzziness of customer demand and the understanding deviation of designer to customer demand in the design stage.Scheme assessment is of great significance for the normal subsequent implementation of the system.A fuzzy assessment method for IEMCS scheme alternatives is proposed to achieve scheme selection.Fuzzy group decision using triangular fuzzy number to express the vague assessment of experts is adopted to determine the index value.TOPSIS is modified by replacing Euclidean distance with contact vector distance in IEMCS scheme alternative assessment.An experiment with eight IEMCS scheme alternatives in a heavy equipment industrial park is given for the validation.The experiment result shows that eight IEMCS scheme alternatives can be assessed.Through the comparisons with other methods,the reliability of the results obtained by the proposed method is discussed.

Research on the Construction of Evaluation Model for the Development of Biopharmaceutical Park in China

Objective To study the influencing factors on the development of biopharmaceutical park,and to construct an evaluation model of the influencing factors for biopharmaceutical park in China.Methods By analyzing various factors affecting biopharmaceutical parks,an evaluation index system of biopharmaceutical parks and an evaluation model of influencing factors of biopharmaceutical park development based on fuzzy group decision making were established.Results and Conclusion Factors such as research and development(R&D)funding investment,incentive for transformation of scientific and technological achievements,and industrial clusters have a greater impact on the development of biopharmaceutical industrial parks in China.Local governments should increase the investment in R&D funding.Besides,they should pay attention to the incentive of transformation of scientific and technological achievements to improve the innovation ability of enterprises.Meanwhile,they should promote the clustering of high-tech enterprises to comprehensively enhance the healthy development of biopharmaceutical parks in China.

Establishing the Forecasting Model with Time Series Data Based on Graph and Particle Swarm Optimization

In recent years,a wide variety of fuzzy time series(FTS)forecasting models have been created and recommended to handle the complicated and ambiguous challenges relating to time series data from real-world sources.However,the accuracy of a model is problem-specific and varies across data sets.But a model’s precision varies between different data sets and depends on the situation at hand.Even though many models assert that they are better than statistics and a single machine learning-based model,increasing forecasting accuracy is still a challenging task.In the fuzzy time series models,the size of the intervals and the fuzzy relationship groups are thought to be crucial variables that affect the model’s forecasting abilities.This study offers a hybrid FTS forecasting model that makes use of both the graph-based clustering technique(GBC)and particle swarm optimization(PSO)for adjusting interval lengths in the universe of discourse(UoD).The suggested model’s forecasting results have been compared to those provided by other current models on a dataset of enrollments at the University of Alabama.For all orders of fuzzy relationships,the suggested model outperforms its counterparts in terms of forecasting accuracy.