关节镜辅助下球囊成形术在胫骨平台骨折患者中的应用效果及对肢体功能的影响研究

目的探讨对胫骨平台骨折患者(FTP)行关节镜下球囊成形术治疗的效果及对患者术后肢体功能恢复产生的影响。方法选取2015年4月-2016年8月在该院收治的全部68例FTP患者为研究对象,所有患者按照随机数字表法分为观察组(关节镜下球囊成形术治疗)和对照组(传统切开手术治疗),对两组疗效、患者肢体功能恢复效果进行回顾性分析。结果观察组相关手术指标(术中出血量、手术时间、术后下床活动时间和术后引流量)均明显优于对照组,膝关节功能恢复优良率为92.18%,明显高于对照组的70.59%,术后生活质量评价量表(SF-36)评分明显高于对照组(P<0.05)。结论在关节镜辅助下对FTP患者行球囊成形术治疗,疗效更为确切,可使患者肢体功能得到更大程度改善,提高患者生活质量。

Location optimization of multiple distribution centers under fuzzy environment

Locating distribution centers optimally is a crucial and systematic task for decision-makers.Optimally located distribution centers can significantly improve the logistics system's efficiency and reduce its operational costs.However,it is not an easy task to optimize distribution center locations and previous studies focused primarily on location optimization of a single distribution center.With growing logistics demands,multiple distribution centers become necessary to meet customers' requirements,but few studies have tackled the multiple distribution center locations(MDCLs) problem.This paper presents a comprehensive algorithm to address the MDCLs problem.Fuzzy integration and clustering approach using the improved axiomatic fuzzy set(AFS) theory is developed for location clustering based on multiple hierarchical evaluation criteria.Then,technique for order preference by similarity to ideal solution(TOPSIS) is applied for evaluating and selecting the best candidate for each cluster.Sensitivity analysis is also conducted to assess the influence of each criterion in the location planning decision procedure.Results from a case study in Guiyang,China,reveals that the proposed approach developed in this study outperforms other similar algorithms for MDCLs selection.This new method may easily be extended to address location planning of other types of facilities,including hospitals,fire stations and schools.

Transit smart card data mining for passenger origin information extraction

The automated fare collection(AFC) system,also known as the transit smart card(SC) system,has gained more and more popularity among transit agencies worldwide.Compared with the conventional manual fare collection system,an AFC system has its inherent advantages in low labor cost and high efficiency for fare collection and transaction data archival.Although it is possible to collect highly valuable data from transit SC transactions,substantial efforts and methodologies are needed for extracting such data because most AFC systems are not initially designed for data collection.This is true especially for the Beijing AFC system,where a passenger's boarding stop(origin) on a flat-rate bus is not recorded on the check-in scan.To extract passengers' origin data from recorded SC transaction information,a Markov chain based Bayesian decision tree algorithm is developed in this study.Using the time invariance property of the Markov chain,the algorithm is further optimized and simplified to have a linear computational complexity.This algorithm is verified with transit vehicles equipped with global positioning system(GPS) data loggers.Our verification results demonstrated that the proposed algorithm is effective in extracting transit passengers' origin information from SC transactions with a relatively high accuracy.Such transit origin data are highly valuable for transit system planning and route optimization.

Estimating hotspots using a Gaussian mixture model from large-scale taxi GPS trace data

The data collected from taxi vehicles using the global positioning system(GPS)traces provides abundant temporal-spatial information,as well as information on the activity of drivers.Using taxi vehicles as mobile sensors in road networks to collect traffic information is an important emerging approach in efforts to relieve congestion.In this paper,we present a hybrid model for estimating driving paths using a density-based spatial clustering of applications with noise(DBSCAN)algorithm and a Gaussian mixture model(GMM).The first step in our approach is to extract the locations from pick-up and drop-off records(PDR)in taxi GPS equipment.Second,the locations are classified into different clusters using DBSCAN.Two parameters(density threshold and radius)are optimized using real trace data recorded from 1100 drivers.A GMM is also utilized to estimate a significant number of locations;the parameters of the GMM are optimized using an expectation-maximum(EM)likelihood algorithm.Finally,applications are used to test the effectiveness of the proposed model.In these applications,locations distributed in two regions(a residential district and a railway station)are clustered and estimated automatically.