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Objective: To cross-culturally translate and validate the Chinese versions of the Oxford Knee Score (OKS) and the Activity and Participation Questionnaire (APQ) in patients with end-stage knee osteoarthritis who are also candidates for knee replacement. Methods: The Chinese version of the OKS and APQ was completed by standard forward–backward translation and adaption. The feasibility was validated by a pretest in 30 patients. The final version together with the Short Form-36 (SF-36), EQ-5D, and EQ visual analog scale were assessed in 150 patients, and the OKS and APQ were repeated in 30 patients after a 2-week interval. The psychometric properties of the OKS and APQ were evaluated for test–retest reliability using intraclass correlation coefficients (ICCs), internal consistency using Cronbach’s α, and construct validity using Spearman’s correlation analysis. Results: All patients were able to understand and complete both the OKS and APQ without difficulty (i.e. no missing data). The ICCs were 0.959 for the OKS, 0.956 for the APQ for total scores, and >0.7 for each item. Cronbach’s α was greater than 0.7, and the corrected item-total correlation was greater than 0.4 for each item of both questionnaires. The OKS and APQ showed better correlations with questions from the pain and function domains than with those from the mental status domains of the SF-36 and EQ-5D. No floor or ceiling effect was identified in either questionnaire. Conclusions: The Chinese versions of the OKS and APQ are easy to understand and complete and showed good reliability and validity. They can be used to assess patient-reported outcomes after undergoing knee replacement in mainland China.

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Abstract. The move towards automated driving is gaining ground. This paper reviews the development process of self-driving technology and discusses the safety and efficiency advantages of autonomous vehicles. The discussion shows that the existing traffic management system, including transport infrastructures and regulations, should be changed accordingly to maximize the advantages of autonomous driving. Thus, this paper subsequently gives an insight of the traffic management from three aspects: fully self-driving traffic infrastructures, mixed traffic infrastructures and regulations. First, it is summarized in detail what should be adjusted in intersections, parking lots, pedestrian crossings, ramps, signs and markings. With the transformation of traffic infrastructures, the advantages of driverless car will be more pronounced on account of increased capacity, reduced delay and land use. Also, this paper indicates that the implementations of strict product liability for self-driving car manufacturers and no-fault tort liability for users are applicable to automated vehicle accidents.

We consider a heterogeneous wireless network serving two classes of users: mobile and fixed. Mobile users can only associate with macro-cells while fixed users can be served by either macro-or small-cells. Multiple service providers (SPs) can operate in the network and each has the same macrocell infrastructure. In contrast, each SP determines a smallcell deployment density by its investment. Each SP is given a fixed total bandwidth, which is split between macro-and smallcell service, and charges a price per unit rate for each type of service. The objective of each SP is then to select the price, bandwidth split, and investment in small-cells to maximize either revenue or social welfare. We first assume a single SP and characterize the optimal strategies for both revenue and social welfare maximization. The deployment density largely depends on the per unit deployment cost of small-cells. We then consider a binary investment game in which each SP has the option of investing in a small-cell network with fixed deployment density, and show that equilibria exist in which one, none, or both SPs invest. In most cases, the pure strategy equilibria are not socially optimal. In addition, there exists asymmetric equilibrium where one SP invests in small-cells while the other doesn't. Numerical results are presented that illustrate the effect of deployment cost on small-cell investment.