This paper describes a self-learning ULR fuzzy controller using temporal back propagation. The ULR fuzzy controller is a multi-layer feed-forward network in which each node performs an unidirectional linear response (ULR) function (node function) on incoming weighted signals. In order to achieve a desired input-output mapping, the weight parameters are updated with a temporal back propagation such that the state variables can follow a given desired trajectory as closely as possible. The temporal back prop agation algorithm is used to train the ULR fuzzy controller to a variety of problems. We demonstrate the effectiveness of the self-learning ULR fuzzy controller by applying it to a benchmark problem in in telligent control-the inverted pendulum system. Experiments show a very good control performance and self-learning capability of the ULR fuzzy controllers.

Nowadays, grid and peer-to-peer (p2p) technologies have become popular solutions for largescale resource sharing and system integration. For escience workflow systems, grid is a convenient way of constructing new services by composing existing services, while p2p is an effective approach to eliminate the performance bottlenecks and enhance the scalability of the systems. However, existing workflow systems focus either on p2p or grid environments and therefore cannot take advantage of both technologies. It is desirable to incorporate the two technologies in workflow systems. SwinDeW-G (Swinburne Decentralised Workflow for Grid) is a novel hybrid decentralised workflow management system facilitating both grid and p2p technologies. It is derived from the former p2p based SwinDeW system but redeveloped as grid services with communications between peers conducted in a p2p fashion. This paper describes the system design and functions of the runtime environment of SwinDeW-G.

Background Hip osteoarthritis is a common disabling condition of the hip joint and is associated with a substantial health burden. We assessed the epidemiological patterns of hip osteoarthritis from 1990 to 2019 by sex, age, and socio-demographic index (SDI). Methods Age-standardized rates (ASRs) were obtained for the incidence and disability-adjusted life years (DALYs) of hip osteoarthritis from 1990 to 2019 for 21 regions, encompassing a total of 204 countries and territories. The estimated annual percentage changes (EAPCs) of ASRs were calculated to evaluate the trends in the incidence and DALYs of hip osteoarthritis over these 30 years. Results Globally, from 1990 to 2019, the age-standardized incidence rate (ASIR) of hip osteoarthritis increased from 17.02 per 100,000 persons to 18.70 per 100,000 persons, with an upward trend in the EAPC of 0.32 (0.29–0.34), whereas the age-standardized DALY rate increased from 11.54 per 100,000 persons to 12.57 per 100,000 persons, with an EAPC of 0.29 (0.27–0.32). In 2019, the EAPCs of the ASIR and age-standardized DALY rate of hip osteoarthritis were positively associated with the SDI of hip osteoarthritis. In 1990 and 2019, the incidence of hip osteoarthritis was unimodally distributed across different age groups, with a peak incidence in the 60–64-year-old age group, whereas the DALYs increased with age. Conclusions The incidence and DALYs of hip osteoarthritis have been increasing globally. The EAPCs of the ASIR and age-standardized DALY rate were particularly significant in developed regions and varied across nations and regions, indicating the urgent need for governments and medical institutions to increase the awareness regarding risk factors, consequences of hip osteoarthritis.

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