Point of interest (POI) recommendation is a significant task in location-based social networks (LBSNs), e.g., Foursquare, Brightkite. It helps users explore the surroundings and help POI owners increase income. While several researches have been proposed for the recommendation services, it lacks integrated analysis on POI recommendation. In this article, the authors propose a unified recommendation framework, which fuses personalized user preference, geographical influence, and social reputation. The TF-IDF method is adopted to measure the interest level and contribution of locations when calculating the similarity between users. Geographical influence includes geographical distance and location popularity. The authors find friends in Brightkite share low common visited POIs. It means friends' interests may vary greatly. Instead of directly getting recommendations from so-called friends in LBSN, the users attain recommendation from others according to their reputation. Finally, experimental results on real-world dataset demonstrate that the proposed method performs much better than other recommendation methods.

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Fault estimation (FE) and fault-tolerant control (FTC) are remarkable techniques, have achieved great success in many applications such as robot, spacecraft, and industrial assembly line. This article aims to design an iterative-learning scheme based FE and FTC method for a class of nonlinear system with iteration-variant state delay and additive measurement noise. A Luenberger observer in iterative version is proposed to achieve the reconstruction of system state information, which consider the historical observation error in order to improve the observation performance in current iteration. To deal with bounded iteration-variant state delay, an iterative-learning scheme based fault estimator is designed and the convergence is proved. Compared with relevant methods which use system output observation residual to revise the FE result of last iteration, the proposed approach uses filtered system output observation residual in order to reduce the effect of measurement noise. Based on the FE result, FTC using signal compensation technique is employed. In addition, an improved particle swarm optimization algorithm is employed for parameters adaptive tuning. Compared with traditional manual adjustment of parameters, the proposed method can find the optimal parameters and save time of parameter tuning. Finally, three examples are provided to verify the effectiveness of the proposed approach.

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Research was conducted to investigate the major factors affecting cloud computing adoption among SMEs. Our theoretical framework is based on the Technological, Organisational and Environmental (TOE) Model, with our early empirical evidence stemming from data collected while conducting semi structured interviews with nine companies. The analysis of the responses identified the following factors as those affecting cloud computing adoption: Relative advantage, uncertainty, geo-restriction, compatibility, complexity, trialability, size, top management support, prior experience, innovativeness, industry, market scope, supplier efforts and external computing support. Our findings are expected to have both theoretical and practical value and important implications for the research community, business practitioners, and policy makers in terms of formulating better strategies for cloud computing adoption.