Brainport智慧街区——应对未来生活的荷兰创新举措

时至今日,建筑师和设计师倾向于在小规模范围内讨论一些未证实的概念和解决方案—普遍以展亭等小型建筑的形式来表达一些创新的概念和想法,也许有一天这些想法会在某些问题中派上用场。但是面对实际生活和急迫的挑战,已经没有时间再提出模糊的概念和乌托邦式的提案了。建筑师和城市设计师现在需要去设计和开发可落地的解决方案来面对当今的挑战,提高城市生活的质量。不过,为了达成和评估预期的结果,动态、创新的实验场地不可或缺,以保证设计师和规划师可以在实践中获得经验。以近期的Brainport智慧街区项目为例,介绍在真实生活中进行实验的整体解决方案,来应对建成环境中的挑战。     

Encoding User Experiences

Guest-Editors Åsmund Izaki and Lucy Helme describe the work that they undertook as part of the Computational Design Research (CDR) group at Aedas|R&D, under the directorship of Christian Derix (2004- 2014), when algorithms were specifically developed ‘to calculate, visualise and stimulate human-centric architectural conditions’. As an initiative, it focused around three main themes - movement and networks, visibility and space, and behaviour and experience, and was applied to large-scale projects such as an urban study for Crossrail and a massing study for Euston Station, as well as to individual buildings such as the Polish Embassy in London.     

A semi-transparent selective undo algorithm for multi-user collaborative editors

Multi-user collaborative editors are useful computer-aided tools to support human-to-human collaboration. For multi-user collaborative editors, selective undo is an essential utility enabling users to undo any editing operations at any time. Collaborative editors usually adopt operational transformation (OT) to address concurrency and consistency issues. However, it is still a great challenge to design an efficient and correct OT algorithm capable of handling both normal do operations and user-initiated undo operations because these two kinds of operations can interfere with each other in various forms. In this paper, we propose a semi-transparent selective undo algorithm that handles both do and undo in a unified framework, which separates the processing part of do operations from the processing part of undo operations. Formal proofs are provided to prove the proposed algorithm under the well-established criteria. Theoretical analysis and experimental evaluation are conducted to show that the proposed algorithm outperforms the prior OT-based selective undo algorithms.     

Evolution from ancient medication to human-centered Healthcare 4.0: A review on health care recommender systems

The evolution of intelligent and data-driven systems has pushed for the tectonic transition from ancient medication to human-centric Healthcare 4.0. The rise of Internet of Things, Internet of Systems, and wireless body area networks has endowed the health care ecosystem with a new digital transformation supported by sophisticated machine learning and artificial intelligence algorithms. Under this umbrella, health care recommendation systems have emerged as a driver for providing patient-centric personalized health care services. Recommendation systems are automatic systems that derive the decisions on the basis of some valid input parameters and vital health information collected through wearable devices, implantable equipments, and various sensor. Therefore, to understand the state-of-the-art developments in the health care ecosystem, this paper provides a comprehensive survey on health care recommendation systems and the associated paradigms. This survey starts from the ancient health care era and move toward the Healthcare 4.0 in a phased manner. The road map from Healthcare 1.0 to Healthcare 4.0 is analyzed to highlight different technology verticals supporting the digital transformation. This study also provides the systematic review of the health care systems, the types of health care systems, and the recommender systems. Moreover, a deep analysis of health care recommender systems and its types is also presented. Finally, the open issues and challenges associated with the adaption and implementation of human-centric Healthcare 4.0 ecosystem are discussed. This is provided to find out the possible research questions and gaps so that the corresponding solutions could be developed in the near future.