Evolutionary algorithms in dynamic environments

The file attached to this record is the author's final peer reviewed version.Evolutionary algorithms (EAs) are widely and often used for solving stationary optimization problems where the fitness landscape or objective function does not change during the course of computation. However, the environments of real world optimization problems may fluctuate or change sharply. If the optimization problem is dynamic, the goal is no longer to find the extrema, but to track their progression through the search space as closely as possible. All kinds of approaches that have been proposed to make EAs suitable for the dynamic environments are surveyed, such as increasing diversity, maintaining diversity, memory based approaches, multi-population approaches and so on

GoGlobal Rural-Urban

This is a book of edited articles and design projects from five years of collaborative international design projects in developed and developing economies, including China, Thailand, Ghana and Japan. Part One contains articles on initiatives including e-commerce models for developing economies, massclusivity and craft design. Part Two is dedicated to design solutions for China’s rural-urban migration issues, which affect 55 million people a year. The project enhances knowledge about the application of design thinking to national-level issues connecting policy to implementation, extending design activity into large-scale social and economic areas. The book follows an exhibition of design outcomes in London and Beijing (2010). Hall developed his chapter (‘Go Global: Ghana’) from a conference paper given at the ‘International Association of Societies of Design Research Conference’, South Korea (2009) and further expanded as a book chapter (with Barker) entitled ‘e-Artisans: Contemporary design for the global market’ in Global Design History (2011). ‘eArtisans’ researched a proposed e-commerce model linking designer-craftsmen with a global Internet sale and distribution model for African countries. The originality lay in proposing and testing knowledge, through design collaborations, in a combination of e-commerce enterprise models; it was significant in deploying the proposed model in an experimental educational initiative. The research was based on previous experience of design, craft and enterprise projects in Thailand and China, and aligned with a creative economies report by UNESCO (2008). The context was a collaboration at the KNUST, Kumasi, Ghana, where action-based research methods resulted in a case study illustrating cultural transfer. Support and partnership were also provided by Aid To Artisans, the British Council and Africa 53. A vital aspect was the discovery of how an e-commerce model changed design concepts and creative proposals. The GoGlobal project has continued with an edited publication, Designing Social City Experiences (Jin Nam and Hall 2013)

室内植物表型平台及性状鉴定研究进展和展望

Plant phenomics is under rapid development in recent years, a research field that is progressing towards integration, scalability, multi-perceptivity and high-throughput analysis. Through combining remote sensing, Internet of Things (IoT), robotics, computer vision, and artificial intelligence techniques such as machine learning and deep learning, relevant research methodologies, biological applications and theoretical foundation of this research domain have been advancing speedily in recent years. This article first introduces the current trends of plant phenomics and its related progress in China and worldwide. Then, it focuses on discussing the characteristics of indoor phenotyping and phenotypic traits that are suitable for indoor experiments, including yield, quality, and stress related traits such as drought, cold and heat resistance, salt stress, heavy metals, and pests. By connecting key phenotypic traits with important biological questions in yield production, crop quality and Stress-related tolerance, we associated indoor phenotyping hardware with relevant biological applications and their plant model systems, for which a range of indoor phenotyping devices and platforms are listed and categorised according to their throughput, sensor integration, platform size, and applications. Additionally, this article introduces existing data management solutions and analysis software packages that are representative for phenotypic analysis. For example, ISA-Tab and MIAPPE ontology standards for capturing metadata in plant phenotyping experiments, PHIS and CropSight for managing complicated datasets, and Python or MATLAB programming languages for automated image analysis based on libraries such as OpenCV, Scikit-Image, MATLAB Image Processing Toolbox. Finally, due to the importance of extracting meaningful information from big phenotyping datasets, this article pays extra attention to the future development of plant phenomics in China, with suggestions and recommendations for the integration of multi-scale phenotyping data to increase confidence in research outcomes, the cultivation of cross-disciplinary researchers to lead the next-generation plant research, as well as the collaboration between academia and industry to enable world-leading research activities in the near future