Advance in Pyrolysis and Gasification of Combustible Solid Waste and Engineering Simulation Study

简要介绍了目前国内城市生活垃圾的产生现状及其主要的处理方法。概述了典型可燃固体废弃物的热解气化实验进展和基于AspenPlus平台模拟的研究现状以及热解气化技术的应用情况。分析总结了热解温度和加热速率对热解产物分布及其产量的影响,以及空气燃料比和气化温度对气化过程的影响。基于AspenPlus平台的热解气化模型能够很好地模拟实际热解气化过程,具有较好的可靠性和实用性。指出可燃固废热解气化实验今后的研究重点及AspenPlus平台模拟研究的着重考虑因素

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

近25 a 银川市城市化进程中热力景观格局演变分析

Abstract： Based on the Landsat images and statistical data，in this paper the urbanization process and thermal landscape dynamics of Yinchuan City were analyzed． The objectives of this study were as follows: ① To analyze the spatial and dynamic characteristics of urban thermal landscapes based on the landscape pattern index; ② To assess the influence of the urbanization process on the thermal landscapes; ③ To understand the relationship between the land use pattern and the land surface temperature ( LST) based on the distribution index． The results showed that there was a significant heat-island effect in Yinchuan City，and the high temperature area was consistent with the built-up area and the unused land． LST declined from the downtown to the suburbs，and an obvious difference of the dynamic characters of the thermal landscapes could be identified at the different stages of urbanization． LST for various land use had obvious discrepancies，the mean LST for the land use types was in a descending order of unused land，construction land，green land and water body． Different land use types had different thermal distribution at different stages of urbanization． The land use pattern influenced the urban thermal environment，especially the spatial distribution of green land and unused land affected LST more significantly

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