Globalization and the Chinese Knowledge Diaspora: An Australian Case Study

In a context of intensified globalisation, knowledge diaspora as “trans-national human capital” have become increasingly valuable to society. With an awareness of a need for more empirical studies especially in Australia, this article concentrates on a group of academics who were working at a major university in Australia and came originally from the Chinese mainland. The study explores their life, work and international research collaborations, using a case study approach with semi-structured interviews as the data collection method. The study found that while globalisation shapes the work and the contributions to Australia, by academics from China, they exert their initiatives to respond to and further reshape globalisation. Equipped with their Chinese cultural and educational backgrounds, academic experience in the West, and active membership in the international knowledge system, the Chinese knowledge diaspora are a modern kind of cosmopolitan literati. They are aware of the impact of globalisation and contribute actively to higher education internationalisation in both Australia and China, have maintained their cultural identity and made good use of their Chinese educational background. Their international collaborations, however, are more likely to be with the scholars from Western countries due to some difficulties they have experienced in China and Australia, and to the current setup of the global knowledge system.postprin

A Computational Strategy for Design and Implementation of Equipment That Addresses Sustainable Agricultural Residue Removal at the Subfield Scale

Agricultural residues are the largest potential near term source of biomass for bioenergy production. Sustainable use of agricultural residues for bioenergy production requires consideration of the important role that residues play in maintaining soil health and productivity. Innovation equipment designs for residue harvesting systems can help economically collect agricultural residues while mitigating sustainability concerns. A key challenge in developing these equipment designs is establishing sustainable reside removal rates at the sub-field scale. Several previous analysis studies have developed methodologies and tools to estimate sustainable agricultural residue removal by considering environmental constraints including soil loss from wind and water erosion and soil organic carbon at field scale or larger but have not considered variation at the sub-field scale. This paper introduces a computational strategy to integrate data and models from multiple spatial scales to investigate how variability of soil, grade, and yield within an individual cornfield can impact sustainable residue removal for bioenergy production. This strategy includes the current modeling tools (i.e., RUSLE2, WEPS, and SCI), the existing data sources (i.e., SSURGO soils, CLIGEN, WINDGEN, and NRCS managements), and the available high fidelity spatial information (i.e., LiDAR slope and crop yield monitor output). Rather than using average or representative values for crop yields, soil characteristics, and slope for a field, county, or larger area, the modeling inputs are based on the same spatial scale as the precision farming data available. There are three challenges for developing an integrated model for sub-field variability of sustainable agricultural residue removal—the computational challenge of iteratively computing with 400 or more spatial points per hectare, the inclusion of geoprocessing tools, and the integration of data from different spatial scales. Using a representative field in Iowa, this paper demonstrates the computational algorithms used and establishes key design parameters for an innovative residue removal equipment design concept

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues from the land can have negative impacts on soil health. Because of this computational tools are needed that can help guide decisions on the amount of agricultural residue that can be sustainably removed. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist that are capable of simultaneously addressing all of the environmental factors that can limit the availability of residue for bioenergy production. This paper presents an integrated framework of models and data that provide a coupled a set of environmental process models and databases that can support agricultural residue removal decisions. Specifically the RUSLE2, WEPS, and Soil Conditioning Index models have been integrated together with the disparate set of databases providing the soils, climate, and management practice data required. The integrated system has been demonstrated for two example cases. In the first case the potential impact of agricultural residue removal is explored. In the second case an aggregate assessment of the agricultural residues available bioenergy production in the state of Iowa is performed

Integration of Feedstock Assembly System and Cellulosic Ethanol Conversion Models to Analyze Bioenergy System Performance

Research barriers continue to exist in all phases of the emerging cellulosic ethanol biorefining industry. These barriers include the identification and development of a sustainable and abundant biomass feedstock, the assembly of viable assembly systems formatting the feedstock and moving it from the field (e.g., the forest) to the biorefinery, and improving conversion technologies. Each of these phases of cellulosic ethanol production are fundamentally connected, but computational tools used to support and inform analysis within each phase remain largely disparate. This paper discusses the integration of a feedstock assembly system modeling toolkit and an Aspen Plus® conversion process model. Many important biomass feedstock characteristics, such as composition, moisture, particle size and distribution, ash content, etc. are impacted and most effectively managed within the assembly system, but generally come at an economic cost. This integration of the assembly system and the conversion process modeling tools will facilitate a seamless investigation of the assembly system conversion process interface. Through the integrated framework, the user can design the assembly system for a particular biorefinery by specifying location, feedstock, equipment, and unit operation specifications. The assembly system modeling toolkit then provides economic valuation, and detailed biomass feedstock composition and formatting information. This data is seamlessly and dynamically used to run the Aspen Plus® conversion process model. The model can then be used to investigate the design of systems for cellulosic ethanol production from field to final product