Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii
Not provided.
Biochemistry & Molecular Biology↗
Engineering topics
Liu, Gary
Publications and source records attributed to Liu, Gary.
On the challenges facing the handling of solid biomass feedstocks: On the Challenges Facing the Handling of Solid Biomass Feedstocks
With a major global emphasis on the management of waste, alternative resources and a shift to environmentally sustainable technologies, demand for large volumes of heterogeneous solid biomass feedstocks for energy or chemical use is expected to rise significantly. In transforming a sporadic supply of a low-value, highly variable product, to continuous and controlled high throughput systems, a thorough understanding of the feedstock properties will increase in importance. Appropriate characterisation tests are necessary to define technical specification and selection criteria for handling equipment and to appraise the requirement and location for additional processes or pre-treatment to be integrated into the handling chain. Such tests may also influence the material characteristics to be used in the conversion process. In this paper, we discuss the main feedstock attributes associated with a number of handling chain phases and the approach to obtain them. Lastly, the framework for a holistic approach to the characterisation and design of biomass feedstock handling systems for further development and practical implementation is also proposed.
09 BIOMASS FUELS↗
BigData Express: Toward Schedulable, Predictable, and High-Performance Data Transfer
Big Data has emerged as a driving force for scientific discoveries. Large scientific instruments (e.g., colliders, and telescopes) generate exponentially increasing volumes of data. To enable scientific discovery, science data must be collected, indexed, archived, shared, and analyzed, typically in a widely distributed, highly collaborative manner. Data transfer is now an essential function for science discoveries, particularly within big data environments. Although significant improvements have been made in the area of bulk data transfer, the currently available data transfer tools and services can not successfully address the high-performance and time-constraint challenges of data transfer required by extreme-scale science applications for the following reasons: disjoint end-to-end data transfer loops, cross-interference between data transfers, and existing data transfer tools and services are oblivious to user requirements (deadline and QoS requirements). Fermilab has been working on the BigData Express project to address these problems. BigData Express seeks to provide a schedulable, predictable, and high-performance data transfer service for big data science. The BigData Express software is being deployed and evaluated at multiple research institutions, which include UMD, StarLight, FNAL, KISTI, KSTAR, SURFnet, Ciena, and other sites. Meanwhile, the BigData Express research team is collaborating with the StarLight International/National Communications Exchange Facility to deploy BigData Express at various research platforms, including Pacific Research Platform, National Research Platform, and Global Research Platform. It is envisioned that we are working toward building a high-performance data transfer federation for big data science.
Lu, Qiming↗