203 research outputs found
LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations
The extraordinary performance of large language models has not only reshaped
the research landscape in the field of NLP but has also demonstrated its
exceptional applicative potential in various domains. However, the potential of
these models in mining relationships from graph data remains under-explored.
Graph neural networks, as a popular research area in recent years, have
numerous studies on relationship mining. Yet, current cutting-edge research in
graph neural networks has not been effectively integrated with large language
models, leading to limited efficiency and capability in graph relationship
mining tasks. A primary challenge is the inability of LLMs to deeply exploit
the edge information in graphs, which is critical for understanding complex
node relationships. This gap limits the potential of LLMs to extract meaningful
insights from graph structures, limiting their applicability in more complex
graph-based analysis. We focus on how to utilize existing LLMs for mining and
understanding relationships in graph data, applying these techniques to
recommendation tasks. We propose an innovative framework that combines the
strong contextual representation capabilities of LLMs with the relationship
extraction and analysis functions of GNNs for mining relationships in graph
data. Specifically, we design a new prompt construction framework that
integrates relational information of graph data into natural language
expressions, aiding LLMs in more intuitively grasping the connectivity
information within graph data. Additionally, we introduce graph relationship
understanding and analysis functions into LLMs to enhance their focus on
connectivity information in graph data. Our evaluation on real-world datasets
demonstrates the framework's ability to understand connectivity information in
graph data
Metal Recovery from Sludge through the Combination of Hydrothermal Sulfidation and Flotation
AbstractThe heavy metal in the waste can react with sulfur and be converted to metal sulfide through the hydrothermal sulfidation. For metal recovery, the synthetic metal sulfide can be enriched through subsequent flotation process. It is a novel way for the recovery of heavy metal from the sludge. In this study, the effects of liquid/solid ratio, mineralizer concentration, precursor concentration and dosage of sulfur on the sulfidation extent and floatation index were investigated. Result shows that with a precursor concentration of 15%, a Zn/S molar ratio of 1:1.2, a liquid/solid ratio of 3:1, the sulfidation extent of zinc in the sludge was greater than 92%, while the flotation recovery of zinc reached up to 45.34%. The toxicity characteristic leaching procedure (TCLP) revealed that stabilization and detoxification of heavy metals occurred during sulfidation
Phenomic selection in slash pine multi-temporally using UAV-multispectral imagery
Genomic selection (GS) is an option for plant domestication that offers high efficiency in improving genetics. However, GS is often not feasible for long-lived tree species with large and complex genomes. In this paper, we investigated UAV multispectral imagery in time series to evaluate genetic variation in tree growth and developed a new predictive approach that is independent of sequencing or pedigrees based on multispectral imagery plus vegetation indices (VIs) for slash pine. Results show that temporal factors have a strong influence on the h2 of tree growth traits. High genetic correlations were found in most months, and genetic gain also showed a slight influence on the time series. Using a consistent ranking of family breeding values, optimal slash pine families were selected, obtaining a promising and reliable predictive ability based on multispectral+VIs (MV) alone or on the combination of pedigree and MV. The highest predictive value, ranging from 0.52 to 0.56, was found in July. The methods described in this paper provide new approaches for phenotypic selection (PS) using high-throughput multispectral unmanned aerial vehicle (UAV) technology, which could potentially be used to reduce the generation time for conifer species and increase the genetic granularity independent of sequencing or pedigrees
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