Northeastern University (China)

About: Northeastern University (China) is a education organization based out in Shenyang, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 36087 authors who have published 36125 publications receiving 426807 citations. The organization is also known as: Dōngběi Dàxué & Northeastern University (东北大学).

Genetic and chemical modification of cells for selective separation and analysis of heavy metals of biological or environmental significance

Abstract: Bioaccumulation describes the ability for microbes or other biological cells to accumulate heavy-metal species from the ambient environment. It has attracted extensive attention in the field of heavy metal remediation and precious metal recovery. Bioaccumulation has also shown great potential for adsorption and preconcentration of ultra-trace levels of heavy metals for their analysis and speciation. Genetic engineering and chemical modification of biological cells open up new avenues for bioaccumulative preconcentration of heavy-metal species for selective analysis and speciation of such metals in combination with spectrometric techniques. We focus on recent progress in genetic and chemical approaches to bioremediation and their applications in selective preconcentration and speciation of heavy-metal species. We also outline the uptake mechanisms of bioaccumulation and key issues in the biosorption of heavy metals and their analysis and speciation. Finally, we discuss future perspectives in the bioaccumulation of heavy-metal species and their analysis and speciation.

Energetics at the Surface of Photoelectrodes and Its Influence on the Photoelectrochemical Properties.

Abstract: Photoelectrochemistry (PEC) holds potential as a direct route for solar energy storage. Its performance is governed by how efficiently photoexcited charges are separated and how fast the charges are transferred to the solution, both of which are highly sensitive to the photoelectrode surfaces near the electrolyte. While other aspects of a PEC system, such as the light-absorbing materials and the catalysts that facilitate charge transfer, have been extensively examined in the past, an underwhelming amount of attention has been paid to the energetics at the photoelectrode/electrolyte interface. The lack of understanding of this interface is an important reason why many photoelectrode materials fail to deliver the expected performance in harvesting solar energy in a PEC system. Using hematite (α-Fe2O3) as a material platform, we present in this Perspective how surface modifications can alter the energetics and the resulting consequences on the overall PEC performance. It has been shown that a detailed unders...

Cost-based variable-length-gram selection for string collections to support approximate queries efficiently

Abstract: Approximate queries on a collection of strings are important in many applications such as record linkage, spell checking, and Web search, where inconsistencies and errors exist in data as well as queries. Several existing algorithms use the concept of "grams," which are substrings of strings used as signatures for the strings to build index structures. A recently proposed technique, called VGRAM, improves the performance of these algorithms by using a carefully chosen dictionary of variable-length grams based on their requencies in the string collection. Since an index structure using fixed-length grams can be viewed as a special case of VGRAM, a fundamental problem arises naturally: what is the relationship between the gram dictionary and the performance of queries? We study this problem in this paper. We propose a dynamic programming algorithm for computing a tight lower bound on the number of common grams shared by two similar strings in order to improve query performance. We analyze how a gram dictionary affects the index structure of the string collection and ultimately the performance of queries. We also propose an algorithm for automatically computing a dictionary of high-quality grams for a workload of queries. Our experiments on real data sets show the improvement on query performance achieved by these techniques. To our best knowledge, this study is the first cost-based quantitative approach to deciding good grams for approximate string queries.

A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness

Abstract: Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.