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| 1 | Potentiometric sensor based on molecularly imprinted polymer for determination of melamine in milk显示文摘 | LIANG Rongning ZHANG Ruiming QIN Wei | 2009 | Sensors and Actuators B: Chemical2009，141，2： | 1 |
| 2 | SIRT1 expression is associated withthe chemotherapy response and prognosis of patients with advancedNSCLC显示文摘 | Zhang T RongN Chen J | 2013 | PLoS One2013，8，79： | 1 |
| 3 | Potentiometric sensor based on molecularly imprinted polymer for determination of melamine in milk显示文摘 | Rongning Liang Ruiming Zhang Wei Qin | 2009 | Chemical2009，，2： | 1 |
| 4 | An effective utiliza- tion of the slag from acid leaching of coal-waste:preparation of water glass with a low-temperature co-melting reaction 显示文摘 | Fang Li Duan Xiaofang Chen Rongning | 2014 | Journal of the Air & Waste Management Association2014，64，8： | 1 |
| 5 | Molecularly im- printed nanopartieles based potentiometric sensor with a nanomolar detection limit 显示文摘 | Liang Rongning Kou Lijuan Chert Zhaopeng | 2013 | Sensors and Actuators B : Chemical2013，188，： | 1 |
| 6 | Potentiometric sensor based on molecularly imprinted polymer for determination of melamine in milk 显示文摘 | Rongning Liang Ruiming Zhang Wei Qin | 2009 | Sens Actuators B2009，141，： | 1 |
| 7 | Potentiometric sensor based on molecularly imprinted polymer for determination of melamine in milk显示文摘 | Rongning Liang Ruiming Zhang Wei Qin | 2009 | Sensors and Actuators B2009，141，： | 1 |
| 8 | Potentiometric sensorbased on molecularly imprinted polymer for determination of melaminein milk显示文摘 | LIANG Rongning ZHANG Ruiming QIN Wei | 2009 | Sensors and Actuators B:Chemical2009，141，2： | 1 |
| 9 | Determination of Hy-droxylated Polychlorinated Biphenyls byoff Line Solid-PhaseExtrac-tion Liquid Chromatography-Tandem Mass Spectrometry using a Mo-lecularly Imprinted Polymer as a Sorbent for Sample Preconcentra-tion显示文摘 | LIANG Rongning ZHAO Yan SU Yang | 2015 | Talanta2015，144，： | 1 |
| 10 | PARAMETER IDENTIFICATION AND TERMINAL STEADY-STATE OPTIMIZATION FOR S SYSTEM IN MICROBIAL CONTINUOUS FERMENTATION显示文摘 | XIAOFANG LI RONGNING QU | 2012 | International Journal of Biomathematics2012，5，2： | 0 |
| 11 | Massive Files Prefetching Model Based on LSTM Neural Network with Cache Transaction Strategy显示文摘In distributed storage systems,file access efficiency has an important impact on the real-time nature of information forensics.As a popular approach to improve file accessing efficiency,prefetching model can fetches data before it is needed according to the file access pattern,which can reduce the I/O waiting time and increase the system concurrency.However,prefetching model needs to mine the degree of association between files to ensure the accuracy of prefetching.In the massive small file situation,the sheer volume of files poses a challenge to the efficiency and accuracy of relevance mining.In this paper,we propose a massive files prefetching model based on LSTM neural network with cache transaction strategy to improve file access efficiency.Firstly,we propose a file clustering algorithm based on temporal locality and spatial locality to reduce the computational complexity.Secondly,we propose a definition of cache transaction according to files occurrence in cache instead of time-offset distance based methods to extract file block feature accurately.Lastly,we innovatively propose a file access prediction algorithm based on LSTM neural network which predict the file that have high possibility to be accessed.Experiments show that compared with the traditional LRU and the plain grouping methods,the proposed model notably increase the cache hit rate and effectively reduces the I/O wait time. | Dongjie Zhu Haiwen Du Yundong Sun Xiaofang Li Rongning Qu Hao Hu Shuangshuang Dong Helen Min Zhou Ning Cao | 2020 | Computers, Materials & Continua2020，，5： | 0 |
| 12 | MINE:A Method of Multi-Interaction Heterogeneous Information Network Embedding显示文摘Interactivity is the most significant feature of network data,especially in social networks.Existing network embedding methods have achieved remarkable results in learning network structure and node attributes,but do not pay attention to the multi-interaction between nodes,which limits the extraction and mining of potential deep interactions between nodes.To tackle the problem,we propose a method called Multi-Interaction heterogeneous information Network Embedding(MINE).Firstly,we introduced the multi-interactions heterogeneous information network and extracted complex heterogeneous relation sequences by the multi-interaction extraction algorithm.Secondly,we use a well-designed multi-relationship network fusion model based on the attention mechanism to fuse multiple interactional relationships.Finally,applying a multitasking model makes the learned vector contain richer semantic relationships.A large number of practical experiments prove that our proposed method outperforms existing methods on multiple data sets. | Dongjie Zhu Yundong Sun Xiaofang Li Haiwen Du Rongning Qu Pingping Yu Xuefeng Piao Russell Higgs Ning Cao | 2020 | Computers, Materials & Continua2020，，6： | 0 |
| 13 | Fusion Recommendation System Based on Collaborative Filtering and Knowledge Graph显示文摘The recommendation algorithm based on collaborative filtering is currently the most successful recommendation method. It recommends items to theuser based on the known historical interaction data of the target user. Furthermore,the combination of the recommended algorithm based on collaborative filtrationand other auxiliary knowledge base is an effective way to improve the performance of the recommended system, of which the Co-Factorization Model(CoFM) is one representative research. CoFM, a fusion recommendation modelcombining the collaborative filtering model FM and the graph embeddingmodel TransE, introduces the information of many entities and their relationsin the knowledge graph into the recommendation system as effective auxiliaryinformation. It can effectively improve the accuracy of recommendations andalleviate the problem of sparse user historical interaction data. Unfortunately,the graph-embedded model TransE used in the CoFM model cannot solve the1-N, N-1, and N-N problems well. To tackle this problem, a novel fusion recommendation model Joint Factorization Machines and TransH Model (JFMH) isproposed, which improves CoFM by replacing the TransE model with TransHmodel. A large number of experiments on two widely used benchmark data setsshow that compared with CoFM, JFMH has improved performance in terms ofitem recommendation and knowledge graph completion, and is more competitivethan multiple baseline methods. | Donglei Lu Dongjie Zhu Haiwen Du Yundong Sun Yansong Wang Xiaofang Li Rongning Qu Ning Cao Russell Higgs | 2022 | Computer Systems Science & Engineering2022，42，9： | 0 |
| 14 | Dis-NDVW: Distributed Network Asset Detection and Vulnerability Warning Platform显示文摘With the rapid development of Internet technology,the issues of network asset detection and vulnerability warning have become hot topics of concern in the industry.However,most existing detection tools operate in a single-node mode and cannot parallelly process large-scale tasks,which cannot meet the current needs of the industry.To address the above issues,this paper proposes a distributed network asset detection and vulnerability warning platform(Dis-NDVW)based on distributed systems and multiple detection tools.Specifically,this paper proposes a distributed message sub-scription and publication system based on Zookeeper and Kafka,which endows Dis-NDVW with the ability to parallelly process large-scale tasks.Meanwhile,Dis-NDVW combines the RangeAssignor,RoundRobinAssignor,and StickyAssignor algorithms to achieve load balancing of task nodes in a distributed detection cluster.In terms of a large-scale task processing strategy,this paper proposes a task partitioning method based on First-In-First-Out(FIFO)queue.This method realizes the parallel operation of task producers and task consumers by dividing pending tasks into different queues according to task types.To ensure the data reliability of the task cluster,Dis-NDVW provides a redundant storage strategy for master-slave partition replicas.In terms of distributed storage,Dis-NDVW utilizes a distributed elastic storage service based on ElasticSearch to achieve distributed storage and efficient retrieval of big data.Experimental verification shows that Dis-NDVW can better meet the basic requirements of ultra-large-scale detection tasks. | Leilei Li Yansong Wang Dongjie Zhu Xiaofang Li Haiwen Du Yixuan Lu Rongning Qu Russell Higgs | 2023 | Computers, Materials & Continua2023，，7： | 0 |
| 15 | Exploration on the Load Balancing Technique for Platform of Internet of Things显示文摘In recent years,the Internet of Things technology has developed rapidly,and smart Internet of Things devices have also been widely popularized.A large amount of data is generated every moment.Now we are in the era of big data in the Internet of Things.The rapid growth of massive data has brought great challenges to storage technology,which cannot be well coped with by traditional storage technology.The demand for massive data storage has given birth to cloud storage technology.Load balancing technology plays an important role in improving the performance and resource utilization of cloud storage systems.Therefore,it is of great practical significance to study how to improve the performance and resource utilization of cloud storage systems through load balancing technology.On the basis of studying the read strategy of Swift,this article proposes a reread strategy based on load balancing of storage resources to solve the problem of unbalanced read load between interruptions caused by random data copying in Swift.The storage asynchronously tracks the I/O conversion to select the storage with the smallest load for asynchronous reading.The experimental results indicate that the proposed strategy can achieve a better load balancing state in terms of storage I/O utilization and CPU utilization than the random read strategy index of Swift. | Donglei Lu Dongjie Zhu Yundong Sun Haiwen Du Xiaofang Li Rongning Qu Yansong Wang Ning Cao Helen Min Zhou | 2021 | Computer Systems Science & Engineering2021，38，9： | 0 |
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