The University of Nottingham Ningbo China

About: The University of Nottingham Ningbo China is a education organization based out in Ningbo, Zhejiang, China. It is known for research contribution in the topics: Computer science & China. The organization has 7491 authors who have published 7153 publications receiving 83555 citations.

Graphene FET Array Biosensor Based on ssDNA Aptamer for Ultrasensitive Hg2+ Detection in Environmental Pollutants.

Abstract: Invisible mercury ion is an extremely poisonous environmental pollutant, therefore, a fast and highly sensitive detection method is of significant importance. In this study, a liquid-gated graphene field-effect transistor (GFET) array biosensor (6 × 6 GFETs on the chip) was fabricated and applied for Hg2+ quantitate detection based on single-stranded DNA (ssDNA) aptamer. The biosensor showed outstanding selectivity to Hg2+ in mixed solutions containing various metal ions. Moreover, the sensing capability of the biosensor was demonstrated by real-time responses and showed a fairly low detection limit of 40 pM, a wide detection ranged from 100 pM to 100 nM and rapid response time below one second. These results suggest that the GFET array biosensor based on ssDNA aptamer offers a simple fabrication procedure and quite fast method for mercury ion contaminant detection and are promising for various analytical applications.

Effect of daidzein on cell growth, cell cycle, and telomerase activity of human cervical cancer in vitro.

Abstract: Phytoestrogens are some plant compounds exhibiting estrogen-like activities. However, some studies have shown that they also affect the growth of some nonhormone-dependent diseases. In this study, daidzein--one of the most common phytoestrogens--was used to investigate its effects on human cervical cancer cells HeLa in vitro. First, the cell growth was measured by using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. Then, the distributions of cell cycle and apoptosis were analyzed with the help of flow cytometry. Finally, the telomerase activity was detected by using real-time quantitative reverse transcription-polymerase chain reaction. The results showed that at the concentrations from 6.25 to 100 micro mol/l, daidzein inhibited the growth of HeLa cells. Flow cytometric analysis showed that cancer cells were arrested at G(0)/G(1) or G(2)/M phase with daidzein. The inductive effects of apoptosis were more obviously observed in low-concentration groups. After HeLa cells were treated with daidzein, the expression of human telomerase catalytic subunit mRNA decreased. These meant that daidzein affected human nonhormone-dependent cervical cancer cells in several ways, including cell growth, cell cycle, and telomerase activity in vitro.

A Personalized Recommendation Algorithm Combining Slope One Scheme and User Based Collaborative Filtering

Abstract: Predicting products a customer would like on the basis of other customers’ ratings for these products has become a well known approach adopted by many personalized recommendation systems on the Internet. With the development of electronic commerce, the number of customers and products grows rapidly, resulted in the sparsity of the rating dataset. Poor quality is one major challenge in collaborative filtering recommender systems. To solve this problem, the paper proposed a personalized recommendation algorithm combining slope one scheme and user based collaborative filtering. This method employs slope one scheme technology to fill the vacant ratings of the user-item matrix where necessary. Then it utilizes the user based collaborative filtering to produce the recommendation. The experiments were made on a common data set using different filtering algorithms. The results show that the proposed recommender algorithm combining slope one scheme and user based collaborative filtering can improve the accuracy of the collaborative filtering recommender system.

Combining Memory-Based and Model-Based Collaborative Filtering in Recommender System

Abstract: Collaborative filtering (CF) technique has been proved to be one of the most successful techniques in recommender systems. Two types of algorithms for collaborative filtering have been researched: memory-based CF and model-based CF. Memory-based approaches identify the similarity between two users by comparing their ratings on a set of items and have suffered from two fundamental problems: sparsity and scalability. Alternatively, the model-based approaches have been proposed to alleviate these problems, but these approaches tend to limit the range of users. This paper presents an approach that combines the advantages of these two kinds of approaches by joining the two methods. Firstly, it employs memory-based CF to fill the vacant ratings of the user-item matrix. Then, it uses the item-based CF as model-based to form the nearest neighbors of every item. At last, it produces prediction of the target user to the target item at real time. The collaborative filtering recommendation method combining memory-based CF and model-based CF can provide better recommendation than traditional collaborative filtering.

Real-Time Primary Frequency Regulation Using Load Power Control by Smart Transformers

Abstract: In the last years, the energy production share of power electronics-based generators has kept increasing with respect to the synchronous generation. These power electronics-resources do not offer rotational inertia, resulting in a decrease of the total system inertia and thus in larger frequency deviations during disturbances. Among several corrective solutions studied in the literature, controlling the load consumption is a promising one. Fast in power response and widely distributed in the grid, load control is able to work as power reserve (both upward and downward) during frequency variations. In this regard, a smart transformer (ST)-based solution is proposed. The ST is able to shape load consumption accurately by means of an online load sensitivity identification-based control, and can support the primary frequency regulation in the power system. This paper presents an ST-based real time frequency regulation (RTFR) controller that varies load consumption by means of a voltage-based load control, in response to the frequency deviation in the power grid. The proposed method is described analytically and tested by means of power-hardware-in-loop experiments in order to show the effectiveness of the RTFR controller in real-time conditions and applications.