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| 1 | Source apportionment and ecological risk assessment of PAHs in surface sediments from the Liaodong Bay,northern China显示文摘The sources and ecological risk from sixteen polycyclic aromatic hydrocarbons in surface sediment in the Liaodong Bay were investigated from 2014 to 2015.The total concentrations of PAHs ranged from 88.5 to 347.1 ng/g,and the high value occurred in the central region of the Liaodong Bay.Cluster analysis identified two site clusters representing the coastal region affected by land-based pollution and the central region of the Liaodong Bay.Principal component analysis-multiple linear regression and diagnostic ratios suggested that PAHs contaminants originated from a mixture of combustion and petroleum sources,and the major was combustion sources.Based on sediment quality guideline,naphthalene,acenaphthylene,acenaphthene,phenanthrene and dibenz[a,h]anthracene may occasionally cause adverse biological effects in some stations.The toxic equivalent concentrations of carcinogenic PAHs indicated low carcinogenic risk for the Liaodong Bay.The ecological risk and toxic pollution levels of PAHs were higher in the central region than in the coastal region along the Liaodong Bay. | ZHANG Yufeng WU Jinhao SONG Lun SONG Yonggang YANG Meng WANG Nianbin HAN Jiabo GUAN Daoming | 2018 | Acta Oceanologica Sinica2018，37，4： | 6 |
| 2 | ia-PNCC: Noise Processing Method for Underwater Target Recognition Convolutional Neural Network显示文摘Underwater target recognition is a key technology for underwater acoustic countermeasure.How to classify and recognize underwater targets according to the noise information of underwater targets has been a hot topic in the field of underwater acoustic signals.In this paper,the deep learning model is applied to underwater target recognition.Improved anti-noise Power-Normalized Cepstral Coefficients(ia-PNCC)is proposed,based on PNCC applied to underwater noises.Multitaper and normalized Gammatone filter banks are applied to improve the anti-noise capacity.The method is combined with a convolutional neural network in order to recognize the underwater target.Experiment results show that the acoustic feature presented by ia-PNCC has lower noise and are wellsuited to underwater target recognition using a convolutional neural network.Compared with the combination of convolutional neural network with single acoustic feature,such as MFCC(Mel-scale Frequency Cepstral Coefficients)or LPCC(Linear Prediction Cepstral Coefficients),the combination of the ia-PNCC with a convolutional neural network offers better accuracy for underwater target recognition. | Nianbin Wang Ming He Jianguo Sun Hongbin Wang Lianke Zhou Ci Chu Lei Chen | 2019 | Computers, Materials & Continua2019，，1： | 3 |
| 3 | Effect of the exposure to suspended solids on the enzymatic activity in the bivalve Sinonovacula constricta显示文摘Aquatic animals are susceptible to sudden changes of their living environment but they adopt strategies to cope with adverse environmental challenges.Contamination by suspended solids,often associated with a dramatic change in the concentrations of important water-quality variables is a frequent occurrence in China's coastal waters and estuaries.Here we studied the impact of suspended solids on the activities of the antioxidant enzymes superoxide dismutase(SOD)and catalase(CAT),as well as adenosine triphosphates(including Na^(+)K^(+)-ATPase,Mg^(++)-ATPase,Ca^(++)-ATPase)and H^(+)K^(+)-ATPase in the gills and visceral mass tissues of the molluscan bivalve Sinonovacula constricta exposed(4,8,12,16,20,and 24 days)to various concentrations of suspended solids.Our results showed that the antioxidant enzymes cooperated closely to effectively scavenge superoxide anion free radicals and H2O2(which can ultimately inhibit gill activity)through the modification of SOD and/or CAT enzymatic activities.ATPases activity(considered to be a sensitive indicator of toxicity)could play an effective role in the maintenance of functional integrity of the plasma membranes as well as some other intracellular functions.After the exposure,a decrease in the Nat K^(+)-ATPase,Mg^(++)-ATPase,and Ca^(++)-ATPase activity of the gills was observed suggesting that they were inhibited by the treatments.These results also indicated that,from day 4 to day 16,exposure to high concentrations of suspended solids had an inhibitory effect on the activity of H^(+)-K^(+)-ATPase in the visceral mass of S.constricta.However,after a period of adaptation the H^(+)-K^(+)-ATPase activity was restored to original levels.Our results suggest that long-term exposure to high levels of suspended solids disturb osmoregulation,gastric acid secretion and digestion,cause oxidative damage,as a consequence of antioxidant enzymes inactivation which eventually damages the gills,affect the food intake and transformation,ultimately resulting in systems failure and eventually death. | Guojun Yang Lun Song Xiaoqian Lu Nianbin Wang Yang Li | 2017 | Aquaculture and Fisheries2017，2，1： | 3 |
| 4 | Using Computing Checkpoint Implement Efficient Coordinated Checkpointing显示文摘 | MEN Chaoguang WANG Nianbin ZHAO Yunlong | 2006 | Chinese Journal of Electronics2006，15，2： | 3 |
| 5 | Learning Domain-Invariant and Discriminative Features for Homogeneous Unsupervised Domain Adaptation显示文摘A classifier trained on the label-rich source dataset tends to perform poorly on the unlabeled target dataset because of the distribution discrepancy across different datasets.Unsupervised domain adaptation aims to transfer knowledge from the labeled source dataset to the unlabeled target dataset to solve this problem.Most of the existing unsupervised domain adaptation methods only concentrate on learning domain-invariant features across different domains,but they neglect the discriminability of the learned features to satisfy the cluster assumption.In this paper,we propose Semantic pairwise centroid alignment(SPCA),which is a point-wise method to learn both domain-invariant and discriminative features for homogeneous unsupervised domain adaptation.SPCA utilizes a novel semantic centroid loss to reduce the intraclass distance in feature space by using source data and target High-confidence centroid points(HCCPs).Then a classifier trained on source features is expected to generalize well on target features.Extensive experiments on visual recognition tasks verify the effectiveness of the proposed SPCA and also demonstrate that both domaininvariant and discriminative features learned by SPCA can significantly boost the performance of homogeneous unsupervised domain adaptation. | ZHANG Yun WANG Nianbin CAI Shaobin | 2020 | Chinese Journal of Electronics2020，29，6： | 2 |
| 6 | Mulfipath passive data acknowledgement on-demand multicast protocol显示文摘 | Shaobin Cai Nianmin Yao Nianbin Wang | 2007 | Computer Communications2007，29，11： | 1 |
| 7 | Isolation and pathogenicity of pathogens from skin ulceration disease and viscera ejection syndrome of the sea cucumber Apostichopus japonicus显示文摘 | Huan Deng Chongbo He Zunchun Zhou Chang Liu Kefei Tan Nianbin Wang Bei Jiang Xianggang Gao Weidong Liu | 2008 | Aquaculture2008，，1： | 1 |
| 8 | Determination of Fluo- roquinolone Residues in Penaeus Japonicas by Microwave-assisted Extraction and Ion-pair High Performance Liquid Chromatography 显示文摘 | Guiying Liu Nianbin Wang Li Wan | 2012 | Journal of Liquid Chromatography & Related Technologies2012，35，： | 1 |
| 9 | PMS-Sorting:A New Sorting Algorithm Based on Similarity显示文摘Borda sorting algorithm is a kind of improvement algorithm based on weighted position sorting algorithm,it is mainly suitable for the high duplication of search results,for the independent search results,the effect is not very good and the computing method of relative score in Borda sorting algorithm is according to the rule of the linear regressive,but position relationship cannot fully represent the correlation changes.aimed at this drawback,the new sorting algorithm is proposed in this paper,named PMS-Sorting algorithm,firstly the position score of the returned results is standardized processing,and the similarity retrieval word string with the query results is combined into the algorithm,the similarity calculation method is also improved,through the experiment,the improved algorithm is superior to traditional sorting algorithm. | Hongbin Wang Lianke Zhou Guodong Zhao Nianbin Wang Jianguo Sun Yue Zheng Lei Chen | 2019 | Computers, Materials & Continua2019，，4： | 0 |
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