Inhibition of microbial activity of activated sludge by ammonia in leachate

Author name used in this publication: X. L. LiAccepted ManuscriptPublishe

Map precipitation from landfill leachate and seawater bittern waste

Author name used in this publication: X. Z. Li2002-2003 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Recovery of ammonium-nitrogen from landfill leachate as a multi-nutrient fertilizer

Author name used in this publication: X. Z. Li2002-2003 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Ammonium removal from landfill leachate by chemical precipitation

Accepted ManuscriptPublishe

Efficiency of biological treatment affected by high strength of ammonium-nitrogen in leachate and chemical precipitation of ammonium-nitrogen as pretreatment

Author name used in this publication: X. Z. Li2000-2001 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Machinery Early Fault Detection Based on Dirichlet Process Mixture Model

© 2013 IEEE. The most commonly used single feature-based anomaly detection method for the complex machinery, such as large wind power equipment, steam turbine generator sets, and reciprocating compressors, exhibits a defect of low-alarm accuracy due to the non-stationary characteristic of the vibration signals. In order to improve the accuracy of fault detection, a novel method based on the Dirichlet process mixture model (DPMM) is proposed. First, the features of the mechanical vibration signals are used to construct the feature space of the equipment. The DPMM modeling method is then applied to self-learn the probabilistic mixture model of the feature space. The normal working condition model is used as the benchmark model. The early fault detection is realized by using a precise difference measurement method based on Kullback-Leibler divergence to calculate the difference between the real-time model and the benchmark model accurately, and by comparing the calculation result with a self-learned alarm threshold. The effectiveness and the adaptability of this novel early fault detection method are verified by comparing it to the single feature-based anomaly detection method and the Gaussian mixture model (GMM)-based early fault detection method

Spatio-Temporal Characteristics of Global Warming in the Tibetan Plateau during the Last 50 Years Based on a Generalised Temperature Zone - Elevation Model

Temperature is one of the primary factors influencing the climate and ecosystem, and examining its change and fluctuation could elucidate the formation of novel climate patterns and trends. In this study, we constructed a generalised temperature zone elevation model (GTEM) to assess the trends of climate change and temporal-spatial differences in the Tibetan Plateau (TP) using the annual and monthly mean temperatures from 1961-2010 at 144 meteorological stations in and near the TP. The results showed the following: (1) The TP has undergone robust warming over the study period, and the warming rate was 0.318°C/decade. The warming has accelerated during recent decades, especially in the last 20 years, and the warming has been most significant in the winter months, followed by the spring, autumn and summer seasons. (2) Spatially, the zones that became significantly smaller were the temperature zones of -6°C and -4°C, and these have decreased 499.44 and 454.26 thousand sq km from 1961 to 2010 at average rates of 25.1% and 11.7%, respectively, over every 5-year interval. These quickly shrinking zones were located in the northwestern and central TP. (3) The elevation dependency of climate warming existed in the TP during 1961-2010, but this tendency has gradually been weakening due to more rapid warming at lower elevations than in the middle and upper elevations of the TP during 1991-2010. The higher regions and some low altitude valleys of the TP were the most significantly warming regions under the same categorizing criteria. Experimental evidence shows that the GTEM is an effective method to analyse climate changes in high altitude mountainous regions

Long-term structural performance monitoring system for the Shanghai Tower

Author name used in this manuscript: You-Lin Xu2012-2013 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Detoxification Center-Based Sampling Missed a Subgroup of Higher Risk Drug Users, a Case from Guangdong, China

BACKGROUND: Injection drug use remains among the most important HIV transmission risk in China. Representativeness of drug users sampled from detoxification centers is questionable. A respondent driven sampling survey was conducted to compare the results with those from the detoxification center in the same city. METHODS: In 2008, two independent surveys were conducted in Dongguan, China, one for community-based drug users using respondent driven sampling and the other for drug users in a compulsory detoxification center as routine sentinel surveillance. Demographic and behavioral information were collected using the same structured questionnaire. Intravenous blood samples were collected to measure antibodies to HIV-1, and syphilis. RESULTS: Compared to those 400 drug users recruited from the detoxification center, the 303 community-based drug users had higher HIV prevalence (14.7% versus 4.0%, P = 0.04), lower syphilis prevalence (4.7% versus 10.8%, P = 0.07), higher proportion of injection drug use (83.9% versus 60.2%, P = 0.01) and syringe sharing (47.8% versus 36.3%, P = 0.10), more likely to be separated (12.4% versus 3.8%, P = 0.01) and being migrants from Guangxi province (31.4% versus 18.0%, P = 0.09), more engaging in commercial sex (64.4% versus 52.5%, P = 0.04). HIV prevalence and rate of syringe sharing were consistently higher among drug users from Guangxi. CONCLUSIONS: Detoxification center-based sampling missed a subgroup with higher HIV prevalence and higher rate of injection drug use. While detoxification center-based sampled can be used to monitor the trend of HIV prevalence and risk behaviors over time, periodic community-based sampling is still necessary to avoid possible systematic error in detoxification center-based samples