货物集装箱扫描技术的发展

恐怖分子利用货物集装箱走私核武器或用于放射性频散装置(RDD)的放射性材料,美国和其他国家目前都在面临这一严重威胁.     

Spatiotemporal Associations of Reservoir Nutrient Characteristics and the Invasive,Harmful Alga Prymnesium parvum in West Texas

Golden alga (Prymnesium parvum) is a harmful alga that has caused ecological and economic harm in freshwater and marine systems worldwide. In inland systems of North America, toxic blooms have nearly eliminated fish populations in some systems. Modifying nutrient profiles through alterations to land or water use may be a viable alternative for golden alga control in reservoirs. The main objective of this study was to improve our understanding of the nutrient dynamics that influence golden alga bloom formation and toxicity in west Texas reservoirs. We examined eight sites in the Upper Colorado River basin, Texas: three impacted reservoirs that have experienced repeated golden alga blooms; two reference reservoirs where golden alga is present but nontoxic; and three confluence sites downstream of the impacted and reference sites. Total, inorganic, and organic nitrogen and phosphorus and their ratios were quantified monthly along with golden alga abundance and ichthyotoxicity between December 2010 and July 2011. Blooms persisted for several months at the impacted sites, which were characterized by high organic nitrogen and low inorganic nitrogen. At impacted sites, abundance was positively associated with inorganic phosphorus and bloom termination coincided with increases in inorganic nitrogen and decreases in inorganic phosphorus in late spring. Management of both inorganic and organic forms of nutrients may create conditions in reservoirs unfavorable to golden alga.     

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen(T-N) impact on the effluent water quality. The objective of this study is to establish two machine learning models—artificial neural networks(ANNs) and support vector machines(SVMs), in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination(R~2), Nash–Sutcliff efficiency(NSE), relative efficiency criteria(d rel). Additionally, Latin-Hypercube one-factor-at-a-time(LH-OAT) and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.     

Curcumin and Its Analogs as a Therapeutic Strategy in Infections Caused by RNA Genome Viruses

Food and Environmental Virology - The use of natural resources for the prevention and treatment of diseases considered fatal to humanity has evolved. Several medicinal plants have nutritional and...