Effects of wastewater effluent-borne nutrients on phytoplankton off the coast of Jeju Island.

The spatiotemporal distributions of nutrients in coastal waters surrounding eight wastewater treatment plants (WWTPs) in four seasons were investigated to determine the effects of WWTP effluents on seawater off Jeju Island, Korea. The highest concentrations of nutrients were observed in the outlets of WWTPs with relatively high ammonium concentrations among dissolved inorganic nitrogen (DIN). The reduced DIN (NO2- and NH4+)/total DIN ratios are used as a potential short-term index for marine environmental conditions. In seawater surrounding the WWTPs, relatively low nutrient concentrations were observed in spring and fall, due to enhanced biological production, which is closely linked to decreased N/P ratios. Because the highest WWTP effluent fluxes of ammonium in this study were similar to the fluxes of nutrients from submarine groundwater discharge, diffusion from bottom sediments, and discharge from land-based fish farm wastewater, WWTP effluent-derived nutrients are potentially important in oligotrophic environments and can be readily utilized by phytoplankton.

ZnO-Impregnated Polyacrylonitrile Nanofiber Filters against Various Phases of Air Pollutants.

The incorporation of metal oxide nanoparticles (NPs) in fiber filters is an effective approach to enhance the specific surface area and surface roughness of the fiber, hence improving their efficiency for fine dust capture and other gas treatment or biological applications. Nevertheless, uneven distribution of NPs limits their practical applications. In this study, a commercial silane coupling agent (3-methacryloxypropyltrimethoxysilane) was used to improve the dispersion of zinc oxide (ZnO) NPs in thin polyacrylonitrile fibers. Scanning electron microscopy (SEM) revealed that the fibers incorporating the silane-modified NPs exhibited better distribution of NPs than those prepared with pristine ZnO NPs. The silane modification enhanced the specific surface area, surface roughness, and fiber porosity. In particular, the nanofiber filter incorporating 12 wt% ZnO NPs modified with 0.5 g silane per g of ZnO NPs maintained a filtration efficiency of 99.76% with a low pressure drop of 44 Pa, excellent antibacterial activity, and could decompose organic methylene blue dye with an efficiency of 85.11% under visible light.

Enhanced Action Recognition Using Multiple Stream Deep Learning with Optical Flow and Weighted Sum.

Various action recognition approaches have recently been proposed with the aid of three-dimensional (3D) convolution and a multiple stream structure. However, existing methods are sensitive to background and optical flow noise, which prevents from learning the main object in a video frame. Furthermore, they cannot reflect the accuracy of each stream in the process of combining multiple streams. In this paper, we present a novel action recognition method that improves the existing method using optical flow and a multi-stream structure. The proposed method consists of two parts: (i) optical flow enhancement process using image segmentation and (ii) score fusion process by applying weighted sum of the accuracy. The enhancement process can help the network to efficiently analyze the flow information of the main object in the optical flow frame, thereby improving accuracy. A different accuracy of each stream can be reflected to the fused score while using the proposed score fusion method. We achieved an accuracy of 98.2% on UCF-101 and 82.4% on HMDB-51. The proposed method outperformed many state-of-the-art methods without changing the network structure and it is expected to be easily applied to other networks.