The growth patterns and water treatment capacity of Nitzschia sp. benthic diatoms in different concentrations of sea cucumber aquaculture wastewater (0%, 10%, 30%, 50%, 80% and 100%) and f/2 medium were studied. Nitzschia sp. grew in different concentrations of aquaculture wastewater and showed remarkable differences in their rate of growth among the treatment groups. Nitzschia sp. grew most quickly (0.83 ind/day) and showed the greatest total chlorophyll-a content in 30% wastewater. The total chlorophyll-a content and growth rate of Nitzschia sp. were strongly correlated (R 2 > 0.98). The total lipid (TL), total protein, exopolysaccharide (EPS) and intracellular polysaccharide (IPS) contents of the diatoms were highest in 100% wastewater and showed significant differences among the experimental groups (p < 0.05). Total nitrogen (TN), ammonium-nitrogen (NH 4 -N) (AN), nitrate nitrogen (NO 3 -N) (NN), nitrite nitrogen (NO 2 -N) (NIN) and total phosphate (TP) contents were significantly reduced after cultivation. TN uptake peaked at 54.58% in 30%wastewater. AN uptake exceeded 95% in 30% wastewater and 100% wastewater.NN uptake peaked at 56.42% in 80% wastewater, whereas TP uptake ranged from 16.80% to 27.69%. These results suggest that Nitzschia sp. biomass can be enhanced via cultivation in low-concentration (30%) wastewater, after which their cultivation may be continued in high-concentration (100%) wastewater, increasing their nutritional value and aiding in the removal of surplus nitrogen and phosphorus in sea cucumber aquaculture wastewater. Application of Nitzschia sp. using the recirculating wastewater-treatment methods described has the potential to reduce environmental harm caused by sea cucumber cultivation and thus achieve sustainable aquaculture. K E Y W O R D Saquaculture, diatom, Nitzschia sp., sea cucumber, sustainability, wastewater
The effects of eutrophication on the absorption kinetic parameters of nitrogen and phosphorus are investigated. The results show that the kinetic characteristics of phosphate, nitrate- nitrogen, ammonia-nitrogen and nitrite-nitrogen of Nitzschia sp. are all generally in accord with the function of Michaelis-Menten equation. The Km of them are 1.81 mg/L, 1.75 mg/L, 0.20 mg/L and 4.53 mg/L, respectively. The maximum uptake rates of nitrate-nitrogen and ammonia- nitrogen are higher than that of nitrite-nitrogen, which indicates that nitrate-nitrogen and ammonia- nitrogen can be uptaken more preferential than that of nitrite-nitrogen. In conclusion, Nitzschia sp. has a fast utilized rate of ammonia-nitrogen, nitrate-nitrogen and phosphorus. This indicates that Nitzschia sp. has a good effect on water quality and it could be applied potentially to purify waste water.
Background Obtaining comprehensive epidemic information for specific global infectious diseases is crucial to travel health. However, different infectious disease information websites may have different purposes, which may lead to misunderstanding by travelers and travel health staff when making accurate epidemic control and management decisions. Objective The objective of this study was to develop a Global Infectious Diseases Epidemic Information Monitoring System (GIDEIMS) in order to provide comprehensive and timely global epidemic information. Methods Distributed web crawler and cloud agent acceleration technologies were used to automatically collect epidemic information about more than 200 infectious diseases from 26 established epidemic websites and Baidu News. Natural language processing and in-depth learning technologies have been utilized to intelligently process epidemic information collected in 28 languages. Currently, the GIDEIMS presents world epidemic information using a geographical map, including date, disease name, reported cases in different countries, and the epidemic situation in China. In order to make a practical assessment of the GIDEIMS, we compared infectious disease data collected from the GIDEIMS and other websites on July 16, 2019. Results Compared with the Global Incident Map and Outbreak News Today, the GIDEIMS provided more comprehensive information on human infectious diseases. The GIDEIMS is currently used in the Health Quarantine Department of Shenzhen Customs District (Shenzhen, China) and was recommended to the Health Quarantine Administrative Department of the General Administration of Customs (China) and travel health–related departments. Conclusions The GIDEIMS is one of the most intelligent tools that contributes to safeguarding the health of travelers, controlling infectious disease epidemics, and effectively managing public health in China.
.Haze significantly impacts various fields, such as autonomous driving, smart cities, and security monitoring. Deep learning has been proven effective in removing haze from images. However, obtaining pixel-aligned hazy and clear paired images in the real world can be challenging. Therefore, synthesized hazed images are often used for training deep networks. These images are typically generated based on parameters such as depth information and atmospheric scattering coefficient. However, this approach may cause the loss of important haze details, leading to color distortion or incomplete dehazed images. To address this problem, this paper proposes a method for synthesizing hazed images using a cycle generative adversarial network (CycleGAN). The CycleGAN is trained with unpaired hazy and clear images to learn the features of the hazy images. Then, the real haze features are added to clear images using the trained CycleGAN, resulting in well-pixel-aligned synthesized hazy and clear paired images that can be used for dehaze training. The results demonstrate that the dataset synthesized using this method efficiently solves the problem associated with traditional synthesized datasets. Furthermore, the dehazed images are restored using a super-resolution algorithm, enabling the obtainment of high-resolution clear images. This method has broadened the applications of deep learning in haze removal, particularly highlighting its potential in the fields of autonomous driving and smart cities.
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