Exploring the key factors affecting the seasonal variation of phytoplankton in the coastal Yellow Sea

Marine phytoplankton play crucial roles in the ocean’s biological pump and have great impacts on global biogeochemical cycles, yet the knowledge of environmental variables controlling their seasonal dynamics needs to be improved further, especially in the coastal ecosystems. In order to explore the determinants affecting the seasonal variation of phytoplankton, here we conducted three surveys during spring, summer and autumn along the coastal Yellow Sea. Among the phytoplankton community, 49 species of diatoms and 9 species of dinoflagellates were observed in spring, 63 species of diatoms and 10 species of dinoflagellates in summer, and 62 species of diatoms and 11 species of dinoflagellates in autumn. These results thus suggested that there were obvious differences in the number of species across the three seasons, of which diatoms were the most diverse group, followed by dinoflagellates. Additionally, diatoms were the most dominant species of the phytoplankton community and varied largely during different seasons. According to the redundancy analysis, the abundance of phytoplankton community was mainly related to water temperature and dissolved inorganic nitrogen (DIN) during the three seasons, indicating that water temperature and DIN could be the key factors controlling the seasonal variability of phytoplankton community along the coastal Yellow Sea. Also, significant correlations were observed between phytoplankton abundance and heavy metals Zn, As, and Hg during the three seasons, suggesting that these metals also had potential influences on the seasonal dynamics of phytoplankton community in the coastal Yellow Sea

Study on a High-Accuracy Real-Time Algorithm to Estimate SOC of Multiple Battery Cells Simultaneously

In traditional battery equalization strategy, open-circuit voltage (OCV) of battery cells was used to judge the difference of SOC between them. However, OCV is not only determined by SOC but also influenced by internal resistance, polarization voltage, capacity, and other nonlinear factors. As a result, OCV is not an ideal indicator of SOC differences, especially in transient conditions. In order to control battery consistency accurately, it is best to use SOC directly as standard for battery consistency judgment and control. To achieve this, an algorithm that can estimate SOC of multiple battery cells simultaneously with low computational complexity and high accuracy is needed. Limited by computing speed of Battery Control Unit (BCU), existing SOC estimation method is hard to estimate SOC of each battery cell simultaneously with high accuracy. In this research, a new SOC estimation strategy was proposed to estimate SOC of multiple battery cells simultaneously for battery equalization control. Battery model is established based on experimental data, and a processor-in-the-loop test system was established to verify the actual performance of the proposed algorithm. Results of simulation and test indicate that the proposed algorithm can estimate SOC of multiple battery cells simultaneously and achieved good real-time performance and high accuracy

Metabolomics in the Development and Progression of Dementia: A Systematic Review

Dementia has become a major global public health challenge with a heavy economic burden. It is urgently necessary to understand dementia pathogenesis and to identify biomarkers predicting risk of dementia in the preclinical stage for prevention, monitoring, and treatment. Metabolomics provides a novel approach for the identification of biomarkers of dementia. This systematic review aimed to examine and summarize recent retrospective cohort human studies assessing circulating metabolite markers, detected using high-throughput metabolomics, in the context of disease progression to dementia, including incident mild cognitive impairment, all-cause dementia, and cognitive decline. We systematically searched the PubMed, Embase, and Cochrane databases for retrospective cohort human studies assessing associations between blood (plasma or serum) metabolomics profile and cognitive decline and risk of dementia from inception through October 15, 2018. We identified 16 studies reporting circulating metabolites and risk of dementia, and six regarding cognitive performance change. Concentrations of several blood metabolites, including lipids (higher phosphatidylcholines, sphingomyelins, and lysophophatidylcholine, and lower docosahexaenoic acid and high-density lipoprotein subfractions), amino acids (lower branched-chain amino acids, creatinine, and taurine, and higher glutamate, glutamine, and anthranilic acid), and steroids were associated with cognitive decline and the incidence or progression of dementia. Circulating metabolites appear to be associated with the risk of dementia. Metabolomics could be a promising tool in dementia biomarker discovery. However, standardization and consensus guidelines for study design and analytical techniques require future development

Study on Temperature Distribution Characteristics of Condensate in Marine Condensator

According to the structure and working principles of marine condensator, utilizing FLUNET, a numerical model of the flow field related to the hot well is established and the results were analysed. The flow field and temperature distribution characteristics of hot well with metal mesh and different heat flow distribution schemes are compared and analysed to improve the operation economy and safety of steam turbine units

A Hybrid Neural Network Model For Predicting The Nitrate Concentration In The Recirculating Aquaculture System

This study was groundbreaking in its application of neural network models for nitrate management in the Recirculating Aquaculture System (RAS). A hybrid neural network model was proposed, which accurately predicted daily nitrate concentration and its trends using six water quality parameters. We conducted a 105-day aquaculture experiment, during which we collected 450 samples from five sets of RAS to train our model (C-L-A model) which incorporates Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and self-Attention. Furthermore, we obtained 90 samples from a standalone RAS as the testing data to evaluate the performance of the model in practical applications. The experimental results proved that the C-L-A model accurately predicted nitrate concentration in RAS and maintained good performance even with a reduced proportion of training data. We recommend using water quality parameters from the past 7 days to forecast future nitrate concentration, as this timeframe allows the model to achieve maximum generalization capability. Additionally, we compared the performance of the C-L-A model with three basic neural network models (CNN, LSTM, self-Attention) as well as three hybrid neural network models (CNN-LSTM, CNN-Attention, LSTM-Attention). The results demonstrated that the C-L-A model (R2=0.956) significantly outperformed the other neural network models (R2=0.901-0.927). Our study suggests that the utilization of neural network models, specifically the C-L-A model, could potentially assist the RAS industry in conserving resources for daily nitrate monitoring.Comment: The content of this paper needs to be further filled and improve

DataSheet_1_Exploring the key factors affecting the seasonal variation of phytoplankton in the coastal Yellow Sea.doc

Marine phytoplankton play crucial roles in the ocean’s biological pump and have great impacts on global biogeochemical cycles, yet the knowledge of environmental variables controlling their seasonal dynamics needs to be improved further, especially in the coastal ecosystems. In order to explore the determinants affecting the seasonal variation of phytoplankton, here we conducted three surveys during spring, summer and autumn along the coastal Yellow Sea. Among the phytoplankton community, 49 species of diatoms and 9 species of dinoflagellates were observed in spring, 63 species of diatoms and 10 species of dinoflagellates in summer, and 62 species of diatoms and 11 species of dinoflagellates in autumn. These results thus suggested that there were obvious differences in the number of species across the three seasons, of which diatoms were the most diverse group, followed by dinoflagellates. Additionally, diatoms were the most dominant species of the phytoplankton community and varied largely during different seasons. According to the redundancy analysis, the abundance of phytoplankton community was mainly related to water temperature and dissolved inorganic nitrogen (DIN) during the three seasons, indicating that water temperature and DIN could be the key factors controlling the seasonal variability of phytoplankton community along the coastal Yellow Sea. Also, significant correlations were observed between phytoplankton abundance and heavy metals Zn, As, and Hg during the three seasons, suggesting that these metals also had potential influences on the seasonal dynamics of phytoplankton community in the coastal Yellow Sea.</p

Analysis and Research on Flow Characteristics of Hot Water Pipeline System

Based on the CFD numerical simulation method, a pipe model was established to study the internal flow characteristics of two different specifications of hot water pipe system, and the related information of the flow field was obtained, such as the internal pressure, flow velocity and flow rate. The results showed that the smoother the pipeline transition was, the smaller the velocity uniformity coefficient would be, and the higher flow field uniformity means the smaller pressure and velocity fluctuations. Therefore, the pipeline vibration will be smaller, and the flow characteristics are greatly improved