Marine Ecological Disasters and Their Physical Controlling Mechanisms in Jiangsu Coastal Area

The studies in this chapter are focused on marine ecological disasters in Jiangsu coastal area. Three kinds of algal blooms occurred in this region, namely, red tide associated with Dinoflagellate, green tide associated with Ulvaprolifera and golden tide associated with Sargassum. Numerical model results demonstrated that red tides in Haizhou Bay originated locally, because most of Dinoflagellates near Zhoushan Islands would be transported northeastward by the Changjiang diluted water, and even the lucky ones that entered the south of Jiangsu coastal area would die in the Subei Shoal due to high turbidity there. Due to the Changjiang diluted water and the prevailing southerly wind, Ulvaprolifera could not drift southward, either. Seawater with high turbidity in the Subei Shoal limited sunlight penetration into deep water column, and further inhibited the growth of Ulvaprolifera suspending in the water column. In this chapter, we use drift bottles and satellite-tracked Argos drifters to provide solid direct dynamic evidence that Ulvaprolifera could drift from the Subei Shoal to Qingdao coastal area and even further north. The sand ridges limited the traveling path of Ulvaprolifera in the Subei Shoal, and wind-driven currents and other baroclinic processes helped Ulvaprolifera travel farther to the north

Prediction of Carbohydrate-Binding Proteins from Sequences Using Support Vector Machines

Carbohydrate-binding proteins are proteins that can interact with sugar chains but do not modify them. They are involved in many physiological functions, and we have developed a method for predicting them from their amino acid sequences. Our method is based on support vector machines (SVMs). We first clarified the definition of carbohydrate-binding proteins and then constructed positive and negative datasets with which the SVMs were trained. By applying the leave-one-out test to these datasets, our method delivered 0.92 of the area under the receiver operating characteristic (ROC) curve. We also examined two amino acid grouping methods that enable effective learning of sequence patterns and evaluated the performance of these methods. When we applied our method in combination with the homology-based prediction method to the annotated human genome database, H-invDB, we found that the true positive rate of prediction was improved

Research on the Evaluation of Multi-channel Online Advertising Combination Effects Based on Channel Click Path

The rapid development of the Internet, mobile and social media has brought a large number of new online advertising and marketing channels to e-commerce enterprises. In order to explore the combinatorial effect of multi-channel online advertising, according to the choice set theory, this paper proposes related research hypotheses based on the classified combination effects of online advertising channels, and then constructs the COX model, extracts relevant variables based on the channel data of individual users from an e-commerce company. Perform regression analysis on the model to obtain the combination effects of the specific advertising channel click order. The results show that the combination of advertising channels from Firm-initiated channels to Customer-initiated channels will have a positive combination effect on purchases, while the combination of advertising channels from brand search to generic search will have a negative combination effect on purchases

A Higher-Order Graph Convolutional Network for Location Recommendation of an Air-Quality-Monitoring Station

The location recommendation of an air-quality-monitoring station is a prerequisite for inferring the air-quality distribution in urban areas. How to use a limited number of monitoring equipment to accurately infer air quality depends on the location of the monitoring equipment. In this paper, our main objective was how to recommend optimal monitoring-station locations based on existing ones to maximize the accuracy of a air-quality inference model for inferring the air-quality distribution of an entire urban area. This task is challenging for the following main reasons: (1) air-quality distribution has spatiotemporal interactions and is affected by many complex external influential factors, such as weather and points of interest (POIs), and (2) how to effectively correlate the air-quality inference model with the monitoring station location recommendation model so that the recommended station can maximize the accuracy of the air-quality inference model. To solve the aforementioned challenges, we formulate the monitoring station location as an urban spatiotemporal graph (USTG) node recommendation problem in which each node represents a region with time-varying air-quality values. We design an effective air-quality inference model-based proposed high-order graph convolution (HGCNInf) that could capture the spatiotemporal interaction of air-quality distribution and could extract external influential factor features. Furthermore, HGCNInf can learn the correlation degree between the nodes in USTG that reflects the spatiotemporal changes in air quality. Based on the correlation degree, we design a greedy algorithm for minimizing information entropy (GMIE) that aims to mark the recommendation priority of unlabeled nodes according to the ability to improve the inference accuracy of HGCNInf through the node incremental learning method. Finally, we recommend the node with the highest priority as the new monitoring station location, which could bring about the greatest accuracy improvement to HGCNInf

Selective photocatalytic decomposition of formic acid over AuPd nanoparticle-decorated TiO2 nanofibers toward high-yield hydrogen production

We present high-yield hydrogen production through selective photocatalytic decomposition of formic acid by using electrospun TiO2 nanofibers decorated with AuPd bimetallic alloy nanoparticles under simulated sunlight irradiation. By using only 5 mg of the AuPd/TiO2 nanofibers containing the 0.75% Au and 0.25% Pd, we could achieve an optimal H2 generation rate of 88.5 μmol h−1 with an apparent quantum yield at 365 nm as 15.6%, which is higher than that of the Pd/TiO2 and Au/TiO2 nanofibers by a factor of 1.6 and 4.5, respectively. The enhanced photocatalytic decomposition of formic acid for H2 generation could be attributed to the stronger electron-sink effect of AuPd alloy nanoparticles, the high selectivity of Pd for the dehydrogenation of formic acid, and the surface plasmon resonance effect of Au. More importantly, we demonstrate that the photocatalytic processes enable re-activation of the AuPd nanoparticles that were poisoned by CO during thermal decomposition of formic acid. As such, the presented AuPd/TiO2 nanofibers are promising materials for re-generation of H2 under mild conditions from liquid storage carrier of hydrogen.Accepted versio

A Novel Multi-Candidate Multi-Correlation Coefficient Algorithm for GOCI-Derived Sea-Surface Current Vector with OSU Tidal Model

The maximum cross-coefficient (MCC) algorithm based on the template matching technique is a typical algorithm for obtaining the sea-surface currents (SSCs) in marginal seas. However, this algorithm has mismatches between images in highly turbid water. In this study, we implemented the MCC algorithm to Geostationary Ocean Color Imager-derived total suspended matter to obtain the SSCs in the Yellow Sea and the East China Sea. We propose a novel vector optimization algorithm, which is combined with the accurate estimate of tidal ellipses from the OSU tidal model. This method considers the three greatest candidate acquisitions from multi-correlation coefficients as potential vectors. The rotation direction of the vector within the tidal oscillation is used to identify and substitute for the spurious vector. The obtained average speed of SSC reached 0.60 m/s, which was close to the buoy-measured average speed of 0.58 m/s. Compared with the existing spurious vector eliminating method, the average angular error was improved by 20%, and the average relative amplitude error was improved by 4% in our case study. On the basis of ensuring data integrity, the inversion accuracy was improved

Observations of a tidal intrusion front in a tidal channel

A visible front indicated by a surface colour change, and sometimes associated with foam or debris lines, was observed in a tidal channel during neap tide. This is an example of a tidal intrusion front occurring in the absence of sudden topographical changes or reversing flows, typically reported to be associated with such fronts. Detailed Acoustic Doppler Current Profiler and conductivity/temperature/depth measurements were taken on repeated transects both with fronts apparent and with fronts absent. The results indicated that the front occurred as a result of stratification, which was sustained by the buoyancy flux and the weak tide-induced mixing during neap ebb tide. The stronger tide-induced mixing during spring tide restrained stratification, leading to the absence of a front. The mechanism of the frontogenesis was the density gradient between the stratified water formed during neap ebb tide, and the more mixed seawater during neap flood tide; thus, the water on the landward (southwestern) side of the front was stratified, and that on the seaward side (northeastern) of the front was vertically well mixed. Gradient Richardson number estimates suggest that the flow between the stratified and mixed water was near the threshold 0.25 for shear instability. Meanwhile, the density gradient would provide an initial baroclinic contribution to velocity convergence, which is indicated by the accumulation of buoyant matter such as foam, grass, and debris into a sharply defined line along the surface. The front migrates with the flood current, with a local maximum towards the eastern side of the channel, leading to an asymmetrical shape with the eastern side of the front driven further into the Tiaozhoumen tidal channel

Design and Fabrication of Full Wheatstone-Bridge-Based Angular GMR Sensors

Since the discovery of the giant magnetoresistive (GMR) effect, GMR sensors have gained much attention in last decades due to their high sensitivity, small size, and low cost. The full Wheatstone-bridge-based GMR sensor is most useful in terms of the application point of view. However, its manufacturing process is usually complex. In this paper, we present an efficient and concise approach to fabricate a full Wheatstone-bridge-based angular GMR sensor by depositing one GMR film stack, utilizing simple patterned processes, and a concise post-annealing procedure based on a special layout. The angular GMR sensor is of good linear performance and achieves a sensitivity of 0.112 mV/V/Oe at the annealing temperature of 260 °C in the magnetic field range from −50 to +50 Oe. This work provides a design and method for GMR-sensor manufacturing that is easy for implementation and suitable for mass production