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There are many factors that affect a player’s overall basketball ability, and different factors will have different effects. The effect is mainly manifested in the difference of offensive and defensive data of basketball players in basketball games. In the basketball field, the formulation of existing training plans mainly relies on the manual observation and personal experience of coaches. This method is inevitably subjective. The pattern recognition of dribbling tactics is one of the important factors. A proper dribbling tactic can make the team achieve better results. In order to discover different dribbling characteristics, reanalyze the connotation and manifestation of basketball speed and strive to analyze the factors that affect basketball speed reasonably and accurately. The deep learning algorithm simulates the thinking process of the human brain neurons through the computer method and then realizes the function of the computer to automatically learn the data characteristics and complete the complex data analysis task. We use artificial intelligence and deep learning to simulate various dribbling tactics of players and find out the rules to improve players’ abilities. The results of the study prove that developing a suitable dribbling tactical model for basketball players can increase their competitive ability by more than 10%, reduce the damage to players, and prolong their careers. Generally speaking, athletes’ injuries can be reduced by more than 15%. This shows that the pattern recognition characteristics and neural mechanisms of dribbling tactics are extremely important to basketball players.

In traditional 3D reconstruction methods, using a single view to predict the 3D structure of an object is a very difficult task. This research mainly discusses human pose recognition and estimation based on 3D multiview basketball sports dataset. The convolutional neural network framework used in this research is VGG11, and the basketball dataset Image Net is used for pretraining. This research uses some modules of the VGG11 network. For different feature fusion methods, different modules of the VGG11 network are used as the feature extraction network. In order to be efficient in computing and processing, the multilayer perceptron in the network model is implemented by a one-dimensional convolutional network. The input is a randomly sampled point set, and after a layer of perceptron, it outputs a feature set of n × 16. Then, the feature set is sent to two network branches, one is to continue to use the perceptron method to generate the feature set of n × 1024, and the other network is used to extract the local features of points. After the RGB basketball sports picture passes through the semantic segmentation network, a picture containing the target object is obtained, and the picture is input to the constructed feature fusion network model. After feature extraction is performed on the RGB image and the depth image, respectively, the RGB feature, the local feature of the point cloud, and the global feature are spliced and fused to form a feature vector of N × 1152. There are three branches for this vector network, which, respectively, predict the object position, rotation, and confidence. Among them, the feature dimensionality reduction is realized by one-dimensional convolution, and the activation function is the ReLU function. After removing the feature mapping module, the accuracy of VC-CNN_v1 dropped by 0.33% and the accuracy of VC-CNN_v2 dropped by 0.55%. It can be seen from the research results that the addition of the feature mapping module improves the recognition effect of the network to a certain extent

The main objective of this study was to assess the effects of disinfection conditions on bromine incorporation into disinfection by-products (DBPs) during chlorination of water with low specific UV absorbance (SUVA). Five classes of DBPs were included: trihalomethanes (THMs), dihaloacetic acids (di-HAAs), trihaloacetic acids (tri-HAAs), dihaloacetonitriles (DHANs) and trihalonitromethanes (THNMs). Results showed that the bromine utilization in DBPs formation was positive related with reaction time, pH and temperature. On the other hand, the bromine substitution factors (BSFs) of DBPs were generally increased with pH (except tri-HAAs) and bromide concentration, but decreased with the reaction time, temperature and chlorine dose. Moreover, the BSFs values varied with DBP classes with the ranking being as following: THNMs≫DHANs≫tri-HAAs>THM≈di-HAAs. These results were mostly similar with the references, yet the pH effect on BSFs as well as the rank of BSFs for different DBP classes may differ with the specific UV absorbance of organic matter.

Two channels Photoplethysmography are acquired by pulse oximeter. The scheme concludes multichannel signals, the conditional sum of cascaded adaptive filters and singular spectrum analysis, and peak selection proposed for monitoring heart rate in extensive exercise.

A comprehensive loop parameters designing method and an auxiliary pulling module for an optical phase-locked loop system were discussed. The experiment successfully demonstrated a pull-in frequency range as wide as 2.4GHz and a frequency accuracy of 2MHz.

To better understand the precursor of disinfection by-products (DBPs) and provide useful information for water utilities to manage the drinking water, a study of DBP formation was conducted through chlorination of leaf organic matter (OM) from phoenix tree and algal OM from Chlorella vulgaris. DBPs investigated include trichloromethane (TCM), trichloroacetic acid (TCAA), dichloroacetic acid (DCAA), chloroacetic acid (CAA), dichloroacetonitrile (DCAN) and trichloroacetonitrile (TCNM). Results show that the specific yields (μg/mg C) of C-DBPs (TCM, CAA, DCAA and TCAA) from leaf OM were higher but the specific yields of N-DBPs (DCAN and TCNM) were lower than those from algal OM. Correlation analysis revealed that C-DBPs yields (μg/L) were significantly (p 0.01) interrelated with each other (r = 0.937-0.996), and for each C-DBP, the hydrophobic OM contributed more to their formation (61-90% of total yields) as compared with hydrophilic OM. In spite of these characteristics, an in-depth examination was conducted revealing that the hydrophobicity and aromaticity of C-DBPs precursors were in the order of TCAA DCAATCM CAA. DCAN precursors were highly variable: they were dominated by hydrophobic OM (leaf OM: 86%) or hydrophilic OM (algal OM: 61%). Hydrophilic OM was the most important precursor for TCNM (76-79% of total yields), followed by hydrophobic neutral and base substances (29-45% of total yields), but the hydrophobic acids exhibited an inhibition role in TCNM formation.

Endothelin-1 (ET-1) is considered to be one of the most potent and long-lasting vasoconstrictive peptides, but the mechanisms on the regulation of ET-1 expression are not fully understood.In this study, we found that microRNA (miR)-125a-5p and miR-125b-5p are highly expressed in vascular endothelial cells (VECs), which can be regulated by oxidized low-density lipoprotein (oxLDL). To explore the function of miR-125a/b-5p in VECs, we examined the roles of potential targets of miR-125a/b-5p that could influence endothelium function. We found that both miR-125a/b-5p can suppress oxLDL-induced ET-1 expression by directly targeting 3' untranslated region of prepro-endothelin-1 (preproET-1) mRNA determined by luciferase reporter assay, western blot, and enzyme immunometric assay. Consistently, inhibitors of miR-125a/b-5p can directly enhance preproET-1 expression. The decreased expressions of miR-125a-5p and miR-125b-5p are negatively associated with upregulation of preproET-1 expression in aorta of stroke-prone spontaneously hypertensive rats (SHR-SPs).Our finding demonstrated that endothelial miR-125a/b-5p inhibits ET-1 expression in VECs, which revealed a novel miRNA-mediated mechanism in vasomotor homeostasis.