Entropy Weight Measure Model of Online Influential Users’ Relative Social Capital

Based on the perspectives of information resource management and social capital measurement, this paper studies how influential users acquire, accumulate, and use their social capital in social networks to explore the general rules, which enterprises use influential users’ relative competitiveness in their topic areas of expertise to advertise precisely. The paper describes the social capital differences among influential users by introducing and calculating users’ relative social capital. Results show that user’s social capital values in different fields are dissimilar, and the scope and intensity of social capital among different users are relative. The proposed method is proved to be effective and reasonable

Information Entropy-based Social Capital Measure Method of Online Influential Users

Measuring online user influence is a major research topic in social marketing performance maximization. In this study, we comprehensively investigate how online influential users gain, accumulate, and use their social capital from the perspective of information resource management and social capital measurement. First, we define the social capital of online influential users and the attribute characters and relationships reflected fully by personality and sociality index data. We then construct a social capital measurement indicator system and information entropy model of online users. After the calculations of this model, we finally forma social capital measure method of online influential users. The rationality and validity of proposed model are tested by experimental study on real datasets

AcoMYB4, an Ananas comosus L. MYB transcription factor, functions in osmotic stress through negative regulation of ABA signaling

Drought and salt stress are the main environmental cues affecting the survival, development, distribution, and yield of crops worldwide. MYB transcription factors play a crucial role in plants’ biological processes, but the function of pineapple MYB genes is still obscure. In this study, one of the pineapple MYB transcription factors, AcoMYB4, was isolated and characterized. The results showed that AcoMYB4 is localized in the cell nucleus, and its expression is induced by low temperature, drought, salt stress, and hormonal stimulation, especially by abscisic acid (ABA). Overexpression of AcoMYB4 in rice and Arabidopsis enhanced plant sensitivity to osmotic stress; it led to an increase in the number stomata on leaf surfaces and lower germination rate under salt and drought stress. Furthermore, in AcoMYB4 OE lines, the membrane oxidation index, free proline, and soluble sugar contents were decreased. In contrast, electrolyte leakage and malondialdehyde (MDA) content increased significantly due to membrane injury, indicating higher sensitivity to drought and salinity stresses. Besides the above, both the expression level and activities of several antioxidant enzymes were decreased, indicating lower antioxidant activity in AcoMYB4 transgenic plants. Moreover, under osmotic stress, overexpression of AcoMYB4 inhibited ABA biosynthesis through a decrease in the transcription of genes responsible for ABA synthesis (ABA1 and ABA2) and ABA signal transduction factor ABI5. These results suggest that AcoMYB4 negatively regulates osmotic stress by attenuating cellular ABA biosynthesis and signal transduction pathways

Effects of nitrogen reduction rates on grain yield and nitrogen utilization in a wheat-maize rotation system in yellow cinnamon soil

Excessive nitrogen (N) fertilizer application severely degrades soil and contaminates the atmosphere and water. A 2-year field experiment was conducted to investigate the effects of different N fertilizer strategies on wheat-summer corn rotation systems in yellow-brown soil areas. The experiment consisted of seven treatments: no N fertilization (CK), conventional fertilization (FP), optimized fertilization (CF), reduced N rates of 10% (90% FP), 20% (80% FP), 30% (70% FP), and a combination of controlled release with conventional urea at 7:3 ratio (CRU). The results indicate that under the condition of 80% FP, both CF and CRU treatments can increase the yield of wheat and corn for two consecutive years. Compared with FP treatment, the wheat yield of CF and CRU treatments increased by 3.62–2.57% and maize yield by 3.53–1.85% with N fertilizer recovery rate (NRE) of crops by 46.2–37.8%. The agronomic N use efficiency (aNUE) under CF treatment increased by 35.4–37.7%, followed by CRU, which increased by 30.5–33.9%. Moreover, compared with FP treatment, both CF and CRU treatment increased the content of organic matter (OM), total N (TN), and hydrolyzed N (HN) in the topsoil layer, and 70% FP treatment significantly reduced the HN content. Both CF and CRU treatments significantly increased the NO3 concentrations in the 0–20 cm soil depth during the wheat and maize season at maturity stages and decreased the residual inorganic N below the plow layer (40–60 cm). During the corn season, the CF and CRU treatments significantly reduced the NO3 concentration in the 40–60 cm soil layer from seedling to jointing. Considering various factors, CRU treatment under 80% FP conditions would be the best fertilization measure for wheat-corn rotation in yellow-brown soil areas

Surface functionalization of vertical graphene significantly enhances the energy storage capability for symmetric supercapacitors

Vertical graphene (VG) sheets, which consist of few-layer graphene vertically aligned on the substrate with three dimensionally interconnected porous network, make them become one of the most promising energy storage electrodes, especially for SCs. Nevertheless, the intrinsic hydrophobic nature of pristine VG sheets severely limited its application in aqueous SCs. Here, electrochemical oxidation strategy is adopted to increase the hydrophilicity of VG sheets by introducing oxygen functional groups so that the aqueous electrolyte can fully be in contact with the VG sheets to improve charge storage performance. Our work demonstrated that the introduction of oxygen functional groups not only greatly improved the hydrophilicity but also generated a pseudo capacitance to increase the specific capacitance. The resulting capacitance of electrochemically oxidized VG for 7 min (denoted as EOVG-7) exhibited three orders of magnitude higher (1605 mF/cm²) compared to pristine VG sheets. Through assembled two EOVG-7 electrodes, a symmetric supercapacitor demonstrated high specific capacitance of 307.5 mF/cm², high energy density of 138.3 μWh/cm2 as well as excellent cyclic stability (84% capacitance retention after 10000 cycles). This strategy provides a promising way for designing and engineering carbon-based aqueous supercapacitors with high performance

Brain-wave (EEG) recognition using transformers, an emerging machine learning technique

Electroencephalograph (EEG) signals play an important role in many aspects of brain science research and applications, and in order to make EEG signals better used in the scientific research, certain algorithms or systems need to be developed for EEG signal recognition. In this dissertation, I try to reproduce and improve two existing state-of-art transformer-based neural network methods of the EEG signal recognition. At present, the recognition of EEG signals mainly relies on convolutional neural networks or recurrent neural networks, but the structure of CNN is relatively fixed, and due to their structure and the characteristics of feature extraction, both CNN and RNN are unable to perceive the global features other than local features. EEG signal, as a signal with global features with global dependencies, new algorithm or system structure is needed. The first method I used in dissertation is Spatial Temporal Tiny Transformer (S3T) from [1]. It is a deep neural network with transformer-based structure. In this model, after some pre-processing of the native EEG signal, the Transformer built in S3T based on the attention mechanism performs attention-related processing on the spatial features of the data, followed by attention processing on the temporal features. Finally, the model slice the data, and then fully-connected layer performs the signal recognition. Although S3T model is powerful in EEG recognition, but for an pure attention mechanism model, S3T can only perceive and obtain the global features of EEG signals mainly. To some extent, it cannot perceive the local temporal features of EEG signals well. In order to solve the problem of obtaining local features and global features simultaneously, I try to use Convolutional Transformer (EEG Conformer). EEG Conformer is a hybrid network of convolutional neural network and Transformer designed by Song and his colleagues in [2]. In this model, first the convolutional part of the model will extract the temporal features as well as spatial features in the local range of EEG signals in a one-dimensional space, respectively. After that, Transformer structure learns the global dependencies on these low level structures. Finally, the fully-connected layer perform signal recognition on these extracted features. Also, I try to do some modification on the model structure or the hyperparameters. The purposes of modification includes: First, reduce the parameter size of the model and keep the recognition correct rate. Second, modify the hyper-parameters in the model to get a better recognition result. However, I have failed on these tries. For the first purpose, I do some ablation and combination work on the model, but did not keep the recognition rate at a satisfied level. The reasons of fail in this part are mainly because the combination or ablation lose important feature extraction ability in the model and result in great drop in recognition rate. For the second purpose, the modification on the hyperparameters only results in the drop of the recognition rate in the both models. The existing model have the parameters adjusted to a saturated level, the rise of them does not improve the recognition ability of the model significantly, but the reduction of them result in great loss in recognition correct rate. In this dissertation, I tried to use two public dataset to verify the recognition ability of the two models, and the results shows that the reproduced models are both acquire competitive recognition ability compared to the existing models which are dominated by the CNN or RNN architectures in EEG signal recognition nowadays. Keywords: EEG recognition; Transformer; attention mechanism; Convolutional Transformer(Conformer); Spatial-Temporal Tiny Transformer(S3T) ; neural network(nn); deep learning; motor imagery(MI).Master of Science (Computer Control and Automation

Effect of Hybrid Laser Arc Welding on the Microstructure and Mechanical and Fracture Properties of 316L Sheet Welded Joints

To explore the influence of different welding modes on the properties of 316L thin-plate welded joints, a new type of laser arc compound gun head similar to a coaxial one was used in this experiment. A high-speed camera was used to record the welding process and analyze the droplet splash behavior of the molten pool. The microstructure, microhardness change, and tensile test results of welded joints under different welding modes were analyzed. The results showed that laser welding (LW) is more prone to molten pool splash than hybrid laser arc welding (HLAW). The HLAW pool area was significantly increased compared with that of LW. The HLAW joint microstructure was more uniform than that of LW, which can improve the microhardness of welded joints. HLAW improved the tensile properties of the joint, with the maximum tensile strength of the joint increasing from 433 to 533 MPa. This test can provide guidance for the HLAW process

Effect of Preheating Temperature on Geometry and Mechanical Properties of Laser Cladding-Based Stellite 6/WC Coating

The effect of 60Si2Mn substrate preheating on the forming quality and mechanical properties of cobalt-based tungsten carbide composite coating was investigated. Substrate preheating was divided into four classes (room temperature, 150 °C, 250 °C, and 350 °C). The morphology, microstructure, and distribution of elements of the coating were analyzed using a two-color laser handheld 3D scanner, a scanning electron microscope (SEM), and an energy dispersive X-ray spectrometer (EDX), respectively. The hardness and wear properties of the cladding layer were characterized through a microhardness tester and a friction wear experiment. The research results show that the substrate preheating temperature is directly proportional to the height of the composite coating. The solidification characteristics of the Stellite 6/WC cladding layer structure are not obviously changed at substrate preheating temperatures of room temperature, 150 °C, and 250 °C. The solidified structure is even more complex at a substrate preheating temperature of 350 °C. At this moment, the microstructure of the cladding layer is mainly various blocky, petaloid, and flower-like precipitates. The hardness and wear properties of the cladding layer are optimal at a substrate preheating temperature of 350 °C in terms of mechanical properties

Intramode Brillouin Scattering Properties of Single-Crystal Lithium Niobate Optical Fiber

Ordinary step-type fiber usually has only one obvious Brillouin scattering gain peak with a low gain coefficient, resulting in a poor sensing performance. As a promising material for nonlinear photonics, lithium niobate can significantly improve the Brillouin gain due to its higher refractive index when replaced with the core material. Furthermore, the higher-order acoustic modes make the Brillouin gain spectrum exhibit multiple scattering peaks, which could improve the performance of sensors. In this study, we simulated the Brillouin scattering properties of different modes of intramode in step-index lithium niobate core fibers. We analyzed the intramode-stimulated Brillouin scattering properties of different pump–Stokes pairs for nine LP modes (LP01, LP11, LP21, LP02, LP31, LP12, LP41, LP22, and LP03) guided in fiber. The results show that both the effective refractive index and Brillouin scattering frequency shift are decreased with the increase in the nine mode orders, and the values of which are 2.2413 to 2.1963, and 21.17 to 20.73 GHz, respectively. The typical back-stimulated Brillouin scattering gain is obtained at 1.7525 m−1·W−1. These simulation results prove that the Brillouin gain of the LiNbO3 optical fiber structure can be significantly improved, which will pave the way for better distributed Brillouin sensing and for improving the transmission capacity of communication systems