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We study a 3D ternary system derived as a sharp-interface limit of the Nakazawa-Ohta density functional theory of triblock copolymers, which combines an interface energy with a long range interaction term. Although both the binary case in 2D and 3D, and the ternary case in 2D, are quite well studied, very little is known about the ternary case in 3D. In particular, it is even unclear whether minimizers are made of finitely many components. In this paper we provide a positive answer to this, by proving that the number of components in a minimizer is bounded from above by some quantity depending only on the total masses and the interaction coefficients. One key difficulty is that the 3D structure prevents us from uncoupling the Coulomb-like long range interaction from the perimeter term, hence the actual shape of minimizers is unknown, not even for small masses. This is due to the lack of a quantitative isoperimetric inequality with two mass constraints in 3D, and it makes the construction of competitors significantly more delicate.

Magnetic MgxCoyZn(1-x-y)Fe2O4 nanoparticles were successfully prepared by the rapid combustion approach, and SEM, XRD, VSM, EDX, and FTIR techniques were applied for their characterization. The influence of the element ratios (Mg2+, Co2+, and Zn2+) in magnetic MgxCoyZn(1-x-y)Fe2O4 nanoparticles on their properties was explored. To acquire a larger specific surface area for better adsorption of methyl blue (MB), magnetic Mg0.4Co0.5Zn0.1Fe2O4 nanoparticles calcined at 400°C for 2 h with 25 mL anhydrous ethanol were selected, and their average particle size and the saturation magnetization were about 81.3 nm and 13.5 emu·g-1, respectively. Adsorption kinetics models and adsorption isotherm models were applied to research the adsorption characteristics of MB onto magnetic Mg0.4Co0.5Zn0.1Fe2O4 nanoparticles. The pseudo-second-order kinetics model ( R 2 > 0.99 ) and Temkin isotherm model ( R 2 = 0.9887 ) were the most consistent with the data, indicating that the adsorption was the chemical multilayer adsorption mechanism, and the process was an exothermic reaction. The E of the Dubinin-Radushkevich (D-R) isotherm model was 0.2347 KJ·mol-1, indicating the adsorption involved physical adsorption besides chemical adsorption. The Δ G 0 and Δ H 0 ( Δ H 0 = − 10.38 KJ·mol-1) of the adsorption process of MB adsorbed onto magnetic Mg0.4Co0.5Zn0.1Fe2O4 nanoparticles measured through the thermodynamic experiment were both less than 0, which proved that the process was a spontaneous exothermic reaction. The adsorption capacity of MB onto magnetic Mg0.4Co0.5Zn0.1Fe2O4 nanoparticles increased with the pH of MB solution increasing from 2 to 4 at room temperature, and it had no significant change when the pH of MB solution was 4-12, while the relative removal rate was 98.75% of the first one after 2 cycles. The electrochemical impedance spectroscopy (EIS) and the cyclic voltammetry (CV) data further demonstrated that MB was adsorbed onto magnetic Mg0.4Co0.5Zn0.1Fe2O4 nanoparticles.

Since the neural network was introduced into the super-resolution (SR) field, many SR deep models have been proposed and have achieved excellent results. However, there are two main drawbacks: one is that the methods based on the best peak-signal-to-noise ratio (PSNR) do not have enough comfortable visual quality; the other is that although the SR models based on generative adversarial network (GAN) have satisfactory visual quality, the structure of the reconstructed image has apparent defects. Therefore, according to the characteristics that human eyes are sensitive to high-frequency components in images, this article proposes an improved image SRGAN model based on high-frequency information fusion (HIFGAN). It builds a feature extraction network for high-frequency information fusion by designing a lightweight spatial attention module and improving the network architecture of enhanced super-resolution GAN (ESRGAN). It makes the generator in the GAN network have better feature recovery ability, reduces the dependence of the later training on the decider and loss function, and makes the generated image structure more consistent with the real situation. In addition, we build a high-frequency loss function to optimize the training of the generator network. Detailed experimental results show that HIFGAN performs excellently in both objective criterion evaluation and subjective visual effect. Compared with the state-of-the-art GAN-based SR networks, the reconstructed image by our model is more precise and complete in texture details.

Due to the complexity of the design of multilayer electromagnetic (EM) wave absorbing materials, it is difficult to establish the relationship between material parameters (type and filling ratios) and EM properties using traditional trial and error methods. Based on the measured EM parameters within a few materials and Boltzmann mixing theory, a database of EM parameters was thereafter built up. In this study, the genetic algorithm (GA) was used to design the multilayer wave-absorbing cement mortar. In order to verify this method, a multilayer mortar was fabricated and measured. The simulated and measured results are well consistent, which convincingly verifies computer-aided design. In addition, the optimized result expresses that the first layer as a matching layer guides EM waves into the interior of the material, while the other layers as absorption layers attenuate EM waves. The multilayer material may not meet the impedance gradient principle but still exhibits better EM wave absorption performance. The reflection loss (RL) of all optimized three layer sample is below –6.89 dB in the full frequency band and the minimum RL is –26.21 dB. This composite absorbing material and the GA method provide more design ideas for the design of future cement-based wave-absorbing materials and save a lot of time and material cost.

Tunnel fan is critical fire-fighting equipment, and its safe and stable operation is very important for the efficiency and safety of tunnel traffic. Existing studies commonly train the fault diagnosis methods with the goal of minimizing mean error which ignores the difference between classes in feature distribution. To solve the problem of inaccurate prediction caused by mean error evaluation, this paper presents a non-neural deep learning model, namely hierarchical cascade forest, which has three characteristics: (1) A hierarchical cascade structure is constructed, of which the output comes from each layer; (2) Each fault class is evaluated and recognized independently, the result of fault classes that are easy to distinguish is output earlier; (3) A confidence-based threshold estimate method is proposed in HCF and used to improve the training method to increase the reliability of HCF. Based on these, HCF improves the cascade forest structure and implements the proper matching of different depth of feature and fault patterns. The effect of HCF is verified through experiments based on the tunnel fans testing rig. Experimented results show that, compared to Deep Forest, the accuracy of HCF increases by 0.6% to 10.8%, and the training time of HCF is reduced 33.24%.

The spring finger collection mechanism is a core component of balers. It is usually used to collect soft straw crops (corn straw, wheat straw, green forage), and rarely used for hard stem crops (such as branch pruning residues). Due to different biophysical characteristics of hard stem crops and soft straw crops, the collection mechanisms are also different. In China, branch pruning residues in apple orchards are collected manually. It is a heavy labor task. To this end, this article uses an improved spring finger mechanism to collect pruning residues in apple orchard branches, effectively replacing manual collection operations. A dynamic motion trajectory simulation platform is developed by analyzing each action using pitchforks or other agricultural tools throughout the entire collection process, and combined with the structure of the spring finger collection mechanism. The platform of the spring finger collection mechanism is improved to collect orchard branches, and redesign and manufacture the core components of the collection mechanism. In addition, comparative tests were performed on4 different placement orientation sunder the conditions of R=40±2 r/min and V=0.9-1.1 m/s. That is, 1) messy, 2) transverse, 3) portrait orientation (the treetop firstly contacts the collection device), and 4) portrait orientation (the thick stems of the branches first touch the collection device). The results indicate that 1) and 2) have similar success rates in picking branches, while 3) and 4) have lower success rates, especially with 4) being the lowest). The analysis found that some branches were directly inserted into the spring finger gap and moved under the machine, resulting in a large amount of losses. This study aims to improve the success rate of picking branches in the bundling machine and verify the relationship between different placement directions and the success rate of picking branches.

Neural network is one of the AI algorithms commonly used to process data, and has an extremely important position in scenarios such as image recognition, classification, and machine translation. With the increase of data volume explosion, the required computing power of neural networks is also significantly increased. The emergence of quantum neural networks improves the computational power of neural networks, but the accuracy of neural networks and quantum neural networks is not high in the face of the complexity and uncertainty of big data. In order to improve the efficiency and accuracy, the cross-fusion of "fuzzy number theory + quantum neural network" is proposed to study the quantum fuzzy neural network (FQNN) based on fuzzy number. The Gaussian fuzzy function is used to generate the corresponding fuzzy affiliation matrix to describe the uncertain information in the data. The fuzzy independent variables are trained through the FQNN model, and the model is output after changing the parameters of the quantum forward propagation layer. Simulation experiments show that the quantum fuzzy neural network model based on fuzzy number is more efficient and accurate in this study compared with the quantum neural network model.

The knowledge graph of computer discipline domain plays a critical role in computer education, and the person event is an important part of the discipline knowledge graph. Adding person events to the graph will make the discipline knowledge graph richer and more interesting, and enhance enthusiasm of students for learning. The most crucial step in building the person event knowledge graph is the extraction of trigger words. Therefore, this paper proposes a method based on the serial fusion of gated recurrent neural network and convolutional neural network (SC-BiGRU-CNN) for person event detection in the computer discipline domain. We extract the global features of the text from the person event sentences through the BiGRU model, and input the extracted global features into the CNN model to further extract the fine-grained features of the text. And then the extracted features are used to classify the event trigger words. In addition, a dataset (CD-PED) for person event detection in the computer discipline domain is constructed to obtain trigger words and their types. We perform experiments on the public dataset MAVEN and the domain dataset CD-PED, respectively. The experimental results show that our approach has significantly improved the [Formula: see text] value compared with the baseline model on the domain dataset CD-PED.