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In this study, the variations in hydrogen content and oxide content in alloys under 0 W, 500 W, 1000 W, 1500 W, 2000 W, 2500 W, 3000 W, and at 20 kHz, 30 kHz, 40 kHz were investigated. Hydrogen content was assessed using porosity and computed tomography, while oxygen content in the alloy was measured using element analyzer and elemental scanning. Compared to other conditions, the melt had the lowest hydrogen and oxide contents at a frequency of 20 kHz and an ultrasonic power of 2500 W, with values of 0.099 cm3/100 g and 0.0015 %, respectively. Experimental observations also indicate that the variations in hydrogen content and oxide content in the alloy during ultrasonic treatment are almost similar. In most cases, lower hydrogen content corresponds to lower oxide content in the same alloy. This is because hydrogen bubbles and oxides become a single entity. At the same time, ultrasonic purification increases the tensile strength of the alloy to 200.1 MPa and the elongation rate to 0.72 %. This study primarily investigates the relationship between hydrogen bubbles and oxides in aluminum melt under different ultrasonic frequencies and power levels, providing significant reference for the purification of various fluids under ultrasonic fields.

Many experimental results confirm that electromagnetic stirring (EMS) has a good purification effect on the melts, either degassing or removing inclusions, but the mechanism of electromagnetic purification still needs to be further explored. This paper takes M174 alloy as an example, and carries out a more in-depth study on the purification effect of electromagnetic field on the basis of numerical simulation, focusing on the amount of hydrogen present in the ingot, the enhancement effect of electromagnetic field stirring on the mechanical properties of the melt was investigated in terms of the treatment temperature, and the current intensity during electromagnetic stirring. The results show that the tensile strength and elongation of the alloy reached 195.3 MPa and 0.69% after electromagnetic stirring at a current intensity of 60 A, which are very close to the theoretical properties of the alloy of 200.3 MPa and 0.74%, respectively. At the same time, the effect of electromagnetic stirring on the presence of oxides and double oxide film in the melt is slightly explored.

Magnitude prediction is a key focus in earthquake science research, and using machine learning models to analyze seismic data, identify pre-seismic anomalies, and improve prediction accuracy is of great scientific and practical significance. Taking the southern part of China’s North–South Seismic Belt (20° N~30° N, 96° E~106° E), where strong earthquakes frequently occur, as an example, we used the sliding time window method to calculate 11 seismicity indicators from the earthquake catalog data as the characteristic parameters of the training model, and compared six machine learning models, including the random forest (RF) and long short-term memory (LSTM) models, to select the best-performing LSTM model for predicting the maximum magnitude of an earthquake in the study area in the coming year. The experimental results show that the LSTM model performs exceptionally well in predicting earthquakes of magnitude 5 < ML ≤ 6 within the time window of the test set, with a prediction success rate of 85%. Additionally, the study explores how different time windows, spatial locations, and parameter choices affect model performance. It found that longer time windows and key seismicity parameters, such as the b-value and the square root of total seismic energy, are crucial for improving prediction accuracy. Finally, we propose a magnitude interval-based assessment method to better predict the actual impacts that different magnitudes may cause. This method demonstrates the LSTM model’s potential in predicting moderate to strong earthquakes and offers new approaches for earthquake early warning and disaster mitigation.

Using different fast charging strategies for lithium-ion batteries can affect the degradation rate of the batteries. In this case, predicting the capacity fade curve can facilitate the application of new batteries. Considering the impact of fast charging strategies on battery aging, a battery capacity degradation trajectory prediction method based on the TM-Seq2Seq (Trend Matching—Sequence-to-Sequence) model is proposed. This method uses data from the first 100 cycles to predict the future capacity fade curve and EOL (end of life) in one-time. First, features are extracted from the discharge voltage-capacity curve. Secondly, a sequence-to-sequence model based on CNN, SE-net, and GRU is designed. Finally, a trend matching loss function is designed based on the common characteristics of capacity fade curves to constrain the encoding features of the sequence-to-sequence model, facilitating the learning of the underlying relationship between inputs and outputs. TM-Seq2Seq model is verified on a public dataset with 132 battery cells and multiple fast charging strategies. The experimental results indicate that, compared to other popular models, the TM-Seq2Seq model has lower prediction errors.

The fault diagnosis of vibration exciter rolling bearing is of great significance to maintain the stability of vibration equipment. When the crack fault of the bearing occurs, the effective fault feature information cannot be extracted because the fault feature information of vibration signal is interfered by the noise around the vibrator. To solve this problem, a fault feature recognition method based on genetic algorithm–optimized Morlet wavelet filter and empirical mode decomposition is proposed. The Morlet wavelet filter optimized by genetic algorithm was used to filter the vibration signal, and then the empirical mode decomposition was applied to the filtered signal. In the envelope spectrum of the reconstructed signal, the characteristic frequency of the rolling bearing crack fault of the vibration exciter could be found accurately. Through simulation and experiment, it is proved that this method can provide theoretical and technical support for the crack fault diagnosis of vibration exciter rolling bearing.

Solutions to most electromagnetic problems use numerical methods, such as the finite element method (FEM), element free method (EFM), and soft computing method. To decrease the computational complexities of these methods, this paper presents an approach for solving electromagnetic problems, called radial basis function (RBF) network-based finite element least square support vector machine (FE-LSSVM). First, the expansion of approximate solutions by the proposed method uses the same structure as the RBF network method. Second, governing equations of electromagnetic problems are transformed to weak integral forms and variational formulations of FEM. Finally, Dirichlet boundary conditions are handled in the LS-SVM framework, which are considered as constraints of an optimization problem. By the Lagrange multiplier method, the quadratic programming problem can be transformed to a problem requiring the solution of a system of equations. The advantages of the proposed method are to directly satisfy the natural boundary conditions of the electromagnetic equations and remarkably improve the calculation accuracy. To verify the efficiency of the method, four categories of electromagnetic problems are investigated by using the proposed method. An analytical method and FEM are also carried out as comparisons to prove the advantages of the method proposed in this paper.

The high integration of power information physical system improves the efficiency of power transmission, but it also brings new threats to power grid. False data injection attacks can use traditional bad data to detect vulnerabilities and maliciously tamper with measurement data to affect the state estimation results. In order to achieve a higher security level for power systems, we propose an earth mover distance method to detect false data injection attacks in smart grids. The proposed method is built on the dynamic correlation of measurement data between adjacent moments. Firstly, a joint-image-transformation-based scheme is proposed to preprocess the measurement data variation, so that the distribution characteristics of measurement data variation are more significant. Secondly, the deviation between the probability distribution of measurement data variation and the histogram are obtained based on the earth’s mover distance. Finally, a reasonable detection threshold is selected to judge whether there are false data injection attacks. The proposed method is tested using IEEE 14 bus system considering the state variable attacks on different nodes. The results verified that the proposed method has a high detection accuracy against false data injection attacks.

Background: We aimed to establish a reference standard of Body Mass Index (BMI) for the growth of preschool children in China. Methods: We monitored and obtained the height and weight of 50702 children aged 3-6 yr in 31 provinces in mainland China in 2014. The reference standard and percentile curve of BMI preschool children aged 3-6 yr old were formulated by using Lambda-Median-Sigma (LMS) method in China. Results: The common grounds of the male and female children were as follows: the percentile maps were similar in shape; the graphs of children aged 4-6 were approximately horn shaped. The differences between male and female children were as follows: the BMI values of male children in the same age group and the same percentile were higher than those of the female children. The change pattern of male children was larger than that of female children. BMI of 3 yr old and 6 yr old children was larger than those of 4 yr old and 5 yr old. During the change from low percentile to high percentile, the BMI values of Chinese male children exceeded WHO to a larger extent, and the BMI values of Chinese female children were substantially consistent with WHO, but the high percentile greater than P95 exceeded WHO. Conclusion: The BMI growth chart developed can be applied in monitoring the growth and nutrition of preschool children in China. We recommend the promotion of the results in the field of preventive health care.

Whole genome amplification (WGA) is crucial for whole genome sequencing to investigate complex genomic alteration at the single-cell or even single-molecule level. Multiple displacement amplification (MDA) and multiple annealing and looping based amplification cycles (MALBAC) are two most widely applied WGA methods, which have different advantages and disadvantages, dependent on research objectives. Herein, we compared the MDA and MALBAC to provide more information on their performance in droplets and tubes. We observed that the droplet method could dramatically reduce the amplification bias and retain the high accuracy of replication than the conventional tube method. Furthermore, the droplet method exhibited higher efficiency and sensitivity for both homozygous and heterozygous single nucleotide variants (SNVs) at the low sequencing depth. In addition, we also found that MALBAC offered a greater uniformity and reproducibility and MDA showed a better efficiency of genomic coverage and SNV detection. Our results provided insights that will allow future decision making.

In this paper, based on computational fluid dynamics (CFD), the simulation study was conducted on the thermosyphon to construct the mathematical configuration of the thermosiphon. Then the CFD data simulation was performed for the thermosiphon and also the experimental comparison was made. The experimental results show that the thermosiphon has the best heat conduction effect under the condition of 500W energy supply and FR=0.4 filling rate. According to the simulation results, the increase of the FR has two different functions. First, the fluid volume becomes larger, and the boiling heat conduction effect can be deepened. Second, the liquid filling effect shall be enhanced and then the liquid surface be strengthened, which will cause many bubbles in the liquid phase of the evaporation zone and thus reduce the thermal conductivity; the most important is that with the liquid-filled effect increased, the gas phase area shall be increased and the pressure will be larger, so as to reduce the steam flow efficiency and overall system efficiency.

The cross-section image of microstructure optical fiber (MOF) is usually characterized by the irregular shape, disordered distribution of the air pores, and multiple sources of noise. The traditional modeling of fiber structure does not work for these MOFs, and it is difficult to obtain the actual cross-section structure. A new method based on the digital image processing technique and finite element method (FEM) is introduced. With this method, the actual cross-section structure of MOFs can be rapidly modeled by gray scale processing, filtering, threshold, and edge detection, which is vital to the simulation of the basic properties of the fiber with FEM precisely. The method is proved to be feasible and reliable in that the dispersion coefficients of an actual fiber simulated are greatly consistent with the experimental results. In addition, the influence of perfect matched layer thinkness, as well as the curve fitting interval selection of the dispersion coefficients on the research results, is explored in the paper, which forms a basis for the correct setting of these parameters. The method has the advantages of strong adaptability, good modeling effect, rapid simulation, and accurate results. Finally, the method applies to all kinds of cross-section modeling of fiber, especially for disordered structure modeling.

Polycrystalline diamond compact bit is the main rock-breaking tool of gas drainage borehole. In order to investigate the influence of geometric parameters of polycrystalline diamond bit on its drilling performance, the integrated force model of polycrystalline diamond bit pressed into rock was established. The stress analysis results show that the tooth rake angle, pressing depth, and horizontal tangential displacement are the main factors to influence the normal load and the axial load of polycrystalline diamond bit. There is an optimal tooth rake angle value of polycrystalline diamond bit cutter when it is used in the soft coal seam. The drilling experiment system was built to verify the drilling performance of drill bits with different geometric structure and different tooth rake angle through drilling the soft coal wall. The experiment results show that the drilling torque and feeding resistance of polycrystalline diamond bit are higher than that of the wing bit at the beginning of drilling the soft coal wall. With the increase in the drilling depth, the feeding resistance of wing bit will increase to exceed that of the polycrystalline diamond bit. So the polycrystalline diamond bits should be selected to drill the gas drainage borehole in soft coal seam. The polycrystalline diamond bit with bigger tooth rake angle cannot cut and break the coal rock effectively, which will reduce the drilling efficiency and the feeding speed. When the tooth rake angle is smaller, polycrystalline diamond bit cannot cut in coal wall effectively, which will increase the feeding resistance. So the polycrystalline diamond bit with tooth rake angle of 15°–20° is suitable for the gas drainage borehole in soft coal seam. With the increase in the rotary speed of polycrystalline diamond bit, the cutting thickness of coal rock decreases and the horizontal tangential displacement increases. When the rotary speed of polycrystalline diamond bit is 290 r/min, the mean drilling torque and mean feeding resistance of polycrystalline diamond bit reach the minimum. Therefore, in order to reach the optimum drilling performance, the working parameters of low drilling pressure and high rotary speed are usually selected during the actual gas drainage borehole in soft coal seam.

With the homemade solid-core silica-based photonic crystal fiber (PCF), we observe the generation of soliton and four-wave mixing (FWM) when the femtosecond pulse is pumped at 0.822 μm, belonging to the normal-dispersion regime. It is generally acknowledged that FWM is unrelated to third-order dispersion, whereas we use the fourth-order Runge-Kutta method to simulate the PCF properties by taking into account the third-order dispersion or not; the result we got is different from the conventional one. The numerical simulations are greatly consistent with the experiment results, and the FWM is dominated by negative fourth-order and negative sixth-order dispersion in the experiment. The main dynamic process is explained as the soliton, and the FWM generation can be induced by third-order dispersion. We indicate that low Raman threshold has played a very active role in this process, and we propose a conception of stimulated FWM effect in this paper.

Automatic microfluidic purification of nucleic acid is predictable to reduce the input of original samples and improve the throughput of library preparation for sequencing. Here, we propose a novel microfluidic system using an external NdFeB magnet to isolate DNA from the polymerase chain reaction (PCR) mixture. The DNA was purified and isolated when the DNA-carrying beads transported to the interface of multi-laminar flow under the influence of magnetic field. Prior to the DNA recovery experiments, COMSOL simulations were carried out to study the relationship between trajectory of beads and magnet positions as well as fluid velocities. Afterwards, the experiments to study the influence of varying velocities and input of samples on the DNA recovery were conducted. Compared to experimental results, the relative error of the final position of beads is less than 10%. The recovery efficiency decreases with increase of input or fluid velocity, and the maximum DNA recovery efficiency is 98.4% with input of l00 ng DNA at fluid velocity of 1.373 mm/s. The results show that simulations significantly reduce the time for parameter adjustment in experiments. In addition, this platform uses a basic two-layer chip to realize automatic DNA isolation without any other liquid switch value or magnet controller.

In this work, the effects of medium composition and fermentation parameters on the simultaneous production of mycelial biomass and exopolymer by medicinal mushroom Hericium erinaceus CZ-2 were investigated in shake flask cultures using one-factor-at-a-time method and orthogonal array design. Results showed that the most suitable carbon, nitrogen, mineral sources, and cofactors for the mycelial biomass and exopolymer production were: corn flour combined with 1 % glucose, yeast extract, KH2PO4 and corn steep liquor. The intuitive analysis of orthogonal array design results indicated that the effects of nutritional requirement on the mycelial growth of Hericium erinaceus CZ-2 were in regular sequence of corn flour combined with 1 % glucose > yeast extract > corn steep liquor > KH2PO4, and those on exopolymer production were in the order of corn flour combined with glucose > KH2PO4 > yeast extract > corn steep liquor. The maximal yield of mycelial biomass (16.07 g/L) was obtained when the composition of the culture medium was (in g/L): corn flour 30, glucose 10, yeast extract 3, KH2PO4 1, CaCO3 0.5, and 15 mL/L of corn steep liquor; while the maximal exopolymer yield (1.314 g/L) was achieved when the composition of medium was (in g/L): corn flour 30, glucose 10, yeast extract 5, KH2PO4 3, CaCO3 0.5, and 15 mL/L of corn steep liquor. In the 15-litre scale-up fermentation, the maximum mycelial biomass yield of 20.50 g/L was achieved using the optimized medium.

In order to improve the drilling performance of polycrystalline diamond compact bit and prolong its service life during drilling in coal rock under the action of wind cycle, the theoretical calculation model of polycrystalline diamond compact bit cutting teeth temperature was derived based on the theory of tribology and heat transfer. The theoretical temperature field of polycrystalline diamond compact bit-cutting teeth was analyzed. Using the joint simulation of EDEM–FLUENT, the temperature variation law of polycrystalline diamond compact bit cutting teeth under the thermo-fluid–solid coupling was analyzed to verify the validity of the theoretical calculation model of polycrystalline diamond compact bit cutting teeth temperature. By building a rotary drilling test platform and conducting drilling experiments on polycrystalline diamond compact bit under different drilling parameters respectively, the correctness of the theoretical model and the simulation data were verified. In addition, a response surface analysis model was established to study the influence of different drilling parameters on the polycrystalline diamond compact bit cutting teeth temperature during drilling in coal rock. The analysis results show that the influence degree of various drilling parameters on the polycrystalline diamond compact bit cutting teeth temperature from large to small is drilling pressure, drilling speed, coal rock properties, and wind speed. Compared with the working condition without wind cycle, the drilling efficiency of polycrystalline diamond compact bit can be increased by 14.38% and the temperature is reduced by 8% when it drills in coal. The drilling efficiency of polycrystalline diamond compact bit can be increased by 17.79% and the temperature is reduced by 10.5% when it drills in coal gangue. [ABSTRACT FROM AUTHOR]