The inclusion of conductive carbon materials into lithium-ion batteries (LIBs) is essential for constructing an electrical network of electrodes. Considering the demand for cells in electric vehicles (e.g., higher energy density and lower cell cost), the replacement of the currently used carbon black with carbon nanotubes (CNTs) seems inevitable. This review discusses how CNTs can contribute to the development of advanced LIBs for EVs. First, the reason for choosing CNTs as a conducting agent for the cathode is discussed in terms of energy density. Second, the reinforcing effect of CNTs on the anode is described with respect to the choice of silicon as the active material. Third, the development of water-based cathode fabrication as well as dry electrode fabrication with aid of CNTs is discussed. Fourth, three technical hurdles, that is, the price, dispersion issue, and entrapped metal impurities, for widespread use of CNTs in LIBs are discussed.

Recently, research on prediction algorithms using deep learning has been actively conducted. In addition, algorithmic trading (auto-trading) based on predictive power of artificial intelligence is also becoming one of the main investment methods in stock trading field, building its own history. Since the possibility of human error is blocked at source and traded mechanically according to the conditions, it is likely to be more profitable than humans in the long run. In particular, for the virtual currency market at least for now, unlike stocks, it is not possible to evaluate the intrinsic value of each cryptocurrencies. So it is far effective to approach them with technical analysis and cryptocurrency market might be the field that the performance of algorithmic trading can be maximized. Currently, the most commonly used artificial intelligence method for financial time series data analysis and forecasting is Long short-term memory(LSTM). However, even t4he LSTM also has deficiencies which constrain its widespread use. Therefore, many improvements are needed in the design of forecasting and investment algorithms in order to increase its utilization in actual investment situations. Meanwhile, Prophet, an artificial intelligence algorithm developed by Facebook (META) in 2017, is used to predict stock and cryptocurrency prices with high prediction accuracy. In particular, it is evaluated that Prophet predicts the price of virtual currencies better than that of stocks. In this study, we aim to show Prophet's virtual currency price prediction accuracy is higher than existing deep learning-based time series prediction method. In addition, we execute mock investment with Prophet predicted value. Evaluating the final value at the end of the investment, most of tested coins exceeded the initial investment recording a positive profit. In future research, we continue to test other coins to determine whether there is a significant difference in the predictive power by coin and therefore can establish investment strategies.

A 9-year-old, intact female, Pungsan dog was referred for purulent vaginal discharge and depression. Abdominal radiographs revealed gas-filled and soft tissue opacity tubular structures in the mid to caudal abdomen. On ultrasonography, fluid-filled uterus and cervix accompanied by hyperechoic foci and reverberation artifacts were identified. Multiple hyperechoic foci were found within the uterine wall, indicating gas content. Escherichia coli was isolated from aerobic and anaerobic bacterial cultures. Radiological differential diagnosis of tubular shaped, dilated gas-filled structure, and gas in the wall of the structure should consider emphysematous pyometra with endometrial pneumatosis in intact female dogs with vulvar discharge.

Soft magnetic powder materials are used throughout industries such as motors and power converters. When manufacturing Fe-based soft magnetic composites, the size and shape of the soft magnetic powder and the microstructure in the powder are closely related to the magnetic properties. In this study, Fe-Si-Al-P alloy powders were manufactured using various manufacturing process parameter sets, and the process parameters of the vacuum induction melt gas atomization process were set as melt temperature, atomization gas pressure, and gas flow rate. Process variable data that records are converted into 6 types of data for each powder recovery section. Process variable data that recorded minute changes were converted into 6 types of data and used as input variables. As output variables, a total of 6 types were designated by measuring the particle size, flowability, apparent density, and sphericity of the manufactured powders according to the process variable conditions. The sensitivity of the input and output variables was analyzed through the Pearson correlation coefficient, and a total of 6 powder characteristics were analyzed by artificial neural network model. The prediction results were compared with the results through linear regression analysis and response surface methodology, respectively.

We complete the survey for finite-source/point-lens (FSPL) giant-source events in 2016–2019 KMTNet microlensing data. The 30 FSPL events show a clear gap in Einstein radius, 9 μas < θE < 26 μas, which is consistent with the gap in Einstein timescales near tE ∼ 0.5 days found by Mr´oz et al. (2017) in an independent sample of point-source/point-lens (PSPL) events. We demonstrate that the two surveys are consistent. We estimate that the 4 events below this gap are due to a power-law distribution of freefloating planet candidates (FFPs) dNFFP/d logM = (0.4 ± 0.2) (M/38 M⊕)−p/star, with 0.9 ≲ p ≲ 1.2. There are substantially more FFPs than known bound planets, implying that the bound planet power-law index γ = 0.6 is likely shaped by the ejection process at least as much as by formation. The mass density per decade of FFPs in the Solar neighborhood is of the same order as that of ‘Oumuamua-like objects. In particular, if we assume that ‘Oumuamua is part of the same process that ejected the FFPs to very wide or unbound orbits, the power-law index is p = 0.89 ± 0.06. If the Solar System’s endowment of Neptune-mass objects in Neptune-like orbits is typical, which is consistent with the results of Poleski et al. (2021), then these could account for a substantial fraction of the FFPs in the Neptune-mass range.

We present the analysis of a planetary microlensing event OGLE-2019-BLG-0362 with a shortduration anomaly (∼0.4 days) near the peak of the light curve, which is caused by the resonant caustic. The event has a severe degeneracy with Δχ2 = 0.9 between the close and the wide binary lens models both with planet-host mass ratio q ≃ 0.007. We measure the angular Einstein radius but not the microlens parallax, and thus we perform a Bayesian analysis to estimate the physical parameters of the lens. We find that the OGLE-2019-BLG-0362L system is a super-Jovian-mass planet Mp = 3.26+0.83 −0.58 MJ orbiting an M dwarf Mh = 0.42+0.34 −0.23 M⊙ at a distance DL = 5.83+1.04 −1.55 kpc. The projected star-planet separation is a⊥ = 2.18+0.58 −0.72 AU, which indicates that the planet lies beyond the snow line of the host star.

The purpose of this study was to examine the extent to which influencing factors impact the transference willingness of farmers. The research method is based on the survey data on transference willingness, and the binary logistic model was used to analyze the influencing factors. The results show that the educational level, annual household income, farm labor, and farmers' understanding of land transfer policy notably influence land transfer. Based on the results, suggestions include improving farmers' social security system, improving a sound market for rural land transfer, and deepening the promotion and explanation of land-related policies.