Search

As the application of visual-spatial skills in academic disciplines, vocational fields and daily life is becoming more and more prominent, it is of great theoretical and practical significance how to make use of big data and artificial intelligence technology to conduct research on the relationship between visual-spatial skills and students' grades. This paper explores and analyses from the perspective of artificial intelligence, combining students' visual-spatial skills and students' specific attribute characteristics to construct an expert system, which defines the prediction of academic performance as a classification problem corresponding to the five categories of excellent, good, moderate, passing, and weak, respectively, and based on which a deep neural network-based classification prediction model for students' performance is designed. The experimental results show that visual-spatial skills plays an important role in the professional learning of science and engineering students, while the classification model designed in this paper has high accuracy in the grade prediction process. This paper not only helps to fill the gaps in the current research field, but is also expected to provide scientific basis for educational practice and promote the development of the education field in a more intelligent and personalized direction.

As the world’s first large grain crop, wheat in its mechanized harvesting process faces a serious problem, namely, when the combine harvester operating parameters are not set reasonably, it leads to increased losses of wheat kernels to an extent exceeding the prescribed standard, of which the loss of scavenging accounts for a large proportion. Excessive grain harvest loss will not only reduce the quality of the wheat harvest but also adversely affect its yield. Real-time monitoring of losses in the harvesting process is key to the dynamic adjustment of operating parameters to decrease machine harvesting losses. This article proposes a grain cleaning loss monitoring device for combine harvesters suitable for wheat crops. It aims to measure the loss of grain cleaning in the process of wheat harvesting in real time and adjust the operating parameters of the harvester timeously by feeding back the data to the driver in real time, so as to decrease the loss of wheat grains in the process of harvesting and achieve the purpose of reducing the loss of harvest. When the device was tested in the field, the wheat variety was Bainong 4199, the yield per mu was 625.83 kg (one mu is 1/15 of a hectare, or approximately 666.67 m2), the mass of grain was 43.21 g, and the water content was 14.2%. After the test, the monitoring error of the loss monitoring device was within 8%, and the average error rate was 6.69%. The test proves that the monitoring device achieves the expected design effect and meets design requirements. The results of this paper are of significance to the intelligent control system of wheat combine harvesters and provide a reference for research into grain cleaning loss monitoring devices for wheat combine harvesters.

Objective: To compare and analyze the curative effect of fracture upper limb fixator and traditional splint in emergency treatment of upper limb fracture. Methods: This is a prospective study. A total of 80 patients with upper limb fractures admitted to the Emergency Department of Sichuan Province Orthopedic Hospital from December 2021 to August 2022 were prospectively selected as subjects. They were divided into two groups according to the random number table method: Patients in the control group were treated with traditional splint, while those in the observation group were treated with medical adjustable upper limb fixator. The clinical efficacy, treatment time, pain, fitness, probability of secondary injury and complications were compared between the two groups. Results: After treatment, the excellent and good rate in the observation group (75.00%) was higher than that in the control group(52.50%). There was no statistically significant difference in the treatment time between the two groups. There was no significant difference in the probability of secondary injury between the two group. Statistically significant difference was observed in the comparison of pain conditions between the two groups. The total fitness rate of the observation group (97.50%) was higher than that of the control group (75.00%). The total incidence of complications in the observation group (2.50%) was lower than that in the control group (20.00%), with a statistically significant difference. Conclusion: Fracture upper limb fixator shows various benefits in first aid of upper limb fractures, such as improving the clinical efficacy of patients with upper limb fractures, ameliorating pain, improving fitness effect, and reducing the probability of complications. [ABSTRACT FROM AUTHOR]

The paper mainly studied the temperature rise characteristics of under-race lubricated high-speed angular contact ball bearings under operational conditions from the perspective of dynamics. The steady-state calculation model of the bearing was established using a thermal network method in consideration of the influence factor of friction power consumption in bearing components based on a dynamic model. Following this, the steady-state change characteristics of the bearing were obtained by solving a thermal balance equation. Through this process, the influence laws of bearing rotation speed, oil supply, and environmental temperature on the friction temperature rise of the bearing were analyzed. Finally, the finite element analysis software ANSYS was employed to provide comparative verification. The results showed that the bearing temperature nonlinearly increased with the increase in inner ring rotation speed, and when it approached a certain critical value, the outer ring temperature exceeded the inner ring temperature. It had an obvious effect on controlling the temperature rise of the bearing inner ring by way of increasing the quantity and reducing the temperature of the lubricating oil supply. Comparative verification showed that the speed–temperature variation tendency from the dynamics-based thermal analysis well agrees with that of the finite element analysis.

Previous studies of GNSS-IR mainly focused on the legacy L1C signal; the potential of modernized signals (L2C and L5Q) has not yet been fully exploited. In this paper, we applied the Multichannel Singular Spectrum Analysis (M-SSA) method to extract common interference patterns from different frequencies simultaneously. The three-frequency (L1C, L2C, and L5Q) signal-to-noise ratio (SNR) measurements from a total of 840 satellite rising and setting arcs, occurring between day of year 250 to 279 in year 2020 and 2021, were used. By comparing GNSS-IR reflector heights obtained from the original and M-SSA-reconstructed SNR time series, we found that M-SSA significantly improves the between-frequency consistency, as shown by an increase in the values of R-squared of linear regression from (0.69, 0.67, 0.89) to (0.95, 0.96, 0.98), and a decrease in RMSE from (0.10 m, 0.10 m, 0.06 m) to (0.04 m, 0.04 m, 0.02 m) for S1C-S2C, S1C-S5Q, and S2C-S5Q pair, respectively. Our results validate (1) the effectiveness of the M-SSA method in extracting common interference patterns from multi-frequency SNR time series, and (2) the superiority of modernized civil signals L2C and L5Q over the legacy L1C signal in GNSS-IR studies. We also emphasize the important role that the L5 signal will play in future GNSS-IR research because of its compatibility and interoperability among different satellite navigation systems.

Although altimeters have been widely used to monitor the spatiotemporal variation of sea-ice thickness, they are unable to separate sea-ice freeboard from snow depth. We use a floating GPS deployed on sea ice to derive the freeboard and snow depth near China's Zhongshan Station. Our results show that the standalone floating GPS can monitor freeboard with a precision of 4.2 cm. If time-varying dynamic ocean topography provided by, for example, a bottom pressure gauge is available, then the precision of GPS-derived freeboard can improve to 1.3 cm. The daily snow depth inverted by GPS interferometric reflectometry captures three precipitation events during our experiment, showing that the floating GPS can monitor the variation in snow depth and observe the freeboard variation at the same time. By studying the relationship between freeboard, snow depth and sea-ice thickness, we find that sea-ice thickness will be greatly underestimated by the negative single-point freeboard under the assumption of hydrostatic equilibrium. As a supplement to existing technologies, the GPS-derived freeboard and snow depth can be used both to evaluate the altimeter observations directly and to improve our understanding of the real-time variation of freeboard and snow depth in the experimental area.

The weighted mean temperature ( Tm) is a critical parameter for precipitable water vapor (PWV) retrieval in global navigation satellite system (GNSS) meteorology. Reanalysis data are an important data source for Tm calculation and Tm empirical model establishment. This study uses radiosonde data to evaluate the accuracy and the spatiotemporal variation of Tm that is derived from four reanalysis data, namely, the release of the fifth-generation accurate global atmospheric reanalysis (ERA5), the modern-era retrospective analysis for research and applications version 2 (MERRA-2), the NCEP/DOE, and the NCEP/NCAR, from 2005 to 2019 in Greenland, due to the paucity of research on the performance of Tm in the polar region that is derived from reanalysis data, particularly on a long temporal scale. The results were as follows: (1) The 15-year mean bias errors (MBEs) and root mean square errors (RMSEs) of Tm that were obtained from the four reanalysis data are 0.267 and 0.691 K for the ERA5, −0.247 and 0.962 K for the MERRA-2, 0.192 and 1.148 K for the NCEP/DOE, and −0.069 and 1.37 K for the NCEP/NCAR. The Tm that was derived from the ERA5 (ERA5 Tm) has the highest accuracy, followed by the MERRA-2 Tm, the NCEP/DOE Tm, and the NCEP/NCAR Tm. (2) In the inter-annual stability of the Tm precision compared with the radiosonde data, the results of the ERA5 are the most stable, followed by the NCEP/DOE Tm, the NCEP/NCAR Tm, and the MERRA-2 Tm. The ERA5 Tm have improved from 2005 to 2019. (3) The Tm accuracy that was computed by the four reanalysis data exhibits significant seasonal variation characteristics in Greenland, as follows: the summer and the autumn accuracy is higher than that in the winter and the spring, which may be related to the variation of the surface temperature (Ts) accuracy. (4) The Tm that was estimated from the four reanalysis data exhibits a consistent spatial distribution, as follows: the Tm is smaller in the middle region of Greenland and is greater at the island’s edge. The comparative study of Tm that is obtained from the four reanalysis data can serve as a reference for future research on Tm model development and water vapor retrieval in polar regions by utilizing reanalysis data.

Bronchopulmonary dysplasia (BPD) is one of the most common complications in premature infants. This disease is caused by long-time use of supplemental oxygen, which seriously affects the lung function of the child and imposes a heavy burden on the family and society. This research aims to adopt the method of ensemble learning in machine learning, combining the Boruta algorithm and the random forest algorithm to determine the predictors of premature infants with BPD and establish a predictive model to help clinicians to conduct an optimal treatment plan. Data were collected from clinical records of 996 premature infants treated in the neonatology department of Liuzhou Maternal and Child Health Hospital in Western China. In this study, premature infants with congenital anomaly, premature infants who died, and premature infants with incomplete data before the diagnosis of BPD were excluded from the data set. After exclusion, we included 648 premature infants in the study. The Boruta algorithm and 10-fold cross-validation were used for feature selection in this study. Six variables were finally selected from the 26 variables, and the random forest model was established. The area under the curve (AUC) of the model was as high as 0.929 with excellent predictive performance. The use of machine learning methods can help clinicians predict the disease so as to formulate the best treatment plan.

The global positioning system (GPS) can provide the daily coordinate time series to help geodesy and geophysical studies. However, due to logistics and malfunctioning, missing values are often “seen” in GPS time series, especially in polar regions. Acquiring a consistent and complete time series is the prerequisite for accurate and reliable statical analysis. Previous imputation studies focused on the temporal relationship of time series, and only a few studies used spatial relationships and/or were based on machine learning methods. In this study, we impute 20 Greenland GPS time series using missForest, which is a new machine learning method for data imputation. The imputation performance of missForest and that of four traditional methods are assessed, and the methods’ impacts on principal component analysis (PCA) are investigated. Results show that missForest can impute more than a 30-day gap, and its imputed time series has the least influence on PCA. When the gap size is 30 days, the mean absolute value of the imputed and true values for missForest is 2.71 mm. The normalized root mean squared error is 0.065, and the distance of the first principal component is 0.013. missForest outperforms the other compared methods. missForest can effectively restore the information of GPS time series and improve the results of related statistical processes, such as PCA analysis.

The common mode error (CME) and optimal noise model are the two most important factors affecting the accuracy of time series in regional Global Navigation Satellite System (GNSS) networks. Removing the CME and selecting the optimal noise model can effectively improve the accuracy of GNSS coordinate time series. The CME, a major source of error, is related to the spatiotemporal distribution; hence, its detrimental effects on time series can be effectively reduced through spatial filtering. Independent component analysis (ICA) is used to filter the time series recorded by 79 GPS stations in Antarctica from 2010 to 2018. After removing stations exhibiting strong local effects using their spatial responses, the filtering results of residual time series derived from principal component analysis (PCA) and ICA are compared and analyzed. The Akaike information criterion (AIC) is then used to determine the optimal noise model of the GPS time series before and after ICA/PCA filtering. The results show that ICA is superior to PCA regarding both the filter results and the consistency of the optimal noise model. In terms of the filtering results, ICA can extract multisource error signals. After ICA filtering, the root mean square (RMS) values of the residual time series are reduced by 14.45%, 8.97%, and 13.27% in the east (E), north (N), and vertical (U) components, respectively, and the associated speed uncertainties are reduced by 13.50%, 8.06% and 11.82%, respectively. Furthermore, different GNSS time series in Antarctica have different optimal noise models with different noise characteristics in different components. The main noise models are the white noise plus flicker noise (WN+FN) and white noise plus power law noise (WN+PN) models. Additionally, the spectrum index of most PN is close to that of FN. Finally, there are more stations with consistent optimal noise models after ICA filtering than there are after PCA filtering.

Objective: To compare and analyze the curative effect of fracture upper limb fixator and traditional splint in emergency treatment of upper limb fracture., Methods: This is a prospective study. A total of 80 patients with upper limb fractures admitted to the Emergency Department of Sichuan Province Orthopedic Hospital from December 2021 to August 2022 were prospectively selected as subjects. They were divided into two groups according to the random number table method: Patients in the control group were treated with traditional splint, while those in the observation group were treated with medical adjustable upper limb fixator. The clinical efficacy, treatment time, pain, fitness, probability of secondary injury and complications were compared between the two groups., Results: After treatment, the excellent and good rate in the observation group (75.00%) was higher than that in the control group(52.50%). There was no statistically significant difference in the treatment time between the two groups. There was no significant difference in the probability of secondary injury between the two group. Statistically significant difference was observed in the comparison of pain conditions between the two groups. The total fitness rate of the observation group (97.50%) was higher than that of the control group (75.00%). The total incidence of complications in the observation group (2.50%) was lower than that in the control group (20.00%), with a statistically significant difference., Conclusion: Fracture upper limb fixator shows various benefits in first aid of upper limb fractures, such as improving the clinical efficacy of patients with upper limb fractures, ameliorating pain, improving fitness effect, and reducing the probability of complications., Competing Interests: Conflicts of interest: None., (Copyright: © Pakistan Journal of Medical Sciences.)