DRNN-based shift decision for automatic transmission

In research on intelligent shift for automatic transmission, the neural network selected has no feedback and lacks an associative memory function. Thus, its adaptability needs to be improved. To achieve this, an automatic shift strategy based on a deep recurrent neural network (DRNN) is proposed. First, a neural network framework was designed in combination with an eight-speed gearbox that matches a particular type of vehicle. Then, the working principle of the DRNN was applied to the shifting process of an automatic gearbox, and the implementation model of the shift logic was established in MATLAB/Stateflow. A data sample obtained from the model was used to train the DRNN. Training and evaluation of the DRNN were accomplished in Python. Finally, a simulation comparison of the DRNN with a back-propagation (BP) neural network proved that after the epochs have been increased, the DRNN has higher precision and adaptation than a BP neural network. This research provides a theoretical basis and technical support for intelligent control of automatic transmission.https://doi.org/10.1177/168781402097529

Study on overburden damage and prevention of runoff disaster in multiseam mining of gully region

Multi-seam mining in gully region has resulted in serious and complex chain disasters, including fissure development, mountain landslides, river blockage, and intensified water inflow. To prevent and control landslides and water inrush disasters, it is crucial to explore the characteristics and laws of overlying strata failure under the coupling effect of gully terrain and repeated mining in coal seams. This study focuses on the mining of multiseam in the gully terrain of Xiqu Coal Mine. The comprehensive analysis method, integrating surface exploration, InSAR dynamic observation, rainfall-runoff analysis, and numerical simulation, is used to analyze the entire process of spatial expansion of overlying strata failure and surface subsidence caused by downward mining of multiseam in the gully region. The results reveal that after the critical mining of the lower coal seam in the gully region, the lower strata beneath the key stratum in interlayered formations are prone to develop cutting failure and vertical fissure, with tensile cracking being the dominant mode of failure. The proportion of shear fractures in the overburden above the key stratum increases significantly, and the gully slope is prone to shear slip under the effects of mining subsidence and gravity. The connection phenomenon between the downward fractures of the slope and the upward fractures of the overburden can even occur. In addition, if the accumulation formed by mountain landslides due to repeated mining blocks the river channel and forms a barrier lake during the flood season, there is a risk of underground water inflow. To prevent such disasters, high-precision terrain synthesized by UAV tilt photogrammetry is used to simulate the rainfall inundation range and time percentage of different durations in Fanshigou watershed during the “100-year return period” rainstorm in Shanxi Province. The research proposes a comprehensive prevention and control method of surface runoff water disaster based on fissure development and surface inundation range, which provides support for gully water disaster prevention and risk assessment in Fanshigou small watershed. This study can serve as a useful reference for the prevention and control of surface geological disasters and the protection of water resources under the condition of multiseam mining in gully regions

The Montreal cognitive assessment: normative data from a large, population-based sample of Chinese healthy adults and validation for detecting vascular cognitive impairment

BackgroundThe Montreal Cognitive Assessment (MoCA) is a valuable tool for detecting cognitive impairment, widely used in many countries. However, there is still a lack of large sample normative data and whose cut-off values for detecting cognitive impairment is considerable controversy.MethodsThe assessment conducted in this study utilizes the MoCA scale, specifically employing the Mandarin-8.1 version. This study recruited a total of 3,097 healthy adults aged over 20 years. We performed multiple linear regression analysis, incorporating age, gender, and education level as predictor variables, to examine their associations with the MoCA total score and subdomain scores. Subsequently, we established normative values stratified by age and education level. Finally, we included 242 patients with vascular cognitive impairment (VCI) and 137 controls with normal cognition, and determined the optimal cut-off value of VCI through ROC curves.ResultsThe participants in this study exhibit a balanced gender distribution, with an average age of 54.46 years (SD = 14.38) and an average education period of 9.49 years (SD = 4.61). The study population demonstrates an average MoCA score of 23.25 points (SD = 4.82). The multiple linear regression analysis indicates that MoCA total score is influenced by age and education level, collectively accounting for 46.8% of the total variance. Higher age and lower education level are correlated with lower MoCA total scores. A score of 22 is the optimal cut-off value for diagnosing vascular cognitive impairment (VCI).ConclusionThis study offered normative MoCA values specific to the Chinese adults. Furthermore, this study indicated that a score of 26 may not represent the most optimal cut-off value for VCI. And for detecting VCI, a score of 22 may be a better cut-off value

Energy dependence of J/ψJ/\psi production in pp collisions with the PACIAE model

In this work we investigate the $J/\psi$ production in proton-proton collisions at the center-of-mass energy ($\sqrt{s}$) equal to 2.76, 5.02, 7, 8 and 13 TeV with a parton and hadron cascade model PACIAE 2.2a. It is based on PYTHIA but extended considering the partonic and hadronic rescatterings before and after hadronization, respectively. In the PYTHIA sector the $J/\psi$ production quantum chromodynamics processes are selected specially and a bias factor is proposed correspondingly. The calculated total cross sections, the differential cross sections as a function of the transverse momentum and the rapidity of $J/\psi$ in the forward rapidity region reproduce the corresponding experimental measurements reasonably well. In the mid-rapidity region, the double differential cross sections at $\sqrt{s}=$ 5.02, 7 and 13 TeV are also in a good agreement with the experimental data. Moreover, we predict the double differential cross section as well as the total cross section of $J/\psi$ at $\sqrt{s}=$ 8 TeV, which could be validated when the experimental data is available.Comment: 6 pages, 8 figures, 3 table

Machine learning techniques based on 18F-FDG PET radiomics features of temporal regions for the classification of temporal lobe epilepsy patients from healthy controls

BackgroundThis study aimed to investigate the clinical application of 18F-FDG PET radiomics features for temporal lobe epilepsy and to create PET radiomics-based machine learning models for differentiating temporal lobe epilepsy (TLE) patients from healthy controls.MethodsA total of 347 subjects who underwent 18F-FDG PET scans from March 2014 to January 2020 (234 TLE patients: 25.50 ± 8.89 years, 141 male patients and 93 female patients; and 113 controls: 27.59 ± 6.94 years, 48 male individuals and 65 female individuals) were allocated to the training (n = 248) and test (n = 99) sets. All 3D PET images were registered to the Montreal Neurological Institute template. PyRadiomics was used to extract radiomics features from the temporal regions segmented according to the Automated Anatomical Labeling (AAL) atlas. The least absolute shrinkage and selection operator (LASSO) and Boruta algorithms were applied to select the radiomics features significantly associated with TLE. Eleven machine-learning algorithms were used to establish models and to select the best model in the training set.ResultsThe final radiomics features (n = 7) used for model training were selected through the combinations of the LASSO and the Boruta algorithms with cross-validation. All data were randomly divided into a training set (n = 248) and a testing set (n = 99). Among 11 machine-learning algorithms, the logistic regression (AUC 0.984, F1-Score 0.959) model performed the best in the training set. Then, we deployed the corresponding online website version (https://wane199.shinyapps.io/TLE_Classification/), showing the details of the LR model for convenience. The AUCs of the tuned logistic regression model in the training and test sets were 0.981 and 0.957, respectively. Furthermore, the calibration curves demonstrated satisfactory alignment (visually assessed) for identifying the TLE patients.ConclusionThe radiomics model from temporal regions can be a potential method for distinguishing TLE. Machine learning-based diagnosis of TLE from preoperative FDG PET images could serve as a useful preoperative diagnostic tool

Advances and trends in microbial production of polyhydroxyalkanoates and their building blocks

With the rapid development of synthetic biology, a variety of biopolymers can be obtained by recombinant microorganisms. Polyhydroxyalkanoates (PHA) is one of the most popular one with promising material properties, such as biodegradability and biocompatibility against the petrol-based plastics. This study reviews the recent studies focusing on the microbial synthesis of PHA, including chassis engineering, pathways engineering for various substrates utilization and PHA monomer synthesis, and PHA synthase modification. In particular, advances in metabolic engineering of dominant workhorses, for example Halomonas, Ralstonia eutropha, Escherichia coli and Pseudomonas, with outstanding PHA accumulation capability, were summarized and discussed, providing a full landscape of diverse PHA biosynthesis. Meanwhile, we also introduced the recent efforts focusing on structural analysis and mutagenesis of PHA synthase, which significantly determines the polymerization activity of varied monomer structures and PHA molecular weight. Besides, perspectives and solutions were thus proposed for achieving scale-up PHA of low cost with customized material property in the coming future