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Based on X ray diffraction and Fourier transform infrared spectroscopy, the microcrystalline structure characteristics and evolution patterns of a total of 6 coal samples from the eastern Junggar, southern Junggar, Tuha, and Kubai coalfields in Xinjiang were investigated. The peak shape of 002 and 100 peaks in low-rank coal is gentle, the peak 002 becomes sharper and the “symmetry” becomes better with the increase of coal rank, and the peak height of the peak 100 gradually increases but the peak width gradually shrinks. The height of the peak 100 gradually increases but the width gradually narrows; with the increase of coal sample metamorphism, the stacking height, elongation and quantity of aromatic laminates increase gradually, the spacing of aromatic laminates decreases on the whole, and the condensation degree of aromatic structures in coal increases gradually; the peak shape of aromatic compounds and oxygen-containing functional groups is sharp, and the peak shape of aliphatic hydrocarbons and hydroxyl functional groups is gentle, which gradually widens and slows down with the rise of coal rank; with the increase of coal rank, the aromaticity, fat chain length, branched chain degree and maturity gradually decrease, while the polycondensation degree and hydrocarbon (oil) generation ability gradually increase. The evolution process of coal is characterized by the shedding of oxygen-containing functional groups, the enrichment of aliphatic hydrocarbons, and the condensation of some aromatics.

Introduction: Stroke is the second leading cause of death globally and a primary factor contributing to disability. Unilateral limb motor impairment caused by stroke is the most common scenario. The bilateral movement pattern plays a crucial role in assisting stroke survivors on the affected side to relearn lost skills. However, motion compensation often lead to decreased coordination between the limbs on both sides. Furthermore, muscle fatigue resulting from imbalanced force exertion on both sides of the limbs can also impact the rehabilitation outcomes.Method: In this study, an assessment method based on muscle synergy indicators was proposed to objectively quantify the impact of motion compensation issues on rehabilitation outcomes. Muscle synergy describes the body’s neuromuscular control mechanism, representing the coordinated activation of multiple muscles during movement. 8 post-stroke hemiplegia patients and 8 healthy subjects participated in this study. During hand-cycling tasks with different resistance levels, surface electromyography signals were synchronously collected from these participants before and after fatigue. Additionally, a simulated compensation experiment was set up for healthy participants to mimic various hemiparetic states observed in patients.Results and discussion: Synergy symmetry and synergy fusion were chosen as potential indicators for assessing motion compensation. The experimental results indicate significant differences in synergy symmetry and fusion levels between the healthy control group and the patient group (p ≤ 0.05), as well as between the healthy control group and the compensation group. Moreover, the analysis across different resistance levels showed no significant variations in the assessed indicators (p > 0.05), suggesting the utility of synergy symmetry and fusion indicators for the quantitative evaluation of compensation behaviors. Although muscle fatigue did not significantly alter the symmetry and fusion levels of bilateral synergies (p > 0.05), it did reduce the synergy repeatability across adjacent movement cycles, compromising movement stability and hindering patient recovery. Based on synergy symmetry and fusion indicators, the degree of bilateral motion compensation in patients can be quantitatively assessed, providing personalized recommendations for rehabilitation training and enhancing its effectiveness.

We present the discovery of three pulsars in the globular cluster (GC) M15 (NGC 7078) by the Five-hundred-meter Aperture Spherical Radio Telescope. PSR J2129+1210J (M15J) is a millisecond pulsar with a spin period of 11.84 ms and a dispersion measure of 66.68 pc cm ^−3 . Both PSR J2129+1210K and L (M15K and L) are long-period pulsars with spin periods of 1928 ms and 3961 ms, respectively. M15L is the GC pulsar with the longest spin period known. We update the timing solutions of M15A to M15H. As predicted by A. Ridolfi et al., the flux density of M15C keeps decreasing, and the latest detection in our data set was on 2022 December 20. We have also detected M15I’s signal for the first time since its discovery. Current timing suggests that it is an isolated pulsar.

We present the discovery of eight isolated millisecond pulsars in globular cluster (GC) NGC 6517 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The spin periods of those pulsars (namely PSR J1801–0857K to R, or NGC 6517K to R) are all shorter than 10 ms. With these discoveries, NGC 6517 is currently the GC with the most known pulsars in the FAST sky. The largest difference in dispersion measure of the pulsars in NGC 6517 is 11.2 cm ^−3 pc, the second among all GCs. The fraction of isolated pulsars in this GC (16 of 17, 94%) is consistent with previous studies indicating an overabundance of isolated pulsars in the densest GCs, especially in those undergoing cluster core collapse. Considering the FAST GC pulsar discoveries, we modeled the GC pulsar population using the empirical Bayesian method described by Turk and Lorimer with the recent counts. Using this approach, we find that the expected number of potential pulsars in GCs seems to be correlated with the central escape velocity; hence, the GCs Liller 1, NGC 6441, M54 (NGC 6715), and ω -Cen (NGC 5139) are expected to host the largest numbers of pulsars.

We present the phase-connected timing solutions of all five pulsars in globular cluster M3 (NGC 5272), namely PSRs M3A to F (PSRs J1342+2822A to F), with the exception of PSR M3C, from FAST archival data. In these timing solutions, those of PSRs M3E and F are obtained for the first time. We find that PSRs M3E and F have low-mass companions and are in circular orbits with periods of 7.1 and 3.0 days, respectively. For PSR M3C, we have not detected its signal in all 41 observations. We found no X-ray counterparts for these pulsars in archival Chandra images in the band of 0.2–20 keV. From the autocorrelation function analysis of M3A and M3B’s dynamic spectra, the scintillation timescale ranges from 7.0 ± 0.3 to 60.0 ± 0.6 minutes, and the scintillation bandwidth ranges from 4.6 ± 0.2 to 57.1 ± 1.1 MHz. The measured scintillation bandwidths from the dynamic spectra indicate strong scintillation, and the scattering medium is anisotropic. From the secondary spectra, we captured a scintillation arc only for PSR M3B with a curvature of 649 ± 23 m ^−1 mHz ^−2 .

Abstract Accurate estimates of aerosol refractive index (RI) are critical for modeling aerosol‐radiation interaction, yet this information is limited for ambient organic aerosols, leading to large uncertainties in estimating aerosol radiative effects. We present a semi‐empirical model that predicts the real RI n of organic aerosol material from its widely measured oxygen‐to‐carbon (O:C) and hydrogen‐to‐carbon (H:C) elemental ratios. The model was based on the theoretical framework of Lorenz‐Lorentz equation and trained with n‐values at 589 nm (n589nm) of 160 pure compounds. The predictions can be expanded to predict n‐values in a wide spectrum between 300 and 1,200 nm. The model was validated with newly measured and literature datasets of n‐values for laboratory secondary organic aerosol (SOA) materials. Uncertainties of n589nm predictions for all SOA samples are within ±5%. The model suggests that n589nm‐values of organic aerosols may vary within a relatively small range for typical O:C and H:C values observed in the atmosphere.

Remote sensing images are very vulnerable to cloud interference during the imaging process. Cloud occlusion, especially thick cloud occlusion, significantly reduces the imaging quality of remote sensing images, which in turn affects a variety of subsequent tasks using the remote sensing images. The remote sensing images miss ground information due to thick cloud occlusion. The thick cloud removal method based on a temporality global–local structure is initially suggested as a solution to this problem. This method includes two stages: the global multi-temporal feature fusion (GMFF) stage and the local single-temporal information restoration (LSIR) stage. It adopts the fusion feature of global multi-temporal to restore the thick cloud occlusion information of local single temporal images. Then, the featured global–local structure is created in both two stages, fusing the global feature capture ability of Transformer with the local feature extraction ability of CNN, with the goal of effectively retaining the detailed information of the remote sensing images. Finally, the local feature extraction (LFE) module and global–local feature extraction (GLFE) module is designed according to the global–local characteristics, and the different module details are designed in this two stages. Experimental results indicate that the proposed method performs significantly better than the compared methods in the established data set for the task of multi-temporal thick cloud removal. In the four scenes, when compared to the best method CMSN, the peak signal-to-noise ratio (PSNR) index improved by 2.675, 5.2255, and 4.9823 dB in the first, second, and third temporal images, respectively. The average improvement of these three temporal images is 9.65%. In the first, second, and third temporal images, the correlation coefficient (CC) index improved by 0.016, 0.0658, and 0.0145, respectively, and the average improvement for the three temporal images is 3.35%. Structural similarity (SSIM) and root mean square (RMSE) are improved 0.33% and 34.29%, respectively. Consequently, in the field of multi-temporal cloud removal, the proposed method enhances the utilization of multi-temporal information and achieves better effectiveness of thick cloud restoration.

Self-supporting run-flat tires (SSRFTs) achieve good zero-pressure driving ability by reinforcing the sidewalls, and the structural shape of sidewall insert rubber (SIR) is critical in influencing the mechanical characteristics of SSRFTs. In this paper, an SSRFT contour model is established by combining the radial tire contour theory and the design elements of SIR. The influence of two design parameters (maximum width L and maximum thickness H) of SIR on the tire stiffness characteristics and the contact characteristics is analyzed in depth, and the accuracy of the model is verified by the tire mechanics bench test. The results show that the radial stiffness of SSRFTs is positively correlated with two design parameters; an increase in L affects the stress concentration at the end of SIR, while a change in H has a more drastic effect on the stress distribution of SIR, leading to a large change in both the location of the deformation of SIR and the maximum equivalent stress; under rated pressure conditions, when L is less than 100 mm, the overlap between SIR and the tread decreases, which in turn makes the contact characteristics of SSRFTs closer to that of a normal tire, and obtains better comfort and abrasion resistance; under zero-pressure conditions, the maximum contact stress of the tread is the smallest when the H is 8 mm, but when H is less than 6 mm, the contact characteristics appear to deteriorate uniformly, and the maximum contact stress continues to rise. The results of the research provide a reference value for the selection of the design parameters for SIR and the optimization of the dynamic performance of SSRFTs.

ObjectiveThis study aimed to investigate the application value of multi-parametric magnetic resonance imaging (MRI) in the diagnosis of iron deposition in the substantia nigra dense zone in Parkinson's disease (PD) and to evaluate the diagnostic value of the correlation among multi-parametric imaging indicators, clinical stage, and disease duration.Materials and methodsThirty-six patients with clinically confirmed PD and 36 healthy controls were enrolled. The disease course was recorded, and PD severity was graded using the Hoehn–Yahr (H–Y) scale. All subjects underwent magnetic sensitivity weighted imaging (SWI), neuromelanin magnetic resonance imaging (NM-MRI), and a T2*mapping sequence. Based on the fusion of the NM-MRI and SWI amplitude maps, phase maps, and T2*MAPPING value maps, NM-MRI was used to delineate the dense zone of the substantia nigra, which was divided into three sub-regions: upper, middle, and lower. In this way, the amplitude, phase, and R2* values of each sub-region and the average value of the sum of the three sub-regions were obtained simultaneously in the SWI amplitude, phase, and T2*MAPPING maps. The multi-parameter imaging indices were compared between the two groups, and the correlation between them and clinical indices was evaluated in the PD group.ResultsThe upper (amplitude, phase value, R2* value), middle, and lower (amplitude) right substantia nigra compact zones were significantly different between the PD and control groups. The upper (phase value, R2* value) and middle (amplitude) areas of the left substantia nigra compact zone were also significantly different between the two groups (all P < 0.05). The mean values (amplitude, phase value, R2* value) of the right substantia nigra densification zone and the mean values (phase value) of the left substantia nigra densification zone were also significantly different (all P < 0.05). Amplitudes in the middle and lower parts of the right substantia nigra dense zone were negatively correlated with the H–Y grade (middle: r = −0.475, P = 0.003; lower: r = −0.331, P = 0.049). Amplitudes of the middle and lower parts of the dense zone of the left substantia nigra were negatively correlated with the H–Y grade (middle: r = −0.342, P = 0.041; lower: r = −0.399, P = 0.016). The average amplitude of the right substantia nigra compact zone was negatively correlated with the H–Y grade (r = −0.367, P = 0.027). The average R2* value of the compact zone of the left substantia nigra was positively correlated with the H–Y grade (r = 0.345, P = 0.040).ConclusionMultiparametric MRI sequence examination has application value in the evaluation of iron deposition in the dense zone of the substantia nigra in PD. Combined with NM-MRI, fusion analysis is beneficial for accurately locating the substantia nigra compact zone and quantitatively analyzing the iron deposition in different sub-regions. Quantitative iron deposition in the middle and lower parts of the substantia nigra dense zone may become an imaging biological indicator for early diagnosis, severity evaluation, and follow-up evaluation of PD and is thus conducive for clinical diagnosis and treatment evaluation.

Thick cloud and cloud shadow often exist in optical remote sensing (RS) images, which limit the application range of RS data. To solve this challenging problem, we propose a multitemporal-based method of cloud removal to reconstruct the large cloud-cover region in RS images. The method can combine the advantages of high accuracy of deep-learning method and low computation cost of traditional method, and reduce the difficulty of establishing multi-temporal dataset to a certain extent. In the proposed method, the complex problem of thick cloud removal is decomposed into two simple processes: coarse stage and refined stage. In the stage of coarse cloud removal, it is found that the cloud-cover images are distributed more regularly in frequency domain than in the space domain. Therefore, the frequency domain residual module is introduced, and the frequency domain attention module is designed to achieve more focused fusion of three-temporal features. In the stage of refined cloud removal, the refined network is designed to retain the effective features of the coarse stage and further refine the reconstruction result based on the image self-correlation. We carried out sufficient experiments to demonstrate the superiority of the proposed method compared with four classical methods, which include WLR, STSCNN, RFRnet and PSTCR. In qualitative experiments, the proposed method has obvious advantages in information accuracy, visual naturalness and semantic rationality. In quantitative experiments, compared with the best of four comparison methods, average values of Peak Signal to Noise Ratio (PSNR) on three-temporal images are improved by 5.1934 dB, 2.7729 dB and 7.2364 dB respectively, and average values of Correlation Coefficient (CC) on three-temporal images are increased by 0.1048, 0.0110 and 0.0966 respectively. Moreover, the proposed method has an obvious advantage in computation cost because it can reconstruct multiple cloud-cover images synchronously.

Aiming at the problem of sparse measurement points due to test conditions in engineering, a smoothing method based on zero-padding in the wavenumber domain is proposed to increase data density. Firstly, the principle of data extension and smoothing is introduced. The core idea of this principle is to extend the discrete data series by zero-padding in the wavenumber domain. The conversion between the spatial and wavenumber domains is achieved using the Discrete Fourier Transform (DFT) and the Inverse Discrete Fourier Transform (IDFT). Then, two sets of two-dimensional discrete random data are extended and smoothed, respectively, and the results verify the effectiveness and feasibility of the algorithm. The method can effectively increase the density of test data in engineering tests, achieve smoothing and extend the application to areas related to data processing.

Abstract Background Endometrial cancer is a common gynecological cancer with annually increasing incidence worldwide. However, the biomarkers that provide prognosis and progression for this disease remain elusive. Results Two eligible human endometrial cancer datasets (GSE17025 and GSE25405) were selected for the study. A total of 520 differentially expressed mRNAs and 30 differentially expressed miRNAs were identified. These mRNAs were mainly enriched in cell cycle, skeletal system development, vasculature development, oocyte maturation, and oocyte meiosis signalling pathways. A total of 160 pairs of differentially expressed miRNAs and mRNAs, including 22 differentially expressed miRNAs and 71 overlapping differentially expressed mRNAs, were validated in endometrial cancer samples using starBase v2.0 project. The prognosis analysis revealed that Cyclin E1 (CCNE1, one of the 82 hub genes, which correlated with hsa-miR-195 and hsa-miR-424) was significantly linked to a worse overall survival in endometrial cancer patients. Conclusions The hub genes and differentially expressed miRNAs identified in this study might be used as prognostic biomarkers for endometrial cancer and molecular targets for its treatment.

A vision-based intelligent robotic grasping system is essential for realizing unmanned operations in industrial manufacturing, and pose estimation plays an import role in this system. In this study, deep learning was used to obtain the 6D pose of an axisymmetric body which was optimal for robotic grasping in industrial stacked scenarios. We propose a method to obtain the 6D pose of an axisymmetric body by detecting the pre-defined keypoints on the side surface. To realize this method and solve other challenges in industrial stacked scenarios, we propose a multitask real-time convolutional neural network (CNN), named Key-Yolact, which involves object detection, instance segmentation, and multiobject 2D keypoint detection. A small CNN as a decision-making subsystem was designed to score multiple predictions of Key-Yolact, and the body with the highest score is considered the best for grasping. Experiments on a self-built stacked dataset showed that Key-Yolact has a practical tradeoff between inference speed and precision. The inference speed of Key-Yolact is higher by 10 FPS, whereas its precision is decreased by only 7% when compared with the classical multitask Keypoint R-CNN. Robotic grasping experiments showed that the proposed design is effective and can be directly applied to industrial scenarios.

Abstract Background Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated. Methods We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed. Results A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC. Conclusions Our study suggests that miR-182 is essential for the biological progression of OC.

Progress in wearable techniques makes the long-term daily electrocardiogram (ECG) monitoring possible. However, the long-term wearable ECGs can be significantly contaminated by various noises, which affect the detection and diagnosis of cardiovascular diseases (CVDs). The situation becomes more serious for wearable ECG screening, where the data are huge, and doctors have no way to visually check the signal quality episode-by-episode. Therefore, automatic and accurate noise rejection for the wearable big-data ECGs is craving. This paper addressed this issue and proposed a noise rejection method for wearable ECGs based on the combination of modified frequency slice wavelet transform (MFSWT) and convolutional neural network (CNN). Wearable ECGs were recorded using the newly developed 12-lead Lenovo smart ECG vest with a sample rate of 500 Hz and a resolution of 16 bit. One thousand 10-s ECG segments were picked up and were manually labeled into three quality types: clinically useful segments with good signal quality (type A), clinically useful segments with poor signal quality (type B), and clinically useless segments (pure noises, type C). Each of the 1,000 10-s ECG segments were transformed into a 2-D time-frequency (T-F) image using the MFSWT, with a pixel size of 200×50. Then, the 2-D grayscale images from MFSWT were fed into a 13-layer CNN model for training the classification models. Results from the standard 5-folder cross-validation showed that the proposed combination method of MFSWT and CNN achieved a highest classification accuracy of 86.3%, which was higher than the comparable methods from continuous wavelet transform (CWT) and artificial neural networks (ANN). The combination of MFSWT and CNN also had a good calculation efficiency. This paper indicated that the combination of MFSWT and CNN is a potential method for automatic identification of noisy segments from wearable ECG recordings.

With the continuous development of economy and society, power demand forecasting has become an important task of the power industry. Accurate power demand forecasting can promote the operation and development of the power supply industry. However, since power consumption is affected by a number of factors, it is difficult to accurately predict the power demand data. With the accumulation of data in the power industry, machine learning technology has shown great potential in power demand forecasting. In this study, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM) are integrated by stacking to build an XLG-LR fusion model to predict power demand. Firstly, preprocessing was carried out on 13 months of electricity and meteorological data. Next, the hyperparameters of each model were adjusted and optimized. Secondly, based on the optimal hyperparameter configuration, a prediction model was built using the training set (70% of the data). Finally, the test set (30% of the data) was used to evaluate the performance of each model. Mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and goodness-of-fit coefficient (R^2) were utilized to analyze each model at different lengths of time, including their seasonal, weekly, and monthly forecast effect. Furthermore, the proposed fusion model was compared with other neural network models such as the GRU, LSTM and TCN models. The results showed that the XLG-LR model achieved the best prediction results at different time lengths, and at the same time consumed the least time compared to the neural network model. This method can provide a more reliable reference for the operation and dispatch of power enterprises and future power construction and planning.

As one of the largest inland megacities in Northwest (NW) China, Xi’an has been facing serious regional haze frequently, especially during winter. The composition of aerosols in Xi’an is highly complex due to its unique basinal topography and unique meteorological conditions. In this study, we characterized the morphology, size, and composition of individual aerosol particles collected during regional haze events at an urban site in Xi’an using Transmission Electron Microscopy (TEM) coupled with Energy-Dispersive X-ray Spectrometry (EDX). Six types of particles were identified based on their morphology and chemical composition, including organic (41.88%), sulfate (32.36%), soot (8.33%), mineral (7.91%), K-rich (5.13%), and fly ash particles (4.49%). These results demonstrate that the organic particles made a larger contribution to haze formation than the secondary inorganic particles during the sampling period. Size distribution and dominance suggest that organic and sulfate particles exert major control on the variation trends of particle size in haze. The coating thickness of organic-cored particles was about 369 nm and that of sulfate-cored particles was about 322 nm, implying that the organic particles were more aged than the sulfate particles. The results presented in this study provide further insights into understanding haze particle formation.

Abstract Because there is no temporal information available, rain removal with a single image is more challenging than that with a video. In this paper, we present a weighted median guided filtering method for rain removal with a single image. It consists of two filtering operations. Firstly, a weighted median filter is convoluted with an input rainy image to obtain a coarse rain-free image; then, guided filter is employed to obtain a refined rain-free image, where the coarse rain-free image is used as a guided image and convoluted with the input rainy image via guided filter. Experimental results show that the proposed method generated comparable results to the state-of-the-art algorithms with low computation cost.

Congestive heart failure (CHF) is a serious pathophysiological condition with high morbidity and mortality, which is hard to predict and diagnose in early age. Artificial intelligence and deep learning combining with cardiac rhythms and physiological time series provide a potential to help in solving it. In this paper, we proposed a novel method that combines a convolutional neural network (CNN) and a distance distribution matrix (DDM) in entropy calculation to classify CHF patients from normal subjects, and demonstrated the effectiveness of this combination. Specifically, three entropy methods were used to generate the distribution matrixes from a 300-point RR interval (i.e., the time interval between the successive cardiac cycles) time series, which are Sample entropy, fuzzy local measure entropy, and fuzzy global measure entropy. Then, three high representative CNN models, i.e., AlexNet, DenseNet, and SE_Inception_v4 were chosen to learn the pattern of the data distributions hidden in the generated distribution matrixes. All data used in our experiments were gathered from the MIT-BIH RR Interval Databases (http://www.physionet.org). A total of 29 CHF patients and 54 normal sinus rhythm subjects were included in this paper. The results showed that the combination of FuzzyGMEn-generated DDM and Inception_v4 model yielded the highest accuracy of 81.85% out of all proposed combinations.

The traditional chassis has the problems of low trafficability and poor stability under complex and changeable unstructured conditions. Thus, a wheel rail composite chassis is proposed. The chassis had a tracked travel mechanism at the front wheels and a wheeled travel mechanism at the rear wheels. This study presents the design, theoretical analysis and dynamic simulation analysis of the chassis. The maximum values of the passability of the wheel–track composite chassis that can be passed were calculated according to the relevant parameters. Furthermore, the chassis was modeled and simulated using RecurDyn to verify whether the values were reasonable. According to different values of the terrain, slope, vertical obstacle height and trench width, the change regularity of the track tension and driving torque of the chassis were obtained. The chassis is designed to improve the vehicle’s ability to operate under complex and diverse unstructured conditions.

On the research about the ultra-high frequency (UHF) re-radiation interference (RRI) from the power lines, traditional high-frequency algorithms need excessive computing resources or have a bad calculation precision. Based on the surface model of RRI, the calculation idea using large element-physical optics (LE-PO) is proposed to calculate the level of RRI. According to the assumption of LE-PO, the electric field integral equation is simplified. When the excitation source is plane wave, Linearly Phased-Rao Wilton Glisson (LP-RWG) basis functions are used to disperse the surface-induced current and the expression of scattering field from the tower of power lines was deduced. Related analysis example is used to analyse the calculation precision and the computing resource of LE-PO. As a result, the computing resources of LE-PO are much less than PO, while the calculation precision of LE-PO is the same as PO.