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Hai-Liang Yuan,1 Xian Zhang,2 Dong-Zhu Peng,1 Guan-Bin Lin,1 Hui-Hui Li,1 Fang-Xian Li,1 Jing-Jing Lu,1 Wei-Wei Chu1 1Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, Ningbo, Zhejiang, People’s Republic of China; 2Department of Endocrinology, the First Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of ChinaCorrespondence: Wei-Wei Chu, Department of Gastroenterology, Beilun Branch of the First Affiliated Hospital of Zhejiang University, No. 1288, Lushan East Road, Beilun District, Ningbo, Zhejiang Province, People’s Republic of China, Tel +86-134 56123811, Fax +86- 574 86100266, Email chuweiwei920@163.comPurpose: Constipation is a common complication of diabetic patients, which has a negative impact on their own health. This study aims to establish and internally validate the risk nomogram of constipation in patients with type 2 diabetes mellitus (T2DM) and to test its predictive ability.Patients and Methods: This retrospective study included 746 patients with T2DM at two medical centers. Among the 746 patients with T2DM, 382 and 163 patients in the Beilun branch of the First Affiliated Hospital of Zhejiang University were enrolled in the training cohort and the validation cohort, respectively. A total of 201 patients in the First Affiliated Hospital of Nanchang University were enrolled in external validation cohorts. The nomogram was established by optimizing the predictive factors through univariate and multivariable logistic regression analysis. The prediction performance of the nomogram was measured by the area under the receiver operating characteristic curve (AUROC), the calibration curve, and the decision curve analysis (DCA). Furthermore, its applicability was internally and independently validated.Results: Among the 16 clinicopathological features, five variables were selected to develop the prediction nomogram, including age, glycated hemoglobin (HbA1c), calcium, anxiety, and regular exercise. The nomogram revealed good discrimination with an area under the receiver operating characteristic curve (AUROC) of 0.908 (95% CI = 0.865– 0.950) in the training cohort, and 0.867 (95% CI = 0.790– 0.944) and 0.816 (95% CI = 0.751– 0.881) in the internal and external validation cohorts, respectively. The calibration curve presented a good agreement between the prediction by the nomogram and the actual observation. The DCA revealed that the nomogram had a high clinical application value.Conclusion: In this study, the nomogram for pretreatment risk management of constipation in patients with T2DM was developed which could help in making timely personalized clinical decisions for different risk populations.Keywords: constipation, type 2 diabetes mellitus, model, nomogram, prediction

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Drug-resistant tuberculosis (DR-TB) is a major contributor to global cases of antimicrobial resistance and remains a public health challenge. To understand the extent and trend of DR-TB under an enhanced multidrug-resistant TB (MDR-TB) management program, we conducted a population-based retrospective study of 1511 Taiwanese MDR-TB cases reported from 2008 to 2019.We obtained patient demographics and clinical and bacteriological information from the National TB Registry and the Infectious Disease Notification System.Of the 1511 MDR-TB patients, 941 were new cases, 485 were previously treated, and 85 had an unknown history of treatment. The male to female ratio was 2.75, and the median age of the patients was 57 years (IQR: 45-72). We observed a significant decline in MDR-TB cases, with annual percentage change (APC) of -4.17%. However, new and previously treated MDR-TB cases had APCs of -1.41% and -9.18%, respectively. The rates of MDR-TB resistance to ethambutol, streptomycin and pyrazinamide were 47.2%, 42.4% and 28.9%, respectively, whereas the rates of resistance to fluoroquinolones and second-line injectable drugs (SLIDs) were 4.1-7.1%, 9.0-14.1%; and the rate of extensively drug-resistant TB was 1.9%, respectively. Furthermore, we observed a decreasing trend of resistance to SLIDs (APCs -7.0% to -8.2%) in new cases and a significant decreasing trend of resistance to moxifloxacin (-24.6%) and levofloxacin (-23.3%) in previously treated cases.The decreasing trend of MDR-TB and resistance to second-line drugs suggested that our programmatic management of TB was effective and that the impact on TB control was profound.

Recently, end-to-end models have been widely used in automatic speech recognition (ASR) systems. Two of the most representative approaches are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. Autoregressive transformers, variants of AED, adopt an autoregressive mechanism for token generation and thus are relatively slow during inference. In this paper, we present a comprehensive study of a CTC Alignment-based Single-Step Non-Autoregressive Transformer (CASS-NAT) for end-to-end ASR. In CASS-NAT, word embeddings in the autoregressive transformer (AT) are substituted with token-level acoustic embeddings (TAE) that are extracted from encoder outputs with the acoustical boundary information offered by the CTC alignment. TAE can be obtained in parallel, resulting in a parallel generation of output tokens. During training, Viterbi-alignment is used for TAE generation, and multiple training strategies are further explored to improve the word error rate (WER) performance. During inference, an error-based alignment sampling method is investigated in depth to reduce the alignment mismatch in the training and testing processes. Experimental results show that the CASS-NAT has a WER that is close to AT on various ASR tasks, while providing a ~24x inference speedup. With and without self-supervised learning, we achieve new state-of-the-art results for non-autoregressive models on several datasets. We also analyze the behavior of the CASS-NAT decoder to explain why it can perform similarly to AT. We find that TAEs have similar functionality to word embeddings for grammatical structures, which might indicate the possibility of learning some semantic information from TAEs without a language model., Published in IEEE Transactions on Audio, Speech, and Language Processing

The fractal structure of icosahedra has been found in metallic-glass forming liquids by pressurization. It is featured by unchanged cluster-connectivity behavior and might be the origin of the dynamic crossover found in supercooled liquids.

Positional fluctuation and covariance during protein dynamics are key observables for understanding the molecular origin of biological functions. A frequently employed potential energy function for describing protein structural variation at the coarse-gained level is elastic network model (ENM). A long-standing issue in biomolecular simulation is thus the parametrization of ENM spring constants from the components of positional covariance matrix (PCM). Based on sensitivity analysis of PCM, the direct-coupling statistics of each spring, which is a specific combination of position fluctuation and covariance, is found to exhibit prominent signal of parameter dependence. This finding provides the basis for devising the objective function and the scheme of running through the effective one-dimensional optimization of every spring by self-consistent iteration. Formal derivation of the positional covariance statistical learning (PCSL) method also motivates the necessary data regularization for stable calculations. Robust convergence of PCSL is achieved in taking an all-atom molecular dynamics trajectory or an ensemble of homologous structures as input data. The PCSL framework can also be generalized with mixed objective functions to capture specific property such as the residue flexibility profile. Such physical chemistry-based statistical learning thus provides a useful platform for integrating the mechanical information encoded in various experimental or computational data.

Unique c-MoS2 nanosheets with corrugated edges mitigate the suppression of LiPSs and assist to reutilize them to boost the electrochemical kinetics in the Li–S battery.

The CR method was used to synthesize a nanorod CoO2/α-MnO2 catalyst with large specific surface area and abundant oxygen vacancies.

Lignin@Fe3O4 nanoparticles adsorb at oil–water interfaces, form Pickering emulsions, induce on-demand magnetic responses to break emulsions, and can sequester oil from water.

In this paper, we theoretically and experimentally demonstrated photothermal nonlinearities of both forward and backward scattering intensities from quasi-perfect absorbing silicon-based metasurface with only λ/7 thickness. The metasurface is efficiently heated up by photothermal effect under laser irradiation, which in turn modulates the scattering spectra via thermo-optical effect. Under a few milliwatt continuous-wave excitation at the resonance wavelength of the metasurface, backward scattering cross-section doubles, and forward scattering cross-section reduces to half. Our study opens up the all-optical dynamical control of the scattering directionality, which would be applicable to silicon photonic devices.

Military Deployment to Southwest Asia and Afghanistan and exposure to toxic airborne particulates has been associated with an increased risk of developing respiratory disease, collectively termed deployment-related respiratory diseases (DRRD). Our knowledge about how particulates mediate respiratory disease is limited, precluding the appropriate recognition or management. Central to this limitation is the lack of understanding of how exposures translate into dysregulated cell identity with dysregulated transcriptional programs. The small airway epithelium is involved in both the pathobiology of DRRD and fine particulate matter deposition. To characterize small airway epithelial cell epigenetic and transcriptional responses to Afghan desert particulate matter (APM) and investigate the functional interactions of transcription factors that mediate these responses, we applied two genomics assays, the assay for transposase accessible chromatin with sequencing (ATAC-seq) and Precision Run-on sequencing (PRO-seq). We identified activity changes in a series of transcriptional pathways as candidate regulators of susceptibility to subsequent insults, including signal-dependent pathways, such as loss of cytochrome P450 or P53/P63, and lineage-determining transcription factors, such as GRHL2 loss or TEAD3 activation. We further demonstrated that TEAD3 activation was unique to APM exposure despite similar inflammatory responses when compared with wood smoke particle exposure and that P53/P63 program loss was uniquely positioned at the intersection of signal-dependent and lineage-determining transcriptional programs. Our results establish the utility of an integrated genomics approach in characterizing responses to exposures and identify genomic targets for the advanced investigation of the pathogenesis of DRRD.

BackgroundNeutrophilic asthma (NA) is associated with increased airway interleukin (IL)-17 and abnormal bacterial community such as dominance of nontypeableHaemophilus influenzae(NTHi), particularly during asthma exacerbations. Bacteria release various products including DNA, but whether they cooperate with IL-17 in exaggerating neutrophilic inflammation is unclear. We sought to investigate the role of bacteria-derived DNA in airway neutrophilic inflammation related to IL-17-high asthma and underlying mechanisms (e.g.Toll-like receptor 9 (TLR9)/IL-36γ signalling axis).MethodsBacterial DNA, IL-8 and IL-36γ were measured in bronchoalveolar lavage fluid (BALF) of people with asthma and healthy subjects. The role of co-exposure to IL-17 and bacterial DNA or live bacteria in neutrophilic inflammation, and the contribution of the TLR9/IL-36γ signalling axis, were determined in cultured primary human airway epithelial cells and alveolar macrophages, and mouse models.ResultsBacterial DNA levels were increased in asthma BALF, which positively correlated with IL-8 and neutrophil levels. Moreover, IL-36γ increased in BALF of NA patients. Bacterial DNA or NTHi infection under an IL-17-high setting amplified IL-8 production and mouse lung neutrophilic inflammation. DNase I treatment in IL-17-exposed and NTHi-infected mouse lungs reduced neutrophilic inflammation. Mechanistically, bacterial DNA-mediated amplification of neutrophilic inflammation is in part dependent on the TLR9/IL-36γ signalling axis.ConclusionsBacterial DNA amplifies airway neutrophilic inflammation in an IL-17-high setting partly through the TLR9 and IL-36γ signalling axis. Our novel findings may offer several potential therapeutic targets including TLR9 antagonists, IL-36γ neutralising antibodies and DNase I to reduce asthma severity associated with exaggerated airway neutrophilic inflammation.

In adaptive learning systems, various models are employed to obtain the optimal learning schedule and review for a specific learner. Models of learning are used to estimate the learner's current recall probability by incorporating features or predictors proposed by psychological theory or empirically relevant to learners' performance. Logistic regression for knowledge tracing has been used widely in modern learner performance modeling. Notably, the learning history included in such models is typically confined to learners' prior accuracy performance without paying attention to learners' response time (RT), such as the performance factors analysis (PFA) model. However, RT and accuracy may give us a more comprehensive picture of a learner's learning trajectory. For example, without considering RT, we cannot estimate whether the learner's performance has reached the automatic or fluent level since these criteria are not accuracy based. Therefore, in the current research, we propose and test new RT-related features to capture learners' correct RT fluctuations around their estimated ideal fluent RT. Our results indicate that the predictiveness of the standard PFA model can be increased by up to 10\% for our test data after incorporating RT-related features, but the complexity of the question format constrains the improvement during practice. If the question is of low complexity and the observed accuracy of the learner can be influenced by guessing, which results in the imprecision measured by accuracy, then the RT-related features provide additional predictive power. In other words, RT-related features are informative when accuracy alone does not completely reflect learners' learning processes.

Background: Melasma is a chronic skin condition that adversely impacts quality of life. Although many therapeutic modalities are available there is no single best treatment for melasma. Oral tranexamic acid has been used for the treatment of this condition but its optimal dose is yet to be established. Objectives: We used network meta-analysis to determine the optimal dose of oral tranexamic acid for the treatment of melasma. Methods: We conducted a comprehensive search of all studies of oral tranexamic acid for the treatment of melasma up to September 2020 using PubMed, EMBASE and the Cochrane Library database. The quality of the studies was evaluated using the Jadad score and the Cochrane’s risk of bias assessment tool. Only high quality randomised controlled trials were selected. Some studies lacked standard deviation of changes from baseline and these were estimated using the correlation coefficient obtained from another similar study. Results: A total of 92 studies were identified of which 6 randomized controlled trials comprising 599 patients were included to form 3 pair-wise network comparisons. The mean age of the patients in these studies ranged from 30.3 to 46.5 years and the treatment duration ranged from 8 to 12 weeks. The Jadad scores ranged from 5 to 8. The optimal dose and duration of oral tranexamic acid was estimated to be 750 mg per day for 12 consecutive weeks. Limitations: Some confounding factors might not have been described in the original studies. Although clear rules were followed, the Melasma Area and Severity Index and the modified Melasma Area and Severity Index were scored by independent physicians and hence inter-observer bias could not be excluded. Conclusion: Oral tranexamic acid is a promising drug for the treatment of melasma. This is the first network meta-analysis to determine the optimal dose of this drug and to report the effects of different dosages. The optimal dose is 250 mg three times per day for 12 weeks, but 250 mg twice daily may be an acceptable option in poorly adherent patients. Our findings will allow physicians to balance drug effects and medication adherence. Personalized treatment plans are warranted.

A Rh(III)-catalyzed tandem reaction for the synthesis of (quinazolin-2-yl)methanone derivatives has been explored from 2,1-benzisoxazoles and α-azido ketones. The transformation involves Rh(III)-catalyzed denitrogenation of α-azido ketones, aza-[4 + 2] cycloaddition, ring opening, and dehydration aromatization processes. Notably, the aza-[4 + 2] cycloaddition of an imine rhodium complex intermediate with 2,1-benzisoxazoles is the key to this reaction.

Cancer is a major cause of death, and its early identification and intervention have potential for clinical actionability and benefits for human health. The studies using whole-genome sequencing (WGS) and large samples analysis of cancer-related genes have been rarely done.We performed WGS to explore germline mutations in coding and non-coding areas of cancer-related genes and non-coding driver genes and regulatory areas. Structural variants (SVs) was also analyzed. We used several tools and a subgrouping method to analyze the variants in 1491 healthy participants. Moreover, 275 cancer-related genes sequencing was carried out in 125 cancer patients.The incidence of familial cancer in the Taiwanese general population is 8.79% (131/1491). Cancer carrier rate of cancer-related genes is about 7.04% (105/1491) for pathogenic/likely pathogenic variants (P/LP) on ClinVar database only, and 28.24% (421/1491) for P/LP and loss of function variants. The carrier frequencies of cancer-related genes P/LP on ClinVar database were as follows: 8.40% (11/131), 7.11% (28/394), and 6.83% (66/966) in FC, 1MC, and nMC, respectively. The SVs and non-coding driver gene variants are uncommon. There are 1.54% (23/1491) of actionable cancer genes in American College of Medical Genetics and Genomics (ACMG), and the germline mutation rate of 275 cancer-related genes is 7.2% (9/125) in cancer patients including 4.0% (5/125) of actionable cancer genes in ACMG. After analyzing the frequencies of P/LP variants on GJB2 and SLC25A13 genes, we suggest that these two genes may not be cancer-related genes and need be re-evaluated.WGS analysis can completely detect germline mutations in cancer carriers. This study use subgrouping approach for samples provides a strategy to study whether a gene or variant is a cancer-related gene or variant in the future studies.

Transformer models have demonstrated their promising potential and achieved excellent performance on a series of computer vision tasks. However, the huge computational cost of vision transformers hinders their deployment and application to edge devices. Recent works have proposed to find and remove the unimportant units of vision transformers. Despite achieving remarkable results, these methods take one dimension of network width into consideration and ignore network depth, which is another important dimension for pruning vision transformers. Therefore, we propose a Width & Depth Pruning (WDPruning) framework that reduces both width and depth dimensions simultaneously. Specifically, for width pruning, a set of learnable pruning-related parameters is used to adaptively adjust the width of transformer. For depth pruning, we introduce several shallow classifiers by using the intermediate information of the transformer blocks, which allows images to be classified by shallow classifiers instead of the deeper classifiers. In the inference period, all of the blocks after shallow classifiers can be dropped so they don’t bring additional parameters and computation. Experimental results on benchmark datasets demonstrate that the proposed method can significantly reduce the computational costs of mainstream vision transformers such as DeiT and Swin Transformer with a minor accuracy drop. In particular, on ILSVRC-12, we achieve over 22% pruning ratio of FLOPs by compressing DeiT-Base, even with an increase of 0.14% Top-1 accuracy.

We examine the Thai government's politicised COVID-19 containment strategies, which have been challenged by Thai protesters. Although we use securitisation theory as an explanatory framework, we argue that researchers using this theory can explain the issues only if they simultaneously use social-conflict theory to explain the interactions between securitising actors and their audiences. By supplementing securitisation theory with social-conflict theory, we have found that the roles of securitising actors and audiences are not fixed. In our case study of Thailand, the Thai government and protesters have played two roles simultaneously: the role of a securitising actor and the role of an audience. This finding suggests that successful securitisation is impermanent; that is, it is subject to change over time. Securitisation may be successful, but the success can only be temporary because as new actors or resources enter the picture, the previously successful securitisation will, at some point, diminish.

Plasmonic structural color, in which vivid colors are generated via resonant nanostructures made of common plasmonic materials, such as noble metals have fueled worldwide interest in backlight-free displays. However, plasmonic colors that were withstanding ultrahigh temperatures without damage remain an unmet challenge due to the low melting point of noble metals. Here, we report the refractory hafnium nitride (HfN) plasmonic crystals that can generate full-visible color with a high image resolution of ∼63,500 dpi while withstanding a high temperature (900 °C). Plasmonic colors that reflect visible light could be attributed to the unique features in plasmonic HfN, a high bulk plasmon frequency of 3.1 eV, whichcould support localized surface plasmon resonance (LSPR) in the visible range. By tuning the wavelength of the LSPR, the reflective optical response can be controlled to generate the colors from blue to red across a wide gamut. The novel refractory plasmonic colors pave the way for emerging applications ranging from reflective displays to solar energy harvesting systems.

In addition to inducing apoptosis, caspase inhibition contributes to necroptosis and/or autophagy depending on the cell type and cellular context. In macrophages, necroptosis can be induced by co-treatment with Toll-like receptor (TLR) ligands (lipopolysaccharide [LPS] for TLR4 and polyinosinic-polycytidylic acid [poly I:C] for TLR3) and a cell-permeable pan-caspase inhibitor zVAD. Here, we elucidated the signaling pathways and molecular mechanisms of cell death. We showed that LPS/zVAD- and poly I:C/zVAD-induced cell death in bone marrow-derived macrophages (BMDMs) was inhibited by receptor-interacting protein kinase 1 (RIP1) inhibitor necrostatin-1 and autophagy inhibitor 3-methyladenine. Electron microscopic images displayed autophagosome/autolysosomes, and immunoblotting data revealed increased LC3II expression. Although zVAD did not affect LPS- or poly I:C-induced activation of IKK, JNK, and p38, it enhanced IRF3 and STAT1 activation as well as type I interferon (IFN) expression. In addition, zVAD inhibited ERK and Akt phosphorylation induced by LPS and poly I:C. Of note, zVAD-induced enhancement of the IRF3/IFN/STAT1 axis was abolished by necrostatin-1, while zVAD-induced inhibition of ERK and Akt was not. Our data further support the involvement of autocrine IFNs action in reactive oxygen species (ROS)-dependent necroptosis, LPS/zVAD-elicited ROS production was inhibited by necrostatin-1, neutralizing antibody of IFN receptor (IFNR) and JAK inhibitor AZD1480. Accordingly, both cell death and ROS production induced by TLR ligands plus zVAD were abrogated in STAT1 knockout macrophages. We conclude that enhanced TRIF-RIP1-dependent autocrine action of IFNβ, rather than inhibition of ERK or Akt, is involved in TLRs/zVAD-induced autophagic and necroptotic cell death via the JAK/STAT1/ROS pathway.

The effects of support materials on catalytic performance were investigated in catalytic removal of toluene. And the Mn–Ce binary oxides as active components were supported on ZrO2, SiO2, γ-Al2O3 and TiO2 support materials. Many techniques, including X-ray diffraction (XRD), Brunauer–Emmett–Teller method (BET), X-ray photoelectron spectroscopy (XPS), temperature-programmed reduction (TPR) and NH3-temperature-programmed desorption (NH3-TPD), were used to characterize physicochemical properties. Among the different catalysts, the MnCe/ZrO2 catalyst with the lowest specific surface area (39.7 m2/g) shows the best catalytic activity. In terms of toluene conversion, the activity order is as follows: MnCe/ZrO2 > MnCe/TiO2 ≈ MnCe/SiO2 > MnCe/Al2O3. The better performance of MnCe/ZrO2 should be attributed to the low-temperature reducibility, and abundant surface species (Mn4+ and lattice oxygen). And XPS and TPR results reveal that more surface abundant Mn and Ce elements generate good interaction in MnCe/ZrO2. The weak interaction between metal oxide and support also boosts the dispersion and complete reduction of MnCe oxides at low temperature. In addition, the in-situ DRIFTS results clarify that the carbonate species are main intermediates in MnCe/ZrO2 sample during surface reaction process.

(1) Background: Breast cancer is the second leading cause of cancer death among women. The accurate prediction of survival intervals will help physicians make informed decisions on treatment strategies or the use of palliative care for patients; (2) Methods: The gene expression is predictive and correlates to patient prognosis. To establish a reliable prediction tool, we collected the RNA-seq data of breast cancer patients, a total of 1187 RNA-seq data (median age 58 years), in FPKM format from the TCGA database. Among them, 144 RNA-seq data with date of death information was selected to establish the SaBrcada-AD dataset. We first normalized the SaBrcada-AD dataset to transcripts per million (TPM) to build survival prediction model SaBrcada. After normalization and dimension raising, the differential gene expression data were used for testing eight different deep learning architectures. Among them, GoogLeNet performed the best. Considering the effect of age on prognosis, we examined all ages between the lower and upper quartiles of patient age for a stratified random sampling test; (3) Results: Stratifying by age based on a cut-off of 61 years of age improved the accuracy of SaBrcada compared to previous findings, resulting in an accuracy of 0.798. We also built a free website tool to provide 5 kinds of predicted survival period information for clinician reference; (4) Conclusions: We established a breast cancer survival analysis prediction model, SaBrcada, and a website tool with the same name. Through this highly reliable survival analysis model and website tool, information on survival intervals will be provided for clinicians as part of precision medicine.

Ce-promoted NiMgAl mixed-oxide (NiCex-C, x = 0, 1,5,10) catalysts were prepared from the quaternary hydrotalcite precursors for CO2 hydrogenation to methane. By engineering the Ce contents, NiCe5-C showed its prior catalytic performance in low-temperature CO2 hydrogenation, being about 3 times higher than that of Ce-free NiCe0-C catalyst (TOF of NiCe5-C and NiCe0-C: 1.19 h-1 vs 0.39 h-1 @ 250 oC). With extensive characterization, it was found that Ce dopants promoted the reduction of NiO by adjusting the interaction between Ni and Mg(Ce)AlOx support. The higest ratio of surface Ni0/(Ni2+Ni0) was obtained over NiCe5-C. Meanwhile, the surface basicity was tailored with Ce dopants. The strongest medium-strength basicity and highest capacity of CO2 adsorption was achieved on NiCe5-C with 5wt% Ce content. The TOF tests indicated a good correlation with medium-strength basicity over the NiCex-C samples. The results showed that the high medium-strength and Ce-promoted surface Ni0 species endows the enhanced low-temperature catalytic performance in CO2 hydrogenation to methane.

At present, research on the loading simulation test bench for overloaded vehicles is mostly limited to the realization of loading simulation under the condition of straight line driving of the vehicle. When simulating the whole vehicle load under straight line driving condition, only accurate road load and inertial load can be obtained to achieve accurate load simulation, and the inertial load required to be simulated by each side device is half of the whole vehicle inertial load. However, straight line driving is only the most ideal driving. The real driving conditions (including ground conditions) of the vehicle are usually composed of steering, longitudinal slope, lateral slope and different ground adhesion coefficients. Therefore, the loads borne by the two independent load simulation systems located on both sides of the vehicle may be different during the load simulation process. In addition, the frequency characteristics of the loads to be simulated by the same side load simulation device at different time sequences may also be different. This paper studies the dynamics modeling, load distribution and error compensation in the whole vehicle load simulation process in order to achieve accurate crawler vehicle load simulation from three aspects.

Based on steady-state experiments, a three-dimensional (3D) simulation study under pulsation conditions was carried out. This was performed to study the effect of pulsation of the supply system on the atomization characteristics of a gas-centered swirl coaxial injector. The results indicated that the spray shape resembled a pagoda when the Klystron effect occurred. When the low-frequency pulsation (200 Hz) was applied to the liquid inlet, the peak of the exit pressure was close to the trough of the inlet mass flow rate. Meanwhile, the exit mass flow rate and pressure were approximately sinusoidal. There was no significant lag effect in the response of the exit mass flow rate to the inlet mass flow rate but with a small sawtooth-like oscillation. Increasing the amplitude of the liquid inlet disturbance amplified the exit mass flow rate fluctuation, which was greater than the set amplitude of the inlet pulsation. The dominant frequency of the injector exit mass flow rate and pressure variation was significantly less than the inlet pulsation frequency when the frequency of the liquid inlet pulsation was increased to 1000 Hz. This was due to the low-pass filtering effect of the annular slit. There was no significant change in the overall spray morphology for the gas inlet pulsation. By applying the same amplitude and frequency of pulsation to the gas and liquid inlets of the injector, the pulsation of the liquid inlet had a stronger effect on the injector exit spray than that of the gas inlet.