Epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China:a hospital-based retrospective study

Traumatic spinal cord injury is potentially catastrophic and can lead to permanent disability or even death.China has the largest population of patients with traumatic spinal cord injury.Previous studies of traumatic spinal cord injury in China have mostly been regional in scope;national-level studies have been rare.To the best of our knowledge,no national-level study of treatment status and economic burden has been performed.This retrospective study aimed to examine the epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China at the national level.We included 13,465 traumatic spinal cord injury patients who were injured between January 2013 and December 2018 and treated in 30 hospitals in 11 provinces/municipalities representing all geographical divisions of China.Patient epidemiological and clinical features,treatment status,and total and daily costs were recorded.Trends in the percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department and cost of care were assessed by annual percentage change using the Joinpoint Regression Program.The percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department did not significantly change overall(annual percentage change,-0.5%and 2.1%,respectively).A total of 10,053(74.7%)patients underwent surgery.Only 2.8%of patients who underwent surgery did so within 24 hours of injury.A total of 2005(14.9%)patients were treated with high-dose(≥500 mg)methylprednisolone sodium succinate/methylprednisolone(MPSS/MP);615(4.6%)received it within 8 hours.The total cost for acute traumatic spinal cord injury decreased over the study period(-4.7%),while daily cost did not significantly change(1.0%increase).Our findings indicate that public health initiatives should aim at improving hospitals’ability to complete early surgery within 24 hours,which is associated with improved sensorimotor recovery,increasing the awareness rate of clinical guidelines related to high-dose MPSS/MP to reduce the use of the treatment with insufficient evidence.

A Model for Detecting Fake News by Integrating Domain-Specific Emotional and Semantic Features

With the rapid spread of Internet information and the spread of fake news,the detection of fake news becomes more and more important.Traditional detection methods often rely on a single emotional or semantic feature to identify fake news,but these methods have limitations when dealing with news in specific domains.In order to solve the problem of weak feature correlation between data from different domains,a model for detecting fake news by integrating domain-specific emotional and semantic features is proposed.This method makes full use of the attention mechanism,grasps the correlation between different features,and effectively improves the effect of feature fusion.The algorithm first extracts the semantic features of news text through the Bi-LSTM(Bidirectional Long Short-Term Memory)layer to capture the contextual relevance of news text.Senta-BiLSTM is then used to extract emotional features and predict the probability of positive and negative emotions in the text.It then uses domain features as an enhancement feature and attention mechanism to fully capture more fine-grained emotional features associated with that domain.Finally,the fusion features are taken as the input of the fake news detection classifier,combined with the multi-task representation of information,and the MLP and Softmax functions are used for classification.The experimental results show that on the Chinese dataset Weibo21,the F1 value of this model is 0.958,4.9% higher than that of the sub-optimal model;on the English dataset FakeNewsNet,the F1 value of the detection result of this model is 0.845,1.8% higher than that of the sub-optimal model,which is advanced and feasible.

Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities.In Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for communication.However,existing JSL recognition systems have faced significant performance limitations due to inherent complexities.In response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning features.Our system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL gestures.Simultaneously,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second stream.Then,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL gestures.After reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the classification.To assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)dataset.Our results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.

Aerodynamic Features of High-Speed Maglev Trains with Different Marshaling Lengths Running on a Viaduct under Crosswinds

The safety and stability of high-speed maglev trains traveling on viaducts in crosswinds critically depend on their aerodynamic characteristics.Therefore,this paper uses an improved delayed detached eddy simulation(IDDES)method to investigate the aerodynamic features of high-speed maglev trains with different marshaling lengths under crosswinds.The effects of marshaling lengths(varying from 3-car to 8-car groups)on the train’s aerodynamic performance,surface pressure,and the flow field surrounding the train were investigated using the three-dimensional unsteady compressible Navier-Stokes(N-S)equations.The results showed that the marshaling lengths had minimal influence on the aerodynamic performance of the head and middle cars.Conversely,the marshaling lengths are negatively correlated with the time-average side force coefficient(CS)and time-average lift force coefficient(Cl)of the tail car.Compared to the tail car of the 3-car groups,the CS and Cl fell by 27.77%and 18.29%,respectively,for the tail car of the 8-car groups.It is essential to pay more attention to the operational safety of the head car,as it exhibits the highest time average CS.Additionally,the mean pressure difference between the two sides of the tail car body increased with the marshaling lengths,and the side force direction on the tail car was opposite to that of the head and middle cars.Furthermore,the turbulent kinetic energy of the wake structure on the windward side quickly decreased as marshaling lengths increased.

Directly predicting N_(2) electroreduction reaction free energy using interpretable machine learning with non-DFT calculated features

Electrocatalytic nitrogen reduction to ammonia has garnered significant attention with the blooming of single-atom catalysts(SACs),showcasing their potential for sustainable and energy-efficient ammonia production.However,cost-effectively designing and screening efficient electrocatalysts remains a challenge.In this study,we have successfully established interpretable machine learning(ML)models to evaluate the catalytic activity of SACs by directly and accurately predicting reaction Gibbs free energy.Our models were trained using non-density functional theory(DFT)calculated features from a dataset comprising 90 graphene-supported SACs.Our results underscore the superior prediction accuracy of the gradient boosting regression(GBR)model for bothΔg(N_(2)→NNH)andΔG(NH_(2)→NH_(3)),boasting coefficient of determination(R^(2))score of 0.972 and 0.984,along with root mean square error(RMSE)of 0.051 and 0.085 eV,respectively.Moreover,feature importance analysis elucidates that the high accuracy of GBR model stems from its adept capture of characteristics pertinent to the active center and coordination environment,unveilling the significance of elementary descriptors,with the colvalent radius playing a dominant role.Additionally,Shapley additive explanations(SHAP)analysis provides global and local interpretation of the working mechanism of the GBR model.Our analysis identifies that a pyrrole-type coordination(flag=0),d-orbitals with a moderate occupation(N_(d)=5),and a moderate difference in covalent radius(r_(TM-ave)near 140 pm)are conducive to achieving high activity.Furthermore,we extend the prediction of activity to more catalysts without additional DFT calculations,validating the reliability of our feature engineering,model training,and design strategy.These findings not only highlight new opportunity for accelerating catalyst design using non-DFT calculated features,but also shed light on the working mechanism of"black box"ML model.Moreover,the model provides valuable guidance for catalytic material design in multiple proton-electron coupling reactions,particularly in driving sustainable CO_(2),O_(2),and N_(2) conversion.

A process-oriented approach for identifying potential landslides considering time-dependent behaviors beyond geomorphological features

Geomorphological features are commonly used to identify potential landslides.Nevertheless,overemphasis on these features could lead to misjudgment.This research proposes a process-oriented approach for potential landslide identification that considers time-dependent behaviors.The method integrates comprehensive remote sensing and geological analysis to qualitatively assess slope stability,and employs numerical analysis to quantitatively calculate aging stability.Specifically,a time-dependent stability calculation method for anticlinal slopes is developed and implemented in discrete element software,incorporating time-dependent mechanical and strength reduction calculations.By considering the time-dependent evolution of slopes,this method highlights the importance of both geomorphological features and time-dependent behaviors in landslide identification.This method has been applied to the Jiarishan slope(JRS)on the Qinghai-Tibet Plateau as a case study.The results show that the JRS,despite having landslide geomorphology,is a stable slope,highlighting the risk of misjudgment when relying solely on geomorphological features.This work provides insights into the geomorphological characterization and evolution history of the JRS and offers valuable guidance for studying slopes with similar landslide geomorphology.Furthermore,the process-oriented method incorporating timedependent evolution provides a means to evaluate potential landslides,reducing misjudgment due to excessive reliance on geomorphological features.

Re-Distributing Facial Features for Engagement Prediction with ModernTCN

Automatically detecting learners’engagement levels helps to develop more effective online teaching and assessment programs,allowing teachers to provide timely feedback and make personalized adjustments based on students’needs to enhance teaching effectiveness.Traditional approaches mainly rely on single-frame multimodal facial spatial information,neglecting temporal emotional and behavioural features,with accuracy affected by significant pose variations.Additionally,convolutional padding can erode feature maps,affecting feature extraction’s representational capacity.To address these issues,we propose a hybrid neural network architecture,the redistributing facial features and temporal convolutional network(RefEIP).This network consists of three key components:first,utilizing the spatial attention mechanism large kernel attention(LKA)to automatically capture local patches and mitigate the effects of pose variations;second,employing the feature organization and weight distribution(FOWD)module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps.Finally,we analyse the temporal changes in video frames through the modern temporal convolutional network(ModernTCN)module to detect engagement levels.We constructed a near-infrared engagement video dataset(NEVD)to better validate the efficiency of the RefEIP network.Through extensive experiments and in-depth studies,we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation(DAiSEE),achieving an accuracy of 90.8%on NEVD and 61.2%on DAiSEE in the fourclass classification task,indicating significant advantages in addressing engagement video analysis problems.

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

Few-shot image recognition based on multi-scale features prototypical network

In order to improve the models capability in expressing features during few-shot learning,a multi-scale features prototypical network(MS-PN)algorithm is proposed.The metric learning algo-rithm is employed to extract image features and project them into a feature space,thus evaluating the similarity between samples based on their relative distances within the metric space.To sufficiently extract feature information from limited sample data and mitigate the impact of constrained data vol-ume,a multi-scale feature extraction network is presented to capture data features at various scales during the process of image feature extraction.Additionally,the position of the prototype is fine-tuned by assigning weights to data points to mitigate the influence of outliers on the experiment.The loss function integrates contrastive loss and label-smoothing to bring similar data points closer and separate dissimilar data points within the metric space.Experimental evaluations are conducted on small-sample datasets mini-ImageNet and CUB200-2011.The method in this paper can achieve higher classification accuracy.Specifically,in the 5-way 1-shot experiment,classification accuracy reaches 50.13%and 66.79%respectively on these two datasets.Moreover,in the 5-way 5-shot ex-periment,accuracy of 66.79%and 85.91%are observed,respectively.

Structural features in the mid-southern section of the Kyushu–Palau Ridge based on satellite altimetry gravity anomaly

The Kyushu–Palau Ridge(KPR),an anti-S-shaped submarine highland at the center of the Philippine Sea Plate(PSP),is considered the residual arc of the Izu–Bonin–Mariana Island Arc,which retains key information about the cessation of the Western Philippine Basin(WPB)expansion and the Parece Vela Basin(PVB)breakup.Herein,using the new generation of satellite altimetry gravity data,high-precision seafloor topography data,and newly acquired ship-borne gravity data,the topographic and gravity characteristics of the KPR mid-southern section and adjacent region are depicted.The distribution characteristics of the faults were delineated using the normalized vertical derivative–total horizontal derivative method(NVDR-THDR)and the minimum curvature potential field separation method.The Moho depth and crustal thickness were inverted using the rapid inversion method for a double-interface model with depth constraints.Based on these results,the crust structure features in the KPR mid-southern section,and the“triangular”structure geological significance where the KPR and Central Basin Rift(CBR)of the WPB intersect are interpreted.The KPR crustal thickness is approximately 6–16 km,with a distinct discontinuity that is slightly thicker than the normal oceanic crust.The KPR mid-southern section crust structure was divided into four segments(S1–S4)from north to south,formed by the CBR eastward extension joint action and clockwise rotation of the PVB expansion axis and the Mindanao fault zone blocking effect.

Intelligent recognition and information extraction of radar complex jamming based on time-frequency features

In modern war,radar countermeasure is becoming increasingly fierce,and the enemy jamming time and pattern are changing more randomly.It is challenging for the radar to efficiently identify jamming and obtain precise parameter information,particularly in low signal-to-noise ratio(SNR)situations.In this paper,an approach to intelligent recognition and complex jamming parameter estimate based on joint time-frequency distribution features is proposed to address this challenging issue.Firstly,a joint algorithm based on YOLOv5 convolutional neural networks(CNNs)is proposed,which is used to achieve the jamming signal classification and preliminary parameter estimation.Furthermore,an accurate jamming key parameters estimation algorithm is constructed by comprehensively utilizing chi-square statistical test,feature region search,position regression,spectrum interpolation,etc.,which realizes the accurate estimation of jamming carrier frequency,relative delay,Doppler frequency shift,and other parameters.Finally,the approach has improved performance for complex jamming recognition and parameter estimation under low SNR,and the recognition rate can reach 98%under−15 dB SNR,according to simulation and real data verification results.

Mesenchymal-epithelial transition factor amplification correlates with adverse pathological features and poor clinical outcome in colorectal cancer

BACKGROUND Colorectal cancer(CRC)is the third most common cancer and the second most common cause of cancer-related mortality worldwide.Mesenchymal-epithelial transition factor(MET)gene participates in multiple tumor biology and shows clinical potential for pharmacological manipulation in tumor treatment.MET amplification has been reported in CRC,but data are very limited.Investigating pathological values of MET in CRC may provide new therapeutic and genetic screening options in future clinical practice.AIM To determine the pathological significance of MET amplification in CRC and to propose a feasible screening strategy.METHODS A number of 205 newly diagnosed CRC patients undergoing surgical resection without any preoperative therapy at Shenzhen Cancer Hospital of Chinese Academy of Medical Sciences were recruited.All patients were without RAS/RAF mutation or microsatellite instability-high.MET amplification and c-MET protein expression were analyzed using fluorescence in situ hybridization(FISH)and immunohistochemistry(IHC),respectively.Correlations between MET aberration and pathological features were detected using the chi-squared test.Progression free survival(PFS)during the two-year follow-up was detected using the Kaplan-Meier method and log rank test.The results of MET FISH and IHC were com pared using one-way ANOVA.RESULTS Polysomy-induced MET amplification was observed in 14.4%of cases,and focal MET amplification was not detected.Polysomy-induced MET amplification was associated with a higher frequency of lymph node metastasis(LNM)(P<0.001)and higher tumor budding grade(P=0.02).In the survival analysis,significant difference was detected between patients with amplified-and non-amplified MET in a two-year follow-up after the first diagnosis(P=0.001).C-MET scores of 0,1+,2+,and 3+were observed in 1.4%,24.9%,54.7%,and 19.0%of tumors,respectively.C-MET overexpression correlated with higher frequency of LNM(P=0.002),but no significant difference of PFS was detected between patients with different protein levels.In terms of concordance between MET FISH and IHC results,MET copy number showed no difference in c-MET IHC 0/1+(3.35±0.18),2+(3.29±0.11)and 3+(3.58±0.22)cohorts,and the MET-to-CEP7 ratio showed no difference in three groups(1.09±0.02,1.10±0.01,and 1.09±0.03).CONCLUSION In CRC,focal MET amplification was a rare event.Polysomy-induced MET amplification correlated with adverse pathological characteristics and poor prognosis.IHC was a poor screening tool for MET amplification.

Oncological features and prognosis of colorectal cancer in human immunodeficiency virus-positive patients: A retrospective study

BACKGROUND Due to the prolonged life expectancy and increased risk of colorectal cancer(CRC)among patients with human immunodeficiency virus(HIV)infection,the prognosis and pathological features of CRC in HIV-positive patients require examination.AIM To compare the differences in oncological features,surgical safety,and prognosis between patients with and without HIV infection who have CRC at the same tumor stage and site.METHODS In this retrospective study,we collected data from HIV-positive and-negative patients who underwent radical resection for CRC.Using random stratified sampling,24 HIV-positive and 363 HIV-negative patients with colorectal adenocarcinoma after radical resection were selected.Using propensity score matching,we selected 72 patients,matched 1:2(HIV-positive:negative=24:48).Differences in basic characteristics,HIV acquisition,perioperative serological indicators,surgical safety,oncological features,and long-term prognosis were compared between the two groups.RESULTS Fewer patients with HIV infection underwent chemotherapy compared to patients without.HIV-positive patients had fewer preoperative and postoperative leukocytes,fewer preoperative lymphocytes,lower carcinoembryonic antigen levels,more intraoperative blood loss,more metastatic lymph nodes,higher node stage,higher tumor node metastasis stage,shorter overall survival,and shorter progression-free survival compared to patients who were HIV-negative.CONCLUSION Compared with CRC patients who are HIV-negative,patients with HIV infection have more metastatic lymph nodes and worse long-term survival after surgery.Standard treatment options for HIV-positive patients with CRC should be explored.

Tissue inhibitor of metalloproteinase-3 expression affects clinicopathological features and prognosis of aflatoxin B1-related hepatocellular carcinoma

BACKGROUND The dysregulation of tissue inhibitor of metalloproteinase-3(TIMP3)was positively correlated with the progression of hepatocellular carcinoma(HCC).However,it is not clear whether TIMP3 expression is associated with the clinico-pathological features and prognosis of aflatoxin B1(AFB1)-related HCC(AHCC).A retrospective study,including 182 patients with AHCC,was conducted to explore the link between TIMP3 expression in cancerous tissues and the clinico-pathological characteristics and prognosis of AHCC.TIMP3 expression was detected by immunohistochemistry and its effects on the clinicopathological features and prognosis of AHCC were evaluated by Kaplan-Meier survival analysis and Cox regression survival analysis.Odds ratio,hazard ratio(HR),median overall survival time(MST),median tumor recurrence-free survival time(MRT),and corresponding 95%confidential interval(CI)was calculated to RESULTS Kaplan-Meier survival analysis showed that compared with high TIMP3 expression,low TIMP3 expression in tumor tissues significantly decreased the MST(36.00 mo vs 18.00 mo)and MRT(32.00 mo vs 16 mo)of patients with AHCC.Multivariate Cox regression survival analysis further proved that decreased expression of TIMP3 increased the risk of death(HR=2.85,95%CI:2.04-4.00)and tumor recurrence(HR=2.26,95%CI:1.57-3.26).Furthermore,decreased expression of TIMP3 protein in tissues with AHCC was significantly correlated with tumor clinicopatho-logical features,such as tumor size,tumor grade and stage,tumor microvessel density,and tumor blood invasion.Additionally,TIMP3 protein expression was also negatively associated with amount of AFB1-DNA adducts in tumor tissues.CONCLUSION These findings indicate that the dysregulation of TIMP3 expression is related to AHCC biological behaviors and affects tumor outcome,suggesting that TIMP3 may act as a prognostic biomarker for AHCC.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): I. Mathematical Framework

This work presents the “nth-Order Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (abbreviated as “nth-FASAM-N”), which will be shown to be the most efficient methodology for computing exact expressions of sensitivities, of any order, of model responses with respect to features of model parameters and, subsequently, with respect to the model’s uncertain parameters, boundaries, and internal interfaces. The unparalleled efficiency and accuracy of the nth-FASAM-N methodology stems from the maximal reduction of the number of adjoint computations (which are considered to be “large-scale” computations) for computing high-order sensitivities. When applying the nth-FASAM-N methodology to compute the second- and higher-order sensitivities, the number of large-scale computations is proportional to the number of “model features” as opposed to being proportional to the number of model parameters (which are considerably more than the number of features).When a model has no “feature” functions of parameters, but only comprises primary parameters, the nth-FASAM-N methodology becomes identical to the extant nth CASAM-N (“nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems”) methodology. Both the nth-FASAM-N and the nth-CASAM-N methodologies are formulated in linearly increasing higher-dimensional Hilbert spaces as opposed to exponentially increasing parameter-dimensional spaces thus overcoming the curse of dimensionality in sensitivity analysis of nonlinear systems. Both the nth-FASAM-N and the nth-CASAM-N are incomparably more efficient and more accurate than any other methods (statistical, finite differences, etc.) for computing exact expressions of response sensitivities of any order with respect to the model’s features and/or primary uncertain parameters, boundaries, and internal interfaces.

Introducing the nth-Order Features Adjoint Sensitivity Analysis Methodology for Nonlinear Systems (nth-FASAM-N): II. Illustrative Example

This work highlights the unparalleled efficiency of the “nth-Order Function/ Feature Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-FASAM-N) by considering the well-known Nordheim-Fuchs reactor dynamics/safety model. This model describes a short-time self-limiting power excursion in a nuclear reactor system having a negative temperature coefficient in which a large amount of reactivity is suddenly inserted, either intentionally or by accident. This nonlinear paradigm model is sufficiently complex to model realistically self-limiting power excursions for short times yet admits closed-form exact expressions for the time-dependent neutron flux, temperature distribution and energy released during the transient power burst. The nth-FASAM-N methodology is compared to the extant “nth-Order Comprehensive Adjoint Sensitivity Analysis Methodology for Nonlinear Systems” (nth-CASAM-N) showing that: (i) the 1st-FASAM-N and the 1st-CASAM-N methodologies are equally efficient for computing the first-order sensitivities;each methodology requires a single large-scale computation for solving the “First-Level Adjoint Sensitivity System” (1st-LASS);(ii) the 2nd-FASAM-N methodology is considerably more efficient than the 2nd-CASAM-N methodology for computing the second-order sensitivities since the number of feature-functions is much smaller than the number of primary parameters;specifically for the Nordheim-Fuchs model, the 2nd-FASAM-N methodology requires 2 large-scale computations to obtain all of the exact expressions of the 28 distinct second-order response sensitivities with respect to the model parameters while the 2nd-CASAM-N methodology requires 7 large-scale computations for obtaining these 28 second-order sensitivities;(iii) the 3rd-FASAM-N methodology is even more efficient than the 3rd-CASAM-N methodology: only 2 large-scale computations are needed to obtain the exact expressions of the 84 distinct third-order response sensitivities with respect to the Nordheim-Fuchs model’s parameters when applying the 3rd-FASAM-N methodology, while the application of the 3rd-CASAM-N methodology requires at least 22 large-scale computations for computing the same 84 distinct third-order sensitivities. Together, the nth-FASAM-N and the nth-CASAM-N methodologies are the most practical methodologies for computing response sensitivities of any order comprehensively and accurately, overcoming the curse of dimensionality in sensitivity analysis.

Analysis of the Gravity-1 Sea Launch System and Technical Features

Sea launch has the characteristics of flexible launching points, high landing area safety, and good economy. In recent years, it has become one of the important launch methods. Since 2019, China has carried out a total of 11 successful sea launches. The Gravity-1(YL-1) sea launch system consists of a launch vehicle system and a sea launch platform. The sea launch program includes roll on/roll off boarding, sea mooring, sea maneuvering, anchoring and positioning, system testing, and formal launch. Through the maiden flight of YL-1, the design and manufacturing technology of large tonnage dedicated launch ship, launch vehicle vertical transfer and roll on/roll off boarding technology, anti-shake technology for sea launch, simple inflatable flexible insulation protective cover technology, and remote wireless measurement and control technology have been fully verified.

Features of Tropical Volcanic Rock and Soil of Jakarta-Bandung HSR and Engineering Countermeasures

The geological features of three types of tropical volcanic rock and soil distributed along Jakarta-Bandung high-speed railway(HSR),including pozzolanic clayey soil,mud shale and deep soft soil,are studied through field and laboratory tests.The paper analyzes the mechanism and causes of engineering geological problems caused by tropical volcanic rock and soil and puts forward measures to control subgrade slope instability by rationally determining project type,making side slope stability control and strengthening waterproofing and drainage.The“zero front slope”tunneling technology at the portal,the simplified excavation method of double-side wall heading and the cross brace construction method of arch protection within the semi-open cut row pile frame in the“mountainside”eccentrically loaded soft soil stratum are adopted to control the instability of tunnel side and front slopes,foundation pits and working faces;CFG or pipe piles shall be used to reinforce soft and expansive foundation or replacement measures shall be taken,and the scheme of blind ditch+double-layer water sealing in ballastless track section shall be put forward to prevent arching deformation of foundation;the treatment measures of CFG pile,pipe pile and vacuum combined piled preloading are adopted to improve the bearing capacity of foundation in deep soft soil section and solve the problems of settlement control and uneven settlement.These engineering countermeasures have been applied during the construction of Jakarta-Bandung HSR and achieved good results.

Immunological features of EGFR-mutant non-small cell lung cancer and clinical practice:a narrative review

Immune checkpoint inhibitors(ICIs)have significantly improved outcomes for patients with advanced driver-negative non-small cell lung cancer(NSCLC).However,targeted therapy remains the preferred treatment for advanced driver-positive NSCLC,including cases with epidermal growth factor receptor(EGFR)mutations.Con-sidering the variability in EGFR-mutant NSCLC,including expression levels of programmed cell death ligand 1(PD-L1),tumor mutation burden(TMB),and other immunological features,the application of immunotherapy in this group is still a subject of investigation.Therefore,we have summarized and analyzed the immunological characteristics and regulatory mechanisms of different EGFR mutations in NSCLC,as well as the current clinical application of immunotherapy in the EGFR-mutant population,to provide a reference for future research.

Classification of mine blasts and microseismic events using starting-up features in seismograms

To find discriminating features in seismograms for the classification of mine seismic events,signal databases of blasts and microseismic events were established based on manual identification.Criteria including the repetition of waveforms,tail decreasing,dominant frequency and occurrence time of day were considered in the establishment of the databases.Signals from databases of different types were drawn into a unified coordinate system.It is noticed that the starting-up angles of the two types tend to be concentrated into two different intervals.However,it is difficult to calculate the starting-up angle directly due to the inaccuracy of the P-wave arrival＇s picking.The slope value of the starting-up trend line,which was obtained by linear regression,was proposed to substitute the angle.Two slope values associated with the coordinates of the first peak and the maximum peak were extracted as the characteristic parameters.A statistical model with correct discrimination rate of greater than 97.1% was established by applying the Fisher discriminant analysis.