Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significan...Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significance. At present, the prediction methods are mainly based on the physicochemical property and statistic analysis of amino acids. However, these methods are suitable for some environments but inapplicable for other environments. In this paper, the multi-sources information fusion theory has been introduced to predict the transmembrane regions. The proposed method is test on a data set of transmembrane proteins. The results show that the proposed method has the ability of predicting the transmembrane regions as a good performance and powerful tool.展开更多
Objective:To explore the candidate indications for function-preserving curative gastrectomy and sentinel lymph node navigation surgery in early gastric cancer(EGC).Methods:The clinicopathological data of 561 patients ...Objective:To explore the candidate indications for function-preserving curative gastrectomy and sentinel lymph node navigation surgery in early gastric cancer(EGC).Methods:The clinicopathological data of 561 patients with EGC who underwent radical gastrectomy for gastric cancer at Peking University Cancer Hospital from November 2010 to November 2020 with postoperative pathological stage pT1 and complete examination data,were collected.Pearson’s Chi-square test was used and binary logistic regression was employed for univariate and multivariate analyses.Combined analysis of multiple risk and protective factors for lymph node metastasis(LNM)of EGC was performed.A negative predictive value(NPV)combination model was built and validated.Results:LNM occurred in 85 of 561 patients with EGC,and the LNM rate was 15.15%.NPV for LNM reached 100%based on three characteristics,including ulcer-free,moderately well differentiation and patient<65years old or tumor located at the proximal 1/3 of the stomach.Regarding lymphatic basin metastasis,multivariate analysis showed that the metastatic proportion of the left gastric artery lymphatic basin was significantly higher in male patients compared with female patients(65.96%vs.38.89%,P<0.05).The proportion of right gastroepiploic artery lymphatic basin metastasis in patients with a maximum tumor diameter>2 cm was significantly greater than that noted in patients with a maximum tumor diameter≤2 cm(60.78%vs.28.13%,P<0.05).Conclusions:Characteristics of lymph node stations/basins metastasis will facilitate precise lymph node resection.The NPV for LNM reaches 100%based on the following two conditions:young and middle-aged EGC patients,well-differentiated tumors,and without ulcers;or well-differentiated tumors,without ulcers,and tumors located in the proximal stomach.These findings can be used as the recommended indications for functionpreserving curative gastrectomy and sentinel lymph node navigation surgery.展开更多
High-precision polar motion(PM) prediction is of important significance in astronomy, geodesy, aviation,hydrographic mapping, interstellar navigation, and so on. SSA can effectively extract the trend and period terms ...High-precision polar motion(PM) prediction is of important significance in astronomy, geodesy, aviation,hydrographic mapping, interstellar navigation, and so on. SSA can effectively extract the trend and period terms of PM,in the process of achieving high-precision medium-and long-term polar motion prediction,it is necessary to solve the end effect problem and overfitting problem of SSA forecasting method;therefore, ARMA was applied to decreasethe end effect, and a suitable combination of reconstructed components was determined to avoid the high variance reaction of SSA overfitting. Based on the decomposition and reconstruction of the PM by SSA, the reconstructed components are determined to participate in the SSA iterative fitting model according to the variance contribution rate. The combination of the reconstructed components representing the polar motion period term and the trend term is determined according to the correlation analysis of the selected reconstructed components. After the above work, the principal component prediction sequence is obtained by fitting the period term and the trend term to convergence, respectively, and then, the SSA end effect is modified, and the residual term is predicted based on ARMA. The test results show that he prediction accuracy of SSA + ARMA at the front of the X and Y directions are improved by 96.90% and 97.53% compared with those of SSA, respectively,and the forecast accuracy of 365 days are improved by 37.93% and 19.53% in the X and Y directions compared with those of Bulletin A, respectively.展开更多
Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satell...Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).展开更多
A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the ...A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”(DOI:https://doi.org/10.1016/j.dt.2018.07.017).Reply to the Note from Li Piani et al is linked to this article.展开更多
OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METH...OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METHODS In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263consecutive cases of CAD patients after PCI in PANDA Ⅲ trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.RESULTS In both the Random Forest Model and the Deep Surv Model, age, renal function(creatinine), cardiac function(LVEF)and post-PCI coronary physiological index(QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age(years)/EF(%) + 1(if creatinine ≥ 2.0 mg/d L) + 1(if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination(C-statistic = 0.651;95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration(Hosmer–Lemeshow χ^(2)= 7.070;P = 0.529) for predicting 2-year patient-oriented composite endpoint(POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis(adjusted HR = 1.89;95% CI: 1.18–3.04;log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.CONCLUSIONS An improved scoring system combining clinical and coronary lesion-based functional variables(ACEF-QFR)was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.展开更多
The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The ris...Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.展开更多
The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst asses...The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.展开更多
Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academi...Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.展开更多
BACKGROUND Pancreatic cancer is a leading cause of cancer-related deaths.Increased activity of the epidermal growth factor receptor(EGFR)is often observed in pancreatic cancer,and the small molecule EGFR inhibitor erl...BACKGROUND Pancreatic cancer is a leading cause of cancer-related deaths.Increased activity of the epidermal growth factor receptor(EGFR)is often observed in pancreatic cancer,and the small molecule EGFR inhibitor erlotinib has been approved for pancreatic cancer therapy by the food and drug administration.Nevertheless,erlotinib alone is ineffective and should be combined with other drugs to improve therapeutic outcomes.We previously showed that certain receptor tyrosine kinase inhibitors can increase mitochondrial membrane potential(Δψm),facilitate tumor cell uptake ofΔψm-sensitive agents,disrupt mitochondrial homeostasis,and subsequently trigger tumor cell death.Erlotinib has not been tested for this effect.AIM To determine whether erlotinib can elevateΔψm and increase tumor cell uptake ofΔψm-sensitive agents,subsequently triggering tumor cell death.METHODSΔψm-sensitive fluorescent dye was used to determine how erlotinib affectsΔψm in pancreatic adenocarcinoma(PDAC)cell lines.The viability of conventional and patient-derived primary PDAC cell lines in 2D-and 3D cultures was measured after treating cells sequentially with erlotinib and mitochondria-targeted ubiquinone(MitoQ),aΔψm-sensitive MitoQ.The synergy between erlotinib and MitoQ was then analyzed using SynergyFinder 2.0.The preclinical efficacy of the twodrug combination was determined using immune-compromised nude mice bearing PDAC cell line xenografts.RESULTS Erlotinib elevatedΔψm in PDAC cells,facilitating tumor cell uptake and mitochondrial enrichment ofΔψm-sensitive agents.MitoQ triggered caspase-dependent apoptosis in PDAC cells in culture if used at high doses,while erlotinib pretreatment potentiated low doses of MitoQ.SynergyFinder suggested that these drugs synergistically induced tumor cell lethality.Consistent with in vitro data,erlotinib and MitoQ combination suppressed human PDAC cell line xenografts in mice more effectively than single treatments of each agent.CONCLUSION Our findings suggest that a combination of erlotinib and MitoQ has the potential to suppress pancreatic tumor cell viability effectively.展开更多
Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the pre...Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident severity.Deep learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and classification.Despite prior success,there is a need for an efficient system recognizing ASL in diverse road conditions.To address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent.The framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved generalization.Sensitivity and factor contribution analyses identify influential predictors.Evaluated on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB dataset.The proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.展开更多
Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of ...Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.展开更多
Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack ...Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).展开更多
Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of th...Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.展开更多
To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior in...To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.展开更多
Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channe...Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channel in a fixed time slot per frame,while the other intra-frame channels are usually recovered by interpolation.However,these approaches suffer from a serious interpolation loss,especially for mobile millimeter-wave communications.To solve this challenging problem,we propose a tensor neural ordinary differential equation(TN-ODE)based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels.Specifically,inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning,we first utilize the neural ODE model to predict future continuous-time channels.To improve the channel prediction accuracy and reduce computational complexity,we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform.Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.展开更多
Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining envir...Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.展开更多
Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial pertur...Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.展开更多
The Myc gene is the essential oncogene in triple-negative breast cancer(TNBC).This study investigates the synergistic effects of combining Myc decoy oligodeoxynucleotides-encapsulated niosomes-selenium hybrid nanocarr...The Myc gene is the essential oncogene in triple-negative breast cancer(TNBC).This study investigates the synergistic effects of combining Myc decoy oligodeoxynucleotides-encapsulated niosomes-selenium hybrid nanocarriers with X-irradiation exposure on the MDA-MB-468 cell line.Decoy and scramble ODNs for Myc transcription factor were designed and synthesized based on promoter sequences of the Bcl2 gene.The nanocarriers were synthesized by loading Myc ODNs and selenium into chitosan(Chi-Se-DEC),which was then encapsulated in niosome-nanocarriers(NISM@Chi-Se-DEC).FT-IR,DLS,FESEM,and hemolysis tests were applied to confirm its characterization and physicochemical properties.Moreover,cellular uptake,cellular toxicity,apoptosis,cell cycle,and scratch repair assays were performed to evaluate its anticancer effects on cancer cells.All anticancer assessments were repeated under X-ray irradiation conditions(fractionated 2Gy).Physicochemical characteristics of niosomes containing SeNPs and ODNs showed that it is synthesized appropriately.It revealed that the anticancer effect of NISM@Chi-Se-DEC can be significantly improved in combination with X-ray irradiation treatment.It can be concluded that NISM@Chi-Se-DEC nanocarriers have the potential as a therapeutic agent for cancer treatment,particularly in combination with radiation therapy and in-vivo experiments are necessary to confirm the efficacy of this nano-drug.展开更多
基金Supported by the National Natural Science Foundation of China (No. 60874105, 61174022)the Program for New Century Excellent Talents in University (No. NCET-08-0345)the Chongqing Natural Science Foundation (No. CSCT, 2010BA2003)
文摘Transmembrane proteins are some special and important proteins in cells. Because of their importance and specificity, the prediction of the transmembrane regions has very important theoretical and practical significance. At present, the prediction methods are mainly based on the physicochemical property and statistic analysis of amino acids. However, these methods are suitable for some environments but inapplicable for other environments. In this paper, the multi-sources information fusion theory has been introduced to predict the transmembrane regions. The proposed method is test on a data set of transmembrane proteins. The results show that the proposed method has the ability of predicting the transmembrane regions as a good performance and powerful tool.
基金Beijing Hospitals Authority Clinical Medicine Development of Special Funding(No.XMLX202119)Open Project funded by Key Laboratory of Carcinogenesis and Translational Research,Ministry of Education/Beijing(No.2022 Open Project-6)。
文摘Objective:To explore the candidate indications for function-preserving curative gastrectomy and sentinel lymph node navigation surgery in early gastric cancer(EGC).Methods:The clinicopathological data of 561 patients with EGC who underwent radical gastrectomy for gastric cancer at Peking University Cancer Hospital from November 2010 to November 2020 with postoperative pathological stage pT1 and complete examination data,were collected.Pearson’s Chi-square test was used and binary logistic regression was employed for univariate and multivariate analyses.Combined analysis of multiple risk and protective factors for lymph node metastasis(LNM)of EGC was performed.A negative predictive value(NPV)combination model was built and validated.Results:LNM occurred in 85 of 561 patients with EGC,and the LNM rate was 15.15%.NPV for LNM reached 100%based on three characteristics,including ulcer-free,moderately well differentiation and patient<65years old or tumor located at the proximal 1/3 of the stomach.Regarding lymphatic basin metastasis,multivariate analysis showed that the metastatic proportion of the left gastric artery lymphatic basin was significantly higher in male patients compared with female patients(65.96%vs.38.89%,P<0.05).The proportion of right gastroepiploic artery lymphatic basin metastasis in patients with a maximum tumor diameter>2 cm was significantly greater than that noted in patients with a maximum tumor diameter≤2 cm(60.78%vs.28.13%,P<0.05).Conclusions:Characteristics of lymph node stations/basins metastasis will facilitate precise lymph node resection.The NPV for LNM reaches 100%based on the following two conditions:young and middle-aged EGC patients,well-differentiated tumors,and without ulcers;or well-differentiated tumors,without ulcers,and tumors located in the proximal stomach.These findings can be used as the recommended indications for functionpreserving curative gastrectomy and sentinel lymph node navigation surgery.
基金supported by the National Natural Science Foundation of China(Grant No.41704015,42271436)the Shandong Natural Science Foundation of China(Grant No.ZR2017MD032,ZR2021MD030)+1 种基金a Project of Shandong Province Higher Education Science and Technology Program(Grant No.J17KA077)Talent introduction plan for Youth Innovation Team in universities of Shandong Province(innovation team of satellite positioning and navigation).
文摘High-precision polar motion(PM) prediction is of important significance in astronomy, geodesy, aviation,hydrographic mapping, interstellar navigation, and so on. SSA can effectively extract the trend and period terms of PM,in the process of achieving high-precision medium-and long-term polar motion prediction,it is necessary to solve the end effect problem and overfitting problem of SSA forecasting method;therefore, ARMA was applied to decreasethe end effect, and a suitable combination of reconstructed components was determined to avoid the high variance reaction of SSA overfitting. Based on the decomposition and reconstruction of the PM by SSA, the reconstructed components are determined to participate in the SSA iterative fitting model according to the variance contribution rate. The combination of the reconstructed components representing the polar motion period term and the trend term is determined according to the correlation analysis of the selected reconstructed components. After the above work, the principal component prediction sequence is obtained by fitting the period term and the trend term to convergence, respectively, and then, the SSA end effect is modified, and the residual term is predicted based on ARMA. The test results show that he prediction accuracy of SSA + ARMA at the front of the X and Y directions are improved by 96.90% and 97.53% compared with those of SSA, respectively,and the forecast accuracy of 365 days are improved by 37.93% and 19.53% in the X and Y directions compared with those of Bulletin A, respectively.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R196),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Pedo-spectroscopy has the potential to provide valuable information about soil physical,chemical,and biological properties.Nowadays,wemay predict soil properties usingVNIRfield imaging spectra(IS)such as Prisma satellite data or laboratory spectra(LS).The primary goal of this study is to investigate machine learning models namely Partial Least Squares Regression(PLSR)and Support Vector Regression(SVR)for the prediction of several soil properties,including clay,sand,silt,organic matter,nitrate NO3-,and calcium carbonate CaCO_(3),using five VNIR spectra dataset combinations(%IS,%LS)as follows:C1(0%IS,100%LS),C2(20%IS,80%LS),C3(50%IS,50%LS),C4(80%IS,20%LS)and C5(100%IS,0%LS).Soil samples were collected at bare soils and at the upper(0–30 cm)layer.The data set has been split into a training dataset 80%of the collected data(n=248)and a validation dataset 20%of the collected data(n=61).The proposed PLSR and SVR models were trained then tested for each dataset combination.According to our results,SVR outperforms PLSR for both:C1(0%IS,100%LS)and C5(100%IS,0%LS).For Soil Organic Matter(SOM)prediction,it achieves(R^(2)=0.79%,RMSE=1.42%)and(R^(2)=0.76%,RMSE=1.3%),respectively.The data fusion has improved the soil property prediction.The highest improvement was obtained for the SOM property(R^(2)=0.80%,RMSE=1.39)when using the SVR model and applying the second Combination C2(20% of IS and 80%LS).
文摘A recently published modeling approach for the penetration into adobe and previous approaches implicitly criticized are reviewed and discussed.This article contains a note on the paper titled“Ballistic model for the prediction of penetration depth and residual velocity in adobe:A new interpretation of the ballistic resistance of earthen masonry”(DOI:https://doi.org/10.1016/j.dt.2018.07.017).Reply to the Note from Li Piani et al is linked to this article.
基金sponsored by Sino Medical,Tianjin,Chinasupported by the Beijing Municipal Science and Technology Project[Z191100006619107 to B.X.]Capital Health Development Research Project[20201–4032 to K.D.].
文摘OBJECTIVES To establish a scoring system combining the ACEF score and the quantitative blood flow ratio(QFR) to improve the long-term risk prediction of patients undergoing percutaneous coronary intervention(PCI).METHODS In this population-based cohort study, a total of 46 features, including patient clinical and coronary lesion characteristics, were assessed for analysis through machine learning models. The ACEF-QFR scoring system was developed using 1263consecutive cases of CAD patients after PCI in PANDA Ⅲ trial database. The newly developed score was then validated on the other remaining 542 patients in the cohort.RESULTS In both the Random Forest Model and the Deep Surv Model, age, renal function(creatinine), cardiac function(LVEF)and post-PCI coronary physiological index(QFR) were identified and confirmed to be significant predictive factors for 2-year adverse cardiac events. The ACEF-QFR score was constructed based on the developmental dataset and computed as age(years)/EF(%) + 1(if creatinine ≥ 2.0 mg/d L) + 1(if post-PCI QFR ≤ 0.92). The performance of the ACEF-QFR scoring system was preliminarily evaluated in the developmental dataset, and then further explored in the validation dataset. The ACEF-QFR score showed superior discrimination(C-statistic = 0.651;95% CI: 0.611-0.691, P < 0.05 versus post-PCI physiological index and other commonly used risk scores) and excellent calibration(Hosmer–Lemeshow χ^(2)= 7.070;P = 0.529) for predicting 2-year patient-oriented composite endpoint(POCE). The good prognostic value of the ACEF-QFR score was further validated by multivariable Cox regression and Kaplan–Meier analysis(adjusted HR = 1.89;95% CI: 1.18–3.04;log-rank P < 0.01) after stratified the patients into high-risk group and low-risk group.CONCLUSIONS An improved scoring system combining clinical and coronary lesion-based functional variables(ACEF-QFR)was developed, and its ability for prognostic prediction in patients with PCI was further validated to be significantly better than the post-PCI physiological index and other commonly used risk scores.
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
基金supported in part by the National Key Research and Development Program of China under 2020AAA0106000the National Natural Science Foundation of China under U20B2060 and U21B2036supported by a grant from the Guoqiang Institute, Tsinghua University under 2021GQG1005
文摘Human mobility prediction is important for many applications.However,training an accurate mobility prediction model requires a large scale of human trajectories,where privacy issues become an important problem.The rising federated learning provides us with a promising solution to this problem,which enables mobile devices to collaboratively learn a shared prediction model while keeping all the training data on the device,decoupling the ability to do machine learning from the need to store the data in the cloud.However,existing federated learningbased methods either do not provide privacy guarantees or have vulnerability in terms of privacy leakage.In this paper,we combine the techniques of data perturbation and model perturbation mechanisms and propose a privacy-preserving mobility prediction algorithm,where we add noise to the transmitted model and the raw data collaboratively to protect user privacy and keep the mobility prediction performance.Extensive experimental results show that our proposed method significantly outperforms the existing stateof-the-art mobility prediction method in terms of defensive performance against practical attacks while having comparable mobility prediction performance,demonstrating its effectiveness.
文摘The scientific community recognizes the seriousness of rockbursts and the need for effective mitigation measures.The literature reports various successful applications of machine learning(ML)models for rockburst assessment;however,a significant question remains unanswered:How reliable are these models,and at what confidence level are classifications made?Typically,ML models output single rockburst grade even in the face of intricate and out-of-distribution samples,without any associated confidence value.Given the susceptibility of ML models to errors,it becomes imperative to quantify their uncertainty to prevent consequential failures.To address this issue,we propose a conformal prediction(CP)framework built on traditional ML models(extreme gradient boosting and random forest)to generate valid classifications of rockburst while producing a measure of confidence for its output.The proposed framework guarantees marginal coverage and,in most cases,conditional coverage on the test dataset.The CP was evaluated on a rockburst case in the Sanshandao Gold Mine in China,where it achieved high coverage and efficiency at applicable confidence levels.Significantly,the CP identified several“confident”classifications from the traditional ML model as unreliable,necessitating expert verification for informed decision-making.The proposed framework improves the reliability and accuracy of rockburst assessments,with the potential to bolster user confidence.
基金the National Natural Science Foundation of China under Grant Nos.U2268204,62172061 and 61662017National Key R&D Program of China under Grant Nos.2020YFB1711800 and 2020YFB1707900+1 种基金the Science and Technology Project of Sichuan Province under Grant Nos.2022YFG0155,2022YFG0157,2021GFW019,2021YFG0152,2021YFG0025,2020YFG0322the Guangxi Natural Science Foundation Project under Grant No.2021GXNSFAA220074.
文摘Predicting students’academic achievements is an essential issue in education,which can benefit many stakeholders,for instance,students,teachers,managers,etc.Compared with online courses such asMOOCs,students’academicrelateddata in the face-to-face physical teaching environment is usually sparsity,and the sample size is relativelysmall.It makes building models to predict students’performance accurately in such an environment even morechallenging.This paper proposes a Two-WayNeuralNetwork(TWNN)model based on the bidirectional recurrentneural network and graph neural network to predict students’next semester’s course performance using only theirprevious course achievements.Extensive experiments on a real dataset show that our model performs better thanthe baselines in many indicators.
基金Supported by NIH/National Cancer Institute Grant,No.R01CA138441 and No.R01CA269452UW Madison Centene Pancreas Cancer Collaborative Award,No.21-8568.
文摘BACKGROUND Pancreatic cancer is a leading cause of cancer-related deaths.Increased activity of the epidermal growth factor receptor(EGFR)is often observed in pancreatic cancer,and the small molecule EGFR inhibitor erlotinib has been approved for pancreatic cancer therapy by the food and drug administration.Nevertheless,erlotinib alone is ineffective and should be combined with other drugs to improve therapeutic outcomes.We previously showed that certain receptor tyrosine kinase inhibitors can increase mitochondrial membrane potential(Δψm),facilitate tumor cell uptake ofΔψm-sensitive agents,disrupt mitochondrial homeostasis,and subsequently trigger tumor cell death.Erlotinib has not been tested for this effect.AIM To determine whether erlotinib can elevateΔψm and increase tumor cell uptake ofΔψm-sensitive agents,subsequently triggering tumor cell death.METHODSΔψm-sensitive fluorescent dye was used to determine how erlotinib affectsΔψm in pancreatic adenocarcinoma(PDAC)cell lines.The viability of conventional and patient-derived primary PDAC cell lines in 2D-and 3D cultures was measured after treating cells sequentially with erlotinib and mitochondria-targeted ubiquinone(MitoQ),aΔψm-sensitive MitoQ.The synergy between erlotinib and MitoQ was then analyzed using SynergyFinder 2.0.The preclinical efficacy of the twodrug combination was determined using immune-compromised nude mice bearing PDAC cell line xenografts.RESULTS Erlotinib elevatedΔψm in PDAC cells,facilitating tumor cell uptake and mitochondrial enrichment ofΔψm-sensitive agents.MitoQ triggered caspase-dependent apoptosis in PDAC cells in culture if used at high doses,while erlotinib pretreatment potentiated low doses of MitoQ.SynergyFinder suggested that these drugs synergistically induced tumor cell lethality.Consistent with in vitro data,erlotinib and MitoQ combination suppressed human PDAC cell line xenografts in mice more effectively than single treatments of each agent.CONCLUSION Our findings suggest that a combination of erlotinib and MitoQ has the potential to suppress pancreatic tumor cell viability effectively.
文摘Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident severity.Deep learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and classification.Despite prior success,there is a need for an efficient system recognizing ASL in diverse road conditions.To address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient Descent.The framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved generalization.Sensitivity and factor contribution analyses identify influential predictors.Evaluated on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB dataset.The proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.
基金supported by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation“Research and System Development of Highway Asset Digitalization Technology inUse Based onHigh-PrecisionMap”(Project Number:202203)in part by Science and Technology Plan Project of Zhejiang Provincial Department of Transportation:Research and Demonstration Application of Key Technologies for Precise Sensing of Expressway Thrown Objects(No.202204).
文摘Traffic prediction already plays a significant role in applications like traffic planning and urban management,but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data.As well as to fulfil both long-termand short-termprediction objectives,a better representation of the temporal dependency and global spatial correlation of traffic data is needed.In order to do this,the Spatiotemporal Graph Neural Network(S-GNN)is proposed in this research as amethod for traffic prediction.The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables.In terms of modelling,the road network is initially represented as a spatiotemporal directed graph,with the features of the samples at the time step being captured by a convolution module.In order to assign varying attention weights to various adjacent area nodes of the target node,the adjacent areas information of nodes in the road network is then aggregated using a graph network.The data is output using a fully connected layer at the end.The findings show that S-GNN can improve short-and long-term traffic prediction accuracy to a greater extent;in comparison to the control model,the RMSE of S-GNN is reduced by about 0.571 to 9.288 and the MAE(Mean Absolute Error)by about 0.314 to 7.678.The experimental results on two real datasets,Pe MSD7(M)and PEMS-BAY,also support this claim.
基金supported by the National Natural Science Foundation of China(81825009,82071505,81901358)the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences(2021-I2MC&T-B-099,2019-I2M-5–006)+2 种基金the Program of Chinese Institute for Brain Research Beijing(2020-NKX-XM-12)the King’s College London-Peking University Health Science Center Joint Institute for Medical Research(BMU2020KCL001,BMU2019LCKXJ012)the National Key R&D Program of China(2021YFF1201103,2016YFC1307000).
文摘Background:Choosing the appropriate antipsychotic drug(APD)treatment for patients with schizophrenia(SCZ)can be challenging,as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers.Previous studies have indicated the association between treatment response and genetic and epigenetic factors,but no effective biomarkers have been identified.Hence,further research is imperative to enhance precision medicine in SCZ treatment.Methods:Participants with SCZ were recruited from two randomized trials.The discovery cohort was recruited from the CAPOC trial(n=2307)involved 6 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,Quetiapine,Aripiprazole,Ziprasidone,and Haloperidol/Perphenazine(subsequently equally assigned to one or the other)groups.The external validation cohort was recruited from the CAPEC trial(n=1379),which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine,Risperidone,and Aripiprazole groups.Additionally,healthy controls(n=275)from the local community were utilized as a genetic/epigenetic reference.The genetic and epigenetic(DNA methylation)risks of SCZ were assessed using the polygenic risk score(PRS)and polymethylation score,respectively.The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis,methylation quantitative trait loci,colocalization,and promoteranchored chromatin interaction.Machine learning was used to develop a prediction model for treatment response,which was evaluated for accuracy and clinical benefit using the area under curve(AUC)for classification,R^(2) for regression,and decision curve analysis.Results:Six risk genes for SCZ(LINC01795,DDHD2,SBNO1,KCNG2,SEMA7A,and RUFY1)involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response.The developed and externally validated prediction model,which incorporated clinical information,PRS,genetic risk score(GRS),and proxy methylation level(proxyDNAm),demonstrated positive benefits for a wide range of patients receiving different APDs,regardless of sex[discovery cohort:AUC=0.874(95%CI 0.867-0.881),R^(2)=0.478;external validation cohort:AUC=0.851(95%CI 0.841-0.861),R^(2)=0.507].Conclusions:This study presents a promising precision medicine approach to evaluate treatment response,which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.Trial registration Chinese Clinical Trial Registry(https://www.chictr.org.cn/),18 Aug 2009 retrospectively registered:CAPOC-ChiCTR-RNC-09000521(https://www.chictr.org.cn/showproj.aspx?proj=9014),CAPEC-ChiCTRRNC-09000522(https://www.chictr.org.cn/showproj.aspx?proj=9013).
基金supported by the Preparation and Characterization of Fogging Agents,Cooperative Project of China(Grant No.1900030040)Preparation and Test of Fogging Agents,Cooperative Project of China(Grant No.2200030085)。
文摘Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency,low cost and eco-friendly.Unfortunately,the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets.Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion.However,the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost.And the stabilization time of the aerosol is still only 30 min,which could not meet the requirements of the perdurable interference.Herein,to improve the stability of water-based aerosol and optimize the complex formulation efficiently,a theoretical calculation method based on thermodynamic entropy theory is proposed.All the factors that influence the shielding effect,including polyol,stabilizer,propellant,water and cosolvent,are considered within calculation.An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition,providing enough time for fighting the electro-optic weapon.Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed,which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process,and gives rise to an enhanced stability of the water-based aerosol.The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability,and provides the powerful guarantee to the homeland security.
基金the National Natural Science Fund of China(61471080)Training Plan for Young Backbone Teachers in Colleges and Universities of Henan Province(2018GGJS171).
文摘To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.
基金supported in part by the National Key Research and Development Program of China(Grant No.2020YFB1805005)in part by the National Natural Science Foundation of China(Grant No.62031019)in part by the European Commission through the H2020-MSCA-ITN META WIRELESS Research Project under Grant 956256。
文摘Channel prediction is critical to address the channel aging issue in mobile scenarios.Existing channel prediction techniques are mainly designed for discrete channel prediction,which can only predict the future channel in a fixed time slot per frame,while the other intra-frame channels are usually recovered by interpolation.However,these approaches suffer from a serious interpolation loss,especially for mobile millimeter-wave communications.To solve this challenging problem,we propose a tensor neural ordinary differential equation(TN-ODE)based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels.Specifically,inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning,we first utilize the neural ODE model to predict future continuous-time channels.To improve the channel prediction accuracy and reduce computational complexity,we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low-dimensional learnable transform.Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.
基金supported by the National Natural Science Foundation of China(Grant No.52374153).
文摘Traditional laboratory tests for measuring rock uniaxial compressive strength(UCS)are tedious and timeconsuming.There is a pressing need for more effective methods to determine rock UCS,especially in deep mining environments under high in-situ stress.Thus,this study aims to develop an advanced model for predicting the UCS of rockmaterial in deepmining environments by combining three boosting-basedmachine learning methods with four optimization algorithms.For this purpose,the Lead-Zinc mine in Southwest China is considered as the case study.Rock density,P-wave velocity,and point load strength index are used as input variables,and UCS is regarded as the output.Subsequently,twelve hybrid predictive models are obtained.Root mean square error(RMSE),mean absolute error(MAE),coefficient of determination(R2),and the proportion of the mean absolute percentage error less than 20%(A-20)are selected as the evaluation metrics.Experimental results showed that the hybridmodel consisting of the extreme gradient boostingmethod and the artificial bee colony algorithm(XGBoost-ABC)achieved satisfactory results on the training dataset and exhibited the best generalization performance on the testing dataset.The values of R2,A-20,RMSE,and MAE on the training dataset are 0.98,1.0,3.11 MPa,and 2.23MPa,respectively.The highest values of R2 and A-20(0.93 and 0.96),and the smallest RMSE and MAE values of 4.78 MPa and 3.76MPa,are observed on the testing dataset.The proposed hybrid model can be considered a reliable and effective method for predicting rock UCS in deep mines.
基金supported by the Joint Funds of the Chinese National Natural Science Foundation (NSFC)(Grant No.U2242213)the National Key Research and Development (R&D)Program of the Ministry of Science and Technology of China(Grant No. 2021YFC3000902)the National Science Foundation for Young Scholars (Grant No. 42205166)。
文摘Ensemble prediction is widely used to represent the uncertainty of single deterministic Numerical Weather Prediction(NWP) caused by errors in initial conditions(ICs). The traditional Singular Vector(SV) initial perturbation method tends only to capture synoptic scale initial uncertainty rather than mesoscale uncertainty in global ensemble prediction. To address this issue, a multiscale SV initial perturbation method based on the China Meteorological Administration Global Ensemble Prediction System(CMA-GEPS) is proposed to quantify multiscale initial uncertainty. The multiscale SV initial perturbation approach entails calculating multiscale SVs at different resolutions with multiple linearized physical processes to capture fast-growing perturbations from mesoscale to synoptic scale in target areas and combining these SVs by using a Gaussian sampling method with amplitude coefficients to generate initial perturbations. Following that, the energy norm,energy spectrum, and structure of multiscale SVs and their impact on GEPS are analyzed based on a batch experiment in different seasons. The results show that the multiscale SV initial perturbations can possess more energy and capture more mesoscale uncertainties than the traditional single-SV method. Meanwhile, multiscale SV initial perturbations can reflect the strongest dynamical instability in target areas. Their performances in global ensemble prediction when compared to single-scale SVs are shown to(i) improve the relationship between the ensemble spread and the root-mean-square error and(ii) provide a better probability forecast skill for atmospheric circulation during the late forecast period and for short-to medium-range precipitation. This study provides scientific evidence and application foundations for the design and development of a multiscale SV initial perturbation method for the GEPS.
基金supported by Zanjan University of Medical Sciences,Zanjan,Iran(Grant Number:A-12-1244-16&Ethical Code:IR.ZUMS.REC.1399.316).
文摘The Myc gene is the essential oncogene in triple-negative breast cancer(TNBC).This study investigates the synergistic effects of combining Myc decoy oligodeoxynucleotides-encapsulated niosomes-selenium hybrid nanocarriers with X-irradiation exposure on the MDA-MB-468 cell line.Decoy and scramble ODNs for Myc transcription factor were designed and synthesized based on promoter sequences of the Bcl2 gene.The nanocarriers were synthesized by loading Myc ODNs and selenium into chitosan(Chi-Se-DEC),which was then encapsulated in niosome-nanocarriers(NISM@Chi-Se-DEC).FT-IR,DLS,FESEM,and hemolysis tests were applied to confirm its characterization and physicochemical properties.Moreover,cellular uptake,cellular toxicity,apoptosis,cell cycle,and scratch repair assays were performed to evaluate its anticancer effects on cancer cells.All anticancer assessments were repeated under X-ray irradiation conditions(fractionated 2Gy).Physicochemical characteristics of niosomes containing SeNPs and ODNs showed that it is synthesized appropriately.It revealed that the anticancer effect of NISM@Chi-Se-DEC can be significantly improved in combination with X-ray irradiation treatment.It can be concluded that NISM@Chi-Se-DEC nanocarriers have the potential as a therapeutic agent for cancer treatment,particularly in combination with radiation therapy and in-vivo experiments are necessary to confirm the efficacy of this nano-drug.
