Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay di...Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.展开更多
Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-...Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.展开更多
Autologous bone marrow-derived mesenchymal stem cells(BMSCs)have been shown to promote osteogenesis;however,the effects of allogeneic BMSCs(allo-BMSCs)on bone regeneration remain unclear.Therefore,we explored the bone...Autologous bone marrow-derived mesenchymal stem cells(BMSCs)have been shown to promote osteogenesis;however,the effects of allogeneic BMSCs(allo-BMSCs)on bone regeneration remain unclear.Therefore,we explored the bone regeneration promotion effect of allo-BMSCs in 3D-printed autologous bone particle(ABP)scaffolds.First,we concurrently printed scaffolds with polycaprolactone,ABPs,and allo-BMSCs for appropriate support,providing bioactive factors and seed cells to promote osteogenesis.In vitro studies showed that ABP scaffolds promoted allo-BMSC osteogenic differentiation.In vivo studies revealed that the implantation of scaffolds loaded with ABPs and allo-BMSCs into canine skull defects for nine months promoted osteogenesis.Further experiments suggested that only a small portion of implanted allo-BMSCs survived and differentiated into vascular endothelial cells,chondrocytes,and osteocytes.The implanted allo-BMSCs released stromal cell-derived factor 1 through paracrine signaling to recruit native BMSCs into the defect,promoting bone regeneration.This study contributes to our understanding of allo-BMSCs,providing information relevant to their future application.展开更多
BACKGROUND Malignant transformation(MT)of mature cystic teratoma(MCT)has a poor prognosis,especially in advanced cases.Concurrent chemoradiotherapy(CCRT)has an inhibitory effect on MT.CASE SUMMARY Herein,we present a ...BACKGROUND Malignant transformation(MT)of mature cystic teratoma(MCT)has a poor prognosis,especially in advanced cases.Concurrent chemoradiotherapy(CCRT)has an inhibitory effect on MT.CASE SUMMARY Herein,we present a case in which CCRT had a reduction effect preoperatively.A 73-year-old woman with pyelonephritis was referred to our hospital.Computed tomography revealed right hydronephrosis and a 6-cm pelvic mass.Endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)revealed squamous cell carci-noma.The patient was diagnosed with MT of MCT.Due to her poor general con-dition and renal malfunction,we selected CCRT,expecting fewer adverse effects.After CCRT,her performance status improved,and the tumor size was reduced;surgery was performed.Five months postoperatively,the patient developed dis-semination and lymph node metastases.Palliative chemotherapy was ineffective.She died 18 months after treatment initiation.CONCLUSION EUS-FNB was useful in the diagnosis of MT of MCT;CCRT suppressed the disea-se and improved quality of life.展开更多
Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predispositio...Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predisposition,environmental exposure,unhealthy lifestyle habits,and existing medical conditions.Although existing machine learning-based methods for predicting stroke patients’health status have made significant progress,limitations remain in terms of prediction accuracy,model explainability,and system optimization.This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence(XAI)for predicting the health status of stroke patients.First,we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients,enabling the parallel prediction of multiple health indicators.Second,we develop a multi-task Area Under Curve(AUC)optimization algorithm based on adaptive low-rank representation,which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization.Additionally,the model’s explainability is analyzed through the stability analysis of SHAP values.Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency.展开更多
Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progr...Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.展开更多
As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as...As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems.This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning,termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation(MAMGBR)model,specifically designed to optimize group-buying recommendations on e-commerce platforms.The core dataset of this study comes from the Chinese maternal and infant e-commerce platform“Beibei,”encompassing approximately 430,000 successful groupbuying actions and over 120,000 users.Themodel focuses on twomain tasks:recommending items for group organizers(Task Ⅰ)and recommending participants for a given group-buying event(Task Ⅱ).In model evaluation,MAMGBR achieves an MRR@10 of 0.7696 for Task I,marking a 20.23%improvement over baseline models.Furthermore,in Task II,where complex interaction patterns prevail,MAMGBR utilizes auxiliary loss functions to effectively model the multifaceted roles of users,items,and participants,leading to a 24.08%increase in MRR@100 under a 1:99 sample ratio.Experimental results show that compared to benchmark models,such as NGCF and EATNN,MAMGBR’s integration ofmulti-head attentionmechanisms,expert networks,and gating mechanisms enables more accurate modeling of user preferences and social associations within group-buying scenarios,significantly enhancing recommendation accuracy and platform group-buying success rates.展开更多
Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–...Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–PM_(2.5)and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM_(2.5)concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R^(2))of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R^(2)increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R^(2)and RMSE of PM_(2.5)retrieval by MTL are 0.84 and 13.76μg m~(-3)respectively.Compared with RF,the R^(2)increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R^(2)and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in Within-EE.For PM_(2.5)retrieval,MTL exhibits an increase of 0.05 in R^(2),a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM_(2.5)retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM_(2.5)concentration and AOD.展开更多
Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecas...Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System global model(ECMWF-IFS)over 14 offshore weather stations along the coast of Shandong Province,this study introduces a multi-task learning(MTL)model(TabNet-MTL),which significantly improves the forecast bias of near-surface wind direction and speed simultaneously.TabNet-MTL adopts the feature engineering method,utilizes mean square error as the loss function,and employs the 5-fold cross validation method to ensure the generalization ability of the trained model.It demonstrates superior skills in wind field correction across different forecast lead times over all stations compared to its single-task version(TabNet-STL)and three other popular single-task learning models(Random Forest,LightGBM,and XGBoost).Results show that it significantly reduces root mean square error of the ECMWF-IFS wind speed forecast from 2.20 to 1.25 m s−1,and increases the forecast accuracy of wind direction from 50%to 65%.As an explainable deep learning model,the weather stations and long-term temporal statistics of near-surface wind speed are identified as the most influential variables for TabNet-MTL in constructing its feature engineering.展开更多
The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches...The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.展开更多
Small cell lung cancer(SCLC)constitutes approximately 15%of all lung cancer cases,characterized by rapid tumor growth,a high pro-liferation rate,and a propensity for early metastasis.1 Approximately one-third of SCLC ...Small cell lung cancer(SCLC)constitutes approximately 15%of all lung cancer cases,characterized by rapid tumor growth,a high pro-liferation rate,and a propensity for early metastasis.1 Approximately one-third of SCLC patients are diagnosed at the limited-stage.Histor-ically,the standard of care for these patients has been 4-6 cycles of concurrent chemoradiotherapy(cCRT),with the exception of a minor-ity of early-stage T1-2N0 patients who may undergo radical surgery.2 Despite an initial high sensitivity to treatment,over 50%of patients experience disease recurrence within two years,with a median overall survival(OS)ranging from 16 to 24 months.For the past three decades,while there have been novel explorations in radiotherapy dosing and fractionation,the treatment paradigm for limited-stage SCLC(LS-SCLC)has remained largely unchanged,with no significant improvement in patient survival outcomes.展开更多
Defective phononic crystals(PnCs)have enabled spatial localization and quantitative amplification of elastic wave energy.Most previous research has focused on applications such as narrow-bandpass filters,ultrasonic se...Defective phononic crystals(PnCs)have enabled spatial localization and quantitative amplification of elastic wave energy.Most previous research has focused on applications such as narrow-bandpass filters,ultrasonic sensors,and piezoelectric energy harvesters,typically operating under the assumption of an external elastic wave incidence.Recently,a novel approach that uses defective PnCs as ultrasonic actuators to generate amplified waves has emerged.However,the existing studies are limited to the generation of either longitudinal or bending waves,with no research addressing the concurrent generation of both.Hence,this paper proposes a straightforward methodology for the concurrent generation and amplification of both wave types utilizing defect modes at independent defect-band frequencies.Bimorph piezoelectric elements are attached to the defect,with each element connected to independent external voltage sources.By precisely adjusting the magnitude and temporal phase differences between the voltage sources,concurrently amplified wave generation is achieved.The paper highlights the advantages of the proposed analytical model.This model is both computationally time-efficient and accurate,in comparison with the COMSOL simulation results.For instance,in case studies,the analytical model reduces the computational time from one hour to mere seconds,while maintaining acceptable error rates of 1%in peak frequencies.This concurrent wave-generation methodology opens new avenues for applications in rotating machinery fault diagnosis,structural health monitoring,and medical imaging.展开更多
Objective:Radiotherapy(RT)is the definitive treatment for stageⅡnasopharyngeal carcinoma(NPC),which is classified as stagesⅠA andⅠB in the latest ninth edition of American Joint Committee on Cancer(AJCC)/Union for ...Objective:Radiotherapy(RT)is the definitive treatment for stageⅡnasopharyngeal carcinoma(NPC),which is classified as stagesⅠA andⅠB in the latest ninth edition of American Joint Committee on Cancer(AJCC)/Union for International Cancer Control(UICC).A crucial question is whether concurrent chemo-radiotherapy(CCRT)could derive additional benefits to this recent“down-staging”subgroup of NPC patients.This study aimed to interrogate clinical and radiomic features for predicting 5-year progression-free survival(PFS)of stageⅡNPC treated with RT alone or CCRT.Methods:Imaging and clinical data of 166 stageⅡNPC(eighth edition AJCC/UICC)patients were collected.Data were allocated into training,internal testing,and external testing sets.For each case,851 radiomic features were extracted and 10 clinical features were collected.Radiomic and clinical features most associated with the 5-year PFS were selected separately.A combined model was developed using multivariate logistic regression by integrating selected features and treatment option to predict 5-year PFS.Model performances were evaluated by area under the receiver operating curve(AUC),prediction accuracy,and decision curve analysis.Survival analyses including Kaplan-Meier analysis and Cox regression model were performed for further analysis.Results:Thirteen radiomic features,three clinical features,and treatment option were considered for model development.The combined model showed higher prognostic performance than using either.For the merged testing set(internal and external testing sets),AUC is 0.76(combined)vs.0.56-0.80(clinical or radiomic alone)and accuracy is 0.75(combined)vs.0.62-0.73(clinical or radiomic alone).Kaplan-Meier analysis using the combined model showed significant discrimination in PFS of the predicted low-risk and high-risk groups in the training and internal testing cohorts(P<0.05).Conclusions:Integrating with clinical and radiomic features could provide prognostic information on 5-year PFS under either treatment regimen,guiding individualized decisions of chemotherapy based on the predicted treatment outcome.展开更多
A novel Additive Manufacturing(AM)-driven concurrent design strategy based on the beam characterization model considering strength constraints is proposed.The lattice topology,radius size,Building Orientation(BO),and ...A novel Additive Manufacturing(AM)-driven concurrent design strategy based on the beam characterization model considering strength constraints is proposed.The lattice topology,radius size,Building Orientation(BO),and structural yield strength can be simultaneously adjusted by integrating the overall process-structure-performance relationship of the AM process into the optimization.Specifically,the transverse isotropic material model is adopted to describe the material properties induced by the layer-by-layer manner of additive manufacturing.To bolster lattice strength performance,the stress constraints and ratio constraints of lattice struts are employed.The Tsai-Wu yield criterion is implemented to characterize the lattice strut's strength,while the P-norm method streamlines the handling of multiple constraints,minimizing computational overhead.Moreover,the gradient-based optimization model is established,where both the individual struts diameters and BO can be designed,and the buckling-prone spatial struts are strategically eliminated to improve the lattice strength further.Furthermore,several typical structures are optimized to verify the effectiveness of the proposed method.The optimized results are quite encouraging since the heterogeneous lattice structures with optimized BO obtained by the strength-based concurrent method show a remarkably improved performance compared to traditional designs.展开更多
Objectives:The optimal treatment strategy for early-stage natural killer/T-cell lymphoma(NKTCL)remains unclear.This study aimed to evaluate and compare the clinical outcomes and adverse events(AEs)associated with two ...Objectives:The optimal treatment strategy for early-stage natural killer/T-cell lymphoma(NKTCL)remains unclear.This study aimed to evaluate and compare the clinical outcomes and adverse events(AEs)associated with two treatment regimens for early-stage NKTCL:pegaspargase with concurrent radiation therapy(P+CCRT)and pegaspargase,gemcitabine,and oxaliplatin(P-GEMOX)with sequential radiation therapy(SERT).Propensity score matching(PSM)was employed to ensure balanced comparison between these regimens.Methods:We assessed the efficacy of P+CCRT from a phase II trial and P-GEMOX combined with SERT using real-world data.PSM was conducted at a 1:1 ratio with a caliper of 0.18 to align baseline characteristics between the treatment groups.Key outcomes analyzed included overall response rate(ORR),complete response rate(CR),progression-free survival(PFS),overall survival(OS),and AEs.Results:Following PSM,the study included 52 patients,with 26 in each treatment group.Baseline characteristics were balanced between the cohorts.The ORR for P+CCRT group was 100.0%compared to 88.5%for P-GEMOX+SERT group,and the CR rates was 100.0%vs.76.9%,respectively.The 3-year OS and PFS rates were both 92.3%for P+CCRT,while P-GEMOX showed 92.3%OS and 80.8%PFS.Adverse events,including hematological toxicity,hepatotoxicity,and coagulation dysfunction,were comparable between the two regimens.Conclusion:P+CCRT is associated with comparable clinical outcomes compared to P-GEMOX+SERT in early-stage NKTCL,with comparable adverse events.Additionally,P+CCRT offers the benefit of a more streamlined treatment regimen with a shorter cycle.Given these encouraging results,further cohort studies are needed to validate these results.展开更多
Accurate forecasting of blast furnace gas(BFG)production is an essential prerequisite for reasonable energy scheduling and management to reduce carbon emissions.Coupling forecasting between BFG generation and consumpt...Accurate forecasting of blast furnace gas(BFG)production is an essential prerequisite for reasonable energy scheduling and management to reduce carbon emissions.Coupling forecasting between BFG generation and consumption dynamics was taken as the research object.A multi-task learning(MTL)method for BFG forecasting was proposed,which integrated a coupling correlation coefficient(CCC)and an inverted transformer structure.The CCC method could enhance key information extraction by establishing relationships between multiple prediction targets and relevant factors,while MTL effectively captured the inherent correlations between BFG generation and consumption.Finally,a real-world case study was conducted to compare the proposed model with four benchmark models.Results indicated significant reductions in average mean absolute percentage error by 33.37%,achieving 1.92%,with a computational time of 76 s.The sensitivity analysis of hyperparameters such as learning rate,batch size,and units of the long short-term memory layer highlights the importance of hyperparameter tuning.展开更多
BACKGROUND Patients with concurrent acute biliary pancreatitis(ABP)and acute cholangitis(AC)may experience exacerbated clinical consequences due to bile duct stones.However,studies exploring this topic remain limited....BACKGROUND Patients with concurrent acute biliary pancreatitis(ABP)and acute cholangitis(AC)may experience exacerbated clinical consequences due to bile duct stones.However,studies exploring this topic remain limited.AIM To compare the clinical presentation and outcomes of patients experiencing AC with and without ABP.METHODS This single-center retrospective cohort study included 358 patients with AC who underwent endoscopic retrograde cholangiopancreatography(ERCP)between January 2016 and December 2017.Patients were divided into two groups:AC with ABP(n=90)and AC without ABP(n=268).Clinical characteristics,laboratory data,ERCP results,primary study outcome[intensive care unit(ICU)admission],and secondary outcomes including 30-day mortality,length of hospital stay,and 30-day readmission rate were analyzed and compared.RESULTS All patients in the AC with ABP group had interstitial pancreatitis.The AC with ABP group had significantly higher white cell count(WBC)counts(13.1×10^(3)/μL vs 10.4×10^(3)/μL,P=0.007)and more abnormal WBC results(61.1%vs 42.3%,P=0.015).Liver biochemical tests,AC severity,ERCP success,adverse events,ICU admissions,30-day mortality,hospital stay,and readmission rates did not differ significantly between the two groups.Univariate analysis showed no significant link between concurrent ABP and ICU admission,although significance was marginal in moderate/severe ABP cases(P=0.051).In the multivariate analysis,age(P=0.035)and cardiovascular dysfunction(P<0.001)were independently associated with length of ICU stay.CONCLUSION Concurrent interstitial ABP and AC did not significantly affect outcomes.Age and cardiovascular dysfunction were stronger predictors of ICU admission and should guide clinical monitoring and management.展开更多
The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.How...The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.展开更多
An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained c...An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained control within in a transaction. Based on the classical nested locking protocol and the speculative concurrency control approach, a two-shadow adaptive concurrency control protocol, which combines the Sacrifice based Optimistic Concurrency Control (OPT-Sacrifice) and High Priority two-phase locking (HP2PL) algorithms together to support both optimistic and pessimistic shadow of each sub-transaction, has been proposed to increase the likelihood of successful timely commitment and to avoid unnecessary replication overload.展开更多
Secure real-time databases must simultaneously satisfy two requirements in guaranteeing data security and minimizing the missing deadlines ratio of transactions. However, these two requirements can conflict with each ...Secure real-time databases must simultaneously satisfy two requirements in guaranteeing data security and minimizing the missing deadlines ratio of transactions. However, these two requirements can conflict with each other and achieve one requirement is to sacrifice the other. This paper presents a secure real-time concurrency control protocol based on optimistic method. The concurrency control protocol incorporates security constraints in a real-time optimistic concurrency control protocol and makes a suitable tradeoff between security and real-time requirements by introducing secure influence factor and real-time influence factor. The experimental results show the concurrency control protocol achieves data security without degrading real-time perform ance significantly.展开更多
文摘Deep neural networks(DNNs)are effective in solving both forward and inverse problems for nonlinear partial differential equations(PDEs).However,conventional DNNs are not effective in handling problems such as delay differential equations(DDEs)and delay integrodifferential equations(DIDEs)with constant delays,primarily due to their low regularity at delayinduced breaking points.In this paper,a DNN method that combines multi-task learning(MTL)which is proposed to solve both the forward and inverse problems of DIDEs.The core idea of this approach is to divide the original equation into multiple tasks based on the delay,using auxiliary outputs to represent the integral terms,followed by the use of MTL to seamlessly incorporate the properties at the breaking points into the loss function.Furthermore,given the increased training dificulty associated with multiple tasks and outputs,we employ a sequential training scheme to reduce training complexity and provide reference solutions for subsequent tasks.This approach significantly enhances the approximation accuracy of solving DIDEs with DNNs,as demonstrated by comparisons with traditional DNN methods.We validate the effectiveness of this method through several numerical experiments,test various parameter sharing structures in MTL and compare the testing results of these structures.Finally,this method is implemented to solve the inverse problem of nonlinear DIDE and the results show that the unknown parameters of DIDE can be discovered with sparse or noisy data.
基金The National Natural Science Foundation of China(62136008,62293541)The Beijing Natural Science Foundation(4232056)The Beijing Nova Program(20240484514).
文摘Cooperative multi-agent reinforcement learning(MARL)is a key technology for enabling cooperation in complex multi-agent systems.It has achieved remarkable progress in areas such as gaming,autonomous driving,and multi-robot control.Empowering cooperative MARL with multi-task decision-making capabilities is expected to further broaden its application scope.In multi-task scenarios,cooperative MARL algorithms need to address 3 types of multi-task problems:reward-related multi-task,arising from different reward functions;multi-domain multi-task,caused by differences in state and action spaces,state transition functions;and scalability-related multi-task,resulting from the dynamic variation in the number of agents.Most existing studies focus on scalability-related multitask problems.However,with the increasing integration between large language models(LLMs)and multi-agent systems,a growing number of LLM-based multi-agent systems have emerged,enabling more complex multi-task cooperation.This paper provides a comprehensive review of the latest advances in this field.By combining multi-task reinforcement learning with cooperative MARL,we categorize and analyze the 3 major types of multi-task problems under multi-agent settings,offering more fine-grained classifications and summarizing key insights for each.In addition,we summarize commonly used benchmarks and discuss future directions of research in this area,which hold promise for further enhancing the multi-task cooperation capabilities of multi-agent systems and expanding their practical applications in the real world.
基金supported by the Science and Technology Development Fund of the Fourth Military Medical University(No.2016XB051)the Military Medical Promotion Plan of the Fourth Military Medical University(No.2016TSA-005)+2 种基金the Science and Technology Program of Guangzhou(No.201604040002)the Youth Development Project of Air Force Medical University(No.21QNPY072)the Xijing Hospital Booster Program(No.XJZT24CZ10).
文摘Autologous bone marrow-derived mesenchymal stem cells(BMSCs)have been shown to promote osteogenesis;however,the effects of allogeneic BMSCs(allo-BMSCs)on bone regeneration remain unclear.Therefore,we explored the bone regeneration promotion effect of allo-BMSCs in 3D-printed autologous bone particle(ABP)scaffolds.First,we concurrently printed scaffolds with polycaprolactone,ABPs,and allo-BMSCs for appropriate support,providing bioactive factors and seed cells to promote osteogenesis.In vitro studies showed that ABP scaffolds promoted allo-BMSC osteogenic differentiation.In vivo studies revealed that the implantation of scaffolds loaded with ABPs and allo-BMSCs into canine skull defects for nine months promoted osteogenesis.Further experiments suggested that only a small portion of implanted allo-BMSCs survived and differentiated into vascular endothelial cells,chondrocytes,and osteocytes.The implanted allo-BMSCs released stromal cell-derived factor 1 through paracrine signaling to recruit native BMSCs into the defect,promoting bone regeneration.This study contributes to our understanding of allo-BMSCs,providing information relevant to their future application.
文摘BACKGROUND Malignant transformation(MT)of mature cystic teratoma(MCT)has a poor prognosis,especially in advanced cases.Concurrent chemoradiotherapy(CCRT)has an inhibitory effect on MT.CASE SUMMARY Herein,we present a case in which CCRT had a reduction effect preoperatively.A 73-year-old woman with pyelonephritis was referred to our hospital.Computed tomography revealed right hydronephrosis and a 6-cm pelvic mass.Endoscopic ultrasound-guided fine-needle biopsy(EUS-FNB)revealed squamous cell carci-noma.The patient was diagnosed with MT of MCT.Due to her poor general con-dition and renal malfunction,we selected CCRT,expecting fewer adverse effects.After CCRT,her performance status improved,and the tumor size was reduced;surgery was performed.Five months postoperatively,the patient developed dis-semination and lymph node metastases.Palliative chemotherapy was ineffective.She died 18 months after treatment initiation.CONCLUSION EUS-FNB was useful in the diagnosis of MT of MCT;CCRT suppressed the disea-se and improved quality of life.
基金funded by the Excellent Talent Training Funding Project in Dongcheng District,Beijing,with project number 2024-dchrcpyzz-9.
文摘Predicting the health status of stroke patients at different stages of the disease is a critical clinical task.The onset and development of stroke are affected by an array of factors,encompassing genetic predisposition,environmental exposure,unhealthy lifestyle habits,and existing medical conditions.Although existing machine learning-based methods for predicting stroke patients’health status have made significant progress,limitations remain in terms of prediction accuracy,model explainability,and system optimization.This paper proposes a multi-task learning approach based on Explainable Artificial Intelligence(XAI)for predicting the health status of stroke patients.First,we design a comprehensive multi-task learning framework that utilizes the task correlation of predicting various health status indicators in patients,enabling the parallel prediction of multiple health indicators.Second,we develop a multi-task Area Under Curve(AUC)optimization algorithm based on adaptive low-rank representation,which removes irrelevant information from the model structure to enhance the performance of multi-task AUC optimization.Additionally,the model’s explainability is analyzed through the stability analysis of SHAP values.Experimental results demonstrate that our approach outperforms comparison algorithms in key prognostic metrics F1 score and Efficiency.
基金jointly supported by the National Natural Science Foundation of China(Grant Nos.42075138 and 42375147)the Program on Key Basic Research Project of Jiangsu(Grant No.BE2023829)。
文摘Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and mitigation.Recently,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging behind.At present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep learning.However,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting applications.Therefore,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and intensity.For TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial features.For real-time intensity estimation,multi-task learning acts as an implicit time-series enhancement.The model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity predictions.Since multiple tasks are correlated,the loss function of 12 h and 24 h are corrected.After testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.
基金supported by the Key Research and Development Program of Heilongjiang Province(No.2022ZX01A35).
文摘As the group-buying model shows significant progress in attracting new users,enhancing user engagement,and increasing platform profitability,providing personalized recommendations for group-buying users has emerged as a new challenge in the field of recommendation systems.This paper introduces a group-buying recommendation model based on multi-head attention mechanisms and multi-task learning,termed the Multi-head Attention Mechanisms and Multi-task Learning Group-Buying Recommendation(MAMGBR)model,specifically designed to optimize group-buying recommendations on e-commerce platforms.The core dataset of this study comes from the Chinese maternal and infant e-commerce platform“Beibei,”encompassing approximately 430,000 successful groupbuying actions and over 120,000 users.Themodel focuses on twomain tasks:recommending items for group organizers(Task Ⅰ)and recommending participants for a given group-buying event(Task Ⅱ).In model evaluation,MAMGBR achieves an MRR@10 of 0.7696 for Task I,marking a 20.23%improvement over baseline models.Furthermore,in Task II,where complex interaction patterns prevail,MAMGBR utilizes auxiliary loss functions to effectively model the multifaceted roles of users,items,and participants,leading to a 24.08%increase in MRR@100 under a 1:99 sample ratio.Experimental results show that compared to benchmark models,such as NGCF and EATNN,MAMGBR’s integration ofmulti-head attentionmechanisms,expert networks,and gating mechanisms enables more accurate modeling of user preferences and social associations within group-buying scenarios,significantly enhancing recommendation accuracy and platform group-buying success rates.
基金supported by the National Natural Science Foundation of China(Grant Nos.42030708,42375138,42030608,42105128,42075079)the Opening Foundation of Key Laboratory of Atmospheric Sounding,China Meteorological Administration(CMA),and the CMA Research Center on Meteorological Observation Engineering Technology(Grant No.U2021Z03),and the Opening Foundation of the Key Laboratory of Atmospheric Chemistry,CMA(Grant No.2022B02)。
文摘Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5μm(PM_(2.5))play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD–PM_(2.5)and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM_(2.5)concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R^(2))of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R^(2)increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55%.The R^(2)and RMSE of PM_(2.5)retrieval by MTL are 0.84 and 13.76μg m~(-3)respectively.Compared with RF,the R^(2)increases by 0.06,the RMSE decreases by 4.55μg m~(-3),and the Within-EE increases by 7.28%.Additionally,compared to DNN,MTL shows an increase of 0.01 in R^(2)and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89%in Within-EE.For PM_(2.5)retrieval,MTL exhibits an increase of 0.05 in R^(2),a decrease of 1.76μg m~(-3)in RMSE,and an increase of 6.83%in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM_(2.5)retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM_(2.5)concentration and AOD.
基金the National Key Research and Development Plan of China[Grant No.2023YFB3002400]the Shanghai 2021 Natural Science Foundation[Grant Nos.21ZR1420400 and 21ZR1419800]+1 种基金the Shanghai 2023 Natural Science Foundation[Grant No.23ZR1463000]the Shandong Provincial Meteorological Bureau Scientific Research Project[Grant No.2023SDBD05].
文摘Accurate short-term forecast of offshore wind fields is still challenging for numerical weather prediction models.Based on three years of 48-hour forecast data from the European Centre for Medium-Range Weather Forecasts Integrated Forecasting System global model(ECMWF-IFS)over 14 offshore weather stations along the coast of Shandong Province,this study introduces a multi-task learning(MTL)model(TabNet-MTL),which significantly improves the forecast bias of near-surface wind direction and speed simultaneously.TabNet-MTL adopts the feature engineering method,utilizes mean square error as the loss function,and employs the 5-fold cross validation method to ensure the generalization ability of the trained model.It demonstrates superior skills in wind field correction across different forecast lead times over all stations compared to its single-task version(TabNet-STL)and three other popular single-task learning models(Random Forest,LightGBM,and XGBoost).Results show that it significantly reduces root mean square error of the ECMWF-IFS wind speed forecast from 2.20 to 1.25 m s−1,and increases the forecast accuracy of wind direction from 50%to 65%.As an explainable deep learning model,the weather stations and long-term temporal statistics of near-surface wind speed are identified as the most influential variables for TabNet-MTL in constructing its feature engineering.
基金supported by the research on key technologies for monitoring and identifying drug abuse of anesthetic drugs and psychotropic drugs,and intervention for addiction(No.2023YFC3304200)the program of a study on the diagnosis of addiction to synthetic cannabinoids and methods of assessing the risk of abuse(No.2022YFC3300905)+1 种基金the program of Ab initio design and generation of AI models for small molecule ligands based on target structures(No.2022PE0AC03)ZHIJIANG LAB.
文摘The accurate prediction of drug absorption,distribution,metabolism,excretion,and toxicity(ADMET)properties represents a crucial step in early drug development for reducing failure risk.Current deep learning approaches face challenges with data sparsity and information loss due to single-molecule representation limitations and isolated predictive tasks.This research proposes molecular properties prediction with parallel-view and collaborative learning(MolP-PC),a multi-view fusion and multi-task deep learning framework that integrates 1D molecular fingerprints(MFs),2D molecular graphs,and 3D geometric representations,incorporating an attention-gated fusion mechanism and multi-task adaptive learning strategy for precise ADMET property predictions.Experimental results demonstrate that MolP-PC achieves optimal performance in 27 of 54 tasks,with its multi-task learning(MTL)mechanism significantly enhancing predictive performance on small-scale datasets and surpassing single-task models in 41 of 54 tasks.Additional ablation studies and interpretability analyses confirm the significance of multi-view fusion in capturing multi-dimensional molecular information and enhancing model generalization.A case study examining the anticancer compound Oroxylin A demonstrates MolP-PC’s effective generalization in predicting key pharmacokinetic parameters such as half-life(T0.5)and clearance(CL),indicating its practical utility in drug modeling.However,the model exhibits a tendency to underestimate volume of distribution(VD),indicating potential for improvement in analyzing compounds with high tissue distribution.This study presents an efficient and interpretable approach for ADMET property prediction,establishing a novel framework for molecular optimization and risk assessment in drug development.
基金supported by the Young Talents Program of Jiangsu Cancer Hospital(grant number:QL201813).
文摘Small cell lung cancer(SCLC)constitutes approximately 15%of all lung cancer cases,characterized by rapid tumor growth,a high pro-liferation rate,and a propensity for early metastasis.1 Approximately one-third of SCLC patients are diagnosed at the limited-stage.Histor-ically,the standard of care for these patients has been 4-6 cycles of concurrent chemoradiotherapy(cCRT),with the exception of a minor-ity of early-stage T1-2N0 patients who may undergo radical surgery.2 Despite an initial high sensitivity to treatment,over 50%of patients experience disease recurrence within two years,with a median overall survival(OS)ranging from 16 to 24 months.For the past three decades,while there have been novel explorations in radiotherapy dosing and fractionation,the treatment paradigm for limited-stage SCLC(LS-SCLC)has remained largely unchanged,with no significant improvement in patient survival outcomes.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea,funded by the Ministry of Education(No.2022R1I1A1A01056406)。
文摘Defective phononic crystals(PnCs)have enabled spatial localization and quantitative amplification of elastic wave energy.Most previous research has focused on applications such as narrow-bandpass filters,ultrasonic sensors,and piezoelectric energy harvesters,typically operating under the assumption of an external elastic wave incidence.Recently,a novel approach that uses defective PnCs as ultrasonic actuators to generate amplified waves has emerged.However,the existing studies are limited to the generation of either longitudinal or bending waves,with no research addressing the concurrent generation of both.Hence,this paper proposes a straightforward methodology for the concurrent generation and amplification of both wave types utilizing defect modes at independent defect-band frequencies.Bimorph piezoelectric elements are attached to the defect,with each element connected to independent external voltage sources.By precisely adjusting the magnitude and temporal phase differences between the voltage sources,concurrently amplified wave generation is achieved.The paper highlights the advantages of the proposed analytical model.This model is both computationally time-efficient and accurate,in comparison with the COMSOL simulation results.For instance,in case studies,the analytical model reduces the computational time from one hour to mere seconds,while maintaining acceptable error rates of 1%in peak frequencies.This concurrent wave-generation methodology opens new avenues for applications in rotating machinery fault diagnosis,structural health monitoring,and medical imaging.
文摘Objective:Radiotherapy(RT)is the definitive treatment for stageⅡnasopharyngeal carcinoma(NPC),which is classified as stagesⅠA andⅠB in the latest ninth edition of American Joint Committee on Cancer(AJCC)/Union for International Cancer Control(UICC).A crucial question is whether concurrent chemo-radiotherapy(CCRT)could derive additional benefits to this recent“down-staging”subgroup of NPC patients.This study aimed to interrogate clinical and radiomic features for predicting 5-year progression-free survival(PFS)of stageⅡNPC treated with RT alone or CCRT.Methods:Imaging and clinical data of 166 stageⅡNPC(eighth edition AJCC/UICC)patients were collected.Data were allocated into training,internal testing,and external testing sets.For each case,851 radiomic features were extracted and 10 clinical features were collected.Radiomic and clinical features most associated with the 5-year PFS were selected separately.A combined model was developed using multivariate logistic regression by integrating selected features and treatment option to predict 5-year PFS.Model performances were evaluated by area under the receiver operating curve(AUC),prediction accuracy,and decision curve analysis.Survival analyses including Kaplan-Meier analysis and Cox regression model were performed for further analysis.Results:Thirteen radiomic features,three clinical features,and treatment option were considered for model development.The combined model showed higher prognostic performance than using either.For the merged testing set(internal and external testing sets),AUC is 0.76(combined)vs.0.56-0.80(clinical or radiomic alone)and accuracy is 0.75(combined)vs.0.62-0.73(clinical or radiomic alone).Kaplan-Meier analysis using the combined model showed significant discrimination in PFS of the predicted low-risk and high-risk groups in the training and internal testing cohorts(P<0.05).Conclusions:Integrating with clinical and radiomic features could provide prognostic information on 5-year PFS under either treatment regimen,guiding individualized decisions of chemotherapy based on the predicted treatment outcome.
基金co-supported by National Key R&D Program of China(No.2022YFB4602003)Key Project of National Natural Science Foundation of China(No.12032018)+2 种基金Guangdong Basic and Applied Basic Research Foundation(No.2022A1515110489)National Natural Science Foundation of China-China Academy of General Technology Joint Fund for Basic Research(No.52375380)National Key Research and Development Program of China(No.2022YFB3402200)。
文摘A novel Additive Manufacturing(AM)-driven concurrent design strategy based on the beam characterization model considering strength constraints is proposed.The lattice topology,radius size,Building Orientation(BO),and structural yield strength can be simultaneously adjusted by integrating the overall process-structure-performance relationship of the AM process into the optimization.Specifically,the transverse isotropic material model is adopted to describe the material properties induced by the layer-by-layer manner of additive manufacturing.To bolster lattice strength performance,the stress constraints and ratio constraints of lattice struts are employed.The Tsai-Wu yield criterion is implemented to characterize the lattice strut's strength,while the P-norm method streamlines the handling of multiple constraints,minimizing computational overhead.Moreover,the gradient-based optimization model is established,where both the individual struts diameters and BO can be designed,and the buckling-prone spatial struts are strategically eliminated to improve the lattice strength further.Furthermore,several typical structures are optimized to verify the effectiveness of the proposed method.The optimized results are quite encouraging since the heterogeneous lattice structures with optimized BO obtained by the strength-based concurrent method show a remarkably improved performance compared to traditional designs.
基金funded by the National Natural Science Foundation of China grant 81700148 and Natural Science Foundation of Guangdong Province grant 2021A1515010093 and 2023A1515011862funded by the National Natural Science Foundation of China grant 82170181funded by the Basic and Applied Basic Research Foundation of Guangdong Province grant 2022B1515120087.
文摘Objectives:The optimal treatment strategy for early-stage natural killer/T-cell lymphoma(NKTCL)remains unclear.This study aimed to evaluate and compare the clinical outcomes and adverse events(AEs)associated with two treatment regimens for early-stage NKTCL:pegaspargase with concurrent radiation therapy(P+CCRT)and pegaspargase,gemcitabine,and oxaliplatin(P-GEMOX)with sequential radiation therapy(SERT).Propensity score matching(PSM)was employed to ensure balanced comparison between these regimens.Methods:We assessed the efficacy of P+CCRT from a phase II trial and P-GEMOX combined with SERT using real-world data.PSM was conducted at a 1:1 ratio with a caliper of 0.18 to align baseline characteristics between the treatment groups.Key outcomes analyzed included overall response rate(ORR),complete response rate(CR),progression-free survival(PFS),overall survival(OS),and AEs.Results:Following PSM,the study included 52 patients,with 26 in each treatment group.Baseline characteristics were balanced between the cohorts.The ORR for P+CCRT group was 100.0%compared to 88.5%for P-GEMOX+SERT group,and the CR rates was 100.0%vs.76.9%,respectively.The 3-year OS and PFS rates were both 92.3%for P+CCRT,while P-GEMOX showed 92.3%OS and 80.8%PFS.Adverse events,including hematological toxicity,hepatotoxicity,and coagulation dysfunction,were comparable between the two regimens.Conclusion:P+CCRT is associated with comparable clinical outcomes compared to P-GEMOX+SERT in early-stage NKTCL,with comparable adverse events.Additionally,P+CCRT offers the benefit of a more streamlined treatment regimen with a shorter cycle.Given these encouraging results,further cohort studies are needed to validate these results.
基金supported by the National Natural Science Foundation of China(No.52474435)China Baowu Low Carbon Metallurgy Innovation Foundation(BWLCF202307).
文摘Accurate forecasting of blast furnace gas(BFG)production is an essential prerequisite for reasonable energy scheduling and management to reduce carbon emissions.Coupling forecasting between BFG generation and consumption dynamics was taken as the research object.A multi-task learning(MTL)method for BFG forecasting was proposed,which integrated a coupling correlation coefficient(CCC)and an inverted transformer structure.The CCC method could enhance key information extraction by establishing relationships between multiple prediction targets and relevant factors,while MTL effectively captured the inherent correlations between BFG generation and consumption.Finally,a real-world case study was conducted to compare the proposed model with four benchmark models.Results indicated significant reductions in average mean absolute percentage error by 33.37%,achieving 1.92%,with a computational time of 76 s.The sensitivity analysis of hyperparameters such as learning rate,batch size,and units of the long short-term memory layer highlights the importance of hyperparameter tuning.
文摘BACKGROUND Patients with concurrent acute biliary pancreatitis(ABP)and acute cholangitis(AC)may experience exacerbated clinical consequences due to bile duct stones.However,studies exploring this topic remain limited.AIM To compare the clinical presentation and outcomes of patients experiencing AC with and without ABP.METHODS This single-center retrospective cohort study included 358 patients with AC who underwent endoscopic retrograde cholangiopancreatography(ERCP)between January 2016 and December 2017.Patients were divided into two groups:AC with ABP(n=90)and AC without ABP(n=268).Clinical characteristics,laboratory data,ERCP results,primary study outcome[intensive care unit(ICU)admission],and secondary outcomes including 30-day mortality,length of hospital stay,and 30-day readmission rate were analyzed and compared.RESULTS All patients in the AC with ABP group had interstitial pancreatitis.The AC with ABP group had significantly higher white cell count(WBC)counts(13.1×10^(3)/μL vs 10.4×10^(3)/μL,P=0.007)and more abnormal WBC results(61.1%vs 42.3%,P=0.015).Liver biochemical tests,AC severity,ERCP success,adverse events,ICU admissions,30-day mortality,hospital stay,and readmission rates did not differ significantly between the two groups.Univariate analysis showed no significant link between concurrent ABP and ICU admission,although significance was marginal in moderate/severe ABP cases(P=0.051).In the multivariate analysis,age(P=0.035)and cardiovascular dysfunction(P<0.001)were independently associated with length of ICU stay.CONCLUSION Concurrent interstitial ABP and AC did not significantly affect outcomes.Age and cardiovascular dysfunction were stronger predictors of ICU admission and should guide clinical monitoring and management.
基金supported by the National Natural Science Foundation of China(Nos.62003115 and 11972130)the Shenzhen Science and Technology Program,China(JCYJ20220818102207015)the Heilongjiang Touyan Team Program,China。
文摘The Low Earth Orbit(LEO)remote sensing satellite mega-constellation has the characteristics of large quantity and various types which make it have unique superiority in the realization of concurrent multiple tasks.However,the complexity of resource allocation is increased because of the large number of tasks and satellites.Therefore,the primary problem of implementing concurrent multiple tasks via LEO mega-constellation is to pre-process tasks and observation re-sources.To address the challenge,we propose a pre-processing algorithm for the mega-constellation based on highly Dynamic Spatio-Temporal Grids(DSTG).In the first stage,this paper describes the management model of mega-constellation and the multiple tasks.Then,the coding method of DSTG is proposed,based on which the description of complex mega-constellation observation resources is realized.In the third part,the DSTG algorithm is used to realize the processing of concurrent multiple tasks at multiple levels,such as task space attribute,time attribute and grid task importance evaluation.Finally,the simulation result of the proposed method in the case of constellation has been given to verify the effectiveness of concurrent multi-task pre-processing based on DSTG.The autonomous processing process of task decomposition and task fusion and mapping to grids,and the convenient indexing process of time window are verified.
文摘An integrated method for concurrency control in parallel real-time databases has been proposed in this paper. The nested transaction model has been investigated to offer more atomic execution units and finer grained control within in a transaction. Based on the classical nested locking protocol and the speculative concurrency control approach, a two-shadow adaptive concurrency control protocol, which combines the Sacrifice based Optimistic Concurrency Control (OPT-Sacrifice) and High Priority two-phase locking (HP2PL) algorithms together to support both optimistic and pessimistic shadow of each sub-transaction, has been proposed to increase the likelihood of successful timely commitment and to avoid unnecessary replication overload.
基金Supported by the Defense Pre-Research Project ofthe"Tenth Five-Year-Plan"of China (413150403)
文摘Secure real-time databases must simultaneously satisfy two requirements in guaranteeing data security and minimizing the missing deadlines ratio of transactions. However, these two requirements can conflict with each other and achieve one requirement is to sacrifice the other. This paper presents a secure real-time concurrency control protocol based on optimistic method. The concurrency control protocol incorporates security constraints in a real-time optimistic concurrency control protocol and makes a suitable tradeoff between security and real-time requirements by introducing secure influence factor and real-time influence factor. The experimental results show the concurrency control protocol achieves data security without degrading real-time perform ance significantly.
