Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential ...Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.展开更多
BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the a...BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.展开更多
This study aimed to develop a multimodal imaging histological model based on computed tomography(CT)images and carcinoembryonic antigen(CEA)values to predict the efficacy of preoperative neoadjuvant therapy in rectal ...This study aimed to develop a multimodal imaging histological model based on computed tomography(CT)images and carcinoembryonic antigen(CEA)values to predict the efficacy of preoperative neoadjuvant therapy in rectal cancer patients.Data were obtained from the Database of Colorectal Cancer of West China Hospital of Sichuan University.A total of 155 patients were enrolled and categorized into good and poor response groups based on pathological evaluation using the tumor regression grade system.Radiomics features were extracted from CT images using PyRadiomics software,and CEA data were collected and processed.Three types of models—a clinical model,a pure radiomics model,and an integrated model—were constructed using logistic regression,support vector machine,random forest(RF),and XGBoost algorithms.The results showed that the integrated model,particularly the RF and XGBoost models,demonstrated the best predictive performance.The RF model achieved an area under the curve(AUC)value of 0.96 in the test set,with accuracy,sensitivity,and specificity of 0.88,0.50,and 1.00,respectively.The XGBoost model had the highest AUC value of 0.97 in the test set,with accuracy,sensitivity,and specificity of 0.91,0.70,and 0.97,respectively.This model can be integrated into existing clinical practice to provide clinicians with additional insights for guiding treatment decisions.Future studies should recruit a larger and more diverse patient population to validate and refine the model,and prospective validation is needed to assess its real-world applicability.展开更多
Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experiment...Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experimental trial-and-error processes.However,existing methods,limited by static structural descriptors such as chemical composition and lattice parameters,fail to account for atomic vibrations,which may introduce spurious correlations and undermine predictive reliability.Here,we propose a deep learning model termed integrating graph and dynamical stability(IGDS)for predicting the synthesizability of inorganic crystals.IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties.Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems,achieving precision and recall values of 0.916/0.863 for ternary compounds.By capturing both static structural descriptors and dynamic features,IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals.This approach bridges the gap between theoretical design concepts and their practical implementation,thereby streamlining the development cycle of new materials and enhancing overall research efficiency.展开更多
Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is pr...Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.展开更多
Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strateg...Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.展开更多
This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data...This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.展开更多
Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especially...Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios.展开更多
Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”co...Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.展开更多
Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve ...Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.展开更多
Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such st...Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.展开更多
Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials t...Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.展开更多
Higher education institutions are becoming increasingly concerned with the retention of their students.This work is motivated by the interest in predicting and reducing student dropout,and consequently in reducing the...Higher education institutions are becoming increasingly concerned with the retention of their students.This work is motivated by the interest in predicting and reducing student dropout,and consequently in reducing the financial losses of said institutions.Based on the characterization of the dropout problem and the application of a knowledge discovery process,an ensemble model is proposed to improve dropout prediction.The ensemble model combines the results of three models:logistic regression,neural networks,and decision tree.As a result,the model can correctly classify 89%of the students as enrolled or dropped and accurately identify 98.1%of dropouts.When compared with the Random Forest ensemble method,the proposed model demonstrates desirable characteristics to assist management in proposing actions to retain students.展开更多
Permeable roads generally exhibit inferior mechanical properties and shorter service life than traditional dense-graded/impermeable roads.Furthermore,the incorporation of recycled aggregates in their construction may ...Permeable roads generally exhibit inferior mechanical properties and shorter service life than traditional dense-graded/impermeable roads.Furthermore,the incorporation of recycled aggregates in their construction may exacerbate these limitations.To address these issues,this study introduced a novel cement-stabilized permeable recycled aggregate material.A total of 162 beam specimens prepared with nine different levels of cement-aggregate ratio were tested to evaluate their permeability,bending load,and bending fatigue life.The experimental results indicate that increasing the content of recycled aggregates led to a reduction in both permeability and bending load.Additionally,the inclusion of recycled aggregates diminished the energy dissipation capacity of the specimens.These findings were used to establish a robust relationship between the initial damage in cement-stabilized permeable recycled aggregate material specimens and their fatigue life,and to propose a predictive model for their fatigue performance.Further,a method for assessing fatigue damage based on the evolution of fatigue-induced strain and energy dissipation was developed.The findings of this study provide valuable insights into the mechanical behavior and fatigue performance of cement-stabilized permeable recycled aggregate materials,offering guidance for the design of low-carbon-emission,permeable,and durable roadways incorporating recycled aggregates.展开更多
In most agricultural areas in the semi-arid region of the southern United States, wheat (Triticum aestivum L.) production is a primary economic activity. This region is drought-prone and projected to have a drier clim...In most agricultural areas in the semi-arid region of the southern United States, wheat (Triticum aestivum L.) production is a primary economic activity. This region is drought-prone and projected to have a drier climate in the future. Predicting the yield loss due to an anticipated drought is crucial for wheat growers. A reliable way for predicting the drought-induced yield loss is to use a plant physiology-based drought index, such as Agricultural Reference Index for Drought (ARID). Since different wheat cultivars exhibit varying levels of sensitivity to water stress, the impact of drought could be different on the cultivars belonging to different drought sensitivity groups. The objective of this study was to develop the cultivar drought sensitivity (CDS) group-specific, ARID-based models for predicting the drought-induced yield loss of winter wheat in the Llano Estacado region in the southern United States by accounting for the phenological phase-specific sensitivity to drought. For the study, the historical (1947-2021) winter wheat grain yield and daily weather data of two locations in the region (Bushland, TX and Clovis, NM) were used. The logical values of the drought sensitivity parameters of the yield models, especially for the moderately-sensitive and highly-sensitive CDS groups, indicated that the yield models reflected the phenomenon of water stress decreasing the winter wheat yields in this region satisfactorily. The reasonable values of the Nash-Sutcliffe Index (0.65 and 0.72), the Willmott Index (0.88 and 0.92), and the percentage error (23 and 22) for the moderately-sensitive and highly-sensitive CDS groups, respectively, indicated that the yield models for these groups performed reasonably well. These models could be useful for predicting the drought-induced yield losses and scheduling irrigation allocation based on the phenological phase-specific drought sensitivity as influenced by cultivar genotype.展开更多
This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mi...This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.展开更多
Self-consolidating concrete(SCC)is an important innovation in concrete technology due to its superior properties.However,predicting its compressive strength remains challenging due to variability in its composition an...Self-consolidating concrete(SCC)is an important innovation in concrete technology due to its superior properties.However,predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes.This study combines machine learning(ML)models with conformal prediction(CP)to address these issues,offering prediction intervals that quantify uncertainty and reliability.A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods,including Random Forest(RF),Gradient Boosting Regressor(GBR),Extreme gradient boosting(XGBoost),and light gradient boosting machine(LGBM),along with CP techniques,including cross-validation plus(CV+)and conformalized quantile regression(CQR)methods.Results demonstrate that LGBM and XGBoost outperform RF,improving R^(2) by 4.5%and 5.7%and reducing Root-mean-square Error(RMSE)by 24.6%and 24.8%,respectively.While CV+yielded narrower but constant intervals,CV+_Gamma and CQR provided adaptive intervals,highlighting trade-offs among precision,adaptability,and coverage reliability.The integration of CP offers a robust framework for uncertainty quantification in SCC strength prediction and marks a significant step forward in ML applications for concrete research.展开更多
AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a...AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a folded protein,and out pops a tertiary structure nearly as good as one from an experiment-based structure.展开更多
The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short...The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short-Term Memory(LSTM)generator,designed to capture temporal relationships in cardiotocography(CTG)data.This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction,normalization,and segmentation to create high-quality input for themodel.It uses convolutional layers to extract spatial information,followed by LSTM layers to model sequences for superior predictive performance.The overall results show that themodel is robust,with an accuracy of 91.5%,precision of 89.8%,recall of 90.4%,and F1-score of 90.1%that outperformed the corresponding baselinemodels,CNN(Convolutional Neural Network)and traditional RNN(Recurrent Neural Network),by 2.3%and 6.1%,respectively.Rather,the ability to detect pregnancy-related abnormalities has considerable therapeutic potential,with the possibility for focused treatments and individualized maternal healthcare approaches,the research team concluded.展开更多
BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise fore...BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this malignancy.AIM To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.METHODS Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020.Least absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient outcomes.The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves.To evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were performed.RESULTS A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte ratio.The area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,respectively.The AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,respectively.The model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding 0.75.Compared with TNM staging,the model demonstrated superior clinical utility.Ultimately,a nomogram was developed via a web-based interface.CONCLUSION This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrated strong predictive ability.Based on these findings,this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.展开更多
基金The National Natural Science Foundation of China-Regional Science“Identification of novel drug targets for lung cancer via Mendelian randomization analysis based on blood proteomics”(62362062)The 2025 Xinjiang University Excellent Graduate Innovation Project“Research on identification of therapeutic targets and predictive factors for mental disorders based on proteomics”(XJDX2025YJS151)。
文摘Traditional psychiatric diagnosis relies on subjective symptom assessment,lacking objective biomarkers that hinder early detection and personalized treatment.Plasma proteins and polygenic risk score(PRS),as potential predictive tools,hold promise for advancing early diagnosis of mental disorders.This study aims to evaluate the predictive potential of proteomic features and PRS in multiple mental illnesses(depression,schizophrenia,and post-traumatic stress disorder(PTSD)).Using participant data from the UK Biobank-Pharma Proteomics Project,we screen protein associations with mental disorders through least absolute shrinkage and selection operator(LASSO)analysis and construct a Cox regression risk prediction model by integrating the PRS.Additionally,we evaluate predictive performance using 6 machine learning methods and Kaplan-Meier survival curves.Our findings reveal distinct predictive patterns across dis-orders.For depression,integrating plasma proteins with PRS significantly improves prediction beyond the clinical model(C-index=0.6322).For schizophrenia,adding plasma proteins enhances predictive performance,whereas PRS provides no significant improvement.For PTSD,neither plasma proteins nor PRS add substantial predictive value beyond clinical variables.Risk stratification analysis demonstrat that all three mental disorders models can clearly distinguish high-risk from low-risk groups(depression:HR=2.34,P<0.001;schizophrenia:HR=5.47,P<0.001;PTSD:HR=3.02,P<0.001).Al-though it shows good performance in short-term prediction,its long-term prediction ability has decreased,and it needs to be further optimized in the future.This study underscores the differential utility of biomarkers across mental disorders and provides a rationale for disorder-specific predictive modeling in precision psychiatry.
文摘BACKGROUND Laparoscopic distal pancreatectomy(LDP)has emerged as the preferred approach for both benign and malignant lesions located in the pancreatic body and tail.Nevertheless,a notable deficiency persists in the absence of a standardized,procedure-specific metric for evaluating and comparing surgical quality.A composite measure termed“textbook outcome(TO)”,which encompasses key short-term endpoints,has been validated in laparoscopic pancreatoduodenectomy but has not yet been established in dedicated LDP cohorts.The definition and prediction of TO in this context could aid in facilitating cross-institutional benchmarking and fostering advancements in quality improvement.AIM To establish procedure-specific criteria for TO and identify independent predictors of TO failure in patients undergoing LDP.METHODS Consecutive patients who underwent LDP at a single high-volume pancreatic center between January 2015 and August 2022 were retrospectively analyzed.TO was defined as the absence of clinically relevant postoperative pancreatic fistula(grade B/C),post-pancreatectomy hemorrhage(grade B/C),severe complications(Clavien-Dindo≥III),readmission within 30 days,and in-hospital or 30-day mortality.Multivariable logistic regression was employed to identify independent predictors of TO failure,and a nomogram was constructed and internally validated.RESULTS Among 405 eligible patients,286(70.6%)attained TO.Multivariable analysis revealed that female sex[odds ratio(OR)=0.62,95%confidence interval(CI):0.39-0.99]conferred a protective effect,while preoperative endoscopic ultrasound-guided fine-needle aspiration(OR=2.66,95%CI:1.05-6.73),pancreatic portal hypertension(OR=2.81,95%CI:1.06-7.45),and cystic-solid(OR=2.51,95%CI:1.34-4.69)or solid lesions(OR=1.91,95%CI:1.06-3.44)were independently associated with TO failure(all P<0.05).The derived nomogram exhibited modest discrimination and calibration when assessed in both the training and validation datasets.CONCLUSION The proposed LDP-specific definition of TO is feasible and discriminative,and the developed nomogram provides an objective tool for individualized risk assessment.
基金supported by the 1-3-5 projects for artificial intelligence(Grant No.:ZYAI24067)West China Hospital,Sichuan University and the medical research project(Grant No.:S2024045),Sichuan Medical Association.
文摘This study aimed to develop a multimodal imaging histological model based on computed tomography(CT)images and carcinoembryonic antigen(CEA)values to predict the efficacy of preoperative neoadjuvant therapy in rectal cancer patients.Data were obtained from the Database of Colorectal Cancer of West China Hospital of Sichuan University.A total of 155 patients were enrolled and categorized into good and poor response groups based on pathological evaluation using the tumor regression grade system.Radiomics features were extracted from CT images using PyRadiomics software,and CEA data were collected and processed.Three types of models—a clinical model,a pure radiomics model,and an integrated model—were constructed using logistic regression,support vector machine,random forest(RF),and XGBoost algorithms.The results showed that the integrated model,particularly the RF and XGBoost models,demonstrated the best predictive performance.The RF model achieved an area under the curve(AUC)value of 0.96 in the test set,with accuracy,sensitivity,and specificity of 0.88,0.50,and 1.00,respectively.The XGBoost model had the highest AUC value of 0.97 in the test set,with accuracy,sensitivity,and specificity of 0.91,0.70,and 0.97,respectively.This model can be integrated into existing clinical practice to provide clinicians with additional insights for guiding treatment decisions.Future studies should recruit a larger and more diverse patient population to validate and refine the model,and prospective validation is needed to assess its real-world applicability.
文摘Accurately predicting the synthesizability of inorganic crystal materials serves as a pivotal tool for the efficient screening of viable candidates,substantially reducing the costs associated with extensive experimental trial-and-error processes.However,existing methods,limited by static structural descriptors such as chemical composition and lattice parameters,fail to account for atomic vibrations,which may introduce spurious correlations and undermine predictive reliability.Here,we propose a deep learning model termed integrating graph and dynamical stability(IGDS)for predicting the synthesizability of inorganic crystals.IGDS employs graph representation learning to construct crystal graphs that precisely capture the static structures of crystals and integrates phonon spectral features extracted from pre-trained machine learning interatomic potentials to represent their dynamic properties.Our model exhibits outstanding performance in predicting the synthesizability of low-energy unsynthesizable crystals across 41 material systems,achieving precision and recall values of 0.916/0.863 for ternary compounds.By capturing both static structural descriptors and dynamic features,IGDS provides a physics-informed method for predicting the synthesizability of inorganic crystals.This approach bridges the gap between theoretical design concepts and their practical implementation,thereby streamlining the development cycle of new materials and enhancing overall research efficiency.
基金the 2023 Liaoning Institute of Science and Technology Doctoral Program Launch Fund(2307B29),covering aspects such as data collection and publication of the paper。
文摘Aiming at the problems that the clock bias prediction model of the Wavelet Neural Network(WNN)is greatly affected by the selection of network parameters,and the Particle Swarm Optimization Wavelet Neural Network is prone to fall into local optima and has insufficient convergence efficiency in clock bias prediction,a short-term clock bias prediction model for BDS-3 based on the Rime Optimization Algorithm(RIME)-optimized Wavelet Neural Network is proposed.Firstly,the specific steps of the WNN model based on the RIME optimization algorithm in clock bias prediction are elaborated in detail.Then,the stability characteristics and training efficiency of the RIME optimization algorithm during the optimization stage are analyzed to determine the population size that suits the characteristics of clock bias data.Finally,using the BDS-3 clock bias data provided by the Wuhan University Data Center,shortterm clock bias prediction experiments with durations of 1 h,3 h,and 6 h are carried out.The experimental results show that in the 6h prediction,the average prediction accuracy of the RIME-WNN model is better than 0.1 ns,which is 93.92%,88.35%,and 48.11%higher than that of the Quadratic Polynomial model,the Grey Model(GM(1,1)),and the PSO-WNN model,respectively.In addition,when the RIMEWNN model predicts different types of Beidou satellites,the maximum difference in the Root Mean Square Error(RMSE)is relatively smaller,which fully demonstrates that the model has a wide and good accuracy adaptability when predicting various types of Beidou satellites.
基金supported by the National Natural Science Foundation of China(Nos.51576097,51976089)the Funding for Outstanding Doctoral Dissertation in Nanjing University of Aeronautics and Astronautics,China(No.BCXJ24-05)the Aeronautical Science Foundation of China(No.2023L060052001).
文摘Quick and accurate determination of the optimal synchrophase angle is crucial for synchrophasing control of multi-propeller aircraft with low noise.This paper proposes a novel noise prediction and optimization strategy,developing a continuous and accurate noise prediction model and obtaining its minimum by solving the Hessian matrix and Fourier-Frobenius matrix.Firstly,a novel propeller noise prediction method uses acoustic simulation pressure signals and improved propeller signatures theory to accurately estimate noise for all synchrophase angles and receiving points.Secondly,a novel optimization approach is proposed to solve the analytical solution of the minimum propeller noise:(A)A noise objective function is established,and use its first derivatives’zeros and Hessian matrix to determine the function minimum.(B)A novel Euler formula transform method is proposed to convert trigonometric polynomials into algebraic polynomials,changing the zeros of the former into those of the latter.(C)Utilize the Fourier-Frobenius matrix method to solve the zeros of algebraic polynomials.To assess the computation time and accuracy,a turboprop aircraft with two six-bladed propellers was analyzed using the computational fluid dynamics and acoustic analogy method,providing acoustic pressure signals at 20 receivers for noise prediction and optimization.The Durand-Kerner and Fourier-Frobenius matrix methods were compared.Results demonstrate that improved propeller signatures theory is more accurate,and the Hessian matrix+Fourier-Frobenius matrix method is faster and more precise than the Hessian matrix+Durand-Kerner method.
基金funded by the National Natural Science Foundation of China(No.52204407)the Natural Science Foundation of Jiangsu Province(No.BK20220595)the China Postdoctoral Science Foundation(No.2022M723689).
文摘This study proposes a multi-scale simplified residual convolutional neural network(MS-SRCNN)for the precise prediction of Mg-Nd binary alloy compositions from scanning electron microscope(SEM)images.A multi-scale data structure is established by spatially aligning and stacking SEM images at different magnifications.The MS-SRCNN significantly reduces computational runtime by over 90%compared to traditional architectures like ResNet50,VGG16,and VGG19,without compromising prediction accuracy.The model demonstrates more excellent predictive performance,achieving a>5%increase in R^(2) compared to single-scale models.Furthermore,the MS-SRCNN exhibits robust composition prediction capability across other Mg-based binary alloys,including Mg-La,Mg-Sn,Mg-Ce,Mg-Sm,Mg-Ag,and Mg-Y,thereby emphasizing its generalization and extrapolation potential.This research establishes a non-destructive,microstructure-informed composition analysis framework,reduces characterization time compared to traditional experiment methods and provides insights into the composition-microstructure relationship in diverse material systems.
基金Fund for funding this research work under Research Support Program for Central labs at King Khalid University through the project number CL/CO/B/6.
文摘Ground water is a crucial ecological resource and source of drinking water to a great percentage of theworld population.The quality of groundwater in an area with industrial emission and air pollution is an especiallyimportant issue that requires proper evaluation.This paper introduces a spatiotemporal deep learning model thatincorporates the use of metaheuristic optimization in predicting groundwater quality in various pollution contexts.Thegiven method is a combination of the Spatial-Temporal-Assisted Deep Belief Network(StaDBN)and a hybrid WhaleOptimization Algorithm and Tiki-Taka Algorithms(WOA-TTA)that would model intricate patterns of contamination.Historical ground water data sets with the hydrochemical data and time are preprocessed and pertinent and nonredundant features are determined with the Addax Optimization Algorithm(AOA).Spatial and temporal dependenciesare explicitly integrated in StaDBN architecture to facilitate representation learning,and network hyperparametersare optimized by the WOA-TTA module to increase the training efficiency and predictive performance.The modelwas coded in Python and tested based on common statistical measures,such as root mean square error(RMSE),Nash Sutcliffe efficiency(NSE),mean absolute error(MAE),and the correlation coefficient(R).The proposedGWQP-StaDBN-WOA-TTA framework demonstrates superior predictive performance and interpretability comparedto conventional machine learning and deep learning models,achieving higher correlation(R=0.963),improvedNash-Sutcliffe efficiency(NSE=0.84),and substantially lower prediction errors(MAE=0.29,RMSE=0.48),therebyvalidating its effectiveness for groundwater quality assessment under industrial and atmospheric pollution scenarios.
基金supported by the National Key Research and Development Program of China(No.2024YFC3506900)Science and Technology Program of Tianjin(No.24ZXZSSS00460)Special Project for Technological Innovation in New Productive Forces of Modern Chinese Medicines(No.24ZXZKSY00010)。
文摘Due to its synergistic effects and reduced side effects,combination therapy has become an important strategy for treating complex diseases.In traditional Chinese medicine(TCM),the“monarch,minister,assistant,envoy”compatibilities theory provides a systematic framework for drug compatibility and has guided the formation of a large number of classic formulas.However,due to the complex compositions and diverse mechanisms of action of TCM,it is difficult to comprehensively reveal its potential synergistic patterns using traditional methods.Synergistic prediction based on molecular compatibility theory provides new ideas for identifying combinations of active compounds in TCM.Compared to resource-intensive traditional experimental methods,artificial intelligence possesses the ability to mine synergistic patterns from multi-omics and structural data,providing an efficient means for modeling and optimizing TCM combinations.This paper systematically reviews the application progress of AI in the synergistic prediction of TCM active compounds and explores the challenges and prospects of its application in modeling combination relationships,thereby contributing to the modernization of TCM theory and methodological innovation.
文摘Pharmaceutical pollution is becoming an increasing threat to aquatic environments since inactive compounds do not break down,and the drug products are accumulated in living organisms.The ability of a drug to dissolve in water(i.e.,LogS)is an important parameter for assessing a drug’s environmental fate,biovailability,and toxicity.LogS is typically measured in a laboratory setting,which can be costly and time-consuming,and does not provide the opportunity to conduct large-scale analyses.This research develops and evaluates machine learning models that can produce LogS estimates and may improve the environmental risk assessments of toxic pharmaceutical pollutants.We used a dataset from the ChEMBL database that contained 8832 molecular compounds.Various data preprocessing and cleaning techniques were applied(i.e.,removing the missing values),we then recorded chemical properties by normalizing and,even,using some feature selection techniques.We evaluated logS with a total of several machine learning and deep learning models,including;linear regression,random forests(RF),support vector machines(SVM),gradient boosting(GBM),and artificial neural networks(ANNs).We assessed model performance using a series of metrics,including root mean square error(RMSE)and mean absolute error(MAE),as well as the coefficient of determination(R^(2)).The findings show that the Least Angle Regression(LAR)model performed the best with an R^(2) value close to 1.0000,confirming high predictive accuracy.The OMP model performed well with good accuracy(R^(2)=0.8727)while remaining computationally cheap,while other models(e.g.,neural networks,random forests)performed well but were too computationally expensive.Finally,to assess the robustness of the results,an error analysis indicated that residuals were evenly distributed around zero,confirming the results from the LAR model.The current research illustrates the potential of AI in anticipating drug solubility,providing support for green pharmaceutical design and environmental risk assessment.Future work should extend predictions to include degradation and toxicity to enhance predictive power and applicability.
基金supported by the National Natural Science Foundation of China(52274055)the Shandong Provincial Natural Science Foundation(ZR2022YQ50)the Taishan Scholar Program of Shandong Province(tsqn202408088)。
文摘Predicting the productivity of multistage fractured horizontal wells plays an important role in exploiting unconventional resources.In recent years,machine learning(ML)models have emerged as a new approach for such studies.However,the scarcity of sufficient real data for model training often leads to imprecise predictions,even though the models trained with real data better characterize geological and engineering features.To tackle this issue,we propose an ML model that can obtain reliable results even with a small amount of data samples.Our model integrates the synthetic minority oversampling technique(SMOTE)to expand the data volume,the support vector machine(SVM)for model training,and the particle swarm optimization(PSO)algorithm for optimizing hyperparameters.To enhance the model performance,we conduct feature fusion and dimensionality reduction.Additionally,we examine the influences of different sample sizes and ML models for training.The proposed model demonstrates higher prediction accuracy and generalization ability,achieving a predicted R^(2)value of up to 0.9 for the test set,compared to the traditional ML techniques with an R^(2)of 0.13.This model accurately predicts the production of fractured horizontal wells even with limited samples,supplying an efficient tool for optimizing the production of unconventional resources.Importantly,the model holds the potential applicability to address similar challenges in other fields constrained by scarce data samples.
文摘Prediction of weaning success from invasive mechanical ventilation remains a challenge in everyday clinical practice.Several prediction scores have been developed to guide success during spontaneous breathing trials to help with weaning decisions.These scores aim to provide a structured framework to support clinical judgment.However,their effectiveness varies across patient populations,and their predictive accuracy remains inconsistent.In this review,we aim to identify the strengths and limitations of commonly used clinical prediction tools in assessing readiness for ventilator liberation.While scores such as the Rapid Shallow Breathing Index and the Integrative Weaning Index are widely adopted,their sensitivity and specificity often fall short in complex clinical settings.Factors such as underlying disease pathophysiology,patient characteristics,and clinician subjectivity impact score performance and reliability.Moreover,disparities in validation across diverse populations limit generalizability.With growing interest in artificial intelligence(AI)and machine learning,there is potential for enhanced prediction models that integrate multidimensional data and adapt to individual patient profiles.However,current AI approaches face challenges related to interpretability,bias,and ethical implementation.This paper underscores the need for more robust,individualized,and transparent prediction systems and advocates for careful integration of emerging technologies into clinical workflows to optimize weaning success and patient outcomes.
基金the National Council for Scientific and Technological Development of Brazil(CNPQ)the Coordination for the Improvement of Higher Education Personnel-Brazil(CAPES)(Grant PROAP 88887.842889/2023-00-PUC/MG,Grant PDPG 88887.708960/2022-00-PUC/MG-INFORMATICA and Finance Code 001)Minas Gerais State Research Support Foundation(FAPEMIG)under Grant No.:APQ-01929-22,and the Pontifical Catholic University of Minas Gerais,Brazil.
文摘Higher education institutions are becoming increasingly concerned with the retention of their students.This work is motivated by the interest in predicting and reducing student dropout,and consequently in reducing the financial losses of said institutions.Based on the characterization of the dropout problem and the application of a knowledge discovery process,an ensemble model is proposed to improve dropout prediction.The ensemble model combines the results of three models:logistic regression,neural networks,and decision tree.As a result,the model can correctly classify 89%of the students as enrolled or dropped and accurately identify 98.1%of dropouts.When compared with the Random Forest ensemble method,the proposed model demonstrates desirable characteristics to assist management in proposing actions to retain students.
基金Project(2024JJ2073)supported by the Science Fund for Distinguished Young Scholars of Hunan Province,ChinaProjects(2023YFC3807205,2019YFC1904704)+4 种基金supported by the National Key R&D Program of ChinaProject(52178443)supported by the National Natural Science Foundation of ChinaProject(2024ZZTS0109)supported by Fundamental Research Funds for the Central Universities of Central South University,China。
文摘Permeable roads generally exhibit inferior mechanical properties and shorter service life than traditional dense-graded/impermeable roads.Furthermore,the incorporation of recycled aggregates in their construction may exacerbate these limitations.To address these issues,this study introduced a novel cement-stabilized permeable recycled aggregate material.A total of 162 beam specimens prepared with nine different levels of cement-aggregate ratio were tested to evaluate their permeability,bending load,and bending fatigue life.The experimental results indicate that increasing the content of recycled aggregates led to a reduction in both permeability and bending load.Additionally,the inclusion of recycled aggregates diminished the energy dissipation capacity of the specimens.These findings were used to establish a robust relationship between the initial damage in cement-stabilized permeable recycled aggregate material specimens and their fatigue life,and to propose a predictive model for their fatigue performance.Further,a method for assessing fatigue damage based on the evolution of fatigue-induced strain and energy dissipation was developed.The findings of this study provide valuable insights into the mechanical behavior and fatigue performance of cement-stabilized permeable recycled aggregate materials,offering guidance for the design of low-carbon-emission,permeable,and durable roadways incorporating recycled aggregates.
文摘In most agricultural areas in the semi-arid region of the southern United States, wheat (Triticum aestivum L.) production is a primary economic activity. This region is drought-prone and projected to have a drier climate in the future. Predicting the yield loss due to an anticipated drought is crucial for wheat growers. A reliable way for predicting the drought-induced yield loss is to use a plant physiology-based drought index, such as Agricultural Reference Index for Drought (ARID). Since different wheat cultivars exhibit varying levels of sensitivity to water stress, the impact of drought could be different on the cultivars belonging to different drought sensitivity groups. The objective of this study was to develop the cultivar drought sensitivity (CDS) group-specific, ARID-based models for predicting the drought-induced yield loss of winter wheat in the Llano Estacado region in the southern United States by accounting for the phenological phase-specific sensitivity to drought. For the study, the historical (1947-2021) winter wheat grain yield and daily weather data of two locations in the region (Bushland, TX and Clovis, NM) were used. The logical values of the drought sensitivity parameters of the yield models, especially for the moderately-sensitive and highly-sensitive CDS groups, indicated that the yield models reflected the phenomenon of water stress decreasing the winter wheat yields in this region satisfactorily. The reasonable values of the Nash-Sutcliffe Index (0.65 and 0.72), the Willmott Index (0.88 and 0.92), and the percentage error (23 and 22) for the moderately-sensitive and highly-sensitive CDS groups, respectively, indicated that the yield models for these groups performed reasonably well. These models could be useful for predicting the drought-induced yield losses and scheduling irrigation allocation based on the phenological phase-specific drought sensitivity as influenced by cultivar genotype.
基金supported by the National Natural Science Foundation of China(No.12372045)the National Key Research and the Development Program of China(Nos.2023YFC2205900,2023YFC2205901)。
文摘This paper solves the problem of model-free dual-arm space robot maneuvering after non-cooperative target capture under high control quality requirements.The explicit system model is unavailable,and the maneuvering mission is disturbed by the measurement noise and the target adversarial behavior.To address these problems,a model-free Combined Adaptive-length Datadriven Predictive Controller(CADPC)is proposed.It consists of a separated subsystem identification method and a combined predictive control strategy.The subsystem identification method is composed of an adaptive data length,thereby reducing sensitivity to undetermined measurement noises and disturbances.Based on the subsystem identification,the combined predictive controller is established,reducing calculating resource.The stability of the CADPC is rigorously proven using the Input-to-State Stable(ISS)theorem and the small-gain theorem.Simulations demonstrate that CADPC effectively handles the model-free space robot post operation in the presence of significant disturbances,state measurement noise,and control input errors.It achieves improved steady-state accuracy,reduced steady-state control consumption,and minimized control input chattering.
基金financially supported by the Natural Sciences and Engineering Research Council of Canada(NSERCGrant No.ALLRP 576708-22)ten industrial partners.
文摘Self-consolidating concrete(SCC)is an important innovation in concrete technology due to its superior properties.However,predicting its compressive strength remains challenging due to variability in its composition and uncertainties in prediction outcomes.This study combines machine learning(ML)models with conformal prediction(CP)to address these issues,offering prediction intervals that quantify uncertainty and reliability.A dataset of over 3000 samples with 17 input variables was used to train four ensemble methods,including Random Forest(RF),Gradient Boosting Regressor(GBR),Extreme gradient boosting(XGBoost),and light gradient boosting machine(LGBM),along with CP techniques,including cross-validation plus(CV+)and conformalized quantile regression(CQR)methods.Results demonstrate that LGBM and XGBoost outperform RF,improving R^(2) by 4.5%and 5.7%and reducing Root-mean-square Error(RMSE)by 24.6%and 24.8%,respectively.While CV+yielded narrower but constant intervals,CV+_Gamma and CQR provided adaptive intervals,highlighting trade-offs among precision,adaptability,and coverage reliability.The integration of CP offers a robust framework for uncertainty quantification in SCC strength prediction and marks a significant step forward in ML applications for concrete research.
基金supported by the U.S.National Natural Science Foundation(CHE-2203505 and MCB-2335137).
文摘AlphaFold[1]has turned everyone into a structural biologist.No need for knowledge of Fourier transforms or spectral density,driven by artificial intelligence(AI),all one needs to do is enter the primary structure of a folded protein,and out pops a tertiary structure nearly as good as one from an experiment-based structure.
文摘The prediction of pregnancy-related hazards must be accurate and timely to safeguard mother and fetal health.This study aims to enhance risk prediction in pregnancywith a novel deep learningmodel based on a Long Short-Term Memory(LSTM)generator,designed to capture temporal relationships in cardiotocography(CTG)data.This methodology integrates CTG signals with demographic characteristics and utilizes preprocessing techniques such as noise reduction,normalization,and segmentation to create high-quality input for themodel.It uses convolutional layers to extract spatial information,followed by LSTM layers to model sequences for superior predictive performance.The overall results show that themodel is robust,with an accuracy of 91.5%,precision of 89.8%,recall of 90.4%,and F1-score of 90.1%that outperformed the corresponding baselinemodels,CNN(Convolutional Neural Network)and traditional RNN(Recurrent Neural Network),by 2.3%and 6.1%,respectively.Rather,the ability to detect pregnancy-related abnormalities has considerable therapeutic potential,with the possibility for focused treatments and individualized maternal healthcare approaches,the research team concluded.
文摘BACKGROUND The prevalence and mortality rates of gastric carcinoma are disproportionately elevated in China,with the disease's intricate and varied characteristics further amplifying its health impact.Precise forecasting of overall survival(OS)is of paramount importance for the clinical management of individuals afflicted with this malignancy.AIM To develop and validate a nomogram model that provides precise gastric cancer prevention and treatment guidance and more accurate survival outcome prediction for patients with gastric carcinoma.METHODS Data analysis was conducted on samples collected from hospitalized gastric cancer patients between 2018 and 2020.Least absolute shrinkage and selection operator,univariate,and multivariate Cox regression analyses were employed to identify independent prognostic factors.A nomogram model was developed to predict gastric cancer patient outcomes.The model's predictability and discriminative ability were evaluated via receiver operating characteristic curves.To evaluate the clinical utility of the model,Kaplan-Meier and decision curve analyses were performed.RESULTS A total of ten independent prognostic factors were identified,including body mass index,tumor-node-metastasis(TNM)stage,radiation,chemotherapy,surgery,albumin,globulin,neutrophil count,lactate dehydrogenase,and platelet-to-lymphocyte ratio.The area under the curve(AUC)values for the 1-,3-,and 5-year survival prediction in the training set were 0.843,0.850,and 0.821,respectively.The AUC values were 0.864,0.820,and 0.786 for the 1-,3-,and 5-year survival prediction in the validation set,respectively.The model exhibited strong discriminative ability,with both the time AUC and time C-index exceeding 0.75.Compared with TNM staging,the model demonstrated superior clinical utility.Ultimately,a nomogram was developed via a web-based interface.CONCLUSION This study established and validated a novel nomogram model for predicting the OS of gastric cancer patients,which demonstrated strong predictive ability.Based on these findings,this model can aid clinicians in implementing personalized interventions for patients with gastric cancer.
