The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primar...The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)III ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures.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
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
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
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
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
Microvascular invasion(MVI)is a critical factor in hepatocellular carcinoma(HCC)prognosis,particularly in hepatitis B virus(HBV)-related cases.This editorial examines a recent study by Xu et al who developed models to...Microvascular invasion(MVI)is a critical factor in hepatocellular carcinoma(HCC)prognosis,particularly in hepatitis B virus(HBV)-related cases.This editorial examines a recent study by Xu et al who developed models to predict MVI and high-risk(M2)status in HBV-related HCC using contrast-enhanced computed tomography(CECT)radiomics and clinicoradiological factors.The study analyzed 270 patients,creating models that achieved an area under the curve values of 0.841 and 0.768 for MVI prediction,and 0.865 and 0.798 for M2 status prediction in training and validation datasets,respectively.These results are comparable to previous radiomics-based approaches,which reinforces the potential of this method in MVI prediction.The strengths of the study include its focus on HBV-related HCC and the use of widely accessible CECT imaging.However,limitations,such as retrospective design and manual segmentation,highlight areas for improvement.The editorial discusses the implications of the study including the need for standardized radiomics approaches and the potential impact on personalized treatment strategies.It also suggests future research directions,such as exploring mechanistic links between radiomics features and MVI,as well as integrating additional biomarkers or imaging modalities.Overall,this study contributes significantly to HCC management,paving the way for more accurate,personalized treatment approaches in the era of precision oncology.展开更多
BACKGROUND Crohn’s disease(CD)patients with intestinal involvement often require surgical intervention due to resistance to medical therapy.Postoperative recurrence remains a significant challenge,with the Rutgeerts ...BACKGROUND Crohn’s disease(CD)patients with intestinal involvement often require surgical intervention due to resistance to medical therapy.Postoperative recurrence remains a significant challenge,with the Rutgeerts score commonly used to predict endoscopic recurrence.AIM To evaluate the relationship between microscopic and macroscopic pathological findings in resected intestinal specimens and the Rutgeerts score to predict endoscopic recurrence in CD patients.METHODS This retrospective cohort study included 32 patients over 18 years of age with intestinal CD who underwent surgery at General Surgery Clinic of Ankara Bilkent City Hospital between November 2019 and October 2023.Resection specimens were histopathologically re-examined,and postoperative colonoscopy reports were classified according to the Rutgeerts score.The association between pathological findings and endoscopic recurrence was analyzed statistically.RESULTS No significant association was found between macroscopic findings and Rutgeerts scores or endoscopic recurrence(P>0.05).However,the presence and severity of neutrophilic cryptitis(P=0.035)and crypt abscesses(P=0.010)in microscopic findings were significantly associated with higher Rutgeerts scores,indicating a parallel increase with endoscopic recurrence.Other microscopic findings showed no significant correlation with Rutgeerts scores or endoscopic recurrence(P>0.05).CONCLUSION The presence of neutrophilic cryptitis and crypt abscesses in resected intestinal specimens of CD patients increases the likelihood of endoscopic recurrence.Early postoperative medical treatment and close endoscopic follow-up may benefit high-risk patients to prevent recurrence,with treatment decisions made by a weekly multidisciplinary council involving General Surgery,Gastroenterology,and Radiology.展开更多
BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of lif...BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.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
A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorith...A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.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
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
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
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
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
The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality...The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality.Conventional models mostly rely on generalized obesity markers including body mass index(BMI),which does not effectively represent oncogenic risk linked with abdominal obesity.Liu et al undertook a large-scale case-control study comprising 6484 firsttime colonoscopy patients at a prominent Chinese hospital between 2020 and 2023 to overcome this restriction.Age,male sex,smoking status,and raised waist-hip ratio(WHR)were found by multivariate logistic regression as independent predictors of advanced colorectal neoplasia(ACN).In a validation cohort of 1891 individuals,a new 7-point risk scoring model was created and stratified into low-(5.0%)ACN prevalence,moderate-(10.3%)and high-risk(17.6%).With C-statistic=0.66 the model showed better discriminating ability than the Asia-Pacific Colorectal Screening(APCS)score(C-statistic=0.63)and the BMI-modified APCS model.These results fit newly published data showing central obesity as a major carcinogenic driver via pro-inflammatory visceral adipokine channels.With the use of WHR,patient risk classification is greatly improved,providing a practical tool to make the most of screening resources in the face of rising CRC incidence rates.Finally,multi-ethnic validation is necessary for the WHR-based scoring model to be considered for integration into global CRC preventive frameworks,since it improves the accuracy of ACN risk prediction.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52278461,52308467,and 52425213).
文摘The uneven distribution of the temperature field in the track structure,caused by various meteorological factors such as extremely low temperatures and snowfall,leads to significant temperature loads and is the primary cause of damage to China Railway Track System(CRTS)III ballastless tracks in cold regions during service.In this study,to predict the temperature of the track structure accurately,we analyzed meteorological data collected from Shenyang,China,and identified the factors that had the most effect on the track temperature field.We propose a temporal convolutional network(TCN)-based temperature field prediction model for ballastless tracks(TCN-Track model),which enhances the ability to extract and fuse local and global features from complex long-term meteorological data.The results indicate that the proposed TCN-Track model performs well in predicting track temperature fields from meteorological data,with a mean absolute error(MAE)ranging from 0.26 to 0.39,a root mean square error(RMSE)ranging from 0.32 to 0.50,and correlation coefficient(R)values ranging from 0.888 to 0.985.Compared with a long short-term memory(LSTM)model,the MAE of the TCN-Track model is reduced by 89.17%and the RMSE by 88.51%.This method offers a new solution for accurately predicting the temperature field of ballastless tracks in cold regions,aiding in predicting and preventing track damage caused by low temperatures.
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.
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.
文摘Microvascular invasion(MVI)is a critical factor in hepatocellular carcinoma(HCC)prognosis,particularly in hepatitis B virus(HBV)-related cases.This editorial examines a recent study by Xu et al who developed models to predict MVI and high-risk(M2)status in HBV-related HCC using contrast-enhanced computed tomography(CECT)radiomics and clinicoradiological factors.The study analyzed 270 patients,creating models that achieved an area under the curve values of 0.841 and 0.768 for MVI prediction,and 0.865 and 0.798 for M2 status prediction in training and validation datasets,respectively.These results are comparable to previous radiomics-based approaches,which reinforces the potential of this method in MVI prediction.The strengths of the study include its focus on HBV-related HCC and the use of widely accessible CECT imaging.However,limitations,such as retrospective design and manual segmentation,highlight areas for improvement.The editorial discusses the implications of the study including the need for standardized radiomics approaches and the potential impact on personalized treatment strategies.It also suggests future research directions,such as exploring mechanistic links between radiomics features and MVI,as well as integrating additional biomarkers or imaging modalities.Overall,this study contributes significantly to HCC management,paving the way for more accurate,personalized treatment approaches in the era of precision oncology.
文摘BACKGROUND Crohn’s disease(CD)patients with intestinal involvement often require surgical intervention due to resistance to medical therapy.Postoperative recurrence remains a significant challenge,with the Rutgeerts score commonly used to predict endoscopic recurrence.AIM To evaluate the relationship between microscopic and macroscopic pathological findings in resected intestinal specimens and the Rutgeerts score to predict endoscopic recurrence in CD patients.METHODS This retrospective cohort study included 32 patients over 18 years of age with intestinal CD who underwent surgery at General Surgery Clinic of Ankara Bilkent City Hospital between November 2019 and October 2023.Resection specimens were histopathologically re-examined,and postoperative colonoscopy reports were classified according to the Rutgeerts score.The association between pathological findings and endoscopic recurrence was analyzed statistically.RESULTS No significant association was found between macroscopic findings and Rutgeerts scores or endoscopic recurrence(P>0.05).However,the presence and severity of neutrophilic cryptitis(P=0.035)and crypt abscesses(P=0.010)in microscopic findings were significantly associated with higher Rutgeerts scores,indicating a parallel increase with endoscopic recurrence.Other microscopic findings showed no significant correlation with Rutgeerts scores or endoscopic recurrence(P>0.05).CONCLUSION The presence of neutrophilic cryptitis and crypt abscesses in resected intestinal specimens of CD patients increases the likelihood of endoscopic recurrence.Early postoperative medical treatment and close endoscopic follow-up may benefit high-risk patients to prevent recurrence,with treatment decisions made by a weekly multidisciplinary council involving General Surgery,Gastroenterology,and Radiology.
文摘BACKGROUND Tumor deposits(TDs)are an independent predictor of poor prognosis in colorec-tal cancer(CRC)patients.Enhanced follow-up and treatment monitoring for TD+patients may improve survival rates and quality of life.However,the detection of TDs relies primarily on postoperative pathological examination,which may have a low detection rate due to sampling limitations.AIM To evaluate the spectral computed tomography(CT)parameters of primary tu-mors and the largest regional lymph nodes(LNs),to determine their value in predicting TDs in CRC.METHODS A retrospective analysis was conducted which included 121 patients with CRC whose complete spectral CT data were available.Patients were divided into the TDs+group and the TDs-group on the basis of their pathological results.Spectral CT parameters of the primary CRC lesion and the largest regional LNs were measured,including the normalized iodine concentration(NIC)in both the arte-rial and venous phases,and the LN-to-primary tumor ratio was calculated.Stati-stical methods were used to evaluate the diagnostic efficacy of each spectral para-meter.RESULTS Among the 121 CRC patients,33(27.2%)were confirmed to be TDs+.The risk of TDs positivity was greater in patients with positive LN metastasis,higher N stage and elevated carcinoembryonic antigen and cancer antigen 19-9 levels.The NIC(LNs in both the arterial and venous phases),NIC(primary tumors in the venous phase),and the LN-to-primary tumor ratio in both the arterial and venous phases were associated with TDs(P<0.05).In mul-tivariate logistic regression analysis,the arterial phase LN-to-primary tumor ratio was identified as an independent predictor of TDs,demonstrating the highest diagnostic performance(area under the curve:0.812,sensitivity:0.879,specificity:0.648,cutoff value:1.145).CONCLUSION The spectral CT parameters of the primary colorectal tumor and the largest regional LNs,especially the LN-to-primary tumor ratio,have significant clinical value in predicting TDs in CRC.
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A new combined model is proposed to obtain predictive data value applied in state estimation for radial power distribution networks. The time delay part of the model is calculated by a recursive least squares algorithm of system identification, which can gradually forget past information. The grey series part of the model uses an equal dimension new information model (EDNIM) and it applies 3 points smoothing method to preprocess the original data and modify remnant difference by GM(1,1). Through the optimization of the coefficient of the model, we are able to minimize the error variance of predictive data. A case study shows that the proposed method achieved high calculation precision and speed and it can be used to obtain the predictive value in real time state estimation of power distribution networks.
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 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.
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.
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.
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The urgent necessity for enhanced risk stratification to improve the efficiency of colonoscopy screening is underscored by the fact that colorectal cancer(CRC)continues to be a primary cause of global cancer mortality.Conventional models mostly rely on generalized obesity markers including body mass index(BMI),which does not effectively represent oncogenic risk linked with abdominal obesity.Liu et al undertook a large-scale case-control study comprising 6484 firsttime colonoscopy patients at a prominent Chinese hospital between 2020 and 2023 to overcome this restriction.Age,male sex,smoking status,and raised waist-hip ratio(WHR)were found by multivariate logistic regression as independent predictors of advanced colorectal neoplasia(ACN).In a validation cohort of 1891 individuals,a new 7-point risk scoring model was created and stratified into low-(5.0%)ACN prevalence,moderate-(10.3%)and high-risk(17.6%).With C-statistic=0.66 the model showed better discriminating ability than the Asia-Pacific Colorectal Screening(APCS)score(C-statistic=0.63)and the BMI-modified APCS model.These results fit newly published data showing central obesity as a major carcinogenic driver via pro-inflammatory visceral adipokine channels.With the use of WHR,patient risk classification is greatly improved,providing a practical tool to make the most of screening resources in the face of rising CRC incidence rates.Finally,multi-ethnic validation is necessary for the WHR-based scoring model to be considered for integration into global CRC preventive frameworks,since it improves the accuracy of ACN risk prediction.
