A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adh...A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.展开更多
With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can b...With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.展开更多
Postoperative pancreatic fistula(POPF)is a frequent complication after pancre-atectomy,leading to increased morbidity and mortality.Optimizing prediction models for POPF has emerged as a critical focus in surgical res...Postoperative pancreatic fistula(POPF)is a frequent complication after pancre-atectomy,leading to increased morbidity and mortality.Optimizing prediction models for POPF has emerged as a critical focus in surgical research.Although over sixty models following pancreaticoduodenectomy,predominantly reliant on a variety of clinical,surgical,and radiological parameters,have been documented,their predictive accuracy remains suboptimal in external validation and across diverse populations.As models after distal pancreatectomy continue to be pro-gressively reported,their external validation is eagerly anticipated.Conversely,POPF prediction after central pancreatectomy is in its nascent stage,warranting urgent need for further development and validation.The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance.Moreover,there is potential for the development of personalized prediction models based on patient-or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF.In the future,prospective multicenter studies and the integration of novel imaging technologies,such as artificial intelligence-based radiomics,may further refine predictive models.Addressing these issues is anticipated to revolutionize risk stratification,clinical decision-making,and postoperative management in patients undergoing pancre-atectomy.展开更多
Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall su...Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.展开更多
This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the aff...This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the affiliated institution of the authors.We apologize for our unintentional mistake.Please note,these changes do not affect our results.展开更多
BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages ...BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.展开更多
The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication e...The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.展开更多
Dilated cardiomyopathy(DCM)is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function,often leading to heart failure(HF)and arrhythmia.The occurrence of atrial fibr...Dilated cardiomyopathy(DCM)is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function,often leading to heart failure(HF)and arrhythmia.The occurrence of atrial fibrillation(AF)is closely related to the progression and prognosis of the disease.In recent years,with the advancement of medical imaging and biomarkers,models for predicting the occurrence of AF in DCM patients have gradually become a research hotspot.This article aims to review the current situation of AF in DCM patients and explore the importance and possible methods of constructing predictive models to provide reference for clinical prevention and treatment.We comprehensively analyzed the risk factors for AF in DCM patients from epidemiological data,pathophysiological mechanisms,clinical and laboratory indicators,electrocardiogram and imaging parameters,and biomarkers,and evaluated the effectiveness of existing predictive models.Through analysis of existing literature and research,this article proposes a predictive model that integrates multiple parameters to improve the accuracy of predicting AF in DCM patients and provide a scientific basis for personalized treatment.展开更多
Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of tra...Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.展开更多
BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challengin...BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.展开更多
Interparticle adhesion force has a controlling effect on the physical and mechanical properties of planetary regolith and rocks.The current research on the adhesion force of planetary regolith and rock particles has b...Interparticle adhesion force has a controlling effect on the physical and mechanical properties of planetary regolith and rocks.The current research on the adhesion force of planetary regolith and rock particles has been primarily based on the assumption of smooth spherical particles to calculate the intergranular adhesion force;this approach lacks consideration for the adhesion force between irregular shaped particles.In our study,an innovative approach was established to directly measure the adhesion force between the arbitrary irregular shaped particles;the probe was modified using simulated lunar soil particles that were a typical representation of planetary regolith.The experimental results showed that for irregular shaped mineral particles,the particle size and mineral composition had no significant influence on the interparticle adhesion force;however,the complex morphology of the contact surface predominantly controlled the adhesion force.As the contact surface roughness increased,the adhesion force gradually decreased,and the rate of decrease gradually slowed;these results were consistent with the change trend predicted via the theoretical models of quantum electrodynamics.Moreover,a theoretical model to predict the adhesion force between the irregular shaped particles was constructed based on Rabinovich’s theory,and the prediction results were correlated with the experimental measurements.展开更多
Traditional microstructure scale parameters have difficulty describing the structure and distribution of a roughmaterial’s surface morphology comprehensively and quantitatively. This study constructs hydrophilic and ...Traditional microstructure scale parameters have difficulty describing the structure and distribution of a roughmaterial’s surface morphology comprehensively and quantitatively. This study constructs hydrophilic and underwateroleophobic surfaces based on polyvinylidene fluoride (PVDF) using a chemical modification method, and the fractaldimension and multifractal spectrum are used to quantitatively characterize the microscopic morphology. A new contactangle prediction model for underwater oleophobic surfaces is established. The results show that the fractal dimension ofthe PVDF surface first increases and then decreases with the reaction time. The uniformity characterized by the multifractalspectrum was generally consistent with scanning electron microscope observations. The contact angle of water droplets onthe PVDF surface is negatively correlated with the fractal dimension, and oil droplets in water are positively correlated.When the fractal dimension is 2.0975, the new contact angle prediction model has higher prediction accuracy. Themaximum and minimum relative deviations of the contact angle between the theoretical and measured data are 18.20%and 0.72%, respectively. For water ring transportation, the larger the fractal dimension and spectral width of the materialsurface, the smaller the absolute value of the spectral difference, the stronger the hydrophilic and oleophobic properties, andthe better the water ring transportation stability.展开更多
The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a...The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a landslide dam.To address this gap,we conducted a study using the smoothed particle hydrodynamics numerical method to investigate the evolution of landslide dams.Our study included 17 numerical simulations to examine the effects of several factors on the geometry of landslide dams,including valley inclination,sliding angle,landslide velocity,and landslide mass repose angle.Based on this,three rapid prediction models were established for calculating the maximum height,the minimum height,and the maximum width of a landslide dam.The results show that the downstream width of a landslide dam remarkably increases with the valley inclination.The position of the maximum dam height along the valley direction is independent of external factors and is always located in the middle of the landslide width area.In contrast,that position of the maximum dam height across the valley direction is significantly influenced by the sliding angle and landslide velocity.To validate our models,we applied them to three typical landslide dams and found that the calculated values of the landslide dam geometry were in good agreement with the actual values.The findings of the current study provide a better understanding of the evolution and geometry of landslide dams,giving crucial guidance for the prediction and early warning of landslide dam disasters.展开更多
The strength of water-bearing rock cannot be obtained in real time and by nondestructive experiments,which is an issue at cultural relics protection sites such as grotto temples.To solve this problem,we conducted a ne...The strength of water-bearing rock cannot be obtained in real time and by nondestructive experiments,which is an issue at cultural relics protection sites such as grotto temples.To solve this problem,we conducted a near-infrared spectrum acquisition experiment in the field and laboratory uniaxial compression strength tests on sandstone that had different water saturation levels.The correlations between the peak height and peak area of the nearinfrared absorption bands of the water-bearing sandstone and uniaxial compressive strength were analyzed.On this basis,a strength prediction model for water-bearing sandstone was established using the long short-term memory full convolutional network(LSTM-FCN)method.Subsequently,a field engineering test was carried out.The results showed that:(1)The sandstone samples had four distinct characteristic absorption peaks at 1400,1900,2200,and 2325 nm.The peak height and peak area of the absorption bands near 1400 nm and 1900 nm had a negative correlation with uniaxial compressive strength.The peak height and peak area of the absorption bands near 2200 nm and 2325 nm had nonlinear positive correlations with uniaxial compressive strength.(2)The LSTM-FCN method was used to establish a strength prediction model for water-bearing sandstone based on near-infrared spectroscopy,and the model achieved an accuracy of up to 97.52%.(3)The prediction model was used to realize non-destructive,quantitative,and real-time determination of uniaxial compressive strength;this represents a new method for the non-destructive testing of grotto rock mass at sites of cultural relics protection.展开更多
The destruction of recombinant bamboo depends on many factors,and the complex ambient temperature is an important factor affecting its basic mechanical properties.To investigate the failure mechanism and stress–strai...The destruction of recombinant bamboo depends on many factors,and the complex ambient temperature is an important factor affecting its basic mechanical properties.To investigate the failure mechanism and stress–strain relationship of recombinant bamboo at different temperatures,eighteen tensile specimens of recombinant bamboo were tested.The results showed that with increasing ambient temperature,the typical failure modes of recombinant bamboo were flush fracture,toothed failure,and serrated failure.The ultimate tensile strength,ultimate strain and elastic modulus of recombinant bamboo decreased with increasing temperature,and the ultimate tensile stress decreased from 154.07 to 96.55 MPa,a decrease of 37.33%,and the ultimate strain decreased from 0.011 to 0.008,a decrease of 26.57%.Based on the Ramberg-Osgood model and the pseudo‒elastic design method,a predictive model was established for the tensile stress–strain relationship of recombinant bamboo considering the temperature level.The model can accurately evaluate the tensile stress–strain relationship of recombinant bamboo under different temperature conditions.展开更多
COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be...COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.展开更多
Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requi...Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.展开更多
High precision and reliable wind speed forecasting have become a challenge for meteorologists.Convective events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb...High precision and reliable wind speed forecasting have become a challenge for meteorologists.Convective events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily life.For accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few decades.As wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)technology.In this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)method.The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer.In the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)model.To optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios.The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.展开更多
BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which...BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(P<0.05).According to the nomogram model’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-pregnancy BMI,Hb level and triglyceride level.The random forest model achieved an AUC of 0.950(95%CI:0.927-0.973),the sensitivity was 84.8%,and the specificity was 91.4%.The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model(P<0.05).CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM.This method is helpful for early diagnosis and appropriate intervention of GDM.展开更多
Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is sign...Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is significant to comprehend the factors motivating women to become entrepreneurs.The non-understanding of the factors can result in the endurance of low living stan-dards and the design of expensive and ineffectual policies.But female involve-ment in entrepreneurship becomes higher in developing economies compared to developed economies.Women Entrepreneurship Index(WEI)plays a vital role in determining the factors that enable theflourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society.Therefore,it is needed to design an automated and accurate WEI prediction model to improve women’s entrepreneurship.In this view,this article develops an automated statistical analysis enabled WEI predic-tive(ASA-WEIP)model.The proposed ASA-WEIP technique aims to effectually determine the WEI.The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing,WEI prediction,and parameter optimiza-tion.For the prediction of WEI,the ASA-WEIP technique makes use of the Deep Belief Network(DBN)model,and the parameter optimization process takes place using Squirrel Search Algorithm(SSA).The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repo-sitory.The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.展开更多
基金the High-Performance Computing Platform of Beijing University of Chemical Technology(BUCT)for supporting this papersupported by the Fundamental Research Funds for the Central Universities(JD2319)+2 种基金the CNOOC Technical Cooperation Project(ZX2022ZCTYF7612)the National Natural Science Foundation of China(51775029,52004014)the Chinese Universities Scientific Fund(XK2020-04)。
文摘A rotating packed bed is a typical chemical process enhancement equipment that can strengthen micromixing and mass transfer.During the operation of the rotating packed bed,the nonreactants and products irregularly adhere to the wire mesh packing in the rotor,thus resulting in an imbalance in the vibration of the rotor,which may cause serious damage to the bearing and material leakage.This study proposes a model prediction for estimating the bearing residual life of a rotating packed bed based on rotor imbalance response analysis.This method is used to determine the influence of the mass on the imbalance in the vibration of the rotor on bearing damage.The major influence on rotor vibration was found to be exerted by the imbalanced mass and its distribution radius,as revealed by the results of orthogonal experiments.Through implementing finite element analysis,the imbalance response curve for the rotating packed bed rotor was obtained,and a correlation among rotor imbalance mass,distribution radius of imbalance mass,and bearing residue life was established via data fitting.The predicted value of the bearing life can be used as the reference basis for an early safety warning of a rotating packed bed to effectively avoid accidents.
基金This work was supported by the Beijing Nova Program[Z211100002121136]Open Fund Project of State Key Laboratory of Lithospheric Evolution[SKL-K202103]+1 种基金Joint Funds of National Natural Science Foundation of China[U19B6003-02]the National Natural Science Foundation of China[42302149].We would like to thank Prof.Zhu Rixiang from the Institute of Geology and Geophysics,Chinese Academy of Sciences.
文摘With continuous hydrocarbon exploration extending to deeper basins,the deepest industrial oil accumulation was discovered below 8,200 m,revealing a new exploration field.Hence,the extent to which oil exploration can be extended,and the prediction of the depth limit of oil accumulation(DLOA),are issues that have attracted significant attention in petroleum geology.Since it is difficult to characterize the evolution of the physical properties of the marine carbonate reservoir with burial depth,and the deepest drilling still cannot reach the DLOA.Hence,the DLOA cannot be predicted by directly establishing the relationship between the ratio of drilling to the dry layer and the depth.In this study,by establishing the relationships between the porosity and the depth and dry layer ratio of the carbonate reservoir,the relationships between the depth and dry layer ratio were obtained collectively.The depth corresponding to a dry layer ratio of 100%is the DLOA.Based on this,a quantitative prediction model for the DLOA was finally built.The results indicate that the porosity of the carbonate reservoir,Lower Ordovician in Tazhong area of Tarim Basin,tends to decrease with burial depth,and manifests as an overall low porosity reservoir in deep layer.The critical porosity of the DLOA was 1.8%,which is the critical geological condition corresponding to a 100%dry layer ratio encountered in the reservoir.The depth of the DLOA was 9,000 m.This study provides a new method for DLOA prediction that is beneficial for a deeper understanding of oil accumulation,and is of great importance for scientific guidance on deep oil drilling.
文摘Postoperative pancreatic fistula(POPF)is a frequent complication after pancre-atectomy,leading to increased morbidity and mortality.Optimizing prediction models for POPF has emerged as a critical focus in surgical research.Although over sixty models following pancreaticoduodenectomy,predominantly reliant on a variety of clinical,surgical,and radiological parameters,have been documented,their predictive accuracy remains suboptimal in external validation and across diverse populations.As models after distal pancreatectomy continue to be pro-gressively reported,their external validation is eagerly anticipated.Conversely,POPF prediction after central pancreatectomy is in its nascent stage,warranting urgent need for further development and validation.The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance.Moreover,there is potential for the development of personalized prediction models based on patient-or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF.In the future,prospective multicenter studies and the integration of novel imaging technologies,such as artificial intelligence-based radiomics,may further refine predictive models.Addressing these issues is anticipated to revolutionize risk stratification,clinical decision-making,and postoperative management in patients undergoing pancre-atectomy.
基金This workwas supported by the Medical and Health Science and Technology Project of Zhejiang Province(No.2021KY180).
文摘Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.
文摘This is an erratum to an already published paper named“Establishment of a prediction model for prehospital return of spontaneous circulation in out-ofhospital patients with cardiac arrest”.We found errors in the affiliated institution of the authors.We apologize for our unintentional mistake.Please note,these changes do not affect our results.
文摘BACKGROUND Cancer patients often suffer from severe stress reactions psychologically,such as anxiety and depression.Prostate cancer(PC)is one of the common cancer types,with most patients diagnosed at advanced stages that cannot be treated by radical surgery and which are accompanied by complications such as bodily pain and bone metastasis.Therefore,attention should be given to the mental health status of PC patients as well as physical adverse events in the course of clinical treatment.AIM To analyze the risk factors leading to anxiety and depression in PC patients after castration and build a risk prediction model.METHODS A retrospective analysis was performed on the data of 120 PC cases treated in Xi'an People's Hospital between January 2019 and January 2022.The patient cohort was divided into a training group(n=84)and a validation group(n=36)at a ratio of 7:3.The patients’anxiety symptoms and depression levels were assessed 2 wk after surgery with the Self-Rating Anxiety Scale(SAS)and the Selfrating Depression Scale(SDS),respectively.Logistic regression was used to analyze the risk factors affecting negative mood,and a risk prediction model was constructed.RESULTS In the training group,35 patients and 37 patients had an SAS score and an SDS score greater than or equal to 50,respectively.Based on the scores,we further subclassified patients into two groups:a bad mood group(n=35)and an emotional stability group(n=49).Multivariate logistic regression analysis showed that marital status,castration scheme,and postoperative Visual Analogue Scale(VAS)score were independent risk factors affecting a patient's bad mood(P<0.05).In the training and validation groups,patients with adverse emotions exhibited significantly higher risk scores than emotionally stable patients(P<0.0001).The area under the curve(AUC)of the risk prediction model for predicting bad mood in the training group was 0.743,the specificity was 70.96%,and the sensitivity was 66.03%,while in the validation group,the AUC,specificity,and sensitivity were 0.755,66.67%,and 76.19%,respectively.The Hosmer-Lemeshow test showed aχ^(2) of 4.2856,a P value of 0.830,and a C-index of 0.773(0.692-0.854).The calibration curve revealed that the predicted curve was basically consistent with the actual curve,and the calibration curve showed that the prediction model had good discrimination and accuracy.Decision curve analysis showed that the model had a high net profit.CONCLUSION In PC patients,marital status,castration scheme,and postoperative pain(VAS)score are important factors affecting postoperative anxiety and depression.The logistic regression model can be used to successfully predict the risk of adverse psychological emotions.
文摘The resurgence of locally acquired malaria cases in the USA and the persistent global challenge of malaria transmission highlight the urgent need for research to prevent this disease. Despite significant eradication efforts, malaria remains a serious threat, particularly in regions like Africa. This study explores how integrating Gregor’s Type IV theory with Geographic Information Systems (GIS) improves our understanding of disease dynamics, especially Malaria transmission patterns in Uganda. By combining data-driven algorithms, artificial intelligence, and geospatial analysis, the research aims to determine the most reliable predictors of Malaria incident rates and assess the impact of different factors on transmission. Using diverse predictive modeling techniques including Linear Regression, K-Nearest Neighbor, Neural Network, and Random Forest, the study found that;Random Forest model outperformed the others, demonstrating superior predictive accuracy with an R<sup>2</sup> of approximately 0.88 and a Mean Squared Error (MSE) of 0.0534, Antimalarial treatment was identified as the most influential factor, with mosquito net access associated with a significant reduction in incident rates, while higher temperatures correlated with increased rates. Our study concluded that the Random Forest model was effective in predicting malaria incident rates in Uganda and highlighted the significance of climate factors and preventive measures such as mosquito nets and antimalarial drugs. We recommended that districts with malaria hotspots lacking Indoor Residual Spraying (IRS) coverage prioritize its implementation to mitigate incident rates, while those with high malaria rates in 2020 require immediate attention. By advocating for the use of appropriate predictive models, our research emphasized the importance of evidence-based decision-making in malaria control strategies, aiming to reduce transmission rates and save lives.
文摘Dilated cardiomyopathy(DCM)is a common myocardial disease characterized by enlargement of the heart cavity and decreased systolic function,often leading to heart failure(HF)and arrhythmia.The occurrence of atrial fibrillation(AF)is closely related to the progression and prognosis of the disease.In recent years,with the advancement of medical imaging and biomarkers,models for predicting the occurrence of AF in DCM patients have gradually become a research hotspot.This article aims to review the current situation of AF in DCM patients and explore the importance and possible methods of constructing predictive models to provide reference for clinical prevention and treatment.We comprehensively analyzed the risk factors for AF in DCM patients from epidemiological data,pathophysiological mechanisms,clinical and laboratory indicators,electrocardiogram and imaging parameters,and biomarkers,and evaluated the effectiveness of existing predictive models.Through analysis of existing literature and research,this article proposes a predictive model that integrates multiple parameters to improve the accuracy of predicting AF in DCM patients and provide a scientific basis for personalized treatment.
文摘Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.
基金Supported by Key Research and Development Program of Shaanxi,No.2020GXLH-Y-019 and 2022KXJ-141Innovation Capability Support Program of Shaanxi,No.2019GHJD-14 and 2021TD-40+1 种基金Science and Technology Talent Support Program of Shaanxi Provincial People's Hospital,No.2021LJ-052023 Natural Science Basic Research Foundation of Shaanxi Province,No.2023-JC-YB-739.
文摘BACKGROUND Surgical site infections(SSIs) are the commonest healthcare-associated infection. In addition to increasing mortality, it also lengthens the hospital stay and raises healthcare expenses. SSIs are challenging to predict, with most models having poor predictability. Therefore, we developed a prediction model for SSI after elective abdominal surgery by identifying risk factors.AIM To analyse the data on inpatients undergoing elective abdominal surgery to identify risk factors and develop predictive models that will help clinicians assess patients preoperatively.METHODS We retrospectively analysed the inpatient records of Shaanxi Provincial People’s Hospital from January 1, 2018 to January 1, 2021. We included the demographic data of the patients and their haematological test results in our analysis. The attending physicians provided the Nutritional Risk Screening 2002(NRS 2002)scores. The surgeons and anaesthesiologists manually calculated the National Nosocomial Infections Surveillance(NNIS) scores. Inpatient SSI risk factors were evaluated using univariate analysis and multivariate logistic regression. Nomograms were used in the predictive models. The receiver operating characteristic and area under the curve values were used to measure the specificity and accuracy of the model.RESULTS A total of 3018 patients met the inclusion criteria. The surgical sites included the uterus(42.2%), the liver(27.6%), the gastrointestinal tract(19.1%), the appendix(5.9%), the kidney(3.7%), and the groin area(1.4%). SSI occurred in 5% of the patients(n = 150). The risk factors associated with SSI were as follows: Age;gender;marital status;place of residence;history of diabetes;surgical season;surgical site;NRS 2002 score;preoperative white blood cell, procalcitonin(PCT), albumin, and low-density lipoprotein cholesterol(LDL) levels;preoperative antibiotic use;anaesthesia method;incision grade;NNIS score;intraoperative blood loss;intraoperative drainage tube placement;surgical operation items. Multivariate logistic regression revealed the following independent risk factors: A history of diabetes [odds ratio(OR) = 5.698, 95% confidence interval(CI): 3.305-9.825, P = 0.001], antibiotic use(OR = 14.977, 95%CI: 2.865-78.299, P = 0.001), an NRS 2002 score of ≥ 3(OR = 2.426, 95%CI: 1.199-4.909, P = 0.014), general anaesthesia(OR = 3.334, 95%CI: 1.134-9.806, P = 0.029), an NNIS score of ≥ 2(OR = 2.362, 95%CI: 1.019-5.476, P = 0.045), PCT ≥ 0.05 μg/L(OR = 1.687, 95%CI: 1.056-2.695, P = 0.029), LDL < 3.37 mmol/L(OR = 1.719, 95%CI: 1.039-2.842, P = 0.035), intraoperative blood loss ≥ 200 mL(OR = 29.026, 95%CI: 13.751-61.266, P < 0.001), surgical season(P < 0.05), surgical site(P < 0.05), and incision grade I or Ⅲ(P < 0.05). The overall area under the receiver operating characteristic curve of the predictive model was 0.926, which is significantly higher than the NNIS score(0.662).CONCLUSION The patient’s condition and haematological test indicators form the bases of our prediction model. It is a novel, efficient, and highly accurate predictive model for preventing postoperative SSI, thereby improving the prognosis in patients undergoing abdominal surgery.
基金supported by the National Natural Science Foundation of China(Nos.U22A20166,52104141,12172230 and U2013603)the Department of Science and Technology of Guangdong Province(No.2019ZT08G315)the Guangdong Basic and Applied Basic Research Foundation(No.2023A1515012654).
文摘Interparticle adhesion force has a controlling effect on the physical and mechanical properties of planetary regolith and rocks.The current research on the adhesion force of planetary regolith and rock particles has been primarily based on the assumption of smooth spherical particles to calculate the intergranular adhesion force;this approach lacks consideration for the adhesion force between irregular shaped particles.In our study,an innovative approach was established to directly measure the adhesion force between the arbitrary irregular shaped particles;the probe was modified using simulated lunar soil particles that were a typical representation of planetary regolith.The experimental results showed that for irregular shaped mineral particles,the particle size and mineral composition had no significant influence on the interparticle adhesion force;however,the complex morphology of the contact surface predominantly controlled the adhesion force.As the contact surface roughness increased,the adhesion force gradually decreased,and the rate of decrease gradually slowed;these results were consistent with the change trend predicted via the theoretical models of quantum electrodynamics.Moreover,a theoretical model to predict the adhesion force between the irregular shaped particles was constructed based on Rabinovich’s theory,and the prediction results were correlated with the experimental measurements.
基金the Natural Science Basic Research Program of Shaanxi(Program No.2023-JC-YB-351)the Scientific Research Program Funded by the Shaanxi Provincial Education Department(Program No.20JS118)the Xi’an Shiyou University Graduate Innovation and Practice Ability Training Plan(YCS21212097,YCS21212092).
文摘Traditional microstructure scale parameters have difficulty describing the structure and distribution of a roughmaterial’s surface morphology comprehensively and quantitatively. This study constructs hydrophilic and underwateroleophobic surfaces based on polyvinylidene fluoride (PVDF) using a chemical modification method, and the fractaldimension and multifractal spectrum are used to quantitatively characterize the microscopic morphology. A new contactangle prediction model for underwater oleophobic surfaces is established. The results show that the fractal dimension ofthe PVDF surface first increases and then decreases with the reaction time. The uniformity characterized by the multifractalspectrum was generally consistent with scanning electron microscope observations. The contact angle of water droplets onthe PVDF surface is negatively correlated with the fractal dimension, and oil droplets in water are positively correlated.When the fractal dimension is 2.0975, the new contact angle prediction model has higher prediction accuracy. Themaximum and minimum relative deviations of the contact angle between the theoretical and measured data are 18.20%and 0.72%, respectively. For water ring transportation, the larger the fractal dimension and spectral width of the materialsurface, the smaller the absolute value of the spectral difference, the stronger the hydrophilic and oleophobic properties, andthe better the water ring transportation stability.
基金funding from the National Natural Science Foundation of China(42207228,51879036,51579032)the Liaoning Revitalization Talents Program(XLYC2002036)the Sichuan Science and Technology Program(2022NSFSC1060)。
文摘The geometry of a landslide dam plays a critical role in its stability and failure mode,and is influenced by the damming process.However,there is a lack of understanding of the factors that affect the 3D geometry of a landslide dam.To address this gap,we conducted a study using the smoothed particle hydrodynamics numerical method to investigate the evolution of landslide dams.Our study included 17 numerical simulations to examine the effects of several factors on the geometry of landslide dams,including valley inclination,sliding angle,landslide velocity,and landslide mass repose angle.Based on this,three rapid prediction models were established for calculating the maximum height,the minimum height,and the maximum width of a landslide dam.The results show that the downstream width of a landslide dam remarkably increases with the valley inclination.The position of the maximum dam height along the valley direction is independent of external factors and is always located in the middle of the landslide width area.In contrast,that position of the maximum dam height across the valley direction is significantly influenced by the sliding angle and landslide velocity.To validate our models,we applied them to three typical landslide dams and found that the calculated values of the landslide dam geometry were in good agreement with the actual values.The findings of the current study provide a better understanding of the evolution and geometry of landslide dams,giving crucial guidance for the prediction and early warning of landslide dam disasters.
基金supported by the Zhejiang Provincial Collaborative Innovation Center of Mountain Geological Hazard Prevention(PCMGH-2021-05)the Special Fund for Fundamental Research Business Expenses of Central Universities(Grant No.600101110102)。
文摘The strength of water-bearing rock cannot be obtained in real time and by nondestructive experiments,which is an issue at cultural relics protection sites such as grotto temples.To solve this problem,we conducted a near-infrared spectrum acquisition experiment in the field and laboratory uniaxial compression strength tests on sandstone that had different water saturation levels.The correlations between the peak height and peak area of the nearinfrared absorption bands of the water-bearing sandstone and uniaxial compressive strength were analyzed.On this basis,a strength prediction model for water-bearing sandstone was established using the long short-term memory full convolutional network(LSTM-FCN)method.Subsequently,a field engineering test was carried out.The results showed that:(1)The sandstone samples had four distinct characteristic absorption peaks at 1400,1900,2200,and 2325 nm.The peak height and peak area of the absorption bands near 1400 nm and 1900 nm had a negative correlation with uniaxial compressive strength.The peak height and peak area of the absorption bands near 2200 nm and 2325 nm had nonlinear positive correlations with uniaxial compressive strength.(2)The LSTM-FCN method was used to establish a strength prediction model for water-bearing sandstone based on near-infrared spectroscopy,and the model achieved an accuracy of up to 97.52%.(3)The prediction model was used to realize non-destructive,quantitative,and real-time determination of uniaxial compressive strength;this represents a new method for the non-destructive testing of grotto rock mass at sites of cultural relics protection.
基金The authors wish to express their gratitude to the National Natural Science Foundation of China(Nos.51208262,51778300)Key Research and Development Project of Jiangsu Province(No.BE2020703)+2 种基金Natural Science Foundation of Jiangsu Province(No.BK20191390)Six Talent Peaks Project of Jiangsu Province(JZ-017)Qinglan Project of Jiangsu Province for financially supporting this study.
文摘The destruction of recombinant bamboo depends on many factors,and the complex ambient temperature is an important factor affecting its basic mechanical properties.To investigate the failure mechanism and stress–strain relationship of recombinant bamboo at different temperatures,eighteen tensile specimens of recombinant bamboo were tested.The results showed that with increasing ambient temperature,the typical failure modes of recombinant bamboo were flush fracture,toothed failure,and serrated failure.The ultimate tensile strength,ultimate strain and elastic modulus of recombinant bamboo decreased with increasing temperature,and the ultimate tensile stress decreased from 154.07 to 96.55 MPa,a decrease of 37.33%,and the ultimate strain decreased from 0.011 to 0.008,a decrease of 26.57%.Based on the Ramberg-Osgood model and the pseudo‒elastic design method,a predictive model was established for the tensile stress–strain relationship of recombinant bamboo considering the temperature level.The model can accurately evaluate the tensile stress–strain relationship of recombinant bamboo under different temperature conditions.
基金The authors extend their appreciation to the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0525.
文摘COVID-19 has significantly impacted the growth prediction of a pandemic,and it is critical in determining how to battle and track the disease progression.In this case,COVID-19 data is a time-series dataset that can be projected using different methodologies.Thus,this work aims to gauge the spread of the outbreak severity over time.Furthermore,data analytics and Machine Learning(ML)techniques are employed to gain a broader understanding of virus infections.We have simulated,adjusted,and fitted several statistical time-series forecasting models,linearML models,and nonlinear ML models.Examples of these models are Logistic Regression,Lasso,Ridge,ElasticNet,Huber Regressor,Lasso Lars,Passive Aggressive Regressor,K-Neighbors Regressor,Decision Tree Regressor,Extra Trees Regressor,Support Vector Regressions(SVR),AdaBoost Regressor,Random Forest Regressor,Bagging Regressor,AuoRegression,MovingAverage,Gradient Boosting Regressor,Autoregressive Moving Average(ARMA),Auto-Regressive Integrated Moving Averages(ARIMA),SimpleExpSmoothing,Exponential Smoothing,Holt-Winters,Simple Moving Average,Weighted Moving Average,Croston,and naive Bayes.Furthermore,our suggested methodology includes the development and evaluation of ensemble models built on top of the best-performing statistical and ML-based prediction methods.A third stage in the proposed system is to examine three different implementations to determine which model delivers the best performance.Then,this best method is used for future forecasts,and consequently,we can collect the most accurate and dependable predictions.
基金supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.RS2022-00167197Development of Intelligent 5G/6G Infrastructure Technology for the Smart City)+2 种基金in part by the National Research Foundation of Korea(NRF),Ministry of Education,through Basic Science Research Program under Grant NRF-2020R1I1A3066543in part by BK21 FOUR(Fostering Outstanding Universities for Research)under Grant 5199990914048in part by the Soonchunhyang University Research Fund.
文摘Intelligent healthcare networks represent a significant component in digital applications,where the requirements hold within quality-of-service(QoS)reliability and safeguarding privacy.This paper addresses these requirements through the integration of enabler paradigms,including federated learning(FL),cloud/edge computing,softwaredefined/virtualized networking infrastructure,and converged prediction algorithms.The study focuses on achieving reliability and efficiency in real-time prediction models,which depend on the interaction flows and network topology.In response to these challenges,we introduce a modified version of federated logistic regression(FLR)that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks.To establish the FLR framework for mission-critical healthcare applications,we provide a comprehensive workflow in this paper,introducing framework setup,iterative round communications,and model evaluation/deployment.Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization,which concludes with the generation of service experience batches for model deployment.To assess the practicality of our approach,we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset(Version 2.0.1)of the Korea Medical Panel Survey.Performance metrics,including end-to-end execution delays,model drop/delivery ratios,and final model accuracies,are captured and compared between the proposed FLR framework and other baseline schemes.Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks,addressing the critical demands of QoS reliability and privacy preservation.
基金This research is funded by Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281755DSR01.
文摘High precision and reliable wind speed forecasting have become a challenge for meteorologists.Convective events,namely,strong winds,thunderstorms,and tornadoes,along with large hail,are natural calamities that disturb daily life.For accurate prediction of wind speed and overcoming its uncertainty of change,several prediction approaches have been presented over the last few decades.As wind speed series have higher volatility and nonlinearity,it is urgent to present cutting-edge artificial intelligence(AI)technology.In this aspect,this paper presents an intelligent wind speed prediction using chicken swarm optimization with the hybrid deep learning(IWSP-CSODL)method.The presented IWSP-CSODL model estimates the wind speed using a hybrid deep learning and hyperparameter optimizer.In the presented IWSP-CSODL model,the prediction process is performed via a convolutional neural network(CNN)based long short-term memory with autoencoder(CBLSTMAE)model.To optimally modify the hyperparameters related to the CBLSTMAE model,the chicken swarm optimization(CSO)algorithm is utilized and thereby reduces the mean square error(MSE).The experimental validation of the IWSP-CSODL model is tested using wind series data under three distinct scenarios.The comparative study pointed out the better outcomes of the IWSP-CSODL model over other recent wind speed prediction models.
文摘BACKGROUND Gestational diabetes mellitus(GDM)is a condition characterized by high blood sugar levels during pregnancy.The prevalence of GDM is on the rise globally,and this trend is particularly evident in China,which has emerged as a significant issue impacting the well-being of expectant mothers and their fetuses.Identifying and addressing GDM in a timely manner is crucial for maintaining the health of both expectant mothers and their developing fetuses.Therefore,this study aims to establish a risk prediction model for GDM and explore the effects of serum ferritin,blood glucose,and body mass index(BMI)on the occurrence of GDM.AIM To develop a risk prediction model to analyze factors leading to GDM,and evaluate its efficiency for early prevention.METHODS The clinical data of 406 pregnant women who underwent routine prenatal examination in Fujian Maternity and Child Health Hospital from April 2020 to December 2022 were retrospectively analyzed.According to whether GDM occurred,they were divided into two groups to analyze the related factors affecting GDM.Then,according to the weight of the relevant risk factors,the training set and the verification set were divided at a ratio of 7:3.Subsequently,a risk prediction model was established using logistic regression and random forest models,and the model was evaluated and verified.RESULTS Pre-pregnancy BMI,previous history of GDM or macrosomia,hypertension,hemoglobin(Hb)level,triglyceride level,family history of diabetes,serum ferritin,and fasting blood glucose levels during early pregnancy were determined.These factors were found to have a significant impact on the development of GDM(P<0.05).According to the nomogram model’s prediction of GDM in pregnancy,the area under the curve(AUC)was determined to be 0.883[95%confidence interval(CI):0.846-0.921],and the sensitivity and specificity were 74.1%and 87.6%,respectively.The top five variables in the random forest model for predicting the occurrence of GDM were serum ferritin,fasting blood glucose in early pregnancy,pre-pregnancy BMI,Hb level and triglyceride level.The random forest model achieved an AUC of 0.950(95%CI:0.927-0.973),the sensitivity was 84.8%,and the specificity was 91.4%.The Delong test showed that the AUC value of the random forest model was higher than that of the decision tree model(P<0.05).CONCLUSION The random forest model is superior to the nomogram model in predicting the risk of GDM.This method is helpful for early diagnosis and appropriate intervention of GDM.
文摘Recently,gender equality and women’s entrepreneurship have gained considerable attention in global economic development.Prior to the design of any policy interventions to increase women’s entrepreneurship,it is significant to comprehend the factors motivating women to become entrepreneurs.The non-understanding of the factors can result in the endurance of low living stan-dards and the design of expensive and ineffectual policies.But female involve-ment in entrepreneurship becomes higher in developing economies compared to developed economies.Women Entrepreneurship Index(WEI)plays a vital role in determining the factors that enable theflourishment of high potential female entrepreneurs which enhances economic welfare and contributes to the economic and social fabric of society.Therefore,it is needed to design an automated and accurate WEI prediction model to improve women’s entrepreneurship.In this view,this article develops an automated statistical analysis enabled WEI predic-tive(ASA-WEIP)model.The proposed ASA-WEIP technique aims to effectually determine the WEI.The proposed ASA-WEIP technique encompasses a series of sub-processes such as pre-processing,WEI prediction,and parameter optimiza-tion.For the prediction of WEI,the ASA-WEIP technique makes use of the Deep Belief Network(DBN)model,and the parameter optimization process takes place using Squirrel Search Algorithm(SSA).The performance validation of the ASA-WEIP technique was executed using the benchmark dataset from the Kaggle repo-sitory.The experimental outcomes stated the better outcomes of the ASA-WEIP technique over the other existing techniques.
