Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub...Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.展开更多
BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive system...BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high...BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.展开更多
Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the ...Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.展开更多
This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations...This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.展开更多
BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular car...BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular carcinoma.AIM To establish noninvasive models for LRF assessment based on liver stiffness measurement(LSM)and to evaluate their clinical performance.METHODS A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort.The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort(132 patients).RESULTS Our study defined indocyanine green retention rate at 15 min(ICGR15)≥10%as mildly impaired LRF and ICGR15≥20%as severely impaired LRF.We constructed predictive models of LRF,named the mLPaM and sLPaM,which involved only LSM,prothrombin time international normalized ratio to albumin ratio(PTAR),age and model for end-stage liver disease(MELD).The area under the curve of the mLPaM model(0.855,0.872,respectively)and sLPaM model(0.869,0.876,respectively)were higher than that of the methods for MELD,albumin bilirubin grade and PTAR in the two cohorts,and their sensitivity and negative predictive value were the highest among these methods in the training cohort.In addition,the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.CONCLUSION The new models had a good predictive performance for LRF and could replace the indocyanine green(ICG)clearance test,especially in patients who are unable to undergo ICG testing.展开更多
BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects t...BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.展开更多
Pyrolysis of methyl ricinoleate(MR)can produce undecylenic acid methyl ester and heptanal which are important chemicals.Atomization feeding favors the heat exchange in the pyrolysis process and hence increases the pro...Pyrolysis of methyl ricinoleate(MR)can produce undecylenic acid methyl ester and heptanal which are important chemicals.Atomization feeding favors the heat exchange in the pyrolysis process and hence increases the product yield.Herein,predictive models to characterize the atomization process were developed.The effect of spray distance on Sauter mean diameter(SMD)of atomized MR droplets was examined,with the optimal spray distance to be 40-50 mm.Temperature mainly affected the physical properties of feedstock,with smaller droplet size obtained at increasing temperature.In addition,pressure had significant influence on SMD and higher pressure resulted in smaller atomized droplets.Then,a model for SMD prediction,combining temperature,pressure,spray distance,and structural parameters of nozzle,was developed through dimensionless analysis.The results showed that SMD was a power function of Reynolds number(Re),Ohnesorge number(Oh),and the ratio of spray distance to diameter of swirl chamber in the nozzle(H/dsc),with the exponents of-1.6618,-1.3205 and 0.1038,respectively.The experimental measured SMD was in good agreement with the calculated values,with the error within±15%.Moreover,the droplet size distribution was studied by establishing the relationship between the standard deviation of droplet size and SMD.This study could provide reference to the regulation and optimization of the atomization process in MR pyrolysis.展开更多
BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpar...BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpartum depression(PPD)in older pregnant women.AIM To analysis the influencing factors and the construction of predictive models for PPD in older pregnant women.METHODS By adopting a cross-sectional survey research design,239 older pregnant women(≥35 years old)who underwent obstetric examinations and gave birth at Suzhou Ninth People's Hospital from February 2022 to July 2023 were selected as the research subjects.When postpartum women of advanced maternal age came to the hospital for follow-up 42 d after birth,the Edinburgh PPD Scale(EPDS)was used to assess the presence of PPD symptoms.The women were divided into a PPD group and a no-PPD group.Two sets of data were collected for analysis,and a prediction model was constructed.The performance of the predictive model was evaluated using receiver operating characteristic(ROC)analysis and the Hosmer-Lemeshow goodness-of-fit test.RESULTS On the 42nd day after delivery,51 of 239 older pregnant women were evaluated with the EPDS scale and found to have depressive symptoms.The incidence rate was 21.34%(51/239).There were statistically significant differences between the PPD group and the no-PPD group in terms of education level(P=0.004),family relationships(P=0.001),pregnancy complications(P=0.019),and mother–infant separation after birth(P=0.002).Multivariate logistic regression analysis showed that a high school education and below,poor family relationships,pregnancy complications,and the separation of the mother and baby after birth were influencing factors for PPD in older pregnant women(P<0.05).Based on the influencing factors,the following model equation was developed:Logit(P)=0.729×education level+0.942×family relationship+1.137×pregnancy complications+1.285×separation of the mother and infant after birth-6.671.The area under the ROC curve of this prediction model was 0.873(95%CI:0.821-0.924),the sensitivity was 0.871,and the specificity was 0.815.The deviation between the value predicted by the model and the actual value through the Hosmer-Lemeshow goodness-of-fit test was not statistically significant(χ^(2)=2.749,P=0.638),indicating that the model did not show an overfitting phenomenon.CONCLUSION The risk of PPD among older pregnant women is influenced by educational level,family relationships,pregnancy complications,and the separation of the mother and baby after birth.A prediction model based on these factors can effectively predict the risk of PPD in older pregnant women.展开更多
Breast cancer is the most prevalent female malignant tumor and significantly threatens the health of affected individuals.1 Recent progress in the identification of the molecular subtypes of breast cancer has ensured ...Breast cancer is the most prevalent female malignant tumor and significantly threatens the health of affected individuals.1 Recent progress in the identification of the molecular subtypes of breast cancer has ensured more personalized and precise treatment strategies.2 This has presented new challenges and opportunities in treatment options and disease prognosis.This Special Issue,titled"Recent advances in breast cancer research",highlights the latest advances in clinical,basic,and translational research on breast cancer.It explores tumor resistance mechanisms and microenvironments to enhance our understanding of drug efficacy and safety.展开更多
This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et...This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.展开更多
To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the stre...To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.展开更多
The objective of the present study was to develop models for egg freshness and shelf-life predictions for the selected evaluation indicators including egg weight,Flaugh unit(HU),and albumen height.Experiments were car...The objective of the present study was to develop models for egg freshness and shelf-life predictions for the selected evaluation indicators including egg weight,Flaugh unit(HU),and albumen height.Experiments were carried out at different storage temperatures for a total period of 29-32 d.All data were collected and fitted in to Arrhenius equation for egg freshness,while the HU data were applied to a probability model for shelf-life prediction.The results showed that egg weight,albumen height,and HU decreased significantly,while albumen pH increased with the extension of storage time.The higher the storage temperature,the faster the egg quality decreased.In addition,the bias factor,accuracy factor,and the standard error of prediction were selected to verify the developed quality models.Maximum rescaled R-square statistic,the Hosmer-Lemeshow goodness-of-fit statistic,and the receiver operating characteristic curve were used to evaluate the goodness-of-fit of the developed probability model for the shelf-life of eggs,which indicated that the presented predictive models can be used to assess egg freshness and predict shelf-life during different storage temperatures.展开更多
With gastric cancer ranking among the most prevalent and deadly malignancies worldwide,early detection and individualized prognosis remain essential for improving patient outcomes.This letter discusses recent advancem...With gastric cancer ranking among the most prevalent and deadly malignancies worldwide,early detection and individualized prognosis remain essential for improving patient outcomes.This letter discusses recent advancements in arti-ficial intelligence(AI)-driven predictive tools for gastric cancer,emphasizing a computed tomography-based radiomic model that achieved a predictive accuracy of area under the curve of 0.893 for treatment response in advanced cases undergoing neoadjuvant immunochemotherapy.AI offers promising avenues for predictive accuracy and personalized treatment planning in gastric oncology.Additionally,this letter highlights the comparison of these AI tools with tra-ditional methodologies,demonstrating their potential to streamline clinical workflows and address existing gaps in risk stratification and early detection.Furthermore,this letter addresses the ethical considerations and the need for robust clinical-AI collaboration to achieve reliable,transparent,and unbiased outcomes.Strengthening cross-disciplinary efforts will be vital for the responsible and effective deployment of AI in this critical area of oncology.展开更多
Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of c...Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.展开更多
Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi ...Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi Ganjiang Decoction syndrome.Methods:Data were collected from over 100 patients diagnosed with Chaihu Guizhi Ganjiang Decoction syndrome at Professor Li Leyu’s endocrinology clinic,Zhongshan Hospital of Traditional Chinese Medicine,Guangdong Province,between April 2021 and April 2022.Body surface temperature data were obtained using the MTI-EXPRO-2013-B infrared thermography system.Principal component analysis(PCA)was applied to differentiate temperature distribution characteristics between genders,and a neural network prediction model was constructed for syndrome diagnosis.Results:Infrared thermography effectively captured surface temperature characteristics of patients with Chaihu Guizhi Ganjiang Decoction syndrome.PCA identified one principal component with a variance explanation rate of 73.953%for females and two principal components with a cumulative variance explanation rate of 77.627%for males.The neural network model demonstrated high predictive performance,with an area under the ROC curve of 0.9743 for the training set and 0.9889 for the validation set.Sensitivity was 1,specificity 0.8636,precision 0.8846,accuracy 0.9333,and the F1 score 0.9388.Conclusion:Infrared thermography provides an innovative,objective,and quantitative method for syndrome differentiation and treatment in TCM.It represents a significant advancement in transitioning from traditional empirical approaches to modern,visualized,and precise diagnosis and treatment.This study underscores the potential of integrating advanced technologies in TCM for enhanced clinical application and modernization.展开更多
Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding ...Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value(GEBV).This study is a fi rst attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits.The performance of GS models in L.vannamei was evaluated in a population consisting of 205 individuals,which were genotyped for 6 359 single nucleotide polymorphism(SNP)markers by specifi c length amplifi ed fragment sequencing(SLAF-seq)and phenotyped for body length and body weight.Three GS models(RR-BLUP,Bayes A,and Bayesian LASSO)were used to obtain the GEBV,and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes.The mean reliability of the GEBVs for body length and body weight predicted by the dif ferent models was 0.296 and 0.411,respectively.For each trait,the performances of the three models were very similar to each other with respect to predictability.The regression coeffi cients estimated by the three models were close to one,suggesting near to zero bias for the predictions.Therefore,when GS was applied in a L.vannamei population for the studied scenarios,all three models appeared practicable.Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.展开更多
In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems ...In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.展开更多
Background:Information addressing soil quality in developing countries often depends on results from small experimental plots,which are later extrapolated to vast areas of agricultural land.This approach often results...Background:Information addressing soil quality in developing countries often depends on results from small experimental plots,which are later extrapolated to vast areas of agricultural land.This approach often results in misin-formation to end-users of land for sustainable soil nutrient management.The objective of this study was to estimate the spatial variability of soil quality index(SQI)at regional scale with predictive models using soil–environmental covariates.Methods:A total of 110 composite soil samples(0–30 cm depth)were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State,Nigeria,and selected soil physical and chemical properties were determined.We employed environmental covariates derived from a digital elevation model(DEM)and Senti-nel-2 imageries for our modelling regime.We measured soil quality using two approaches[total data set(TDS)and minimum data set(MDS)].Two scoring functions were also applied,linear(L)and non-linear(NL),yielding four indices(MDS_L,MDS_NL,TDS_L,and TDS_NL).Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis(PCA).Random forest(RF),support vector regression(SVR),regression kriging(RK),Cubist regression,and geographically weighted regression(GWR)were applied to predict SQI in unsampled locations.Results:The computed SQI via MDS_L was classified into five classes:≤0.38,0.38–0.48,0.48–0.58,0.58–0.68,and≥0.68,representing very low(classⅤ),low(classⅣ),moderate(classⅢ),high(classⅡ)and very high(classⅠ)soil quality,respectively.GWR model was robust in predicting soil quality(R^(2)=0.21,CCC=0.39,RMSE=0.15),while RF was a model with inferior performance(R^(2)=0.02,CCC=0.32,RMSE=0.15).Soil quality was high in the southern region and low in the northern region.High soil quality class(>49%)and moderate soil quality class(>14%)dominate the study area in all predicted models used.Conclusions:Structural stability index,sand content,soil oganic carbon content,and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices,while land surface water index,Sentinel-2 near-infrared band,plane curvature,and clay index were the most important variables affecting soil quality variability.The MDS_L and GWR are effective and useful models to identify the key soil properties for assessing soil quality,which can provide guidance for site-specific management of soils developed on diverse parent materials.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.
基金Supported by Capital’s Funds for Health Improvement and Research,No.2024-4-4135.
文摘BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2305002Beijing Natural Science Foundation,No.7232079+1 种基金Middle-aged and Young Talent Incubation Programs(Clinical Research)of Beijing Youan Hospital,No.BJYAYY-YN2022-12,No.BJYAYY-YN2022-13,and No.BJYAYY-YN2022-01the China Postdoctoral Science Foundation,No.2023M732410 and No.2024T170595.
文摘BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.
基金This study was funded by GCRF UK and was carried out as part of project CoNTINuE-Capacity building in technology-driven innovation in healthcare.
文摘Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
文摘This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.
基金Startup Fund for Scientific Research of Fujian Medical University,No.2018QH1052Fujian Health Research Talents Training Program,No.2019-1-42.
文摘BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular carcinoma.AIM To establish noninvasive models for LRF assessment based on liver stiffness measurement(LSM)and to evaluate their clinical performance.METHODS A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort.The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort(132 patients).RESULTS Our study defined indocyanine green retention rate at 15 min(ICGR15)≥10%as mildly impaired LRF and ICGR15≥20%as severely impaired LRF.We constructed predictive models of LRF,named the mLPaM and sLPaM,which involved only LSM,prothrombin time international normalized ratio to albumin ratio(PTAR),age and model for end-stage liver disease(MELD).The area under the curve of the mLPaM model(0.855,0.872,respectively)and sLPaM model(0.869,0.876,respectively)were higher than that of the methods for MELD,albumin bilirubin grade and PTAR in the two cohorts,and their sensitivity and negative predictive value were the highest among these methods in the training cohort.In addition,the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.CONCLUSION The new models had a good predictive performance for LRF and could replace the indocyanine green(ICG)clearance test,especially in patients who are unable to undergo ICG testing.
文摘BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.
基金the National Natural Science Foundation of China(grant number 21776261)the Zhejiang Province Public Welfare Technology Application Research Project(grant number 2017C31016)the China Postdoctoral Science Foundation(grant number 2017M612029)。
文摘Pyrolysis of methyl ricinoleate(MR)can produce undecylenic acid methyl ester and heptanal which are important chemicals.Atomization feeding favors the heat exchange in the pyrolysis process and hence increases the product yield.Herein,predictive models to characterize the atomization process were developed.The effect of spray distance on Sauter mean diameter(SMD)of atomized MR droplets was examined,with the optimal spray distance to be 40-50 mm.Temperature mainly affected the physical properties of feedstock,with smaller droplet size obtained at increasing temperature.In addition,pressure had significant influence on SMD and higher pressure resulted in smaller atomized droplets.Then,a model for SMD prediction,combining temperature,pressure,spray distance,and structural parameters of nozzle,was developed through dimensionless analysis.The results showed that SMD was a power function of Reynolds number(Re),Ohnesorge number(Oh),and the ratio of spray distance to diameter of swirl chamber in the nozzle(H/dsc),with the exponents of-1.6618,-1.3205 and 0.1038,respectively.The experimental measured SMD was in good agreement with the calculated values,with the error within±15%.Moreover,the droplet size distribution was studied by establishing the relationship between the standard deviation of droplet size and SMD.This study could provide reference to the regulation and optimization of the atomization process in MR pyrolysis.
基金This study was reviewed and approved by the Ethics Committee of Suzhou Ninth People's Hospital.
文摘BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpartum depression(PPD)in older pregnant women.AIM To analysis the influencing factors and the construction of predictive models for PPD in older pregnant women.METHODS By adopting a cross-sectional survey research design,239 older pregnant women(≥35 years old)who underwent obstetric examinations and gave birth at Suzhou Ninth People's Hospital from February 2022 to July 2023 were selected as the research subjects.When postpartum women of advanced maternal age came to the hospital for follow-up 42 d after birth,the Edinburgh PPD Scale(EPDS)was used to assess the presence of PPD symptoms.The women were divided into a PPD group and a no-PPD group.Two sets of data were collected for analysis,and a prediction model was constructed.The performance of the predictive model was evaluated using receiver operating characteristic(ROC)analysis and the Hosmer-Lemeshow goodness-of-fit test.RESULTS On the 42nd day after delivery,51 of 239 older pregnant women were evaluated with the EPDS scale and found to have depressive symptoms.The incidence rate was 21.34%(51/239).There were statistically significant differences between the PPD group and the no-PPD group in terms of education level(P=0.004),family relationships(P=0.001),pregnancy complications(P=0.019),and mother–infant separation after birth(P=0.002).Multivariate logistic regression analysis showed that a high school education and below,poor family relationships,pregnancy complications,and the separation of the mother and baby after birth were influencing factors for PPD in older pregnant women(P<0.05).Based on the influencing factors,the following model equation was developed:Logit(P)=0.729×education level+0.942×family relationship+1.137×pregnancy complications+1.285×separation of the mother and infant after birth-6.671.The area under the ROC curve of this prediction model was 0.873(95%CI:0.821-0.924),the sensitivity was 0.871,and the specificity was 0.815.The deviation between the value predicted by the model and the actual value through the Hosmer-Lemeshow goodness-of-fit test was not statistically significant(χ^(2)=2.749,P=0.638),indicating that the model did not show an overfitting phenomenon.CONCLUSION The risk of PPD among older pregnant women is influenced by educational level,family relationships,pregnancy complications,and the separation of the mother and baby after birth.A prediction model based on these factors can effectively predict the risk of PPD in older pregnant women.
文摘Breast cancer is the most prevalent female malignant tumor and significantly threatens the health of affected individuals.1 Recent progress in the identification of the molecular subtypes of breast cancer has ensured more personalized and precise treatment strategies.2 This has presented new challenges and opportunities in treatment options and disease prognosis.This Special Issue,titled"Recent advances in breast cancer research",highlights the latest advances in clinical,basic,and translational research on breast cancer.It explores tumor resistance mechanisms and microenvironments to enhance our understanding of drug efficacy and safety.
文摘This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.
基金Funded by State Railway Administration Research Project(No.2023JS007)National Natural Science Foundation of China(No.52438002)+1 种基金Research and Development Programs for Science and Technology of China Railways Corporation(No.J2023G003)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘To investigate the influence of coarse aggregate parent rock properties on the elastic modulus of concrete,the mineralogical properties and stress-strain curves of granite and dolomite parent rocks,as well as the strength and elastic modulus of mortar and concrete prepared with mechanism aggregates of the corresponding lithology,and the stress-strain curves of concrete were investigated.In this paper,a coarse aggregate and mortar matrix bonding assumption is proposed,and a prediction model for the elastic modulus of mortar is established by considering the lithology of the mechanism sand and the slurry components.An equivalent coarse aggregate elastic modulus model was established by considering factors such as coarse aggregate particle size,volume fraction,and mortar thickness between coarse aggregates.Based on the elastic modulus of the equivalent coarse aggregate and the remaining mortar,a prediction model for the elastic modulus of the two and three components of concrete in series and then in parallel was established,and the predicted values differed from the measured values within 10%.It is proposed that the coarse aggregate elastic modulus in highstrength concrete is the most critical factor affecting the elastic modulus of concrete,and as the coarse aggregate elastic modulus increases by 27.7%,the concrete elastic modulus increases by 19.5%.
基金supported by the Key Research and Development Program of Shandong Province(No.2019GNC106024)the Shandong Poultry Industry Innovation Team Construction Project(SDAIT-11-14)the High-level Talent Research Fund of Qingdao Agricultural University(No.6631120080/1111317),China.
文摘The objective of the present study was to develop models for egg freshness and shelf-life predictions for the selected evaluation indicators including egg weight,Flaugh unit(HU),and albumen height.Experiments were carried out at different storage temperatures for a total period of 29-32 d.All data were collected and fitted in to Arrhenius equation for egg freshness,while the HU data were applied to a probability model for shelf-life prediction.The results showed that egg weight,albumen height,and HU decreased significantly,while albumen pH increased with the extension of storage time.The higher the storage temperature,the faster the egg quality decreased.In addition,the bias factor,accuracy factor,and the standard error of prediction were selected to verify the developed quality models.Maximum rescaled R-square statistic,the Hosmer-Lemeshow goodness-of-fit statistic,and the receiver operating characteristic curve were used to evaluate the goodness-of-fit of the developed probability model for the shelf-life of eggs,which indicated that the presented predictive models can be used to assess egg freshness and predict shelf-life during different storage temperatures.
文摘With gastric cancer ranking among the most prevalent and deadly malignancies worldwide,early detection and individualized prognosis remain essential for improving patient outcomes.This letter discusses recent advancements in arti-ficial intelligence(AI)-driven predictive tools for gastric cancer,emphasizing a computed tomography-based radiomic model that achieved a predictive accuracy of area under the curve of 0.893 for treatment response in advanced cases undergoing neoadjuvant immunochemotherapy.AI offers promising avenues for predictive accuracy and personalized treatment planning in gastric oncology.Additionally,this letter highlights the comparison of these AI tools with tra-ditional methodologies,demonstrating their potential to streamline clinical workflows and address existing gaps in risk stratification and early detection.Furthermore,this letter addresses the ethical considerations and the need for robust clinical-AI collaboration to achieve reliable,transparent,and unbiased outcomes.Strengthening cross-disciplinary efforts will be vital for the responsible and effective deployment of AI in this critical area of oncology.
文摘Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.
基金Zhongshan Science and Technology Bureau Project“The Application of Infrared Thermography in the Syndrome Differentiation of Chaihu Guizhi Ganjiang Decoction”(Project No.2021B1066)Zhongshan Science and Technology Bureau Project“Exploring the Diagnostic Approach of the TCM Syndrome Type‘Chaihu Guizhi Ganjiang Decoction’Based on Infrared Thermal Imaging Systems and Digital Modeling Methods of Ancient and Modern Literature”(Project No.2022B1131)。
文摘Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi Ganjiang Decoction syndrome.Methods:Data were collected from over 100 patients diagnosed with Chaihu Guizhi Ganjiang Decoction syndrome at Professor Li Leyu’s endocrinology clinic,Zhongshan Hospital of Traditional Chinese Medicine,Guangdong Province,between April 2021 and April 2022.Body surface temperature data were obtained using the MTI-EXPRO-2013-B infrared thermography system.Principal component analysis(PCA)was applied to differentiate temperature distribution characteristics between genders,and a neural network prediction model was constructed for syndrome diagnosis.Results:Infrared thermography effectively captured surface temperature characteristics of patients with Chaihu Guizhi Ganjiang Decoction syndrome.PCA identified one principal component with a variance explanation rate of 73.953%for females and two principal components with a cumulative variance explanation rate of 77.627%for males.The neural network model demonstrated high predictive performance,with an area under the ROC curve of 0.9743 for the training set and 0.9889 for the validation set.Sensitivity was 1,specificity 0.8636,precision 0.8846,accuracy 0.9333,and the F1 score 0.9388.Conclusion:Infrared thermography provides an innovative,objective,and quantitative method for syndrome differentiation and treatment in TCM.It represents a significant advancement in transitioning from traditional empirical approaches to modern,visualized,and precise diagnosis and treatment.This study underscores the potential of integrating advanced technologies in TCM for enhanced clinical application and modernization.
基金Supported by the National High Technology Research and Development Program of China(863 Program)(No.2012AA10A404)the National Natural Science Foundation of China(No.31502161)Financially Supported by Qingdao National Laboratory for Marine Science and Technology(No.2015ASKJ02)
文摘Genomic selection(GS)can be used to accelerate genetic improvement by shortening the selection interval.The successful application of GS depends largely on the accuracy of the prediction of genomic estimated breeding value(GEBV).This study is a fi rst attempt to understand the practicality of GS in Litopenaeus vannamei and aims to evaluate models for GS on growth traits.The performance of GS models in L.vannamei was evaluated in a population consisting of 205 individuals,which were genotyped for 6 359 single nucleotide polymorphism(SNP)markers by specifi c length amplifi ed fragment sequencing(SLAF-seq)and phenotyped for body length and body weight.Three GS models(RR-BLUP,Bayes A,and Bayesian LASSO)were used to obtain the GEBV,and their predictive ability was assessed by the reliability of the GEBV and the bias of the predicted phenotypes.The mean reliability of the GEBVs for body length and body weight predicted by the dif ferent models was 0.296 and 0.411,respectively.For each trait,the performances of the three models were very similar to each other with respect to predictability.The regression coeffi cients estimated by the three models were close to one,suggesting near to zero bias for the predictions.Therefore,when GS was applied in a L.vannamei population for the studied scenarios,all three models appeared practicable.Further analyses suggested that improved estimation of the genomic prediction could be realized by increasing the size of the training population as well as the density of SNPs.
基金supported by National High Technology Research and Development Program of China (863 Program)(No. 2009AA04Z162)National Nature Science Foundation of China(No. 60825302, No. 60934007, No. 61074061)+1 种基金Program of Shanghai Subject Chief Scientist,"Shu Guang" project supported by Shang-hai Municipal Education Commission and Shanghai Education Development FoundationKey Project of Shanghai Science and Technology Commission, China (No. 10JC1403400)
文摘In this paper, a low-dimensional multiple-input and multiple-output （MIMO） model predictive control （MPC） configuration is presented for partial differential equation （PDE） unknown spatially-distributed systems （SDSs）. First, the dimension reduction with principal component analysis （PCA） is used to transform the high-dimensional spatio-temporal data into a low-dimensional time domain. The MPC strategy is proposed based on the online correction low-dimensional models, where the state of the system at a previous time is used to correct the output of low-dimensional models. Sufficient conditions for closed-loop stability are presented and proven. Simulations demonstrate the accuracy and efficiency of the proposed methodologies.
文摘Background:Information addressing soil quality in developing countries often depends on results from small experimental plots,which are later extrapolated to vast areas of agricultural land.This approach often results in misin-formation to end-users of land for sustainable soil nutrient management.The objective of this study was to estimate the spatial variability of soil quality index(SQI)at regional scale with predictive models using soil–environmental covariates.Methods:A total of 110 composite soil samples(0–30 cm depth)were collected by stratified random sampling schemes at 2–5 km intervals across the Cross River State,Nigeria,and selected soil physical and chemical properties were determined.We employed environmental covariates derived from a digital elevation model(DEM)and Senti-nel-2 imageries for our modelling regime.We measured soil quality using two approaches[total data set(TDS)and minimum data set(MDS)].Two scoring functions were also applied,linear(L)and non-linear(NL),yielding four indices(MDS_L,MDS_NL,TDS_L,and TDS_NL).Eleven soil quality indicators were used as TDS and were further screened for MDS using principal component analysis(PCA).Random forest(RF),support vector regression(SVR),regression kriging(RK),Cubist regression,and geographically weighted regression(GWR)were applied to predict SQI in unsampled locations.Results:The computed SQI via MDS_L was classified into five classes:≤0.38,0.38–0.48,0.48–0.58,0.58–0.68,and≥0.68,representing very low(classⅤ),low(classⅣ),moderate(classⅢ),high(classⅡ)and very high(classⅠ)soil quality,respectively.GWR model was robust in predicting soil quality(R^(2)=0.21,CCC=0.39,RMSE=0.15),while RF was a model with inferior performance(R^(2)=0.02,CCC=0.32,RMSE=0.15).Soil quality was high in the southern region and low in the northern region.High soil quality class(>49%)and moderate soil quality class(>14%)dominate the study area in all predicted models used.Conclusions:Structural stability index,sand content,soil oganic carbon content,and mean weight diameter of aggregates were the parameters used in establishing regional soil quality indices,while land surface water index,Sentinel-2 near-infrared band,plane curvature,and clay index were the most important variables affecting soil quality variability.The MDS_L and GWR are effective and useful models to identify the key soil properties for assessing soil quality,which can provide guidance for site-specific management of soils developed on diverse parent materials.
