Optimizing prediction models for pancreatic fistula after pancreatectomy:Current status and future perspectives

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.

Equivalency and unbiasedness of grey prediction models

In order to deeply research the structure discrepancy and modeling mechanism among different grey prediction models, the equivalence and unbiasedness of grey prediction models are analyzed and verified. The results show that all the grey prediction models that are strictly derived from x^（0）（k） ＋az^（1）（k） = b have the identical model structure and simulation precision. Moreover, the unbiased simulation for the homogeneous exponential sequence can be accomplished. However, the models derived from dx^（1）/dt ＋ ax^（1）= b are only close to those derived from x^（0）（k） ＋ az^（1）（k） = b provided that ｜a｜ has to satisfy｜a｜ 0.1; neither could the unbiased simulation for the homogeneous exponential sequence be achieved. The above conclusions are proved and verified through some theorems and examples.

Risk prediction models for hepatocellular carcinoma in different populations

Hepatocellular carcinoma （HCC） is a malignant disease with limited therapeutic options due to its aggressive progression. It places heaW burden on most low and middle income countries to treat HCC patients. Nowadays accurate HCC risk predictions can help making decisions on the need for HCC surveillance and antiviral therapy. HCC risk prediction models based on major risk factors of HCC are useful and helpful in providing adequate surveillance strategies to individuals who have different risk levels. Several risk prediction models among cohorts of different populations for estimating HCC incidence have been presented recently by using simple, efficient, and ready-to-use parameters. Moreover, using predictive scoring systems to assess HCC development can provide suggestions to improve clinical and public health approaches, making them more cost-effective and effort-effective, for inducing personalized surveillance programs according to risk stratification. In this review, the features of risk prediction models of HCC across different populations were summarized, and the perspectives of HCC risk prediction models were discussed as well.

Rapid prediction models for 3D geometry of landslide dam considering the damming process

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.

Generalized Nonlinear Irreducible Auto-Correlation and Its Applications in Nonlinear Prediction Models Identification

There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-(dependency) (IAD) and generalized irreducible auto-(dependency) (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.

Health monitoring and comparative analysis of time-dependent effect using different prediction models for self-anchored suspension bridge with extra-wide concrete girder

The structural health status of Hunan Road Bridge during its two-year service period from April 2015 to April 2017 was studied based on monitored data.The Hunan Road Bridge is the widest concrete self-anchored suspension bridge in China at present.Its structural changes and safety were evaluated using the health monitoring data,which included deformations,detailed stresses,and vibration characteristics.The influences of the single and dual effects comprising the ambient temperature changes and concrete shrinkage and creep(S&C)were analyzed based on the measured data.The ANSYS beam finite element model was established and validated by the measured bridge completion state.The comparative analyses of the prediction results of long-term concrete S&C effects were conducted using CEB-FIP 90 and B3 prediction models.The age-adjusted effective modulus method was adopted to simulate the aging behavior of concrete.Prestress relaxation was considered in the stepwise calculation.The results show that the transverse deviations of the towers are noteworthy.The spatial effect of the extra-wide girder is significant,as the compressive stress variations at the girder were uneven along the transverse direction.General increase and decrease in the girder compressive stresses were caused by seasonal ambient warming and cooling,respectively.The temperature gradient effects in the main girder were significant.Comparisons with the measured data showed that more accurate prediction results were obtained with the B3 prediction model,which can consider the concrete material parameters,than with the CEB-FIP 90 model.Significant deflection of the midspan girder in the middle region will be caused by the deviations of the cable anchoring positions at the girder ends and tower tops toward the midspan due to concrete S&C.The increase in the compressive stresses at the top plate and decrease in the stresses at the bottom plate at the middle midspan will be significant.The pre-deviations of the towers toward the sidespan and pre-lift of the midspan girder can reduce the adverse influences of concrete S&C on the structural health of the self-anchored suspension bridge with extra-wide concrete girder.

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies,all public tertiary hospitals in China began to use online outpatient appointment systems.However,the phenomenon of patient no-shows in online outpatient appointments is becoming more serious.The objective of this study is to design a prediction model for patient no-shows,thereby assisting hospitals in making relevant decisions,and reducing the probability of patient no-show behavior.We used 382,004 original online outpatient appointment records,and divided the data set into a training set(N_(1)=286,503),and a validation set(N_(2)=95,501).We used machine learning algorithms such as logistic regression,k-nearest neighbor(KNN),boosting,decision tree(DT),random forest(RF)and bagging to design prediction models for patient no-show in online outpatient appointments.The patient no-show rate of online outpatient appointment was 11.1%(N=42,224).From the validation set,bagging had the highest area under the ROC curve and AUC value,which was 0.990,followed by random forest and boosting models,which were 0.987 and 0.976,respectively.In contrast,compared with the previous prediction models,the area under ROC and AUC values of the logistic regression,decision tree,and k-nearest neighbors were lower at 0.597,0.499 and 0.843,respectively.This study demonstrates the possibility of using data from multiple sources to predict patient no-shows.The prediction model results can provide decision basis for hospitals to reduce medical resource waste,develop effective outpatient appointment policies,and optimize operations.

Prediction models for recurrence in patients with small bowel bleeding

Obscure gastrointestinal bleeding(OGIB)has traditionally been defined as gastrointestinal bleeding whose source remains unidentified after bidirectional endoscopy.OGIB can present as overt bleeding or occult bleeding,and small bowel lesions are the most common causes.The small bowel can be evaluated using capsule endoscopy,device-assisted enteroscopy,computed tomography enterography,or magnetic resonance enterography.Once the cause of smallbowel bleeding is identified and targeted therapeutic intervention is completed,the patient can be managed with routine visits.However,diagnostic tests may produce negative results,and some patients with small bowel bleeding,regardless of diagnostic findings,may experience rebleeding.Predicting those at risk of rebleeding can help clinicians form individualized surveillance plans.Several studies have identified different factors associated with rebleeding,and a limited number of studies have attempted to create prediction models for recurrence.This article describes prediction models developed so far for identifying patients with OGIB who are at greater risk of rebleeding.These models may aid clinicians in forming tailored patient management and surveillance.

Speed prediction models for car and sports utility vehicleat locations along four-lane median divided horizontal curves

Sites with varying geometric features were analyzed to develop the 85 th percentile speed prediction models for car and sports utility vehicle(SUV) at 50 m prior to the point of curvature(PC), PC, midpoint of a curve(MC), point of tangent(PT) and 50 m beyond PT on four-lane median divided rural highways. The car and SUV speed data were combined in the analysis as they were found to be normally distributed and not significantly different. Independent parameters representing geometric features and speed at the preceding section were logically selected in stepwise regression analyses to develop the models. Speeds at various locations were found to be dependent on some combinations of curve length, curvature and speed in the immediately preceding section of the highway. Curve length had a significant effect on the speed at locations 50 m prior to PC, PC and MC. The effect of curvature on speed was observed only at MC. The curve geometry did not have a significant effect on speed from PT onwards. The speed at 50 m prior to PC and curvature is the most significant parameter that affects the speed at PC and MC, respectively. Before entering a horizontal curve, drivers possibly perceive the curve based on its length. Longer curve encourages drivers to maintain higher speed in the preceding tangent section. Further, drivers start experiencing the effect of curvature only after entering the curve and adjust speed accordingly. Practitioners can use these findings in designing consistent horizontal curve for vehicle speed harmony.

Prognostic prediction models for postoperative patients with stageⅠtoⅢcolorectal cancer based on machine learning

BACKGROUND Colorectal cancer(CRC)is characterized by high heterogeneity,aggressiveness,and high morbidity and mortality rates.With machine learning(ML)algorithms,patient,tumor,and treatment features can be used to develop and validate models for predicting survival.In addition,important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.AIM To construct prognostic prediction models and screen important variables for patients with stageⅠtoⅢCRC.METHODS More than 1000 postoperative CRC patients were grouped according to survival time(with cutoff values of 3 years and 5 years)and assigned to training and testing cohorts(7:3).For each 3-category survival time,predictions were made by 4 ML algorithms(all-variable and important variable-only datasets),each of which was validated via 5-fold cross-validation and bootstrap validation.Important variables were screened with multivariable regression methods.Model performance was evaluated and compared before and after variable screening with the area under the curve(AUC).SHapley Additive exPlanations(SHAP)further demonstrated the impact of important variables on model decision-making.Nomograms were constructed for practical model application.RESULTS Our ML models performed well;the model performance before and after important parameter identification was consistent,and variable screening was effective.The highest pre-and postscreening model AUCs 95%confidence intervals in the testing set were 0.87(0.81-0.92)and 0.89(0.84-0.93)for overall survival,0.75(0.69-0.82)and 0.73(0.64-0.81)for disease-free survival,0.95(0.88-1.00)and 0.88(0.75-0.97)for recurrence-free survival,and 0.76(0.47-0.95)and 0.80(0.53-0.94)for distant metastasis-free survival.Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets.The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors.The nomograms were created.CONCLUSION We constructed a comprehensive,high-accuracy,important variable-based ML architecture for predicting the 3-category survival times.This architecture could serve as a vital reference for managing CRC patients.

Construction and optimization of traditional Chinese medicine constitution prediction models based on deep learning

Objective To cater to the demands for personalized health services from a deep learning per-spective by investigating the characteristics of traditional Chinese medicine(TCM)constitu-tion data and constructing models to explore new prediction methods.Methods Data from students at Chengdu University of Traditional Chinese Medicine were collected and organized according to the 24 solar terms from January 21,2020,to April 6,2022.The data were used to identify nine TCM constitutions,including balanced constitution,Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,phlegm dampness constitution,damp heat constitution,stagnant blood constitution,Qi stagnation constitution,and specific-inherited predisposition constitution.Deep learning algorithms were employed to construct multi-layer perceptron(MLP),long short-term memory(LSTM),and deep belief network(DBN)models for the prediction of TCM constitutions based on the nine constitution types.To optimize these TCM constitution prediction models,this study in-troduced the attention mechanism(AM),grey wolf optimizer(GWO),and particle swarm op-timization(PSO).The models’performance was evaluated before and after optimization us-ing the F1-score,accuracy,precision,and recall.Results The research analyzed a total of 31655 pieces of data.(i)Before optimization,the MLP model achieved more than 90%prediction accuracy for all constitution types except the balanced and Qi deficiency constitutions.The LSTM model's prediction accuracies exceeded 60%,indicating that their potential in TCM constitutional prediction may not have been fully realized due to the absence of pronounced temporal features in the data.Regarding the DBN model,the binary classification analysis showed that,apart from slightly underperforming in predicting the Qi deficiency constitution and damp heat constitution,with accuracies of 65%and 60%,respectively.The DBN model demonstrated considerable discriminative power for other constitution types,achieving prediction accuracy rates and area under the receiver op-erating characteristic(ROC)curve(AUC)values exceeding 70%and 0.78,respectively.This indicates that while the model possesses a certain level of constitutional differentiation abili-ty,it encounters limitations in processing specific constitutional features,leaving room for further improvement in its performance.For multi-class classification problem,the DBN model’s prediction accuracy rate fell short of 50%.(ii)After optimization,the LSTM model,enhanced with the AM,typically achieved a prediction accuracy rate above 75%,with lower performance for the Qi deficiency constitution,stagnant blood constitution,and Qi stagna-tion constitution.The GWO-optimized DBN model for multi-class classification showed an increased prediction accuracy rate of 56%,while the PSO-optimized model had a decreased accuracy rate to 37%.The GWO-PSO-DBN model,optimized with both algorithms,demon-strated an improved prediction accuracy rate of 54%.Conclusion This study constructed MLP,LSTM,and DBN models for predicting TCM consti-tution and improved them based on different optimisation algorithms.The results showed that the MLP model performs well,the LSTM and DBN models were effective in prediction but with certain limitations.This study also provided a new technology reference for the es-tablishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.

Comparison of various prediction models in the effect of laparoscopic sleeve gastrectomy on type 2 diabetes mellitus in the Chinese population 5 years after surgery

Background:The effect of bariatric surgery on type 2 diabetes mellitus(T2DM)control can be assessed based on predictive models of T2DM remission.Various models have been externally verified internationally.However,long-term validated results after laparoscopic sleeve gastrectomy(LSG)surgery are lacking.The best model for the Chinese population is also unknown.Methods:We retrospectively analyzed Chinese population data 5 years after LSG at Beijing Shijitan Hospital in China between March 2009 and December 2016.The independent t-test,Mann–Whitney U test,and chi-squared test were used to compare characteristics between T2DM remission and non-remission groups.We evaluated the predictive efficacy of each model for longterm T2DM remission after LSG by calculating the area under the curve(AUC),sensitivity,specificity,Youden index,positive predictive value(PPV),negative predictive value(NPV),and predicted-to-observed ratio,and performed calibration using Hosmer–Lemeshow test for 11 prediction models.Results:We enrolled 108 patients,including 44(40.7%)men,with a mean age of 35.5 years.The mean body mass index was 40.3±9.1 kg/m^(2),the percentage of excess weight loss(%EWL)was(75.9±30.4)%,and the percentage of total weight loss(%TWL)was(29.1±10.6)%.The mean glycated hemoglobin A1c(HbA1c)level was(7.3±1.8)%preoperatively and decreased to(5.9±1.0)%5 years after LSG.The 5-year postoperative complete and partial remission rates of T2DM were 50.9%[55/108]and 27.8%[30/108],respectively.Six models,i.e.,"ABCD",individualized metabolic surgery(IMS),advanced-DiaRem,DiaBetter,Dixon et al’s regression model,and Panunzi et al’s regression model,showed a good discrimination ability(all AUC>0.8).The"ABCD"(sensitivity,74%;specificity,80%;AUC,0.82[95%confidence interval[CI]:0.74–0.89]),IMS(sensitivity,78%;specificity,84%;AUC,0.82[95%CI:0.73–0.89]),and Panunzi et al’s regression models(sensitivity,78%;specificity,91%;AUC,0.86[95%CI:0.78–0.92])showed good discernibility.In the Hosmer–Lemeshow goodness-of-fit test,except for DiaRem(P<0.01),DiaBetter(P<0.01),Hayes et al(P=0.03),Park et al(P=0.02),and Ramos-Levi et al’s(P<0.01)models,all models had a satifactory fit results(P>0.05).The P values of calibration results of the"ABCD"and IMS were 0.07 and 0.14,respectively.The predicted-to-observed ratios of the"ABCD"and IMS were 0.87 and 0.89,respectively.Conclusion:The prediction model IMS was recommended for clinical use because of excellent predictive performance,good statistical test results,and simple and practical design features.

Critical appraisal of novel prediction models and risk calculators for post-hepatectomy liver failure and complications: practicability and generalisability in the real-world setting

Over the last few decades,the evolution of liver resection has progressed through numerous milestones in peri-operative management,operative techniques and novel technologies that have dramatically improved patient safety and outcomes(1).Consequently,such developments have enabled surgeons to embark on liver resections of lesions in technically challenging locations,whereby extended resection or bilovascular reconstruction may be required to ensure oncologic clearance.In the context of extended resections or resection of lesions from heavily diseased livers,concerns remain regarding the adequacy of the remnant future liver remnant(FLR)and liver function,placing patients at risk of the clinical phenomenon known as post-hepatectomy liver failure(PHLF).Although relatively uncommon,PHLF has a reported incidence of up to 32%in the literature and remains an important cause of post-hepatectomy morbidity and mortality(2).Presently,several definitions have been proposed to describe PHLF,the most recent of which was proposed by the International Study Group of Liver Surgery(ISGLS).In this definition,PHLF was defined as an increased international normalized ratio(INR)or hyperbilirubinemia on or after post-operative day 5,with further stratification of severity grades(A,B or C)based on the extent of clinical management(3).While definitions in PHLF assist in providing a common diagnostic framework among physicians,establishing predictors in PHLF is conceivably more helpful as it allows surgeons to have important decision-making details prior to planned liver resection.

Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models

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 R2 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.

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

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.

Review on fatigue life prediction models of welded joint

Fatigue assessment of welded joint is still far from being completely solved now,since many influencing factors coexist and some important ones should be considered in the developed life prediction models reasonably.Thus,such influencing factors of welded joint fatigue are firstly summarized in this work;and then,the existing life prediction models are reviewed from two aspects,i.e.,uniaxial and multiaxial ones;finally,significant conclusions of existing experimental and theoretical researches and some suggestions on improving the fatigue assessment of welded joints,especially for the low-cycle fatigue with the occurrence of ratchetting,are provided.

Clinical Prediction Models for Hepatitis B Virus-related Acute-on-chronic Liver Failure:A Technical Report

Background and Aims:It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure(HBV-ACLF).This study systematically summarized and evaluated the quality and performance of available clinical prediction models(CPMs).Methods:A keyword search of articles on HBV-ACLF CPMs published in PubMed from January 1995 to April 2020 was performed.Both the quality and performance of the CPMs were assessed.Results:Fifty-two CPMs were identified,of which 31 were HBV-ACLF specific.The modeling data were mostly derived from retrospective(83.87%)and single-center(96.77%)cohorts,with sample sizes ranging from 46 to 1,202.Three-month mortality was the most common endpoint.The Asian Pacific Association for the Study of the Liver consensus(51.92%)and Chinese Medical Association liver failure guidelines(40.38%)were commonly used for HBV-ACLF diagnosis.Serum bilirubin(67.74%),the international normalized ratio(54.84%),and hepatic encephalopathy(51.61%)were the most frequent variables used in models.Model discrimination was commonly evaluated(88.46%),but model calibration was seldom performed.The model for end-stage liver disease score was the most widely used(84.62%);however,varying performance was reported among the studies.Conclusions:Substantial limitations lie in the quality of HBV-ACLF-specific CPMs.Disease severity of study populations may impact model performance.The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.

Generalized and species-specific prediction models for aboveground biomass in semi-steppe rangelands

Aims The accurate estimation of aboveground biomass in vegetation is critical for global carbon accounting.Regression models provide an easy estimation of aboveground biomass at large spatial and temporal scales.Yet,only few prediction models are available for aboveground biomass in rangelands,as compared with forests.In addition to the development of prediction models,we tested whether such prediction models vary with plant growth forms and life spans,and with the inclusion of site and/or quadrat-specific factors.Methods We collected dataset of aboveground biomass from destructive harvesting of 8088 individual plants belonging to 79 species in 735 quadrats across 35 sites in semi-steppe rangelands in Iran.A logarithmic transformation of the power-law model was used to develop simple prediction models for the easy estimation of above-ground biomass using plant coverage and vegetation density as predictors for the species-specific model,multispecies and plants of different growth forms and life spans.In addition,additive and multiplicative linear regression models were developed by using plant coverage and one categorical variable from the site and/or quadrat-specific factors.Important Findings The log-transformed power-law model based on plant coverage pre-cisely predicted aboveground biomass across the whole dataset for ei-ther most of the species-specific model,multispecies or plants of the same growth forms(shrubs,forbs or graminoids)and life spans(annuals,biennials or perennials).The addition of vegetation density as a single or in a compound predictor variable had relatively poor performance com-pared with the model having plant coverage only.Although generalizing at the levels of plant group forms and/or life spans did not substantially enhance the model-fit and validation of the plant coverage-based mul-tispecies model,the inclusion of plant growth forms or life spans as a categorical predictor variable had performed well.Generalized models in this study will greatly contribute to the accurate and easy predic-tion of aboveground biomass in the studied rangelands and will be also useful to rangeland practitioners and ecological modellers interested in the global relationship between biodiversity and aboveground biomass productivity across space and time in natural rangelands.

Review of wax sedimentations prediction models for crude-oil transportation pipelines

Wax sedimentation in pipelines is a severe crude oil production and transportation challenge.Pipeline surface roughness is experienced at the early stages of the problem;with time,the effective pipe cross-sectional area is reduced due to pipeline wax plugging,causing pumping pressure increase,equipment failures,and blockages,resulting in unnecessary downtime costs and pipeline abandonments in the worst situation.This paper reviews mathematical and experimental loops models used for pipeline solid wax predictions and calculations as functions of pressure,temperature,and fluid composition;by assessing model's Assumptions,strengths and weaknesses.It is found that most mathematical models applied molecular-diffusion mechanisms in modeling and neglected shear effects;which resulted in wax over-prediction.Experimental loop was time-consuming due to mounting and dismounting of test section during wax deposition measurements;our modification has included sensor-integration to detect,measure,and analyze wax deposition;Reliable wax predictions models are essential to properly design pipelines and adopt cost-effective strategies for wax deposition prevention,control,and removal.

Establishing prognostic models for intrahepatic cholangiocarcinoma based on immune cells

BACKGROUND Intrahepatic cholangiocarcinoma(ICC)is a malignant liver tumor that is challenging to treat and manage and current prognostic models for the disease are inefficient or ineffective.Tumor-associated immune cells are critical for tumor development and progression.The main goal of this study was to establish models based on tumor-associated immune cells for predicting the overall survival of patients undergoing surgery for ICC.AIM To establish 1-year and 3-year prognostic models for ICC after surgical resection.METHODS Immunohistochemical staining was performed for CD4,CD8,CD20,pan-cytokeratin(CK),and CD68 in tumors and paired adjacent tissues from 141 patients with ICC who underwent curative surgery.Selection of variables was based on regression diagnostic procedures and goodness-of-fit tests(PH assumption).Clinical parameters and pathological diagnoses,combined with the distribution of immune cells in tumors and paired adjacent tissues,were utilized to establish 1-and 3-year prognostic models.RESULTS This is an important application of immune cells in the tumor microenvironment.CD4,CD8,CD20,and CK were included in the establishment of our prognostic model by stepwise selection,whereas CD68 was not significantly associated with the prognosis of ICC.By integrating clinical data associated with ICC,distinct prognostic models were derived for 1-and 3-year survival outcomes using variable selection.The 1-year prediction model yielded a C-index of 0.7695%confidence interval(95%CI):0.65-0.87 and the 3-year prediction model produced a C-index of 0.69(95%CI:0.65-0.73).Internal validation yielded a C-index of 0.761(95%CI:0.669-0.853)for the 1-year model and 0.693(95%CI:0.642-0.744)for the 3-year model.CONCLUSION We developed Cox regression models for 1-year and 3-year survival predictions of patients with ICC who underwent resection,which has positive implications for establishing a more comprehensive prognostic model for ICC based on tumor immune microenvironment and immune cell changes in the future.