BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling techn...BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.展开更多
Delirium,a complex neurocognitive syndrome,frequently emerges following surgery,presenting diverse manifestations and considerable obstacles,especially among the elderly.This editorial delves into the intricate phenom...Delirium,a complex neurocognitive syndrome,frequently emerges following surgery,presenting diverse manifestations and considerable obstacles,especially among the elderly.This editorial delves into the intricate phenomenon of postoperative delirium(POD),shedding light on a study that explores POD in elderly individuals undergoing abdominal malignancy surgery.The study examines pathophysiology and predictive determinants,offering valuable insights into this challenging clinical scenario.Employing the synthetic minority oversampling technique,a predictive model is developed,incorporating critical risk factors such as comorbidity index,anesthesia grade,and surgical duration.There is an urgent need for accurate risk factor identification to mitigate POD incidence.While specific to elderly patients with abdominal malignancies,the findings contribute significantly to understanding delirium pathophysiology and prediction.Further research is warranted to establish standardized predictive for enhanced generalizability.展开更多
In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making d...In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.展开更多
This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from tra...This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.展开更多
There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We defi...There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.展开更多
Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the develo...Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.展开更多
Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on...Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To exploit the benefits presented by the laser hardening process, it is necessary to develop an integrated strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive modelling approach for predicting the hardened surface physical and geometrical attributes. The laser surface transformation hardening of cylindrical AISI 4340 steel workpieces is modeled using the conventional regression equation method as well as artificial neural network method. The process parameters included in the study are laser power, beam scanning speed, and the workpiece rotational speed. The upper and the lower limits for each parameter are chosen considering the start of the transformation hardening and the maximum hardened zone without surface melting. The resulting models are able to predict the depths representing the maximum hardness zone, the hardness drop zone, and the overheated zone without martensite transformation. Because of its ability to model highly nonlinear problems, the ANN based model presents the best modelling results and can predict the hardness profile with good accuracy.展开更多
Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we s...Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions.Classification of all-cause,30-day readmission outcomes were modeled using logistic regression,artificial neural network,and Easy Ensemble.F1 statistic,sensitivity,and positive predictive value were used to evaluate the model performance.Results:We identified 14 most influential data features(4 numeric features and 10 categorical features)and evaluated 3 machine learning models with numerous sampling methods(oversampling,undersampling,and hybrid techniques).The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble)for predicting readmission,whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions:Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes.But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.展开更多
To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital d...To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital drill holes in aluminum alloy 6061.Firstly,four cutting control parameters(tool rotation speed,tool revolution speed,axial feeding pitch and tool revolution radius)and affecting cutting forces are identified after orbital drilling kinematics analysis.Secondly,hybrid level orthogonal experiment method is utilized in modeling experiment.By nonlinear regression analysis,two quadratic prediction models for axial and radial forces are established,where the above four control parameters are used as input variables.Then,model accuracy and cutting control parameters are analyzed.Upon axial and radial forces models,two optimal combinations of cutting control parameters are obtained for processing a13mm hole,corresponding to the minimum axial force and the radial force respectively.Finally,each optimal combination is applied in verification experiment.The verification experiment results of cutting force are in good agreement with prediction model,which confirms accracy of the research method in practical production.展开更多
Predictive microbiology was utilized to model Staphylococcus aureus （S. aureus） growth and staphylococcal enterotoxin A （SEA） production in milk in this study. The modifed logistic model, modifed Gompertz model an...Predictive microbiology was utilized to model Staphylococcus aureus （S. aureus） growth and staphylococcal enterotoxin A （SEA） production in milk in this study. The modifed logistic model, modifed Gompertz model and Baranyi model were applied to model growth data of S. aureus between 15℃ and 37℃. Model comparisons indicated that Baranyi model described the growth data more accurately than two others with a mean square error of 0.0129. Growth rates generated from Baranyi model matched the observed ones with a bias factor of 0.999 and an accuracy factor of 1.01, and ft a square root model with respect to temperature; other two modifed models both overestimated the observed ones. SEA amount began to be detected when the cell number reached106.4 cfu ？ mL-1, and showed the linear correlation with time. Besides, the rate of SEA production ftted an exponential relationship as a function of temperature. Predictions based on the study could be applied to indicate possible growth of S. aureus and prevent the occurrence of staphylococcal food poisoning.展开更多
Sources of uncertainty or error that arise in attempting to scale up the results of laboratory-scale sediment transport studies for predictive modeling of geomorphic systems include: (i) model imperfec...Sources of uncertainty or error that arise in attempting to scale up the results of laboratory-scale sediment transport studies for predictive modeling of geomorphic systems include: (i) model imperfection, (ii) omission of important processes, (iii) lack of knowledge of initial conditions, (iv) sensitivity to initial conditions, (v) unresolved heterogeneity, (vi) occurrence of external forcing, and (vii) inapplicability of the factor of safety concept. Sources of uncertainty that are unimportant or that can be controlled at small scales and over short time scales become important in large-scale applications and over long time scales. Control and repeatability, hallmarks of laboratory-scale experiments, are usually lacking at the large scales characteristic of geomorphology. Heterogeneity is an important concomitant of size, and tends to make large systems unique. Uniqueness implies that prediction cannot be based upon first-principles quantitative modeling alone, but must be a function of system history as well. Periodic data collection, feedback, and model updating are essential where site-specific prediction is required.展开更多
Oil wells on the North Slope of Alaska pass through deep deposits of permafrost. The heat transferred during their operation causes localized thawing, resulting in ground subsidence adjacent to the well casings. This ...Oil wells on the North Slope of Alaska pass through deep deposits of permafrost. The heat transferred during their operation causes localized thawing, resulting in ground subsidence adjacent to the well casings. This subsidence has a damaging effect, causing the casings to compress, deform, and potentially fail. This paper presents the results of a laboratory study of the thaw consolidation strain of deep permafrost and its predictive modeling. Tests were performed to determine strains due to thaw and post-thaw loading, as well as soil index properties. Results, together with data from an earlier testing program, were used to produce empirical models for predicting strains and ground subsidence. Four distinct strain cases were analyzed with three models by multiple regression analyses, and the best-fitting model was selected for each case. Models were further compared in a ground subsidence prediction using a shared subsurface profile. Laboratory results indicate that strains due to thaw and post-thaw testing in deep core permafrost are insensitive to depth and are more strongly influenced by stress redistributions and the presence of ice lenses and inclusions. Modeling results show that the most statistically valid and useful models were those constructed using moisture content, porosity, and degree of saturation. The applicability of these models was validated by comparison with results from Finite Element modeling.展开更多
Background:Accurate nestling age is valuable for studies on nesting strategies,productivity,and impacts on repro-ductive success.Most aging guides consist of descriptions and photographs that are time consuming to rea...Background:Accurate nestling age is valuable for studies on nesting strategies,productivity,and impacts on repro-ductive success.Most aging guides consist of descriptions and photographs that are time consuming to read and subjective to interpret.The Western Bluebird(Sialia mexicana)is a secondary cavity-nesting passerine that nests in coniferous and open deciduous forests.Nest box programs for cavity-nesting species have provided suitable nesting locations and opportunities for data collection on nestling growth and development.Methods:We developed models for predicting the age of Western Bluebird nestlings from morphometric meas-urements using model training and validation.These were developed for mass,tarsus,and two different culmen measurements.Results:Our models were accurate to within less than a day,and each model worked best for a specific age range.The mass and tarsus models can be used to estimate the ages of Western Bluebird nestlings 0-10 days old and were accurate to within 0.5 days for mass and 0.7 days for tarsus.The culmen models can be used to estimate ages of nest-lings 0-15 days old and were also accurate to within less than a day.The daily mean,minimum,and maximum values of each morphometric measurement are provided and can be used in the field for accurate nestling age estimations in real time.Conclusions:The model training and validation procedures used here demonstrate that this method can create aging models that are highly accurate.The methods can be applied to any passerine species provided sufficient nest-ling morphometric data are available.展开更多
One of the most controversial minerals in their origin and occurrence around the world is manganese deposits.The Abu Zenima area is rated one of the most economically important places where manganese ore deposits(Mn O...One of the most controversial minerals in their origin and occurrence around the world is manganese deposits.The Abu Zenima area is rated one of the most economically important places where manganese ore deposits(Mn ODs)are located in the southwest Sinai microplate,Egypt.These deposits are confined with the Um Bogma Formation(UBF)and the reserves of this region are relatively small.In this study,optical and radar data are used in a new challenge as an attempt to reach the closest controls and setting of Mn ODs.Moreover,Frequency Ratio(FR)and Logistic Regression(LogR)predictive models are applied to integrate different geospatial thematic maps,to predict new potential resource zones for increasing the ranges of mining quarries.Landsat8 OLI,Sentinel-2A Multi Spectral Instrument and Radar(Sentinel-1B)data are combined for mapping Mn ODs locations and their relationship with geological structures and the surrounding rocks.Band ratio,Principal and Independent Component Analysis techniques and four classification algorithms were implemented to the optical’VNIR and SWIR bands.Unusually,the interferometric processing steps for Sentinel-1 data were made for understanding the tectonic features in the area.The FR and LogR models are validated during fieldwork with known Mn ODs locations.Results indicate that processed images are capable of differentiation of UBF which broadly distributed in the central and southern parts of the area.Mn ODs possibly formed by thermal events that attributed to paleo-volcanic events before the rift stage.The high accuracy of LogR model(0.902)suggests that high Mn ODs potential zones are identified within the intersected fault zones near granitic units.This integration is recommended for discriminating hydrothermally Mn ODs in other arid geographic regions.展开更多
Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. ...Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.展开更多
This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative ...This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.展开更多
Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop cont...Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.展开更多
BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction...BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.展开更多
Objective To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram.Methods Data from elderly individuals(age≥65 years)histologicall...Objective To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram.Methods Data from elderly individuals(age≥65 years)histologically diagnosed with brain glioma were sourced from the Surveillance,Epidemiology,and End Results(SEER)database.The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio.Additionally,data obtained from Tangdu Hospital constituted an external validation cohort for the study.The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator(LASSO)and multivariate Cox regression analysis,enabling the construction of a nomogram.Model performance was evaluated using C-index,ROC curves,calibration plot and decision curve analysis(DCA).Results A cohort of 20483 elderly glioma patients was selected from the SEER database.Five prognostic factors(age,marital status,histological type,stage,and treatment)were found to significantly impact overall survival(OS)and cancer-specific survival(CSS),with tumor location emerging as a sixth variable independently linked to CSS.Subsequently,nomogram models were developed to predict the probabilities of survival at 6,12,and 24 months.The assessment findings from the validation queue indicate a that the model exhibited strong performance.Conclusion Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients.They can potentially assist in risk stratification and clinical decision-making.展开更多
Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of se...Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.展开更多
基金Supported by Discipline Advancement Program of Shanghai Fourth People’s Hospital,No.SY-XKZT-2020-2013.
文摘BACKGROUND Postoperative delirium,particularly prevalent in elderly patients after abdominal cancer surgery,presents significant challenges in clinical management.AIM To develop a synthetic minority oversampling technique(SMOTE)-based model for predicting postoperative delirium in elderly abdominal cancer patients.METHODS In this retrospective cohort study,we analyzed data from 611 elderly patients who underwent abdominal malignant tumor surgery at our hospital between September 2020 and October 2022.The incidence of postoperative delirium was recorded for 7 d post-surgery.Patients were divided into delirium and non-delirium groups based on the occurrence of postoperative delirium or not.A multivariate logistic regression model was used to identify risk factors and develop a predictive model for postoperative delirium.The SMOTE technique was applied to enhance the model by oversampling the delirium cases.The model’s predictive accuracy was then validated.RESULTS In our study involving 611 elderly patients with abdominal malignant tumors,multivariate logistic regression analysis identified significant risk factors for postoperative delirium.These included the Charlson comorbidity index,American Society of Anesthesiologists classification,history of cerebrovascular disease,surgical duration,perioperative blood transfusion,and postoperative pain score.The incidence rate of postoperative delirium in our study was 22.91%.The original predictive model(P1)exhibited an area under the receiver operating characteristic curve of 0.862.In comparison,the SMOTE-based logistic early warning model(P2),which utilized the SMOTE oversampling algorithm,showed a slightly lower but comparable area under the curve of 0.856,suggesting no significant difference in performance between the two predictive approaches.CONCLUSION This study confirms that the SMOTE-enhanced predictive model for postoperative delirium in elderly abdominal tumor patients shows performance equivalent to that of traditional methods,effectively addressing data imbalance.
文摘Delirium,a complex neurocognitive syndrome,frequently emerges following surgery,presenting diverse manifestations and considerable obstacles,especially among the elderly.This editorial delves into the intricate phenomenon of postoperative delirium(POD),shedding light on a study that explores POD in elderly individuals undergoing abdominal malignancy surgery.The study examines pathophysiology and predictive determinants,offering valuable insights into this challenging clinical scenario.Employing the synthetic minority oversampling technique,a predictive model is developed,incorporating critical risk factors such as comorbidity index,anesthesia grade,and surgical duration.There is an urgent need for accurate risk factor identification to mitigate POD incidence.While specific to elderly patients with abdominal malignancies,the findings contribute significantly to understanding delirium pathophysiology and prediction.Further research is warranted to establish standardized predictive for enhanced generalizability.
文摘In a competitive digital age where data volumes are increasing with time, the ability to extract meaningful knowledge from high-dimensional data using machine learning (ML) and data mining (DM) techniques and making decisions based on the extracted knowledge is becoming increasingly important in all business domains. Nevertheless, high-dimensional data remains a major challenge for classification algorithms due to its high computational cost and storage requirements. The 2016 Demographic and Health Survey of Ethiopia (EDHS 2016) used as the data source for this study which is publicly available contains several features that may not be relevant to the prediction task. In this paper, we developed a hybrid multidimensional metrics framework for predictive modeling for both model performance evaluation and feature selection to overcome the feature selection challenges and select the best model among the available models in DM and ML. The proposed hybrid metrics were used to measure the efficiency of the predictive models. Experimental results show that the decision tree algorithm is the most efficient model. The higher score of HMM (m, r) = 0.47 illustrates the overall significant model that encompasses almost all the user’s requirements, unlike the classical metrics that use a criterion to select the most appropriate model. On the other hand, the ANNs were found to be the most computationally intensive for our prediction task. Moreover, the type of data and the class size of the dataset (unbalanced data) have a significant impact on the efficiency of the model, especially on the computational cost, and the interpretability of the parameters of the model would be hampered. And the efficiency of the predictive model could be improved with other feature selection algorithms (especially hybrid metrics) considering the experts of the knowledge domain, as the understanding of the business domain has a significant impact.
基金Project supported by the National Natural Science Foundation ofChina (No. 40101014) and by the Science and technology Committee of Zhejiang Province (No. 001110445) China
文摘This article presents two approaches for automated building of knowledge bases of soil resources mapping. These methods used decision tree and Bayesian predictive modeling, respectively to generate knowledge from training data. With these methods, building a knowledge base for automated soil mapping is easier than using the conventional knowledge acquisition approach. The knowledge bases built by these two methods were used by the knowledge classifier for soil type classification of the Longyou area, Zhejiang Province, China using TM bi-temporal imageries and GIS data. To evaluate the performance of the resultant knowledge bases, the classification results were compared to existing soil map based on field survey. The accuracy assessment and analysis of the resultant soil maps suggested that the knowledge bases built by these two methods were of good quality for mapping distribution model of soil classes over the study area.
文摘There has been evidence of crime in the US since colonization. In this article, we analyze the crime statistics of San Francisco and its resolution of crime recorded from January to September of the year 2018. We define resolution of crime as a target variable and study its relationship with other variables. We make several classification models to predict resolution of crime using several data mining techniques and suggest the best model for predicting resolution.
基金Supported by the National Center for Advancing Translational Sciences,No.UL1 TR002377.
文摘Artificial intelligence(AI)and digital twin models of various systems have long been used in industry to test products quickly and efficiently.Use of digital twins in clinical medicine caught attention with the development of Archimedes,an AI model of diabetes,in 2003.More recently,AI models have been applied to the fields of cardiology,endocrinology,and undergraduate medical education.The use of digital twins and AI thus far has focused mainly on chronic disease management,their application in the field of critical care medicine remains much less explored.In neurocritical care,current AI technology focuses on interpreting electroencephalography,monitoring intracranial pressure,and prognosticating outcomes.AI models have been developed to interpret electroencephalograms by helping to annotate the tracings,detecting seizures,and identifying brain activation in unresponsive patients.In this mini-review we describe the challenges and opportunities in building an actionable AI model pertinent to neurocritical care that can be used to educate the newer generation of clinicians and augment clinical decision making.
文摘Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To exploit the benefits presented by the laser hardening process, it is necessary to develop an integrated strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive modelling approach for predicting the hardened surface physical and geometrical attributes. The laser surface transformation hardening of cylindrical AISI 4340 steel workpieces is modeled using the conventional regression equation method as well as artificial neural network method. The process parameters included in the study are laser power, beam scanning speed, and the workpiece rotational speed. The upper and the lower limits for each parameter are chosen considering the start of the transformation hardening and the maximum hardened zone without surface melting. The resulting models are able to predict the depths representing the maximum hardness zone, the hardness drop zone, and the overheated zone without martensite transformation. Because of its ability to model highly nonlinear problems, the ANN based model presents the best modelling results and can predict the hardness profile with good accuracy.
基金supported in part by the Key Research and Development Program for Guangdong Province(No.2019B010136001)in part by Hainan Major Science and Technology Projects(No.ZDKJ2019010)+3 种基金in part by the National Key Research and Development Program of China(No.2016YFB0800803 and No.2018YFB1004005)in part by National Natural Science Foundation of China(No.81960565,No.81260139,No.81060073,No.81560275,No.61562021,No.30560161 and No.61872110)in part by Hainan Special Projects of Social Development(No.ZDYF2018103 and No.2015SF 39)in part by Hainan Association for Academic Excellence Youth Science and Technology Innovation Program(No.201515)
文摘Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with diabetes.Methods:In this retrospective cohort study,we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with readmissions.Classification of all-cause,30-day readmission outcomes were modeled using logistic regression,artificial neural network,and Easy Ensemble.F1 statistic,sensitivity,and positive predictive value were used to evaluate the model performance.Results:We identified 14 most influential data features(4 numeric features and 10 categorical features)and evaluated 3 machine learning models with numerous sampling methods(oversampling,undersampling,and hybrid techniques).The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble)for predicting readmission,whereas the other two algorithms led to much smaller differences between the training and testing datasets.Conclusions:Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with diabetes.But more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.
基金Supported by the National Natural Science Foundation of China(50975141)the Aviation Science Fund(20091652018,2010352005)the National Science and Technology Major Project of the Ministry of Science and Technology of China(2012ZX04003031-4)
文摘To optimize cutting control parameters and provide scientific evidence for controlling cutting forces,cutting force modeling and cutting control parameter optimization are researched with one tool adopted to orbital drill holes in aluminum alloy 6061.Firstly,four cutting control parameters(tool rotation speed,tool revolution speed,axial feeding pitch and tool revolution radius)and affecting cutting forces are identified after orbital drilling kinematics analysis.Secondly,hybrid level orthogonal experiment method is utilized in modeling experiment.By nonlinear regression analysis,two quadratic prediction models for axial and radial forces are established,where the above four control parameters are used as input variables.Then,model accuracy and cutting control parameters are analyzed.Upon axial and radial forces models,two optimal combinations of cutting control parameters are obtained for processing a13mm hole,corresponding to the minimum axial force and the radial force respectively.Finally,each optimal combination is applied in verification experiment.The verification experiment results of cutting force are in good agreement with prediction model,which confirms accracy of the research method in practical production.
基金Supported by"Academic Backbone"Project of Northeast Agricultural University(15XG26)the National High-level Talents Special Support Program of China
文摘Predictive microbiology was utilized to model Staphylococcus aureus （S. aureus） growth and staphylococcal enterotoxin A （SEA） production in milk in this study. The modifed logistic model, modifed Gompertz model and Baranyi model were applied to model growth data of S. aureus between 15℃ and 37℃. Model comparisons indicated that Baranyi model described the growth data more accurately than two others with a mean square error of 0.0129. Growth rates generated from Baranyi model matched the observed ones with a bias factor of 0.999 and an accuracy factor of 1.01, and ft a square root model with respect to temperature; other two modifed models both overestimated the observed ones. SEA amount began to be detected when the cell number reached106.4 cfu ？ mL-1, and showed the linear correlation with time. Besides, the rate of SEA production ftted an exponential relationship as a function of temperature. Predictions based on the study could be applied to indicate possible growth of S. aureus and prevent the occurrence of staphylococcal food poisoning.
基金Knowledge Innovation Project of CAS No.KZCX1-10-04
文摘Sources of uncertainty or error that arise in attempting to scale up the results of laboratory-scale sediment transport studies for predictive modeling of geomorphic systems include: (i) model imperfection, (ii) omission of important processes, (iii) lack of knowledge of initial conditions, (iv) sensitivity to initial conditions, (v) unresolved heterogeneity, (vi) occurrence of external forcing, and (vii) inapplicability of the factor of safety concept. Sources of uncertainty that are unimportant or that can be controlled at small scales and over short time scales become important in large-scale applications and over long time scales. Control and repeatability, hallmarks of laboratory-scale experiments, are usually lacking at the large scales characteristic of geomorphology. Heterogeneity is an important concomitant of size, and tends to make large systems unique. Uniqueness implies that prediction cannot be based upon first-principles quantitative modeling alone, but must be a function of system history as well. Periodic data collection, feedback, and model updating are essential where site-specific prediction is required.
文摘Oil wells on the North Slope of Alaska pass through deep deposits of permafrost. The heat transferred during their operation causes localized thawing, resulting in ground subsidence adjacent to the well casings. This subsidence has a damaging effect, causing the casings to compress, deform, and potentially fail. This paper presents the results of a laboratory study of the thaw consolidation strain of deep permafrost and its predictive modeling. Tests were performed to determine strains due to thaw and post-thaw loading, as well as soil index properties. Results, together with data from an earlier testing program, were used to produce empirical models for predicting strains and ground subsidence. Four distinct strain cases were analyzed with three models by multiple regression analyses, and the best-fitting model was selected for each case. Models were further compared in a ground subsidence prediction using a shared subsurface profile. Laboratory results indicate that strains due to thaw and post-thaw testing in deep core permafrost are insensitive to depth and are more strongly influenced by stress redistributions and the presence of ice lenses and inclusions. Modeling results show that the most statistically valid and useful models were those constructed using moisture content, porosity, and degree of saturation. The applicability of these models was validated by comparison with results from Finite Element modeling.
文摘Background:Accurate nestling age is valuable for studies on nesting strategies,productivity,and impacts on repro-ductive success.Most aging guides consist of descriptions and photographs that are time consuming to read and subjective to interpret.The Western Bluebird(Sialia mexicana)is a secondary cavity-nesting passerine that nests in coniferous and open deciduous forests.Nest box programs for cavity-nesting species have provided suitable nesting locations and opportunities for data collection on nestling growth and development.Methods:We developed models for predicting the age of Western Bluebird nestlings from morphometric meas-urements using model training and validation.These were developed for mass,tarsus,and two different culmen measurements.Results:Our models were accurate to within less than a day,and each model worked best for a specific age range.The mass and tarsus models can be used to estimate the ages of Western Bluebird nestlings 0-10 days old and were accurate to within 0.5 days for mass and 0.7 days for tarsus.The culmen models can be used to estimate ages of nest-lings 0-15 days old and were also accurate to within less than a day.The daily mean,minimum,and maximum values of each morphometric measurement are provided and can be used in the field for accurate nestling age estimations in real time.Conclusions:The model training and validation procedures used here demonstrate that this method can create aging models that are highly accurate.The methods can be applied to any passerine species provided sufficient nest-ling morphometric data are available.
文摘One of the most controversial minerals in their origin and occurrence around the world is manganese deposits.The Abu Zenima area is rated one of the most economically important places where manganese ore deposits(Mn ODs)are located in the southwest Sinai microplate,Egypt.These deposits are confined with the Um Bogma Formation(UBF)and the reserves of this region are relatively small.In this study,optical and radar data are used in a new challenge as an attempt to reach the closest controls and setting of Mn ODs.Moreover,Frequency Ratio(FR)and Logistic Regression(LogR)predictive models are applied to integrate different geospatial thematic maps,to predict new potential resource zones for increasing the ranges of mining quarries.Landsat8 OLI,Sentinel-2A Multi Spectral Instrument and Radar(Sentinel-1B)data are combined for mapping Mn ODs locations and their relationship with geological structures and the surrounding rocks.Band ratio,Principal and Independent Component Analysis techniques and four classification algorithms were implemented to the optical’VNIR and SWIR bands.Unusually,the interferometric processing steps for Sentinel-1 data were made for understanding the tectonic features in the area.The FR and LogR models are validated during fieldwork with known Mn ODs locations.Results indicate that processed images are capable of differentiation of UBF which broadly distributed in the central and southern parts of the area.Mn ODs possibly formed by thermal events that attributed to paleo-volcanic events before the rift stage.The high accuracy of LogR model(0.902)suggests that high Mn ODs potential zones are identified within the intersected fault zones near granitic units.This integration is recommended for discriminating hydrothermally Mn ODs in other arid geographic regions.
文摘Efficient water quality monitoring and ensuring the safety of drinking water by government agencies in areas where the resource is constantly depleted due to anthropogenic or natural factors cannot be overemphasized. The above statement holds for West Texas, Midland, and Odessa Precisely. Two machine learning regression algorithms (Random Forest and XGBoost) were employed to develop models for the prediction of total dissolved solids (TDS) and sodium absorption ratio (SAR) for efficient water quality monitoring of two vital aquifers: Edward-Trinity (plateau), and Ogallala aquifers. These two aquifers have contributed immensely to providing water for different uses ranging from domestic, agricultural, industrial, etc. The data was obtained from the Texas Water Development Board (TWDB). The XGBoost and Random Forest models used in this study gave an accurate prediction of observed data (TDS and SAR) for both the Edward-Trinity (plateau) and Ogallala aquifers with the R<sup>2</sup> values consistently greater than 0.83. The Random Forest model gave a better prediction of TDS and SAR concentration with an average R, MAE, RMSE and MSE of 0.977, 0.015, 0.029 and 0.00, respectively. For the XGBoost, an average R, MAE, RMSE, and MSE of 0.953, 0.016, 0.037 and 0.00, respectively, were achieved. The overall performance of the models produced was impressive. From this study, we can clearly understand that Random Forest and XGBoost are appropriate for water quality prediction and monitoring in an area of high hydrocarbon activities like Midland and Odessa and West Texas at large.
基金supported by the National Natural Science Foundation of China (62073303,61673356)Hubei Provincial Natural Science Foundation of China (2015CFA010)the 111 Project(B17040)。
文摘This article focuses on dynamic event-triggered mechanism(DETM)-based model predictive control(MPC) for T-S fuzzy systems.A hybrid dynamic variables-dependent DETM is carefully devised,which includes a multiplicative dynamic variable and an additive dynamic variable.The addressed DETM-based fuzzy MPC issue is described as a “min-max” optimization problem(OP).To facilitate the co-design of the MPC controller and the weighting matrix of the DETM,an auxiliary OP is proposed based on a new Lyapunov function and a new robust positive invariant(RPI) set that contain the membership functions and the hybrid dynamic variables.A dynamic event-triggered fuzzy MPC algorithm is developed accordingly,whose recursive feasibility is analysed by employing the RPI set.With the designed controller,the involved fuzzy system is ensured to be asymptotically stable.Two examples show that the new DETM and DETM-based MPC algorithm have the advantages of reducing resource consumption while yielding the anticipated performance.
基金supported by the Major Science and Technology Projects of Gansu Province(Grant No.20ZD7GF011)Gansu Province Higher Education Industry Support Plan Project:Research on the Collaborative Operation of Solar Thermal Storage+Wind-Solar Hybrid Power Generation--Based on“Integrated Energy Demonstration of Wind-Solar Energy Storage in Gansu Province”(Project No.2022CYZC-34).
文摘Parallel connection of multiple inverters is an important means to solve the expansion,reserve and protection of distributed power generation,such as photovoltaics.In view of the shortcomings of traditional droop control methods such as weak anti-interference ability,low tracking accuracy of inverter output voltage and serious circulation phenomenon,a finite control set model predictive control(FCS-MPC)strategy of microgrid multiinverter parallel system based on Mixed Logical Dynamical(MLD)modeling is proposed.Firstly,the MLD modeling method is introduced logical variables,combining discrete events and continuous events to form an overall differential equation,which makes the modeling more accurate.Then a predictive controller is designed based on the model,and constraints are added to the objective function,which can not only solve the real-time changes of the control system by online optimization,but also effectively obtain a higher tracking accuracy of the inverter output voltage and lower total harmonic distortion rate(Total Harmonics Distortion,THD);and suppress the circulating current between the inverters,to obtain a good dynamic response.Finally,the simulation is carried out onMATLAB/Simulink to verify the correctness of the model and the rationality of the proposed strategy.This paper aims to provide guidance for the design and optimal control of multi-inverter parallel systems.
基金Supported by Guizhou Provincial Science and Technology Projects,No.[2021]013 and No.[2021]053Doctor Foundation of Guizhou Provincial People's Hospital,No.GZSYBS[2021]07.
文摘BACKGROUND Sarcopenia may be associated with hepatocellular carcinoma(HCC)following hepatectomy.But traditional single clinical variables are still insufficient to predict recurrence.We still lack effective prediction models for recent recurrence(time to recurrence<2 years)after hepatectomy for HCC.AIM To establish an interventable prediction model to estimate recurrence-free survival(RFS)after hepatectomy for HCC based on sarcopenia.METHODS We retrospectively analyzed 283 hepatitis B-related HCC patients who underwent curative hepatectomy for the first time,and the skeletal muscle index at the third lumbar spine was measured by preoperative computed tomography.94 of these patients were enrolled for external validation.Cox multivariate analysis was per-formed to identify the risk factors of postoperative recurrence in training cohort.A nomogram model was developed to predict the RFS of HCC patients,and its predictive performance was validated.The predictive efficacy of this model was evaluated using the receiver operating characteristic curve.RESULTS Multivariate analysis showed that sarcopenia[Hazard ratio(HR)=1.767,95%CI:1.166-2.678,P<0.05],alpha-fetoprotein≥40 ng/mL(HR=1.984,95%CI:1.307-3.011,P<0.05),the maximum diameter of tumor>5 cm(HR=2.222,95%CI:1.285-3.842,P<0.05),and hepatitis B virus DNA level≥2000 IU/mL(HR=2.1,95%CI:1.407-3.135,P<0.05)were independent risk factors associated with postoperative recurrence of HCC.Based on the sarcopenia to assess the RFS model of hepatectomy with hepatitis B-related liver cancer disease(SAMD)was established combined with other the above risk factors.The area under the curve of the SAMD model was 0.782(95%CI:0.705-0.858)in the training cohort(sensitivity 81%,specificity 63%)and 0.773(95%CI:0.707-0.838)in the validation cohort.Besides,a SAMD score≥110 was better to distinguish the high-risk group of postoperative recurrence of HCC.CONCLUSION Sarcopenia is associated with recent recurrence after hepatectomy for hepatitis B-related HCC.A nutritional status-based prediction model is first established for postoperative recurrence of hepatitis B-related HCC,which is superior to other models and contributes to prognosis prediction.
基金supported by the Fund for Distinguished Young Scientists of the Department of Science and Technology of Shaanxi Province(No.2023-JC-JQ-68).
文摘Objective To determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram.Methods Data from elderly individuals(age≥65 years)histologically diagnosed with brain glioma were sourced from the Surveillance,Epidemiology,and End Results(SEER)database.The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio.Additionally,data obtained from Tangdu Hospital constituted an external validation cohort for the study.The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator(LASSO)and multivariate Cox regression analysis,enabling the construction of a nomogram.Model performance was evaluated using C-index,ROC curves,calibration plot and decision curve analysis(DCA).Results A cohort of 20483 elderly glioma patients was selected from the SEER database.Five prognostic factors(age,marital status,histological type,stage,and treatment)were found to significantly impact overall survival(OS)and cancer-specific survival(CSS),with tumor location emerging as a sixth variable independently linked to CSS.Subsequently,nomogram models were developed to predict the probabilities of survival at 6,12,and 24 months.The assessment findings from the validation queue indicate a that the model exhibited strong performance.Conclusion Our nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients.They can potentially assist in risk stratification and clinical decision-making.
基金supported in part by the National Natural Science Foundation of China under Grant 62172192,U20A20228,and 62171203in part by the Science and Technology Demonstration Project of Social Development of Jiangsu Province under Grant BE2019631。
文摘Currently,applications accessing remote computing resources through cloud data centers is the main mode of operation,but this mode of operation greatly increases communication latency and reduces overall quality of service(QoS)and quality of experience(QoE).Edge computing technology extends cloud service functionality to the edge of the mobile network,closer to the task execution end,and can effectivelymitigate the communication latency problem.However,the massive and heterogeneous nature of servers in edge computing systems brings new challenges to task scheduling and resource management,and the booming development of artificial neural networks provides us withmore powerfulmethods to alleviate this limitation.Therefore,in this paper,we proposed a time series forecasting model incorporating Conv1D,LSTM and GRU for edge computing device resource scheduling,trained and tested the forecasting model using a small self-built dataset,and achieved competitive experimental results.
