The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal...The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal outcomes reported in previous studies and sought to apply an innovative approach to improve these results. To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, encompassing both import and transit containers. The RandomizedSearchCV algorithm from Python’s Scikit-learn library was utilized in the methodological approach to yield the optimal activation function for prediction accuracy. The results indicated that “ajaLT”, a fusion of the Logistic and Hyperbolic Tangent Activation Functions, provided the best prediction accuracy, reaching a high of 82%. Despite these encouraging findings, it’s crucial to recognize the study’s limitations. While Fusion of Activation Functions is a promising method, further evaluation is necessary across different container types and port operations to ascertain the broader applicability and generalizability of these findings. The original value of this study lies in its innovative application of FAFs to CDT. Unlike previous studies, this research evaluates the method based on prediction accuracy rather than training time. It opens new avenues for machine learning engineers and researchers in applying FAFs to enhance prediction accuracy in CDT modeling, contributing to a previously underexplored area.展开更多
The alpha–beta filter algorithm has been widely researched for various applications,for example,navigation and target tracking systems.To improve the dynamic performance of the alpha–beta filter algorithm,a new pred...The alpha–beta filter algorithm has been widely researched for various applications,for example,navigation and target tracking systems.To improve the dynamic performance of the alpha–beta filter algorithm,a new prediction learning model is proposed in this study.The proposed model has two main components:(1)the alpha–beta filter algorithm is the main prediction module,and(2)the learning module is a feedforward artificial neural network(FF‐ANN).Furthermore,the model uses two inputs,temperature sensor and humidity sensor data,and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings.Using the novel proposed technique,prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network,and also reduces the root mean square error(RMSE)and mean absolute error(MAE).We carried out different experiments with different experimental setups.The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter.A higher prediction accuracy was achieved,and the MAE and RMSE were 35.1%–38.2%respectively.The final proposed model results show increased performance when compared to traditional methods.展开更多
Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with diffe...Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.展开更多
Crop Yield Prediction(CYP)is critical to world food production.Food safety is a top priority for policymakers.They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an ag...Crop Yield Prediction(CYP)is critical to world food production.Food safety is a top priority for policymakers.They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business.Crop Yield(CY)is a complex variable influenced by multiple factors,including genotype,environment,and their interactions.CYP is a significant agrarian issue.However,CYP is the main task due to many composite factors,such as climatic conditions and soil characteristics.Machine Learning(ML)is a powerful tool for supporting CYP decisions,including decision support on which crops to grow in a specific season.Generally,Artificial Neural Networks(ANN)are usually used to predict the behaviour of complex non-linear models.As a result,this research paper attempts to determine the correlations between climatic variables,soil nutrients,and CYwith the available data.InANN,threemethods,Levenberg-Marquardt(LM),Bayesian regularisation(BR),and scaled conjugate gradient(SCG),are used to train the neural network(NN)model and then compared to determine prediction accuracy.The performance measures of the training,as declared above,such as Mean Squared Error(MSE)and correlation coefficient(R),were determined to assess the ANN models that had been built.The experimental study proves that LM training algorithms are better,while BR and SCG have minimal performance.展开更多
This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk facto...This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.展开更多
[Objective] To discuss the effects of major mapping methods for DNA sequence on the accuracy of protein coding regions prediction,and to find out the effective mapping methods.[Method] By taking Approximate Correlatio...[Objective] To discuss the effects of major mapping methods for DNA sequence on the accuracy of protein coding regions prediction,and to find out the effective mapping methods.[Method] By taking Approximate Correlation（AC） as the full measure of the prediction accuracy at nucleotide level,the windowed narrow pass-band filter（WNPBF） based prediction algorithm was applied to study the effects of different mapping methods on prediction accuracy.[Result] In DNA data sets ALLSEQ and HMR195,the Voss and Z-Curve methods are proved to be more effective mapping methods than paired numeric（PN）,Electron-ion Interaction Potential（EIIP） and complex number methods.[Conclusion] This study lays the foundation to verify the effectiveness of new mapping methods by using the predicted AC value,and it is meaningful to reveal DNA structure by using bioinformatics methods.展开更多
Background:Recently,machine learning(ML)has become attractive in genomic prediction,but its superiority in genomic prediction over conventional(ss)GBLUP methods and the choice of optimal ML methods need to be investig...Background:Recently,machine learning(ML)has become attractive in genomic prediction,but its superiority in genomic prediction over conventional(ss)GBLUP methods and the choice of optimal ML methods need to be investigated.Results:In this study,2566 Chinese Yorkshire pigs with reproduction trait records were genotyped with the GenoBaits Porcine SNP 50 K and PorcineSNP50 panels.Four ML methods,including support vector regression(SVR),kernel ridge regression(KRR),random forest(RF)and Adaboost.R2 were implemented.Through 20 replicates of fivefold cross-validation(CV)and one prediction for younger individuals,the utility of ML methods in genomic prediction was explored.In CV,compared with genomic BLUP(GBLUP),single-step GBLUP(ssGBLUP)and the Bayesian method BayesHE,ML methods significantly outperformed these conventional methods.ML methods improved the genomic prediction accuracy of GBLUP,ssGBLUP,and BayesHE by 19.3%,15.0% and 20.8%,respectively.In addition,ML methods yielded smaller mean squared error(MSE)and mean absolute error(MAE)in all scenarios.ssGBLUP yielded an improvement of 3.8% on average in accuracy compared to that of GBLUP,and the accuracy of BayesHE was close to that of GBLUP.In genomic prediction of younger individuals,RF and Adaboost.R2_KRR performed better than GBLUP and BayesHE,while ssGBLUP performed comparably with RF,and ssGBLUP yielded slightly higher accuracy and lower MSE than Adaboost.R2_KRR in the prediction of total number of piglets born,while for number of piglets born alive,Adaboost.R2_KRR performed significantly better than ssGBLUP.Among ML methods,Adaboost.R2_KRR consistently performed well in our study.Our findings also demonstrated that optimal hyperparameters are useful for ML methods.After tuning hyperparameters in CV and in predicting genomic outcomes of younger individuals,the average improvement was 14.3% and 21.8% over those using default hyperparameters,respectively.Conclusion:Our findings demonstrated that ML methods had better overall prediction performance than conventional genomic selection methods,and could be new options for genomic prediction.Among ML methods,Adaboost.R2_KRR consistently performed well in our study,and tuning hyperparameters is necessary for ML methods.The optimal hyperparameters depend on the character of traits,datasets etc.展开更多
A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax depositi...A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.展开更多
The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical...The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical and clinical studies （Wang and Li, 2013; Wang et al., 2014）. There are two kinds of extremely useful markers in Y chromosome, single nucle- otide polymorphism （SNP） and short tandem repeats （STRs）. With a very low mutation rate on the order of 3.0 x 10-8 mutations/nucleotide/generation （Xue et al., 2009）, SNP markers have been used in constructing a robust phylogeny tree linking all the Y chromosome lineages from world pop- ulations （Karafet et al., 2008）. Those lineages determined by the pattern of SNPs are called haplogroups. That is to say, we have to genotype an appropriate number of SNPs in order to assign a given Y chromosome to a haplogroup. Compared with SNPs, the mutation rates of STR markers are about four to five orders of magnitude higher （Gusmgo et al., 2005; Ballantyne et al., 2010）. Typing STR has advantages of saving time and cost compared with typing SNPs in phylogenetic assignment of a Y chromosome （Wang et al., 2010）. A set of STR values for an individual is called a haplotype. Because of the disparity in mutation rates between SNP and STR, one SNP haplogroup could actually comprise many STR haplotypes （Wang et al., 2010）. It is most interesting that STR variability is clustered more by haplogroups than by populations （Bosch et al., 1999; Behar et al., 2004）, which indicates that STR haplotypes could be used to infer the haplogroup information of a given Y chromosome. There has been increasing interest in this cost- effective strategy for predicting the haplogroup from a given STR haplotype when SNP data are unavailable. For instance, Vadim Urasin＇s YPredictor （http：//predictor.ydna.ru/）, Whit Atheys＇ haplogroup predictor （http：//www.hprg.com/hapest5/） （Athey, 2005, 2006）, and haplogroup classifier of Arizona University （Schlecht et al., 2008） have been widely employed in previous studies for haplogroup prediction （Larmuseau et al., 2010; Bembea et al., 2011; Larmuseau et al., 2012; Tarlykov et al., 2013）.展开更多
Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reduc...Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.展开更多
The deviation control of directional drilling is essentially the controlling of two angles of the wellbore actually drilled, namely, the inclination and azimuth. In directional drilling the bit trajectory never coinci...The deviation control of directional drilling is essentially the controlling of two angles of the wellbore actually drilled, namely, the inclination and azimuth. In directional drilling the bit trajectory never coincides exactly with the planned path, which is usually a plane curve with straight, building, holding, and dropping sections in succession. The drilling direction is of course dependant on the direction of the resultant forces acting on the bit and it is quite a tough job to hit the optimum target at the hole bottom as required. The traditional passive methods for correcting the drilling path have not met the demand to improve the techniques of deviation control. A method for combining wellbore surveys to obtain a composite, more accurate well position relies on accepting the position of the well from the most accurate survey instrument used in a given section of the wellbore. The error in each position measurement is the sum of many independent root sources of error effects. The relationship between surveys and other influential factors is considered, along with an analysis of different points of view. The collaborative work describes, establishes a common starting point of wellbore position uncertainty model, definition of what constitutes an error model, mathematics of position uncertainty calculation and an error model for basic directional service.展开更多
Among the advantages of using industrial robots for machining applications instead of machine tools are flexibility, cost effectiveness, and versatility. Due to the kinematics of the articulated robot, the system beha...Among the advantages of using industrial robots for machining applications instead of machine tools are flexibility, cost effectiveness, and versatility. Due to the kinematics of the articulated robot, the system behaviour is quite different compared with machine tools. Two major questions arise in implementing robots in machining tasks: one is the robot’s stiffness, and the second is the achievable machined part accuracy, which varies mainly due to the huge variety of robot models. This paper proposes error prediction model in the application of industrial robot for machining tasks, based on stiffness and accuracy limits. The research work includes experimental and theoretical parts. Advanced machining and inspection tools were applied, as well as a theoretical model of the robot structure and stiffness based on the form-shaping function approach. The robot machining performances, from the workpiece accuracy point of view were predicted.展开更多
Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficie...Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.展开更多
Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimens...Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimensionality reduction,temporal modeling,and robust prediction,especially for multi-day forecasting.A novel hybrid model,SLHS-TCN-XGBoost,is proposed for power demand forecasting,leveraging SLHS(dimensionality reduction),TCN(temporal feature learning),and XGBoost(ensemble prediction).Applied to the three-year electricity load dataset of Seoul,South Korea,the model’s MAE,RMSE,and MAPE reached 112.08,148.39,and 2%,respectively,which are significantly reduced in MAE,RMSE,and MAPE by 87.37%,87.35%,and 87.43%relative to the baseline XGBoost model.Performance validation across nine forecast days demonstrates superior accuracy,with MAPE as low as 0.35%and 0.21%on key dates.Statistical Significance tests confirm significant improvements(p<0.05),with the highest MAPE reduction of 98.17%on critical days.Seasonal and temporal error analyses reveal stable performance,particularly in Quarter 3 and Quarter 4(0.5%,0.3%)and nighttime hours(<1%).Robustness tests,including 5-fold cross-validation and Various noise perturbations,confirm the model’s stability and resilience.The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting,with future optimization potential in data preprocessing,algorithm integration,and interpretability.展开更多
Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process mi...Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.展开更多
Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to...Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.展开更多
Determining an optimal sample size is a key step in designing field surveys,and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon(SOC).Based on 550 s...Determining an optimal sample size is a key step in designing field surveys,and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon(SOC).Based on 550 soil sampling points in the nearsurface layer(0 to 20 cm)in a representative region of northern China's agro-pastoral ecotone,we studied effects of four interpolation methods such as ordinary kriging(OK),universal kriging(UK),inverse distance weighting(IDW)and radial basis function(RBF)and random subsampling(50,100,200,300,400,and 500)on the prediction accuracy of SOC estimation.When the Shannon's Diversity Index(SHDI)and Shannon's Evenness Index(SHEI)was 2.01 and 0.67,the OK method appeared to be a superior method,which had the smallest root mean square error(RMSE)and the mean error(ME)nearest to zero.On the contrary,the UK method performed poorly for the interpolation of SOC in the present study.The sample size of 200 had the most accurate prediction;50 sampling points produced the worst prediction accuracy.Thus,we used 200 samples to estimate the study area's soil organic carbon density(SOCD)by the OK method.The total SOC storage to a depth of 20 cm in the study area was 117.94 Mt,and its mean SOCD was 2.40 kg/m2.The SOCD kg/(C⋅m2)of different land use types were in the following order:woodland(3.29)>grassland(2.35)>cropland(2.19)>sandy land(1.55).展开更多
This letter addresses the study by Jayabalan et al,which underscores the liver outcome score(LOS)and hemoglobin(Hb)as key prognostic markers for patients with autoimmune liver disease overlap syndromes(AILDOS),with pa...This letter addresses the study by Jayabalan et al,which underscores the liver outcome score(LOS)and hemoglobin(Hb)as key prognostic markers for patients with autoimmune liver disease overlap syndromes(AILDOS),with particular relevance to the autoimmune hepatitis-primary biliary cholangitis(AIH-PBC)subgroup.The findings indicate that an LOS threshold of 6 achieves high sensitivity and specificity in predicting liver-related mortality among AIH-PBC patients.Moreover,low Hb levels emerge as a significant mortality predictor across all AILDOS cases.These results contribute valuable perspectives on risk stratification in AILDOS,highlighting the promise of non-invasive prognostic tools.Future studies with larger cohorts are needed to substantiate LOS and Hb as robust markers for clinical application.展开更多
BACKGROUND Despite improved survival rates in rectal cancer treatment,many patients experience low anterior resection syndrome(LARS).The preoperative LARS score(POLARS)aims to address the limitations of LARS assessmen...BACKGROUND Despite improved survival rates in rectal cancer treatment,many patients experience low anterior resection syndrome(LARS).The preoperative LARS score(POLARS)aims to address the limitations of LARS assessment by predicting outcomes preoperatively to enhance surgical planning.AIM To investigate the predictive accuracy of POLARS in assessing the occurrence of LARS.METHODS This study enrolled a total of 335 patients who underwent laparoscopic or robotic low anal sphincter-preserving surgery for rectal tumors.Patients were categorized into three groups according to their POLARS score:no LARS(score 0-20),minor LARS(score 21-29),and major LARS(score 30-42).The QLQ-C30/CR29 scores were compared among these groups,and the agreement between POLARS predictions and the actual LARS scores was analyzed.RESULTS The study population was divided into three groups:major LARS(n=51,27.42%),minor LARS(n=109,58.6%),and no LARS(n=26,13.98%).Significant differences in the QLQ-C30 scales of social function,diarrhea,and financial impact were detected between the no LARS and major LARS groups(P<0.05)and between the minor LARS and major LARS groups(P<0.05).Similarly,significant differences were detected in the QLQ-CR29 scales for blood and mucus in the stool,fecal incontinence,and stool frequency between the no LARS and minor LARS groups(P<0.05),as well as between the minor LARS and major LARS groups(P<0.05).The predictive precision for major LARS using the POLARS score was 82.35%(42/51),with a recall of 35.89%(42/117).The mean absolute error(MAE)between the POLARS score and the actual LARS score was 8.92±5.47.In contrast,the XGBoost(extreme gradient boosting)model achieved a lower MAE of 6.29±4.77,with a precision of 84.39%and a recall of 74.05%for predicting major LARS.CONCLUSION The POLARS score demonstrated effectiveness and precision in predicting major LARS,thereby providing valuable insights into postoperative symptoms and patient quality of life.However,the XGBoost model exhibited superior performance with a lower MAE and higher recall for predicting major LARS compared to the POLARS model.展开更多
The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications,particularly for capacitive energy storage.Predicting t...The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications,particularly for capacitive energy storage.Predicting the relative permittivity of particle/polymer nanocomposites from the microstructure is of great significance.However,the classical effective medium theory and physics-based numerical calculation represented by finite element method are time-consuming and cumbersome for complex structures and nonlinear problem.The work explores a novel architecture combining the convolutional neural network(ConvNet)and finite element method(FEM)to predict the relative permittivity of nanocomposite dielectrics with incorporated barium titanite(BT)particles in polyvinylidene fluoride(PVDF)matrix.The ConvNet was trained and evaluated on big datasets with 14266 training data and 3514 testing data generated form a programmatic algorithm.Through numerical experiments,we demonstrate that the trained network can efficiently provide an accurate agreement between the ConvNet model and FEM by virtue of the significant evaluation metrics R2,which reaches as high as 0.9783 and 0.9375 on training and testing data,respectively.The strong universality of the presented method allows for an extension to fast and accurately predict other properties of the nanocomposite dielectrics.展开更多
文摘The purpose of this study was to address the challenges in predicting and classifying accuracy in modeling Container Dwell Time (CDT) using Artificial Neural Networks (ANN). This objective was driven by the suboptimal outcomes reported in previous studies and sought to apply an innovative approach to improve these results. To achieve this, the study applied the Fusion of Activation Functions (FAFs) to a substantial dataset. This dataset included 307,594 container records from the Port of Tema from 2014 to 2022, encompassing both import and transit containers. The RandomizedSearchCV algorithm from Python’s Scikit-learn library was utilized in the methodological approach to yield the optimal activation function for prediction accuracy. The results indicated that “ajaLT”, a fusion of the Logistic and Hyperbolic Tangent Activation Functions, provided the best prediction accuracy, reaching a high of 82%. Despite these encouraging findings, it’s crucial to recognize the study’s limitations. While Fusion of Activation Functions is a promising method, further evaluation is necessary across different container types and port operations to ascertain the broader applicability and generalizability of these findings. The original value of this study lies in its innovative application of FAFs to CDT. Unlike previous studies, this research evaluates the method based on prediction accuracy rather than training time. It opens new avenues for machine learning engineers and researchers in applying FAFs to enhance prediction accuracy in CDT modeling, contributing to a previously underexplored area.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2020‐0‐01441,Artificial Intelligence Convergence Research Center(Chungnam National University))“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS‐004).
文摘The alpha–beta filter algorithm has been widely researched for various applications,for example,navigation and target tracking systems.To improve the dynamic performance of the alpha–beta filter algorithm,a new prediction learning model is proposed in this study.The proposed model has two main components:(1)the alpha–beta filter algorithm is the main prediction module,and(2)the learning module is a feedforward artificial neural network(FF‐ANN).Furthermore,the model uses two inputs,temperature sensor and humidity sensor data,and a prediction algorithm is used to predict actual sensor readings from noisy sensor readings.Using the novel proposed technique,prediction accuracy is significantly improved while adding the feed‐forward backpropagation neural network,and also reduces the root mean square error(RMSE)and mean absolute error(MAE).We carried out different experiments with different experimental setups.The proposed model performance was evaluated with the traditional alpha–beta filter algorithm and other algorithms such as the Kalman filter.A higher prediction accuracy was achieved,and the MAE and RMSE were 35.1%–38.2%respectively.The final proposed model results show increased performance when compared to traditional methods.
基金the National Natural Science Foundation of China(No.32273185)the National Key R&D Program of China(No.2020YFD0901205)the Marine Fishery Resources Investigation and Exploration Program of the Ministry of Agriculture and Rural Affairs of China in 2021(No.D-8006-21-0215)。
文摘Albacore tuna(Thunnus alalunga)is one of the target species of tuna longline fishing,and waters near the Cook Islands are a vital albacore tuna fishing ground.Marine environmental data are usually presented with different spatial resolutions,which leads to different results in tuna fishery prediction.Study on the impact of different spatial resolutions on the prediction accuracy of albacore tuna fishery to select the best spatial resolution can contribute to better management of albacore tuna resources.The nominal catch per unit effort(CPUE)of albacore tuna is calculated according to vessel monitor system(VMS)data collected from Chinese distantwater fishery enterprises from January 1,2017 to May 31,2021.A total of 26 spatiotemporal and environmental factors,including temperature,salinity,dissolved oxygen of 0–300 m water layer,chlorophyll-a concentration in the sea surface,sea surface height,month,longitude,and latitude,were selected as variables.The temporal resolution of the variables was daily and the spatial resolutions were set to be 0.5°×0.5°,1°×1°,2°×2°,and 5°×5°.The relationship between the nominal CPUE and each individual factor was analyzed to remove the factors irrelavant to the nominal CPUE,together with a multicollinearity diagnosis on the factors to remove factors highly related to the other factors within the four spatial resolutions.The relationship models between CPUE and spatiotemporal and environmental factors by four spatial resolutions were established based on the long short-term memory(LSTM)neural network model.The mean absolute error(MAE)and root mean square error(RMSE)were used to analyze the fitness and accuracy of the models,and to determine the effects of different spatial resolutions on the prediction accuracy of the albacore tuna fishing ground.The results show the resolution of 1°×1°can lead to the best prediction accuracy,with the MAE and RMSE being 0.0268 and 0.0452 respectively,followed by 0.5°×0.5°,2°×2°and 5°×5°with declining prediction accuracy.The results suggested that 1)albacore tuna fishing ground can be predicted by LSTM;2)the VMS records the data in detail and can be used scientifically to calculate the CPUE;3)correlation analysis,and multicollinearity diagnosis are necessary to improve the prediction accuracy of the model;4)the spatial resolution should be 1°×1°in the forecast of albacore tuna fishing ground in waters near the Cook Islands.
文摘Crop Yield Prediction(CYP)is critical to world food production.Food safety is a top priority for policymakers.They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business.Crop Yield(CY)is a complex variable influenced by multiple factors,including genotype,environment,and their interactions.CYP is a significant agrarian issue.However,CYP is the main task due to many composite factors,such as climatic conditions and soil characteristics.Machine Learning(ML)is a powerful tool for supporting CYP decisions,including decision support on which crops to grow in a specific season.Generally,Artificial Neural Networks(ANN)are usually used to predict the behaviour of complex non-linear models.As a result,this research paper attempts to determine the correlations between climatic variables,soil nutrients,and CYwith the available data.InANN,threemethods,Levenberg-Marquardt(LM),Bayesian regularisation(BR),and scaled conjugate gradient(SCG),are used to train the neural network(NN)model and then compared to determine prediction accuracy.The performance measures of the training,as declared above,such as Mean Squared Error(MSE)and correlation coefficient(R),were determined to assess the ANN models that had been built.The experimental study proves that LM training algorithms are better,while BR and SCG have minimal performance.
文摘This letter addressed the impactful study by Zhong et al,which introduced a risk prediction and stratification model for surgical adverse events following minimally invasive esophagectomy.By identifying key risk factors such as chronic obstructive pulmonary disease and hypoalbuminemia,the model demonstrated strong predictive accuracy and offered a pathway to personalized perioperative care.This correspondence highlighted the clinical significance,emphasizing its potential to optimize patient outcomes through tailored inter-ventions.Further prospective validation and application across diverse settings are essential to realize its full potential in advancing esophageal surgery practices.
基金Supported by Ningxia Natural Science Foundation (NZ1024)the Scientific Research the Project of Ningxia Universities (201027)~~
文摘[Objective] To discuss the effects of major mapping methods for DNA sequence on the accuracy of protein coding regions prediction,and to find out the effective mapping methods.[Method] By taking Approximate Correlation（AC） as the full measure of the prediction accuracy at nucleotide level,the windowed narrow pass-band filter（WNPBF） based prediction algorithm was applied to study the effects of different mapping methods on prediction accuracy.[Result] In DNA data sets ALLSEQ and HMR195,the Voss and Z-Curve methods are proved to be more effective mapping methods than paired numeric（PN）,Electron-ion Interaction Potential（EIIP） and complex number methods.[Conclusion] This study lays the foundation to verify the effectiveness of new mapping methods by using the predicted AC value,and it is meaningful to reveal DNA structure by using bioinformatics methods.
基金supported by grants from the National Key Research and Development Project(2019YFE0106800)Modern Agriculture Science and Technology Key Project of Hebei Province(19226376D)China Agriculture Research System of MOF and MARA.
文摘Background:Recently,machine learning(ML)has become attractive in genomic prediction,but its superiority in genomic prediction over conventional(ss)GBLUP methods and the choice of optimal ML methods need to be investigated.Results:In this study,2566 Chinese Yorkshire pigs with reproduction trait records were genotyped with the GenoBaits Porcine SNP 50 K and PorcineSNP50 panels.Four ML methods,including support vector regression(SVR),kernel ridge regression(KRR),random forest(RF)and Adaboost.R2 were implemented.Through 20 replicates of fivefold cross-validation(CV)and one prediction for younger individuals,the utility of ML methods in genomic prediction was explored.In CV,compared with genomic BLUP(GBLUP),single-step GBLUP(ssGBLUP)and the Bayesian method BayesHE,ML methods significantly outperformed these conventional methods.ML methods improved the genomic prediction accuracy of GBLUP,ssGBLUP,and BayesHE by 19.3%,15.0% and 20.8%,respectively.In addition,ML methods yielded smaller mean squared error(MSE)and mean absolute error(MAE)in all scenarios.ssGBLUP yielded an improvement of 3.8% on average in accuracy compared to that of GBLUP,and the accuracy of BayesHE was close to that of GBLUP.In genomic prediction of younger individuals,RF and Adaboost.R2_KRR performed better than GBLUP and BayesHE,while ssGBLUP performed comparably with RF,and ssGBLUP yielded slightly higher accuracy and lower MSE than Adaboost.R2_KRR in the prediction of total number of piglets born,while for number of piglets born alive,Adaboost.R2_KRR performed significantly better than ssGBLUP.Among ML methods,Adaboost.R2_KRR consistently performed well in our study.Our findings also demonstrated that optimal hyperparameters are useful for ML methods.After tuning hyperparameters in CV and in predicting genomic outcomes of younger individuals,the average improvement was 14.3% and 21.8% over those using default hyperparameters,respectively.Conclusion:Our findings demonstrated that ML methods had better overall prediction performance than conventional genomic selection methods,and could be new options for genomic prediction.Among ML methods,Adaboost.R2_KRR consistently performed well in our study,and tuning hyperparameters is necessary for ML methods.The optimal hyperparameters depend on the character of traits,datasets etc.
文摘A hard problem that hinders the movement of waxy crude oil is wax deposition in oil pipelines.To ensure the safe operation of crude oil pipelines,an accurate model must be developed to predict the rate of wax deposition in crude oil pipelines.Aiming at the shortcomings of the ENN prediction model,which easily falls into the local minimum value and weak generalization ability in the implementation process,an optimized ENN prediction model based on the IRSA is proposed.The validity of the new model was confirmed by the accurate prediction of two sets of experimental data on wax deposition in crude oil pipelines.The two groups of crude oil wax deposition rate case prediction results showed that the average absolute percentage errors of IRSA-ENN prediction models is 0.5476% and 0.7831%,respectively.Additionally,it shows a higher prediction accuracy compared to the ENN prediction model.In fact,the new model established by using the IRSA to optimize ENN can optimize the initial weights and thresholds in the prediction process,which can overcome the shortcomings of the ENN prediction model,such as weak generalization ability and tendency to fall into the local minimum value,so that it has the advantages of strong implementation and high prediction accuracy.
基金supported by the National Excellent Youth Science Foundation of China(No.31222030)the National Natural Science Foundation of China(No.91131002)+3 种基金the Shanghai Rising-Star Program(No.12QA1400300)the China Ministry of Education Scientific Research Major Project(Nos. 311016 and 113022A)the MOE University Doctoral Research Supervisor's Funds(No.20120071110021)the Shanghai Professional Development Funding(No.2010001)
文摘The paternally inherited Y chromosome has been widely used in forensics for personal identification, in anthropology and population genetics to understand origin and migration of human populations, and also in medical and clinical studies （Wang and Li, 2013; Wang et al., 2014）. There are two kinds of extremely useful markers in Y chromosome, single nucle- otide polymorphism （SNP） and short tandem repeats （STRs）. With a very low mutation rate on the order of 3.0 x 10-8 mutations/nucleotide/generation （Xue et al., 2009）, SNP markers have been used in constructing a robust phylogeny tree linking all the Y chromosome lineages from world pop- ulations （Karafet et al., 2008）. Those lineages determined by the pattern of SNPs are called haplogroups. That is to say, we have to genotype an appropriate number of SNPs in order to assign a given Y chromosome to a haplogroup. Compared with SNPs, the mutation rates of STR markers are about four to five orders of magnitude higher （Gusmgo et al., 2005; Ballantyne et al., 2010）. Typing STR has advantages of saving time and cost compared with typing SNPs in phylogenetic assignment of a Y chromosome （Wang et al., 2010）. A set of STR values for an individual is called a haplotype. Because of the disparity in mutation rates between SNP and STR, one SNP haplogroup could actually comprise many STR haplotypes （Wang et al., 2010）. It is most interesting that STR variability is clustered more by haplogroups than by populations （Bosch et al., 1999; Behar et al., 2004）, which indicates that STR haplotypes could be used to infer the haplogroup information of a given Y chromosome. There has been increasing interest in this cost- effective strategy for predicting the haplogroup from a given STR haplotype when SNP data are unavailable. For instance, Vadim Urasin＇s YPredictor （http：//predictor.ydna.ru/）, Whit Atheys＇ haplogroup predictor （http：//www.hprg.com/hapest5/） （Athey, 2005, 2006）, and haplogroup classifier of Arizona University （Schlecht et al., 2008） have been widely employed in previous studies for haplogroup prediction （Larmuseau et al., 2010; Bembea et al., 2011; Larmuseau et al., 2012; Tarlykov et al., 2013）.
文摘Data is always a crucial issue of concern especially during its prediction and computation in digital revolution.This paper exactly helps in providing efficient learning mechanism for accurate predictability and reducing redundant data communication.It also discusses the Bayesian analysis that finds the conditional probability of at least two parametric based predictions for the data.The paper presents a method for improving the performance of Bayesian classification using the combination of Kalman Filter and K-means.The method is applied on a small dataset just for establishing the fact that the proposed algorithm can reduce the time for computing the clusters from data.The proposed Bayesian learning probabilistic model is used to check the statistical noise and other inaccuracies using unknown variables.This scenario is being implemented using efficient machine learning algorithm to perpetuate the Bayesian probabilistic approach.It also demonstrates the generative function forKalman-filer based prediction model and its observations.This paper implements the algorithm using open source platform of Python and efficiently integrates all different modules to piece of code via Common Platform Enumeration(CPE)for Python.
文摘The deviation control of directional drilling is essentially the controlling of two angles of the wellbore actually drilled, namely, the inclination and azimuth. In directional drilling the bit trajectory never coincides exactly with the planned path, which is usually a plane curve with straight, building, holding, and dropping sections in succession. The drilling direction is of course dependant on the direction of the resultant forces acting on the bit and it is quite a tough job to hit the optimum target at the hole bottom as required. The traditional passive methods for correcting the drilling path have not met the demand to improve the techniques of deviation control. A method for combining wellbore surveys to obtain a composite, more accurate well position relies on accepting the position of the well from the most accurate survey instrument used in a given section of the wellbore. The error in each position measurement is the sum of many independent root sources of error effects. The relationship between surveys and other influential factors is considered, along with an analysis of different points of view. The collaborative work describes, establishes a common starting point of wellbore position uncertainty model, definition of what constitutes an error model, mathematics of position uncertainty calculation and an error model for basic directional service.
文摘Among the advantages of using industrial robots for machining applications instead of machine tools are flexibility, cost effectiveness, and versatility. Due to the kinematics of the articulated robot, the system behaviour is quite different compared with machine tools. Two major questions arise in implementing robots in machining tasks: one is the robot’s stiffness, and the second is the achievable machined part accuracy, which varies mainly due to the huge variety of robot models. This paper proposes error prediction model in the application of industrial robot for machining tasks, based on stiffness and accuracy limits. The research work includes experimental and theoretical parts. Advanced machining and inspection tools were applied, as well as a theoretical model of the robot structure and stiffness based on the form-shaping function approach. The robot machining performances, from the workpiece accuracy point of view were predicted.
文摘Photovoltaic(PV)power forecasting is essential for balancing energy supply and demand in renewable energy systems.However,the performance of PV panels varies across different technologies due to differences in efficiency and how they process solar radiation.This study evaluates the effectiveness of deep learning models in predicting PV power generation for three panel technologies:Hybrid-Si,Mono-Si,and Poly-Si,across three forecasting horizons:1-step,12-step,and 24-step.Among the tested models,the Convolutional Neural Network—Long Short-Term Memory(CNN-LSTM)architecture exhibited superior performance,particularly for the 24-step horizon,achieving R^(2)=0.9793 and MAE 0.0162 for the Poly-Si array,followed by Mono-Si(R^(2)=0.9768)and Hybrid-Si arrays(R^(2)=0.9769).These findings demonstrate that the CNN-LSTM model can provide accurate and reliable PV power predictions for all studied technologies.By identifying the most suitable predictive model for each panel technology,this study contributes to optimizing PV power forecasting and improving energy management strategies.
基金supported by Mahasarakham University for Piyapatr Busababodhin’s work.Guoqing Chen’s research was supported by Chengdu Jincheng College Green Data Integration Intelligence Research and Innovation Project(No.2025-2027)the High-Quality Development Research Center Project in the Tuojiang River Basin(No.TJGZL2024-07)+1 种基金the Open Fund ofWuhan Gravitation and Solid Earth Tides,National Observation and Research Station(No.WHYWZ202406)the Scientific Research Fund of the Institute of Seismology,CEA,and the National Institute of Natural Hazards,MEM(No.IS202236328).
文摘Existing power forecasting models struggle to simultaneously handle high-dimensional,noisy load data while capturing long-term dependencies.This critical limitation necessitates an integrated approach combining dimensionality reduction,temporal modeling,and robust prediction,especially for multi-day forecasting.A novel hybrid model,SLHS-TCN-XGBoost,is proposed for power demand forecasting,leveraging SLHS(dimensionality reduction),TCN(temporal feature learning),and XGBoost(ensemble prediction).Applied to the three-year electricity load dataset of Seoul,South Korea,the model’s MAE,RMSE,and MAPE reached 112.08,148.39,and 2%,respectively,which are significantly reduced in MAE,RMSE,and MAPE by 87.37%,87.35%,and 87.43%relative to the baseline XGBoost model.Performance validation across nine forecast days demonstrates superior accuracy,with MAPE as low as 0.35%and 0.21%on key dates.Statistical Significance tests confirm significant improvements(p<0.05),with the highest MAPE reduction of 98.17%on critical days.Seasonal and temporal error analyses reveal stable performance,particularly in Quarter 3 and Quarter 4(0.5%,0.3%)and nighttime hours(<1%).Robustness tests,including 5-fold cross-validation and Various noise perturbations,confirm the model’s stability and resilience.The SLHS-TCN-XGBoost model offers an efficient and reliable solution for power demand forecasting,with future optimization potential in data preprocessing,algorithm integration,and interpretability.
基金supported by a grant (12-TI-C04) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government
文摘Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.
基金Funding is provided by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.The stock data is usually non-stationary,and attributes are non-correlative to each other.Several traditional Stock Technical Indicators(STIs)may incorrectly predict the stockmarket trends.To study the stock market characteristics using STIs and make efficient trading decisions,a robust model is built.This paper aims to build up an Evolutionary Deep Learning Model(EDLM)to identify stock trends’prices by using STIs.The proposed model has implemented the Deep Learning(DL)model to establish the concept of Correlation-Tensor.The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange(NSE)-India,a Long Short Term Memory(LSTM)is used.The datasets encompassed the trading days from the 17^(th) of Nov 2008 to the 15^(th) of Nov 2018.This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends.The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one.The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%,56.25%,and 57.95%on the datasets of HDFC,Yes Bank,and SBI,respectively.Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.
基金This research was supported by the National Key R and D Program of China(2016YFC0500901 and 2016YFC0500907)the National Natural Science Foundation of China(Grant Nos.31971466 and 41807525)the One Hundred Person Project of the Chinese Academy of Sciences(Y551821).
文摘Determining an optimal sample size is a key step in designing field surveys,and is particularly important for detecting the spatial pattern of highly variable properties such as soil organic carbon(SOC).Based on 550 soil sampling points in the nearsurface layer(0 to 20 cm)in a representative region of northern China's agro-pastoral ecotone,we studied effects of four interpolation methods such as ordinary kriging(OK),universal kriging(UK),inverse distance weighting(IDW)and radial basis function(RBF)and random subsampling(50,100,200,300,400,and 500)on the prediction accuracy of SOC estimation.When the Shannon's Diversity Index(SHDI)and Shannon's Evenness Index(SHEI)was 2.01 and 0.67,the OK method appeared to be a superior method,which had the smallest root mean square error(RMSE)and the mean error(ME)nearest to zero.On the contrary,the UK method performed poorly for the interpolation of SOC in the present study.The sample size of 200 had the most accurate prediction;50 sampling points produced the worst prediction accuracy.Thus,we used 200 samples to estimate the study area's soil organic carbon density(SOCD)by the OK method.The total SOC storage to a depth of 20 cm in the study area was 117.94 Mt,and its mean SOCD was 2.40 kg/m2.The SOCD kg/(C⋅m2)of different land use types were in the following order:woodland(3.29)>grassland(2.35)>cropland(2.19)>sandy land(1.55).
文摘This letter addresses the study by Jayabalan et al,which underscores the liver outcome score(LOS)and hemoglobin(Hb)as key prognostic markers for patients with autoimmune liver disease overlap syndromes(AILDOS),with particular relevance to the autoimmune hepatitis-primary biliary cholangitis(AIH-PBC)subgroup.The findings indicate that an LOS threshold of 6 achieves high sensitivity and specificity in predicting liver-related mortality among AIH-PBC patients.Moreover,low Hb levels emerge as a significant mortality predictor across all AILDOS cases.These results contribute valuable perspectives on risk stratification in AILDOS,highlighting the promise of non-invasive prognostic tools.Future studies with larger cohorts are needed to substantiate LOS and Hb as robust markers for clinical application.
基金Supported by the Natural Science Foundation of Jiangsu Higher Education Institutions,No.22KJB510027the Project of the State Administration of Traditional Chinese Medicine of China,No.GZY-ZJ-KJ-24031.
文摘BACKGROUND Despite improved survival rates in rectal cancer treatment,many patients experience low anterior resection syndrome(LARS).The preoperative LARS score(POLARS)aims to address the limitations of LARS assessment by predicting outcomes preoperatively to enhance surgical planning.AIM To investigate the predictive accuracy of POLARS in assessing the occurrence of LARS.METHODS This study enrolled a total of 335 patients who underwent laparoscopic or robotic low anal sphincter-preserving surgery for rectal tumors.Patients were categorized into three groups according to their POLARS score:no LARS(score 0-20),minor LARS(score 21-29),and major LARS(score 30-42).The QLQ-C30/CR29 scores were compared among these groups,and the agreement between POLARS predictions and the actual LARS scores was analyzed.RESULTS The study population was divided into three groups:major LARS(n=51,27.42%),minor LARS(n=109,58.6%),and no LARS(n=26,13.98%).Significant differences in the QLQ-C30 scales of social function,diarrhea,and financial impact were detected between the no LARS and major LARS groups(P<0.05)and between the minor LARS and major LARS groups(P<0.05).Similarly,significant differences were detected in the QLQ-CR29 scales for blood and mucus in the stool,fecal incontinence,and stool frequency between the no LARS and minor LARS groups(P<0.05),as well as between the minor LARS and major LARS groups(P<0.05).The predictive precision for major LARS using the POLARS score was 82.35%(42/51),with a recall of 35.89%(42/117).The mean absolute error(MAE)between the POLARS score and the actual LARS score was 8.92±5.47.In contrast,the XGBoost(extreme gradient boosting)model achieved a lower MAE of 6.29±4.77,with a precision of 84.39%and a recall of 74.05%for predicting major LARS.CONCLUSION The POLARS score demonstrated effectiveness and precision in predicting major LARS,thereby providing valuable insights into postoperative symptoms and patient quality of life.However,the XGBoost model exhibited superior performance with a lower MAE and higher recall for predicting major LARS compared to the POLARS model.
基金supported by the National Natural Science Foundation of China(Nos.52107018 and 51937007)National Key Research and Development Program of China(No.2021YFB2401502).
文摘The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications,particularly for capacitive energy storage.Predicting the relative permittivity of particle/polymer nanocomposites from the microstructure is of great significance.However,the classical effective medium theory and physics-based numerical calculation represented by finite element method are time-consuming and cumbersome for complex structures and nonlinear problem.The work explores a novel architecture combining the convolutional neural network(ConvNet)and finite element method(FEM)to predict the relative permittivity of nanocomposite dielectrics with incorporated barium titanite(BT)particles in polyvinylidene fluoride(PVDF)matrix.The ConvNet was trained and evaluated on big datasets with 14266 training data and 3514 testing data generated form a programmatic algorithm.Through numerical experiments,we demonstrate that the trained network can efficiently provide an accurate agreement between the ConvNet model and FEM by virtue of the significant evaluation metrics R2,which reaches as high as 0.9783 and 0.9375 on training and testing data,respectively.The strong universality of the presented method allows for an extension to fast and accurately predict other properties of the nanocomposite dielectrics.
