Conventional Regression Methods Research Articles

Optimization method of parameters inverse identification for hot deformation constitutive model of 2Cr13 martensitic stainless steel using genetic algorithm

Constitutive models are considered a key prerequisite to investigate the thermal deformation behavior of materials. Accurate identification of their parameters is crucial for enhancing the predictive precision of the models. A novel optimization method for accurate inverse identification of parameters using the genetic algorithm (GA) in the Modified Zerilli-Armstrong (MZA) model was proposed in this work. Under conditions of temperatures ranging from 1223 K to 1473 K at intervals of 50 K and strain rates of 0.01, 0.1, 1, and 5 s−1, uniaxial isothermal hot compression tests were performed on 2Cr13 martensitic stainless steel (MSS) employing a Gleeble-1500D thermal simulation test machine. Based on the experimental data, the conventional linear regression method was utilized to solve the MZA model of 2Cr13 MSS. Initial values for the material constants I1, S1, C5, and C6 to be optimized were assigned drawing from the calculated results. The GA-based iteratively optimized MZA (G-ZA) model was established by minimizing the mean squared error as an objective function between the experimental and predicted flow stress values. Compared to the MZA model, a significant enhancement in predictive performance was achieved with the G-ZA model, while good generalization was also demonstrated. Both models were successfully integrated into the Forge® finite element analysis software through the secondary development of user subroutines. Numerical simulation results indicated that the G-ZA model demonstrated a better agreement with the experimental data in predicting the load-displacement curve. This validated that a more accurate constitutive model for the hot deformation of 2Cr13 MSS can be effectively constructed using the GA-based parameter inverse identification strategy.

Exploring Post-Experience Evaluation of Gastronomy Tourism Experiences Through Airbnb

ABSTRACT This research delves into factors impacting the post-evaluation of Airbnb gastronomic experiences based on expectation disconfirmation theory. Utilizing big data techniques, including topic modeling, sentiment analysis, image analysis, and conventional OLS regression methods, the study scrutinized 196,265 online reviews and 1,331 images from Airbnb Experiences Website. The findings reveal that review sentiments, image vibrancy, host credibility, and the count of reviews play pivotal roles in determining gastronomy experience ratings. Furthermore, Using Latent Dirichlet Allocation, the reviews revealed six main topics in order of importance: food city tours, social interactions, host, local food culture, beverage appreciation, and hands-on learning. These results illuminate the primary considerations of gastronomy tourists, providing guidance for hosts to better design their experiences and offering insights for other stakeholders in this sector.

Bayesian inverse inference of material properties from microstructure images

In this paper, we introduce a Bayesian framework designed for inverse inference, aiming to predict material properties/process parameters from microstructure images. The integration of Bayesian inference techniques with deep generative models establishes a robust tool for applications in materials science, particularly in material characterization and property control. This integration provides a novel approach to clarifying the reliability of predictions. The application of this framework to a sample problem involving the prediction of material properties from artificial dual-phase steel microstructures demonstrates its capability to estimate these properties while accounting for prediction uncertainties. Moreover, even in comparison to conventional regression methods in terms of point estimation, the proposed framework exhibits superior accuracy in prediction. These results clearly illustrate that the framework presented in this paper constitutes a powerful tool for achieving efficient material design.

Modeling the thermal transport properties of hydrogen and its mixtures with greenhouse gas impurities: A data-driven machine learning approach

This study introduces machine learning (ML) algorithms to predict hydrogen (H2) thermodynamic properties for geological storage, focusing on its mixtures with natural gas, nitrogen (N2), and carbon dioxide (CO2). Employing 1167 data samples, this research utilizes multiple linear regression (MLR), random forest (RF), gradient boosting (GB), and decision tree (DT) models, enhanced by sensitivity analysis for feature engineering. GB model's superior accuracy and efficiency over conventional statistical linear regression methods. The finding reveals that the GB model achieves superior performance, with an R2 value of 0.9998 for thermal capacity and the lowest mean absolute error (MAE)/root mean square error (RMSE) across all H2 properties—density (1.51%, 2.06 kg/m3), viscosity (0.000078%, 0.000117 cp), thermal conductivity (0.000427%, 0.000849 W/m.K), and thermal capacity (8.79%, 26.06 J/g.K). Moreover, this ML-based approach not only demonstrates remarkable accuracy and efficiency but also suggests the potential of smart paradigms to further enhance the safety and transportability of underground hydrogen storage projects. Ultimately, this work will help the reservoir simulation at field scale to be more robust and reliable.

Trends in plant cover derived from vegetation plot data using ordinal zero‐augmented beta regression

AbstractQuestionsPlant cover values in vegetation plot data are bounded between 0 and 1, and cover is typically recorded in discrete classes with non‐equal intervals. Consequently, cover data are skewed and heteroskedastic, which hampers the application of conventional regression methods. Recently developed ordinal beta regression models consider these statistical difficulties. Our primary question is whether we can detect species trends in vegetation plot time series data with this modelling approach. A second question is whether trends in cover have additional value compared to trends in occurrence, which are easier to assess for practitioners.LocationThe Netherlands, Western Europe.MethodsWe used vegetation plot data collected from 10,000 fixed plots which were surveyed once every four years during 1999–2022. We used the ordinal zero‐augmented beta regression (OZAB) model, a hierarchical model consisting of a logistic regression for presence and an ordinal beta regression for cover. We adapted the OZAB model for longitudinal data and produced estimates of cover and occurrence for each four‐year period. Thereafter we assessed trends in cover and in occurrence across all periods.ResultsWe found evidence of a trend in cover in 318 out of the 721 species (44%) with sufficient data. Most species showed similar directional trends in occurrence and percent cover. No trend in occurrence was detected for 64 species that had evidence of a trend in cover. Declining species had stronger relative changes in cover than in occurrence.ConclusionsOur model enables researchers to detect trends in cover using longitudinal vegetation plot data. Cover trends often corroborated trends in occurrence, but we also regularly found trends in cover even in the absence of evidence for trends in occurrence. Our approach thus contributes to a more complete picture of (changes in) vegetation composition based on large monitoring data sets.

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of China traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people’s awareness of the importance of local culture, awakens people’s sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in China, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Temperature analysis and prediction for road-rail steel truss cable-stayed bridges based on the structural health monitoring

A growing number of road-rail steel truss bridges have been built worldwide in recent years. Due to the complex cross-sectional structure and increasing span length, these main girders are prone to developing uneven temperature fields and significant local thermal effects, which can lead to excessive temperature stresses and structural damage. An accurate assessment of the temperature distribution in road-rail steel truss girders is critical as it aids in the thermal response analysis of the bridge and the structural safety evaluation. The paper analyses the temperature distribution of the road-rail steel truss girder base on the light conditions and the structure of the main girder. A modified method for calculating the effective temperature (ET) and temperature difference (TD) is also proposed. Comparing with the conventional regional weighted averaging method, the modified method improves the accuracy of ET calculation by 20 %. A prediction method based on the long short-term memory (LSTM) network is proposed, which estimates ET and TD for road-rail steel truss girders based on the environment around the bridge. The deep learning-based method improves accuracy by 40 % compared with the conventional linear regression method for ET prediction. Meanwhile, the proposed method alleviates the current lack of prediction methods for TD of road-rail steel truss girders. A detailed analysis of the temperature of a road-rail steel truss bridge is also presented based on the structural health monitoring (SHM) system. A negative vertical temperature difference was observed in the fringe truss. The maximum transverse temperature difference (TTD) was much greater than the maximum vertical temperature difference (VTD). The above features deserve extra attention from bridge designers.

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy

IntroductionThe correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no prediction model yet.Material and methodsThe data were collected from the National Free Preconception Health Examination Project in China. A sum of 455 neonates (42 SGA births and 423 non-LGA births) were included. A training set (n = 319) and a test set (n = 136) were created from the dataset at random. To develop prediction models for LGA neonates, conventional logistic regression (LR) method and six machine learning methods were used in this study. Recursive feature elimination approach was performed by choosing 10 features which made a big contribution to the prediction models. And the Shapley Additive Explanation model was applied to interpret the most important characteristics that affected forecast outputs.ResultsThe random forest (RF) model had the highest average area under the receiver-operating-characteristic curve (AUC) for predicting LGA in the test set (0.843, 95% confidence interval [CI]: 0.714–0.974). Except for the logistic regression model (AUC: 0.603, 95%CI: 0.440–0.767), other models’ AUCs displayed well. Thereinto, the RF algorithm’s final prediction model using 10 characteristics achieved an average AUC of 0.821 (95% CI: 0.693–0.949).ConclusionThe prediction model based on machine learning might be a promising tool for the prenatal prediction of LGA births in women with radiation exposure before pregnancy.

Opportunities offered by latent-based multiblock strategies to integrate biomarkers of chemical exposure and biomarkers of effect in environmental health studies

Modern environmental epidemiology benefits from a new generation of technologies that enable comprehensive profiling of biomarkers, including environmental chemical exposure and omic datasets. The integration and analysis of large and structured datasets to identify functional associations is constrained by computational challenges that cannot be overcome using conventional regression methods. Some extensions of Partial Least Squares (PLS) regression have been developed to efficently integrate multiple datasets, including Multiblock PLS (MB-PLS) and Sequential and Orthogonalized PLS; however, these approaches remain seldom applied in environmental epidemiology. To address that research gap, this study aimed to assess and compare the applicability of PLS-based multiblock models in an observational case study, where biomarkers of exposure to environmental chemicals and endogenous biomarkers of effect were simultaneously integrated to highlight biological links related to a health outcome. The methods were compared with and without sparsity coupling two metrics to support the variable selection: Variable Importance in Projection (VIP) and Selectivity Ratio (SR). The framework was applied to a case-study dataset mimicking the structure of 36 environmental exposure biomarkers (E-block), 61 inflammation biomarkers (M-block), and their relationships with the gestational age at delivery of 161 mother-infant pairs. The results showed an overall consistency in the selected variables across models, although some specific selection patterns were identified. The block-scaled concatenation-based approaches (e.g. MB-PLS) tended to select more variables from the E-block, while these methods were unable to identify certain variables in the M-block. Overall, the number of variables selected using the SR criterion was higher than using the VIP criterion, with lower predictive performances. The multiblock models coupled to VIP, appeared to be the methods of choice for identifying relevant variables with similar statistical performances. Overall, the use of multiblock PLS-based methods appears to be a good strategy to efficiently support the variable selection process in modern environmental epidemiology.

Does artificial intelligence predict orthognathic surgical outcomes better than conventional linear regression methods?

To evaluate the performance of an artificial intelligence (AI) model in predicting orthognathic surgical outcomes compared to conventional prediction methods. Preoperative and posttreatment lateral cephalograms from 705 patients who underwent combined surgical-orthodontic treatment were collected. Predictors included 254 input variables, including preoperative skeletal and soft-tissue characteristics, as well as the extent of orthognathic surgical repositioning. Outcomes were 64 Cartesian coordinate variables of 32 soft-tissue landmarks after surgery. Conventional prediction models were built applying two linear regression methods: multivariate multiple linear regression (MLR) and multivariate partial least squares algorithm (PLS). The AI-based prediction model was based on the TabNet deep neural network. The prediction accuracy was compared, and the influencing factors were analyzed. In general, MLR demonstrated the poorest predictive performance. Among 32 soft-tissue landmarks, PLS showed more accurate prediction results in 16 soft-tissue landmarks above the upper lip, whereas AI outperformed in six landmarks located in the lower border of the mandible and neck area. The remaining 10 landmarks presented no significant difference between AI and PLS prediction models. AI predictions did not always outperform conventional methods. A combination of both methods may be more effective in predicting orthognathic surgical outcomes.

The Specific Application and Meaning Expression of Folk Art in Urban Landscape Design Under the Optimized Interactive Genetic Algorithm

With the rapid and stable growth of China's economy and the rapid development of urban construction, urban complexes have sprung up in major cities. The lag in urban ecological environmental protection, maintenance and construction has brought great pressure to the needs of today's urban residents in terms of life, safety, and sense of belonging. In the major cities with many high-rise buildings in contemporary China, land resources are becoming more and more scarce, and the urban ecological environment is in urgent need of recycling, and due to the blind imitation of Western culture and design mode and the neglect of my country's traditional regional culture, the urban landscape lacks interaction, resonance, and sense of belonging with citizens, and the phenomenon of landscape similarity emerges in various cities, focusing on the landscape space of urban complexes. There are also these problems. Urban residents urgently need a third space that can adjust their physical, mental, and spiritual needs. How to design an urban complex landscape that meets the aesthetic needs and humanistic needs of contemporary cities and has regional characteristics has become the first important task of my research. Folk art is an artistic treasure created by the working people in their production and life. Folk art is the embodiment of cultural regionality and the foundation of national culture. It awakens people's awareness of the importance of local culture, awakens people's sense of belonging, and is closer to the local public life. Today, the living soil and social and humanistic environment of folk art are in the process of urbanization in our country, and there is a trend of gradual disappearance of lifestyle changes. How to make the contemporary urban complex landscape an organic soil for the survival, expression, application, and development of folk art is an important task in contemporary urban landscape design. Based on optimization, related concepts such as symbols, folk art symbols, urban complexes, urban complex landscape design, etc. have been sorted out. The relevant experimental results show that the construction land accuracy of the logistic regression model based on genetic algorithm has increased from 78.0% to 85.3%. kappa increased from 74.5% to 81.2%. Research shows that the logistic regression parameter optimization method based on genetic algorithm has better simulation effect than the conventional logistic regression method and is more suitable for the situation of unbalanced data distribution and many data features in the simulation of large-scale urban land dynamic changes.

Stellar Occultations in the Era of Data Mining and Modern Regression Models: Using Gaussian Processes to Analyze Light Curves and Improve Predictions

Gaussian process (GP) regression is a nonparametric Bayesian approach that has been used successfully in various astronomical domains, especially in time-domain astronomy. The most common applications are the smoothing of data for interpolation and the detection of periodicities. The ability to create unbiased data-driven models without a predefined physical model can be a major advantage over conventional regression methods. Prior knowledge can be included by setting boundary conditions or constraining hyperparameter values, while unknown hyperparameters are optimized during the conditioning of the model. We have adapted and transformed previous approaches of GP regression and introduce three new applications for this regression method, especially in the context of stellar occultations: the modeling of occultation light curves, the correction of public JPL ephemerides of minor planets based on publicly available image data of the Zwicky Transient Facility, and the detection of natural satellites. We used data from observations of stellar occultations to validate the models and achieved promising results in all cases, and thus we confirmed the flexibility of GP regression models. Considering various existing use cases in addition to our novel applications, GP regression can be used to model diverse data sets addressing a wide range of problems. The accuracy of the model depends on the input data and on the set boundary conditions. Generally, high-quality data allow the usage of loose boundary conditions, while low-quality data require more restrictive boundary conditions to avoid overfitting.

Leveraging Remotely Sensed and Climatic Data for Improved Crop Yield Prediction in the Chi Basin, Thailand

Predictions of crop production in the Chi basin are of major importance for decision support tools in countries such as Thailand, which aims to increase domestic income and global food security by implementing the appropriate policies. This research aims to establish a predictive model for predicting crop production for an internal crop growth season prior to harvest at the province scale for fourteen provinces in Thailand’s Chi basin between 2011 and 2019. We provide approaches for reducing redundant variables and multicollinearity in remotely sensed (RS) and meteorological data to avoid overfitting models using correlation analysis (CA) and the variance inflation factor (VIF). The temperature condition index (TCI), the normalized difference vegetation index (NDVI), land surface temperature (LSTnighttime), and mean temperature (Tmean) were the resulting variables in the prediction model with a p-value < 0.05 and a VIF < 5. The baseline data (2011–2017: June to November) were used to train four regression models, which revealed that eXtreme Gradient Boosting (XGBoost), random forest (RF), and XGBoost achieved R2 values of 0.95, 0.94, and 0.93, respectively. In addition, the testing dataset (2018–2019) displayed a minimum root-mean-square error (RMSE) of 0.18 ton/ha for the optimal solution by integrating variables and applying the XGBoost model. Accordingly, it is estimated that between 2020 and 2022, the total crop production in the Chi basin region will be 7.88, 7.64, and 7.72 million tons, respectively. The results demonstrated that the proposed model is proficient at greatly improving crop yield prediction accuracy when compared to a conventional regression method and that it may be deployed in different regions to assist farmers and policymakers in making more informed decisions about agricultural practices and resource allocation.

On the Efficiency of the newly Proposed Convex Olanrewaju-Olanrewaju Lo-oλγ(|θ|) Penalized Regression-Type Estimator via GLMs Technique.

In this article, we proposed a novel convex penalized regression-type estimator, termed Olanrewaju-Olanrewaju penalized regression-type estimator, denoted by Lo-oλγ(|θ|) for ultra and high-dimensional data consideration whenever the number of covariates (p) is strictly greater than the sample size(n⁡). The newly proposed penalized estimator was formulated via the unique combination of arctangent and hyperbolic functions. It was noted that the newly proposed estimator could efficiently work under the constraint p<n formulated for, and as well as for the conventional regression methods of n>p. The proposed convex penalized regression-type estimator was formulated via three symmetric distributional noises of Gaussian, Laplace, and Normal Inverse Gaussian (NIG), considered being the three certified symmetric distributions that belong to the exponential family of distributions. The regularity axioms and oracle properties of the newly proposed penalized estimator were worked-out, as well as the parameter estimation for the penalized regression-type estimator for the three symmetric noises via canonical expressible form of Generalized Linear Models (GLMs) and first-order Karush-Kuhn-Tucker (KKT) condition. The Lo-oλγ(|θ|) regression-type estimator was subjected to simulation studies of well-generated symmetric observations. The US Longley macroeconomic dataset with the constraint p<n was fitted to Lo-oλγ(|θ|) regression-type estimator under the umbrella of three symmetric distributional noises. In conclusion, Gaussian noise for Lo-oλγ(|θ|) penalized regression-type estimator under the umbrella of p<n for the simulation and real life analyzes was considered effectively efficient because of its most reduced Mean Square Error (mse) measured error-type in comparison to Gaussian, Laplace, and NIG noises for LASSO and Atan penalties; and as well for Laplace, and NIG noises for Lo-oλγ(|θ|).

On a class of linear regression methods

In this paper, a unified study is presented for the design and analysis of a broad class of linear regression methods. The proposed general framework includes the conventional linear regression methods (such as the least squares regression and the Ridge regression) and some new regression methods (e.g. the Landweber regression and Showalter regression), which have recently been introduced in the fields of optimization and inverse problems. The strong consistency, the reduced mean squared error, the asymptotic Gaussian property, and the best worst case error of this class of linear regression methods are investigated. Various numerical experiments are performed to demonstrate the consistency and efficiency of the proposed class of methods for linear regression.

A Method for Evaluating Reservoir Permeability Based on Machine Learning Flow Unit Index

Abstract The H formation of the Y gas field in the X depression belongs to a low-permeability tight sandstone reservoir affected by sedimentation, diagenesis, and cementation. The lithology and pore structure of the target layer are complex, with strong physical heterogeneity and complex pore-permeability relationships. Conventional core pore permeability regression and nuclear magnetic resonance software-defined radio methods do not satisfy the requirements for precise evaluation in terms of permeability calculation accuracy. Based on the principle of the flow zone index (FZI) method, this study analyzed the influence of pore structure on permeability and extracted three pore structure characterization parameters, namely, the maximum pore throat radius (Rmax), displacement pressure (Pd), and average throat radius (R), from the mercury injection capillary pressure curve. The relationship between the FZI and pore structure is clarified. Therefore, the FZI in this area can characterize the permeability differences within different flow units. Based on the flow unit theory, a refined evaluation model for three types of reservoirs was established in the study area. By analyzing the response characteristics and correlation of conventional logging curves using machine learning, three optimization combination curves were selected, and a multiparameter fitting equation for the FZI was established, which was applied to predict the permeability of new wells. The results showed that the calculated permeability was highly consistent with the core analysis results, thereby providing a theoretical basis for the precise evaluation of low-permeability tight reservoirs.

Modeling stream baseflow nitrate concentration in an agricultural watershed using neural network and bootstrap method

The evaluation of water resource vulnerability to nonpoint source pollution in the presence of uncertainty remains a crucial concern. To enhance the accuracy of the assessment and pinpoint the key factors that affect watershed water quality, the integration of an artificial neural network (ANN) into the evaluation process is imperative. The research involved the collection of streams baseflow samples from thirty-eight sites between 1994 and 1999 in the Tomorrow-Waupaca River Watershed in central Wisconsin, USA, with a focus on examining the relationship between nitrate concentrations and a range of environmental and land use variables extracted from the watershed GIS database. This study utilized ANN methodology, combined with a bootstrap technique that employed a random resampling approach from a single input dataset, to simulate monthly stream baseflow nitrate concentrations. The effectiveness and predictive ability of the ANN model were assessed by comparing it to conventional multivariate regression methods. Through the use of ANN, more precise outcomes can be achieved while taking into account the uncertainty associated with the analysis. The findings demonstrated that the ANN outperformed both the multivariate linear regression and nonlinear quadratic response surface models in explaining the variance of stream nitrate and in external prediction consistency. This study also highlighted several key variables, such as the areal percentage of agricultural land and grassland, stream order, and the average slope of the groundwater flow path, that significantly impacted the stream baseflow nitrate concentrations in this watershed that was dominated by dairy farming and groundwater. Of these variables, the percentage of agricultural land emerged as the most significant factor.

The machine learning methods to analyze the using strategy of antiplatelet drugs in ischaemic stroke patients with gastrointestinal haemorrhage

BackgroundFor ischaemic stroke patients with gastrointestinal haemorrhage, stopping antiplatelet drugs or reducing the dose of antiplatelet drugs was a conventional clinical therapy method. But not a study to prove which way was better. And the machinery learning methods could help to obtain which way more suit for some patients.MethodsData from consecutive ischaemic stroke patients with gastrointestinal haemorrhage were prospectively collected. The outcome was a recurrent stroke rate, haemorrhage events, mortality and favourable functional outcome (FFO). We analysed the data using conventional logistic regression methods and a supervised machine learning model. We used unsupervised machine learning to group and analyse data characters. ResultsThe patients of stopping antiplatelet drugs had a lower rate of bleeding events (p = 0.125), mortality (p = 0.008), rate of recurrence of stroke (p = 0.161) and distribution of severe patients (mRS 3–6) (p = 0.056). For Logistic regression, stopping antiplatelet drugs (OR = 2.826, p = 0.030) was related to lower mortality. The stopping antiplatelet drugs in the supervised machine learning model related to mortality (AUC = 0.95) and FFO (AUC = 0.82). For group by unsupervised machine learning, the patients of better prognosis had more male (p < 0.001), younger (p < 0.001), had lower NIHSS score (p < 0.001); and had a higher value of serum lipid level (p < 0.001).ConclusionsFor ischemic stroke patients with gastrointestinal haemorrhage, stopping antiplatelet drugs had a better prognosis. Patients who were younger, male, with lesser NIHSS scores at admission, with the fewest history of a medical, higher value of diastolic blood pressure, platelet, blood lipid and lower INR could have a better prognosis.

Prediction of soluble solids content using near-infrared spectra and optical properties of intact apple and pulp applying PLSR and CNN

Soluble solids content (SSC) is one of the most important internal quality attributes of fruit and could be predicted using near-infrared (NIR) spectra and optical properties. Partial least squares regression (PLSR) is a conventional regression method in SSC prediction. In recent years, deep learning methods represented by convolutional neural network (CNN) was suggested to be implied in spectral analysis. However, researchers are inevitably facing problems with regard to the selection of spectral pretreatment methods and the evaluation of the performance of the chosen regression. This study employed PLSR and CNN regression to predict SSC of apple based on the collected diffuse reflectance spectra of intact apple, total reflectance and total transmittance spectra of apple pulp, and the calculated optical property spectra, i.e., absorption coefficient and reduced scattering coefficient spectra of apple pulp. Five different spectral pretreatment methods were exerted on these spectra. Results showed that at a given regression (PLSR or CNN), the built models based on the diffuse reflectance spectra of intact apple had the best SSC prediction, and the built models based on pulp’s reduced scattering coefficient spectra had the poorest prediction performance. The best prediction performance was achieved by PLSR models using Savitzky-Golay with multiple scattering correction (Rp = 0.96, RMSEP = 0.54 %) and CNN regressions using Savitzky-Golay with standard normal variational transformation (Rp = 0.95, RMSEP = 0.59 %), respectively. Additionally, when the unknown original spectra were used for modeling, CNN had a better performance compared to PLSR, indicating the outstanding preponderance of CNN in spectral analysis. This study provides an effective reference for the selection of chemometric method based on NIR spectra.

Robust low-rank learning multi-output regression for incipient sediment motion in sewer pipes

The existing incipient sediment motion models typically apply conventional regression methods considering either velocity or shear stress. In the current study, incipient sediment motion is analyzed through a simultaneous and joint analysis of velocity and shear stress using the robust low-rank learning (RLRL) multi-output regression technique. Moreover, the experimental data compiled from five different channels are utilized to develop a generic incipient sediment motion model valid for a channel of any cross-sectional shape. The efficiency of the developed method is examined and compared against the available conventional regression models. The experimental results indicate that the RLRL model yields better results than its counterparts. In particular, while cross-section specific models fail to provide accurate estimates for shear stress or velocity for other cross sections, the proposed model provides satisfactory results for all channel shapes. The better performance of the recommended approach can be attributed to the joint modeling of the shear stress and the velocity which is realized by capturing the correlation between these parameters in terms of a low rank output mixing matrix which enhances the prediction performance of the approach.