Multivariate Adaptive Regression Splines Algorithm Research Articles

A fast and automatic establishment method of the calculation formula for the tunnel face safety factor based on analytical solution

Assessing rock tunnel face stability when constructing mountain tunnels is of utmost importance because it is influenced by various factors, including the tunnel diameter, depth, excavation dimension, geological strength index, uniaxial compressive strength, and Hoek-Brown constant. This study established a tunnel face stability database based on analytical solutions that considered the correlation between the influencing parameters. Finally, a safety factor prediction equation was constructed by combining response surface methodology and the multivariate adaptive regression spline algorithm. The results of this study find applications at construction sites and provide a reference for the rapid evaluation of rock tunnel faces during construction.

Mini Percutaneous Nephrolithotomy vs Standard Percutaneous Nephrolithotomy: A Perioperative Decision Support System for Surgical Success Comparison.

This study aimed to rank the features that are important in terms of safety and effectiveness in choosing the surgical method and providing appropriate care to the patient by using the variables examined before and after the surgery to evaluate the success of mini percutaneous nephrolithotomy and standard percutaneous nephrolithotomy surgeries. The features evaluated before and after surgery were ranked according to their importance in the features considered, using Multivariate Adaptive Regression Splines (MARS), LASSO, Ridge, Elastic_net, and Random Forest algorithms as variable selection techniques. There are 278 samples in the relevant data set. Type of surgery (100%), intercostal access (97.75%), kidney opening procedure (94.25%), postoperative creatinine (59.22%), hydronephrosis (52.23%), the number of entries (41.61%), and pre- and post-operative hemoglobin difference (45.13%) were determined as the most critical variables. The MARS algorithm showed the most successful performance, with the lowest mean absolute error (MAE) value of 0.3622, the lowest root mean square error (RMSE) value of 0.3960, and the highest R2 value of 0.3405. Clinical decision support systems can be helpful in eliminating errors and reducing costs. It can also improve the quality of healthcare and aid in the early diagnosis of diseases. Computer-aided decision-making systems can be developed using the results of such products. These systems can provide doctors with better information about their patient's treatment options and improve decision-making. It can contribute to patients being better informed about the surgery results and taking an active role. In conclusion, this study provides essential information that should be included in the surgical decision-making process for patients using medications and with a history of percutaneous nephrolithotomy.

Morphological characterization of the Polatli sheep in terms of live weight using data mining algorithms.

The aim of this research is both to estimate the live weight (LW) of Polatli sheep (Ile de France × Akkaraman (G1)) by considering some body measurements (withers height (WH), rump height (RH), body length (BL), chest depth (CD), chest width (CD), chest girth (CG), cannon bone circumference (CBC)), age, and sex variables as independent variables using C&RT (Classification and Regression Tree), CHAID (Chi-square Automatic Interaction Detector), and MARS (Multivariate Adaptive Regression Splines) algorithms and to determine the significant independent variables in the estimation of live weight. For this purpose, a total of 210 sheep were used, including 180 females and 30 males of different ages, for the estimation of LW. The calculated Pearson correlation coefficients between LW and WH, RH, BL, CD, CW, CG, and CBC characteristics are 0.897, 0.896, 0.853, 0.948, 0.550, 0.914, and 0.798, respectively (p < 0.05). In the application of data mining algorithms as prediction models, a cross-validation of 10 was used, while for tree-based algorithms, the parent node was set to 10 and the child node to 5. While CHAID and C&RT algorithms each used 8 independent variables to explain the variation observed in LW, the MARS algorithm used 9 independent variables. In Polatli sheep, the sheep with the highest live weight was found in the node with age > 3 and CD > 36 cm cutting point in the CHAID algorithm (93.571 kg). In the C&RT algorithm, it was predicted to be (91.316 kg) when age > 0, CD > 36.5 cm, and CBC > 9.5 cm. When evaluated considering commonly used criteria, the prediction performances of CHAID, C&RT, and MARS algorithms were calculated as follows: RMSE (root mean square error) values are "5.788, 5.103, 4.005"; SDR (standard deviation ratio) values are "0.254, 0.224, 0.176"; MAPE (mean absolute percentage error) values are "7.555, 6.675, 5.682"; Adj-Rsq (adjusted R-squared) values are "0.935, 0.950, 0.969"; and AIC (Akaike information criterion) values are "741.436, 688.489, 582.792," respectively. In terms of prediction performance, among the tree algorithms (CHAID and C&RT), C&RT was found to be the best, while considering all performance measures, it was observed that the MARS algorithm exhibited the best performance. Consequently, it has been determined that C&RT and MARS algorithms can be safely employed in morphological characterization studies for the identification of indirect criteria and the formation of elite herds in terms of LW. This decision allows for the reliable use of these algorithms to facilitate the selection of indirect variables and the establishment of elite populations in breeding programs focusing on live weight.

Using fat thickness and longissimus thoracis traits real-time ultrasound measurements in Black Belly ewe lambs to predict carcass tissue composition through multiresponse multivariate adaptive regression splines algorithm

The main idea of the current study was to estimate carcass tissue composition using fat thickness and longissimus thoracis (LT) traits real-time ultrasound measurements (USM) in Black Belly ewe lambs through multiresponse multivariate adaptive regression splines (MARS) algorithms. Twenty-four hours before slaughter, subcutaneous (SFT) and kidney-fat thickness (KFT), LT depth (LTD), width (LTA, cm) and area (LTMA) were measured in 60 lambs (BW of 26.40 ± 7.01 kg). Information on carcass and non-carcass components was recorded after slaughter. The total carcass muscle (TCM), total carcass bone (TCB), and total carcass fat (TCF) had a low to high correlation (P < 0.01) with BW, cold carcass weight (CCW), and LTD, SFT, KFT, and LDMA. The CCW (%65.58) and SFT (%16.70) were the most effective variables, whilst LTD (%9.57) and LTMA (%8.15) were the lowest variables for determining TCB, TCM, and TCF. The multiresponse MARS algorithm provides an accurate and efficient means of estimating TCF, TCB, and TCM.

Predicting body weight of South African Sussex cattle at weaning using multivariate adaptive regression splines and classification and regression tree data mining algorithms

ABSTRACT The use of multivariate adaptive regression splines (MARS) and classification and regression tree (CART) to estimate the live body weight at weaning age of the Sussex cattle breed remain poorly understood in South Africa. This study was conducted to examine the effect of linear body measurements on body weight at weaning using MARS and CART algorithms. The body weight and linear body measurements included sternum height, withers height, heart girth, hip height, body length, rump length and rump width were collected from 101 Sussex cattle (female = 57 and male = 44) at weaning. Goodness of fit criterions was used to select the best data mining algorithms. The results showed that MARS showed higher predictive performance in the criteria as compared to CART algorithm. The findings of the study suggest that MARS algorithm can be used to estimate the BW at weaning age in Sussex cattle breed. These findings might be helpful to cattle farmers in the selection criterions of breeding stock at weaning age.

The consumer price index prediction using machine learning approaches: Evidence from the United States

The consumer price index (CPI) is one of the most important macroeconomic indicators for determining inflation, and accurate predictions of CPI changes are important for a country's economic development. This study uses multivariate linear regression (MLR), support vector regression (SVR), autoregressive distributed lag (ARDL), and multivariate adaptive regression splines (MARS) to predict the CPI of the United States. Data from January 2017 to February 2022 were randomly selected and divided into two stages: 80 % for training and 20% for testing. The US CPI was modeled for the observed period and relied on a mix of elements, including crude oil price, world gold price, and federal fund effective rate. Evaluation metrics—mean absolute percentage value, mean absolute error, root mean square error, R-squared, and correlation of determination—were employed to estimate forecasted values. The MLR, SVR, ARDL, and MARS models attained high accuracy parameters, while the MARS algorithm generated higher accuracy in US CPI forecasts than the others in the testing phase. These outputs could support the US government in overseeing economic policies, sectors, and social security, thereby boosting national economic development.

Prediction of carcass tissues composition using the neck and shoulder traits in hair lambs with multiresponse multivariate adaptive regression splines

The present study aimed to predict the carcass tissue composition of hair sheep lambs using a multiresponse multivariate adaptive regression splines algorithm. The left half of sixty-six hair lambs were divided into five commercial cuts (neck, shoulder, rib, loin, and leg), each cut was weighed and dissected in total soft tissue (fat and muscle, TSTW) and bone (BOW). The independent variables included variables obtained from neck and shoulder dissection: weights of the neck (NWE) and shoulder (SWE), neck soft STW (NSTW), neck BW (NBOW), shoulder STW (SSTW), and shoulder BW (SBOW). The prediction of hot carcass weight (HCW), cold carcass weight (CCW), carcass soft tissue weight (CSTW), and carcass bone weight (CBWE) had an R2 that ranged from 0.90 to 0.96. It is concluded that some neck traits and all shoulder traits could be used to predict the carcass tissue weights of hair-suckling lambs correctly.

Predicting Earthquake-Induced Landslides by Using a Stochastic Modeling Approach: A Case Study of the 2001 El Salvador Coseismic Landslides

In January and February 2001, El Salvador was hit by two strong earthquakes that triggered thousands of landslides, causing 1259 fatalities and extensive damage. The analysis of aerial and SPOT-4 satellite images allowed us to map 6491 coseismic landslides, mainly debris slides and flows that occurred in volcanic epiclastites and pyroclastites. Four different multivariate adaptive regression splines (MARS) models were produced using different predictors and landslide inventories which contain slope failures triggered by an extreme rainfall event in 2009 and those induced by the earthquakes of 2001. In a predictive analysis, three validation scenarios were employed: the first and the second included 25% and 95% of the landslides, respectively, while the third was based on a k-fold spatial cross-validation. The results of our analysis revealed that: (i) the MARS algorithm provides reliable predictions of coseismic landslides; (ii) a better ability to predict coseismic slope failures was observed when including susceptibility to rainfall-triggered landslides as an independent variable; (iii) the best accuracy is achieved by models trained with both preparatory and trigger variables; (iv) an incomplete inventory of coseismic slope failures built just after the earthquake event can be used to identify potential locations of yet unreported landslides.

MD-MARS: Maintainability Framework Based on Data Flow Prediction Using Multivariate Adaptive Regression Splines Algorithm in Wireless Sensor Network

The demand for Wireless Sensor Networks is increasing day by day because of their diverse nature. Due to the limited energy, it is a complex task to retract the sensor node after deployment. So, there is a requirement for network maintainability before the deployment phase for its smooth working. It is achieved in three phases: hardware of the sensor node, communication and external environmental phase. This paper focuses on network maintainability in the communication phase. A novel framework MD-MARS is presented to enhance the network maintainability. This framework is classified into three phases namely analysis of performance parameters, data flow optimization and maintainability evaluation. In the initial phase, the performance parameter is analyzed using NS2 simulator. The next phase deals with data flow optimization using a machine learning algorithm. It reduces congestion and enhances network performance. The proposed algorithm is finely tuned to different degrees using the Grid Search approach to achieve the highest accuracy. The best model is selected based on accuracy and minimizes the prediction error. This algorithm predicts with the highest accuracy of 99.83%, lowest being 21.17%. Maintainability is achieved in the last phase using the total time taken to optimize the data flow. Several observations of repair time are determined for the best-tune model during the prediction of optimized data flow. These observations are used to calculate the mean time to repair, standard deviation, probability density function, maintainability and repair rate. The maximum maintainability achieved in this paper is 97.67% at a repair time of 26.07 milliseconds.

Use of Multivariate Adaptive Regression Splines Algorithm to Predict Body Weight from Body Measurements of Anatolian buffaloes in Türkiye.

Anatolian buffalo is an important breed reared for meat and milk in various regions of Türkiye. The present study was performed to estimate body weight (BW) from several body measurements, such as tail length (TL), shoulder height (SH), withers height (WH), body length (BL), chest circumference (CC), shank diameter (SD) and birth weight (BiW). The data set was taken from Muş Province of Türkiye. In this respect, 171 Anatolian buffaloes were used. To estimate the BW, different proportions of the training and test sets were used with the MARS algorithm. The optimal MARS was determined at a proportion of 70-30%. The MARS model displays the heaviest BW that can be produced by Anatolian buffalo according to tail length, body length, chest circumference and shoulder height. In conclusion, it could be suggested that the MARS algorithm may allow animal breeders to obtain an elite population and to determine the body measurements affecting BW as indirect selection criteria for describing the breed description of Anatolian buffalo and aiding sustainable meat production and rural development in Türkiye.

The Use of Some Nonlinear Functions to Explain Growth in Japanese Quails with Multivariate Adaptive Regression Splines Algorithm

The study aimed was to determine the best nonlinear function describing the growth stages of the Japanese quail breed. To this aim, growth functions such as exponential, logistic, von Bertalanffy, Brody, and Gompertz were used as nonlinear functions is used in the description of the body weight-age relationship of male and female Japanese quails. The Multivariate Adaptive Regression Splines (MARS) data mining algorithm was applied to the individual growth parameters obtained from the determined as the best fit model, and the relationship between sex and growth parameters with it has been revealed. The study dataset was 1267 body weight-age records collected from the hatching to the 6th week of age of 181 Japanese quails consisting of 90 females and 91 males. Each model was applied separately for both males and females. Model fit criteria such as coefficient of determination (R2), adjusted coefficient of determination (R2adj), Akaike's information criterion (AIC), and Bayes information criterion (BIC) were used to evaluate the performances of the growth functions used individually. All the statistical analyses were made by the R package program. The growth curve models were ranked in the form of Logistic &gt; Gompertz &gt; von Bertalanffy &gt; Brody &gt; Exponential according to the goodness of fit criteria. The most suitable model among the non-linear models in terms of performance was logistic. When the relationship between the growth curve parameters and body weight of the logistic model was explained with the MARS algorithm, the goodness of fit criteria showed that the obtained MARS model showed reliable performance. In addition, Pearson’s correlation coefficient between real and estimated body weight was found quite strong for the MARS algorithm (r=0.935). The results showed that the MARS algorithm can be presented as a good reference for breeders to establish breed standards and selection strategies for Japanese quails in growth parameters for breeding purposes.

High-dimensional multinomial multiclass severity scoring of COVID-19 pneumonia using CT radiomics features and machine learning algorithms

We aimed to construct a prediction model based on computed tomography (CT) radiomics features to classify COVID-19 patients into severe-, moderate-, mild-, and non-pneumonic. A total of 1110 patients were studied from a publicly available dataset with 4-class severity scoring performed by a radiologist (based on CT images and clinical features). The entire lungs were segmented and followed by resizing, bin discretization and radiomic features extraction. We utilized two feature selection algorithms, namely bagging random forest (BRF) and multivariate adaptive regression splines (MARS), each coupled to a classifier, namely multinomial logistic regression (MLR), to construct multiclass classification models. The dataset was divided into 50% (555 samples), 20% (223 samples), and 30% (332 samples) for training, validation, and untouched test datasets, respectively. Subsequently, nested cross-validation was performed on train/validation to select the features and tune the models. All predictive power indices were reported based on the testing set. The performance of multi-class models was assessed using precision, recall, F1-score, and accuracy based on the 4 × 4 confusion matrices. In addition, the areas under the receiver operating characteristic curves (AUCs) for multi-class classifications were calculated and compared for both models. Using BRF, 23 radiomic features were selected, 11 from first-order, 9 from GLCM, 1 GLRLM, 1 from GLDM, and 1 from shape. Ten features were selected using the MARS algorithm, namely 3 from first-order, 1 from GLDM, 1 from GLRLM, 1 from GLSZM, 1 from shape, and 3 from GLCM features. The mean absolute deviation, skewness, and variance from first-order and flatness from shape, and cluster prominence from GLCM features and Gray Level Non Uniformity Normalize from GLRLM were selected by both BRF and MARS algorithms. All selected features by BRF or MARS were significantly associated with four-class outcomes as assessed within MLR (All p values < 0.05). BRF + MLR and MARS + MLR resulted in pseudo-R2 prediction performances of 0.305 and 0.253, respectively. Meanwhile, there was a significant difference between the feature selection models when using a likelihood ratio test (p value = 0.046). Based on confusion matrices for BRF + MLR and MARS + MLR algorithms, the precision was 0.856 and 0.728, the recall was 0.852 and 0.722, whereas the accuracy was 0.921 and 0.861, respectively. AUCs (95% CI) for multi-class classification were 0.846 (0.805–0.887) and 0.807 (0.752–0.861) for BRF + MLR and MARS + MLR algorithms, respectively. Our models based on the utilization of radiomic features, coupled with machine learning were able to accurately classify patients according to the severity of pneumonia, thus highlighting the potential of this emerging paradigm in the prognostication and management of COVID-19 patients.

The Effect of Different Doses of 1-Methylcyclopropene on Postharvest Physiology and Predicting Ethylene Production through Multivariate Adaptive Regression Splines in Cocktail Tomato

Maintaining the postharvest quality of tomatoes, which are essential in the world vegetable trade, is very important; otherwise, storage may cause severe quality and economic losses. This study aimed to determine the effects of 1-MCP treatments on quality and storage time to prevent quality loss and deterioration in tomatoes due to high temperature and to predict ethylene production through the Multivariate Adaptive Regression Splines algorithm during long-term storage. For this purpose, same-sized fruits were divided into three different groups. Two groups were treated with 1-MCP (625 and 1250 ppb), and the untreated fruit was the control. Then, the tomatoes were stored for 39 days at a temperature of 20 °C and relative humidity of 85–90%. The results obtained from the present study showed that the 1-MCP treatments prolonged the life and maintained the quality of tomato fruit during storage. The ethylene production and respiration rate were significantly lower in 1-MCP-treated fruit than in the untreated fruit during the storage period (p &lt; 0.05). According to the Multivariate Adaptive Regression Splines algorithm, it was observed that increasing doses of 1-MCP inhibited ethylene production. Furthermore, weight loss and respiratory rate were effective on ethylene production at 100 and 43.9%, respectively. In conclusion, treating the fruit with 1250 ppb of 1-MCP was determined to be the best practice for maintaining all quality criteria during storage.

Analysis of earthquake hazards prediction with multivariate adaptive regression splines

&lt;p&gt;&lt;span&gt;Earthquake research has not yielded promising results, either in the form of causes or revealing the timing of their future events. Many methods have been developed, one of which is related to data mining, such as the use of hybrid neural networks, support vector regressor, fuzzy modeling, clustering, and others. Earthquake research has uncertain parameters and to obtain optimal results an appropriate method is needed. In general, several predictive data mining methods are grouped into two categories, namely parametric and non-parametric. This study uses a non-parametric method with multivariate adaptive regression spline (MARS) and conic multivariate adaptive regression spline (CMARS) as the backward stage of the MARS algorithm. The results of this study after parameter testing and analysis obtained a mathematical model with 16 basis functions (BF) and 12 basis functions contributing to the model and 4 basis functions not contributing to the model. Based on the level of variable contribution, it can be written that the epicenter distance is 100 percent, the magnitude is 31.1 percent, the location temperature is 5.5 percent, and the depth is 3.5 percent. It can be concluded that the results of the prediction analysis of areas in Lombok with the highest earthquake hazard level are Malaka, Genggelang, Pemenang, Tanjung, Tegal Maja, Senggigi, Mangsit. Meninting, and Malimbu.&lt;/span&gt;&lt;/p&gt;

A new framework for missing data estimation and reconstruction based on the geographical input information, data mining, and multi-criteria decision-making; theory and application in missing groundwater data of Damghan Plain, Iran

In the present study, a new framework is developed based on the geographical data (GD), data mining techniques (DI), and Hesitant fuzzy-multicriteria decision-making methods (HF-MCDA) for modeling groundwater table (GWT) missing data in Damghan plain. The GD is used as inputs in the presented approach, and available GWT is used as output. The different DI, including artificial neural network (ANN), tree model M5 (M5), multivariate adaptive regression spline (MARS), least-square support vector machine (LSSVM), random forest (RF), and extreme learning machine (ELM), are employed for establishing a relation between GD and GWT and estimating missing GWT. However, there is this challenge that one of the DI is better because there are different criteria for selecting the best DI, including error criteria, uncertainty, and computation time. Moreover, there is hesitation about the choice of weight criteria. In this condition, HF-MCDA is a practical choice. According to the results, M5 (by values of 5.485 m, 10.811 m, and 0.998 for MAE, RMSE, and R2, respectively) and LSSVM (by values of 3.043 m, 17.005 m, and 0.997 for MAE, RMSE, and R2, respectively) have accurate results than other investigated DI. In contrast, ELM has the worst results in terms of accuracy. M5 and RF have the best and worst performance based on the time computation term. The results of bootstrap uncertainty show that LSSVM has minimum uncertainty (by the value of 1.349 m for d_factor), and ELM has maximum uncertainty (by the value of 1.570 m for d_factor). Finally, according to the results of HF-MCDA, M5, LSSVM has first and the second rank with a closed score. Besides, the MARS algorithm is placed B in the third rank with a slight difference from M5 and LSSVM. Based on the high and closed scores of M5, LSSVM, and MARS, these methods can be used to find missing GWT data.

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

Detection of correct pregnancy status in lactating dairy cattle using MARS data mining algorithm

In this study, it is aimed to determine pregnancy outcomes by using multivariate adaptive regression splines (MARS) algorithm for classification type problems. For this purpose, data obtained from a private dairy farm in the Konya region of Türkiye in 2020 were used to determine pregnancy outcomes in Holstein dairy cattle. It has been determined how to perform statistical analyses on solving classification-type problems with the MARS algorithm and how to use R packages (caret and earth) by creating an R script file. After the analysis, the MARS estimation equation was created and in finding the probability of being pregnant: While lactation period, cow age, number of lactations, insemination number, and total lactation milk yield variables are important, it was seen that 7-day mean milk yield and last lactation milk yield were not significant. Using the train function of the caret package, the number of terms that produce the highest accuracy and the degree of interaction are determined. Goodness-of-fit tests of the optimum model were calculated. Within the scope of the evaluation of the generalization ability of the model, training and test sets were created, the classification success graph of the MARS algorithm, the model building phase were summarized, and the generalization ability of the established model was measured. When the pregnancy status is taken as a positive reference, the correct classification rate (sensitivity) of the animal with positive pregnancy status was found to be 0.9574. The correct classification rate (specificity) of pregnant animals was found to be 0.8370. The overall classification ratio of the training set (accuracy) was found to be 0.8777. The area under the ROC curve (AUC) was found to be 0.947, which indicates that the optimum specificity value is close to 1.

Use of linear modeling, multivariate adaptive regression splines and decision trees in body weight prediction in goats

Use of robust regression algorithms for better prediction of body weight (BW) is receiving increased attention. The present study therefore aimed at predicting BW from chest circumference, breed and sex of a total of 1,012 goats. The animals comprised 332 matured West African Dwarf (WAD) (197 bucks and 135 does), 374 Red Sokoto (RS) (216 bucks and 158 does) and 306 Sahel (SH) (172 bucks and 134 does) randomly selected in Nasarawa State, north central Nigeria. BW prediction was made using automatic linear modeling (ALM), multivariate adaptive regression splines (MARS), classification and regression tree (CART), chi-square automatic interaction detection (CHAID) and exhaustive CHAID. The predictive ability of each statistical approach was measured using goodness of fit criteria i.e. Pearson?s correlation coefficient (r), Coefficient of determination (R2), Adjusted coefficient of determination (Adj. R2), Root-mean-square error (RMSE), Mean absolute percentage error (MAPE), Mean absolute deviation (MAD), Global relative approximation error (RAE), Standard deviation ratio (SD ratio), Akaike?s information criterion (AIC) and Akaike?s information criterion corrected (AICc). Male RS and SH goats had significantly (P&lt;0.05) higher BW and CC compared to their female counterparts while in WAD, male goats had significantly (P&lt;0.05) higher CC (57.88?0.51 vs. 55.45?0.55). CC was determined to be the trait of paramount importance in BW prediction, as expected. Among the five models, MARS algorithm gave the best fit in BW prediction with r, R2, Adj. R2, SDratio, RMSE, RAE, MAPE, MAD, AIC and AICc values of 0.966, 0.933, 0.932, 0.26, 1.078, 0.045, 3.245, 0.743, 186.0 and 187.0, respectively. The present information may guide the choice of model which may be exploited in the selection and genetic improvement of animals including feed and health management and marketing purposes, and especially in the identification of the studied breed?s standards.

Can TKA outcomes be predicted with computational simulation? Generation of a patient specific planning tool

BackgroundComputer simulations of knee movement allow Total Knee Arthroplasty (TKA) dynamic outcomes to be studied. This study aims to build a model predicting patient reported outcome from a simulation of post-operative TKA joint dynamics. MethodsLandmark localisation was performed on 239 segmented pre-operative computerized tomography (CT) scans to capture patient specific soft tissue attachments. The pre-operative bones and 3D implant files were registered to post-operative CT scans following TKA. Each post-operative knee was simulated undergoing a deep knee bend with assumed ligament balancing of the extension space. The kinematic results from this simulation were used in a Multivariate Adaptive Regression Spline algorithm, predicting attainment of a Patient Acceptable Symptom State (PASS) score in captured 12 month post-operative Knee Injury and Osteoarthritis Outcome Scores (KOOS). An independent series of 250 patients was evaluated by the predictive model to assess how the predictive model behaved in a pre-operative planning context. ResultsThe generated predictive algorithm, called the Dynamic Knee Score (DKS) contained features, in order of significance, related to tibio-femoral force, patello-femoral motion and tibio-femoral motion. Area Under the Curve for predicting attainment of the PASS KOOS Score was 0.64. The predictive model produced a bimodal spread of predictions, reflecting a tendency to either strongly prefer one alignment plan over another or be ambivalent. ConclusionA predictive algorithm relating patient reported outcome to the outputs of a computational simulation of a deep knee bend has been derived (the DKS). Simulation outcomes related to tibio-femoral balance had the highest correlation with patient reported outcome.

Soft computing analysis of thermohydraulic enhancement using twisted tapes in a flat-plate solar collector: Sensitivity analysis and multi-objective optimization

Flat-plate solar collectors are a cost-effective technology for water heating in residential buildings. Comprehensive sensitivity analysis and multi-objective optimization of five crucial factors in a flat-plate solar collector equipped with twisted tapes are investigated. The studied factors are the Nusselt number (Nu), friction factor (f), solar collector efficiency (η), thermal performance factor (ξ), and the temperature difference of the heat transfer fluid across the device (ΔT). The simulation of functions is carried out by four soft computing methods, such as the linear and cubic form of multivariate adaptive regression splines (MARS), group method of data handling (GMDH), and multivariate polynomial regression (MPR). The linear form of the MARS algorithm shows the highest accuracy in predicting Nu, f, and η by R2 = 0.99986, 0.9984, and 0.98821, respectively. The Reynolds number (Re) is from 4000 to 14,000, and the twisted tape ratio (Y) is between 0 and 15. The simulated annealing optimization is employed to find the global optima. According to the study, Nu, η, ξ, and ΔT reach their maximum when f is minimum, which occurs for Y = 5 and Re in the 13,200 to 13,400 range for different global solar radiation levels. The maximum temperature difference happens at noon, about 6.54 K at the Re = 5362.5 and Y = 5 with the collector surface area by 2 m2. When the appropriate working conditions are met so that these factors are simultaneously optimal, ΔT, hence the efficiency, can be increased.