Optimal Set Of Variables Research Articles

The influence of low-progressive scoliosis and its atlas on selected features of the pelvis and spine among 7-15-year-old youth of both sexes and environments

The purpose of this study was to demonstrate the significant effect of low-progressive scoliosis and the location of its atlas on the values of selected features describing the pelvis and spine. Material and Methods. The study was carried out in a group of 2,361 subjects aged 7 to 15 years, in 6 semiannual consecutive editions. This allowed to record 16,608 observations of 31 features describing posture by photogrammetric method. Results. Three influential traits were selected for multiple regression analysis with selection of a subset of the optimal set of variables like the deviation of the spinous process to the left or right from the vertical and the location of the atlas of this deviation. Conclusions. (1) The effect of low-progressive scoliosis and its atlas on selected features of the spine and pelvis is multidirectional and varied. (2) Left and right low-progressive scoliosis significantly and positively affects almost all features of the spine and pelvis. Significant negative influence is episodic. The features with a significant relationship are Delta angle, the length of lumbar lordosis and the angle of pelvic torsion to the left and right. (3) The low-progressive scoliosis and its atlas show significant and negative effects on the angle of thoracic kyphosis and the length and height of lumbar lordosis.

The effect of the length, angle, height, and depth of lumbar lordosis on selected features of thoracic spine

The purpose of this study was to demonstrate the significant effect of the values of the angle, depth, height, and length of lumbar lordosis on selected features of the thoracic spine. Material and Methods. The study was carried out in a group of 2,361 children aged 7 to 15 years, in 6 semiannual consecutive editions. This allowed to record 16,608 observations of 31 features describing posture by photogrammetric method. Results. The following influential features like angle, depth, heights, and lengths of lumbar lordosis were selected for multiple regression analysis with selection of a subset of the optimal set of variables. Conclusions The presented statistical analysis shows the mutual influence of the values of the selected characteristics. The study showed no independent feature, however, the degree of influence varied. This demonstrates the interrelatedness of the different spinal segments. Corrective actions that change the value of one postural trait force the change of others in a positive or negative way.

Development of feature extraction method for near infrared spectroscopy using stepwise bayesian linear regression

Near infrared (NIR) spectra contain information regarding the analyte as well as uninformative wavelengths. To build high-performance data-driven models, key wavelengths with a strong correlation to the analyte must be selected. This study proposes a feature selection method called stepwise Bayesian linear regression (SBLR) for eliminating unrelated wavelengths, thereby enhancing the robustness of the constructed model. First, a random wavelength is selected from an optimal variable set, and the other wavelengths are placed in a candidate variable set. A Bayesian linear regression (BLR) is implemented by adding a new variable from the candidate set or removing a variable from the optimal set in each step. Furthermore, the BLR model is utilized to perform the F-test. Comparing with the critical value of the F-test with a significance level of α, the test determines whether the variable is retained in the optimal set. Finally, the extracted variables are used to construct a BLR model. The performance and generalization ability of the proposed method were validated. The physical explanation of extracted wavelengths is consistent with the perspective of chemical analysis based on the experiment, which provides a good understanding of the collected NIR spectral data. In addition, compared with traditional algorithms, such as partial least squares regression, least absolute shrinkage and selection operator, and stepwise regression, the proposed method reserves only a few of the effective wavelengths from the full NIR spectra. The proposed method demonstrates potential for key wavelength selection in NIR spectroscopy.

Novel comprehensive variable selection algorithm based on multi-weight vector optimal selection and bootstrapping soft shrinkage

The dimensionality of spectral data is increasing with the advancements in spectral technology. Therefore, there is an urgent need to develop high-performance variable selection algorithms for chemometrics applications. This study proposes a novel multi-weight optimal-bootstrap soft shrinkage (MWO-BOSS) method for variable selection based on the bootstrap soft shrinkage (BOSS) algorithm, comprising three effective improvement strategies. First, the optimal weight vector of six weight vectors are used as weights of the selection variables, rather than the absolute value of the regression coefficients based only on a single weight vector. Second, in each loop, a step-by-step strategy is implemented to determine the optimal set of variables. Finally, a smoothing operation is added to the weight vector to improve the anti-noise performance of the algorithm. The performance of the MWO-BOSS algorithm was tested on the four spectral datasets corn protein, corn oil, soil, and beer and compared with six high-performance algorithms, namely interval partial least squares (iPLS), Moving Window Partial Least-Squares(MWPLS), competitive adaptive reweighted sampling (CARS), variable combinatorial population analysis (VCPA), VCPA-IRIV and BOSS. The results show that the MWO-BOSS algorithm effectively improves the predictive ability of the model, with MWO-BOSS-Step-S providing the best results among the four tested datasets.

Multi-objective optimization for comparative energy and economic analyses of a novel evacuated solar collector prototype (ICSSWH) under different weather conditions

This paper aims at optimizing the energy performance of an innovative integrated collector-storage solar water heater (ICSSWH) prototype. The system incorporates a high-vacuum gap, which significantly reduces convective thermal losses, and a serpentine absorber system coated with a low-E layer to minimize radiative thermal losses.The methodology is based on a detailed dynamic simulation model, implemented in the MatLab environment, suitable conceived to accurately predict the energy performance and economic profitability of the ICSSWH under various weather conditions and energy market prices. This approach aims to initially identify the most influential geometrical, thermophysical and optical parameters that impact the energy performance of the system through the assessment of different objective functions. After identifying the most influential parameters, the range of existence with the interval step for each variable is defined, and the optimization procedure is conducted to determine the optimal set of variables that maximize/minimize the selected objective functions.To demonstrate the effectiveness of the proposed methodology, a proof of concept was conducted to optimize the energy performance of novel ICSSWH by considering nineteen parameters with the aim of maximizing/minimizing three different objective functions. The results of this analysis yielded various sets of optimised parameters for the ICSSWH. Therefore, by implementing the set of optimised parameters into the dynamic simulation model, the energy performance of the optimised and non-optimised ICSSWH collectors was evaluated in 42 different weather zones and compared to a reference case scenario.

What Constitutes the High-Quality Soundscape in Human Habitats? Utilizing a Random Forest Model to Explore Soundscape and Its Geospatial Factors Behind.

Soundscape is the production of sounds and the acoustic environment, and it emphasizes peoples' perceiving and experiencing process in the context. To this end, this paper focuses on the Pearl River Delta in China, and implements an empirical study based on the soundscape evaluation data from the Participatory Soundscape Sensing (PSS) system, and the geospatial data from multiple sources. The optimal variable set with 24 features are successfully used to establish a random forest model to predict the soundscape comfort of a new site (F1 = 0.61). Results show that the acoustic factors are most important to successfully classify soundscape comfort (averaged relative importance of 17.45), subsequently ranking by built environment elements (11.28), temporal factors (9.59), and demographic factors (9.14), while landscape index (8.60) and land cover type (7.71) seem to have unclear importance. Furthermore, the partial dependence analysis provides the answers about the appropriate threshold or category of various variables to quantitatively or qualitatively specify the necessary management and control metrics for maintaining soundscape quality. These findings suggest that mainstreaming the soundscape in the coupled natural-human systems and clarifying the mechanisms between soundscape perception and geospatial factors can be beneficial to create a high-quality soundscape in human habitats.

Mapping Forest Stock Volume Based on Growth Characteristics of Crown Using Multi-Temporal Landsat 8 OLI and ZY-3 Stereo Images in Planted Eucalyptus Forest

Labeled as a fast-growing tree species, eucalyptus has outstanding carbon sequestration capacity. Forest stock volume (FSV) is regarded as an important parameter for evaluating the quality of planted eucalyptus forests. However, it is an intractable problem to map FSV of planted eucalyptus forests using optical images because of growth characteristics of the crown and low saturation levels. To improve the accuracy of FSV in planted eucalyptus forests, time series Landsat 8 OLI (LC8) images and ZY-3 stereo images were acquired in the study area. Additionally, then, three composite images were proposed using acquired Landsat 8 OLI images based on the size and shape of eucalyptus crowns, and several spectra variables were extracted from these composite images. Furthermore, corrected canopy height model (CCHM) was also extracted from ZY-3 stereo images. Meanwhile, four models (random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and multiple linear regression (MLR)) were used to estimate the FSV with various variable sets using the importance of the alternative variables ranked by RF. The results show that the sensitivity between proposed spectral variables and FSV is significantly improved using proposed composed images based on the growth characteristics of the crown, especially for young eucalyptus forests. After adding CCHM and stand age to the optimal variable set, the average relative root mean square error (rRMSE) of estimated FSV decreased from 41.01% to 29.94% for single LC8 images and from 32.64% to 26.47% for proposed composite LC8 images, respectively. After using the variable set extracted from composite LC8 images, the number of samples with overestimated FSV was significantly decreased for the young forest. Furthermore, forest height plays an important role in improving the accuracy of mapping FSV, whether young or mature eucalyptus forest. It was also proved that composite images related to crown close and CCHM have great potential to delay the saturation phenomenon for mapping FSV in planted eucalyptus forest.

Wind Turbine with Configurable Feedback Scheme for Minimal Environmental Impact and Maximum Efficiency

This work consists of a study of wind turbine configurations, analyzing their efficiency, and proposing optimization stages for wind energy conversion technologies. This has made it possible to establish a reconfigurable model of adaptive turbines, under the criteria of minimum environmental impacton fauna, maximum energy efficiency, and dynamic updating to new technologies. The method consisted of the identification of the parameters of the wind system, its analysis, and the generalization of the architecture. A set of optimization variables was obtained, which allowed proposing of innovative techniques for adapting the configurable model of the system. The developed model provides a basis for the identification of parameters, online diagnosis, configurable optimization stages on the installed wind turbines, improves efficiency, environmental remediation and regeneration of the flow pattern conditions and optimal environmental variables of the system, as well as recycling. programmed in the technological update stage.
 Keywords: Wind turbines, dynamic optimization, reconfigurable systems, environmental regeneration.

Genomic Prediction Using LD-Based Haplotypes in Combined Pig Populations.

The size of reference population is an important factor affecting genomic prediction. Thus, combining different populations in genomic prediction is an attractive way to improve prediction ability. However, combining multireference population roughly cannot increase the prediction accuracy as well as expected in pig. This may be due to different linkage disequilibrium (LD) pattern differences between population. In this study, we used the imputed whole-genome sequencing (WGS) data to construct LD-based haplotypes for genomic prediction in combined population to explore the impact of different single-nucleotide polymorphism (SNP) densities, variant representation (SNPs or haplotype alleles), and reference population size on the prediction accuracy for reproduction traits. Our results showed that genomic best linear unbiased prediction (GBLUP) using the WGS data can improve prediction accuracy in multi-population but not within-population. Not only the genomic prediction accuracy of the haplotype method using 80 K chip data in multi-population but also GBLUP for the multi-population (3.4–5.9%) was higher than that within-population (1.2–4.3%). More importantly, we have found that using the haplotype method based on the WGS data in multi-population has better genomic prediction performance, and our results showed that building haploblock in this scenario based on low LD threshold (r 2 = 0.2–0.3) produced an optimal set of variables for reproduction traits in Yorkshire pig population. Our results suggested that whether the use of the haplotype method based on the chip data or GBLUP (individual SNP method) based on the WGS data were beneficial for genomic prediction in multi-population, while simultaneously combining the haplotype method and WGS data was a better strategy for multi-population genomic evaluation.

The Associations of rs1799724 and rs361525 With the Risk of Ankylosing Spondylitis Are Dependent on HLA-B27 Status in a Chinese Han Population.

ObjectivesHuman leucocyte antigen B27 (HLA-B27) is an important biomarker for ankylosing spondylitis (AS). However, delay in the diagnosis of AS is still common in clinical practice. Several single nucleotide polymorphisms (SNPs) in the coding gene of tumor necrosis factor alpha (TNFα) have been reported to be AS susceptibility loci. Our aim was to explore whether SNPs in TNFα could be used to improve the performance of HLA-B27 for predicting AS.MethodsFive SNPs (rs1799964, rs1800630, rs1799724, rs1800629, and rs361525) spanning TNFα were genotyped by qPCR-Invader assay in 93 AS patients and 107 healthy controls for association analysis and linkage disequilibrium (LD) analysis. Random forest algorithm was utilized to construct the predictive classifiers for AS. HLA-B was genotyped by PCR-sequence-based typing in a subset of the HLA-B27-positive subjects (38 AS patients and 5 healthy controls).ResultsThe T allele of rs1799724 was verified to significantly increase the risk of AS (OR = 4.583, p < 0.0001), while the A allele of rs361525 showed an association with the reduced AS risk (OR = 0.168, p = 0.009). In addition, the rs1799964T-rs1800630C-rs1799724T-rs1800629G-rs361525G haplotype was significantly associated with a higher risk of AS (p < 0.0001). The optimal set of variables for classifiers to predict AS only consisted of HLA-B27. Strong associations with HLA-B27 status were found in both rs1799724 (p < 0.0001) and rs361525 (p = 0.001), and all the analyzed HLA-B27-positive subjects carried HLA-B*27:04 or HLA-B*27:05.ConclusionIn the Chinese Han population, the minor allele T of rs1799724 could increase the risk of AS, while the minor allele A of rs361525 protects individuals from AS. However, the contributions of rs1799724 and rs361525 to AS risk were dependent on HLA-B27 status, suggesting the importance of taking the independence and specificity into consideration in AS susceptibility loci studies.

Wpływ środowiska, wieku, płci, wysokości i masy ciała oraz asymetrii miednicy w płaszczyźnie strzałkowej i czołowej na wielkości cech zespołu kręgosłup-miednica wśród 7-15-letnich dzieci i młodzieży

Introduction. A human being tries to change their lifestyle through cultural activities in order to maintain a state of balance with the environment according to progressive adaptive changes in body posture, forced by the changing environment and smaller energy resources. Aim. Demonstration of the influence of the environment, age, sex, body height and weight as well as pelvic asymmetry in the sagittal and frontal plane on the values of selected features describing the spine-pelvic syndrome. Material and methods. The research was carried out in a group of 2,361 children and adolescents aged 7 to 15 years, in 6 consecutive semiannual editions. It was possible to register 16,608 observations of 29 features describing body posture by using the photogrammetric method. Results. Due to the number of obtained measurements and the results of statistical calculations, only significant relationships were included in the analysis. Five influencing parameters such as environment, age, sex, body weight and height were selected for multiple regression analysis with the selection of a subset of the optimal set of variables. The set of variables included the features of the spine-pelvis syndrome. Conclusions. 1. The influence of environment, sex, age, body height and weight, as well as pelvis asymmetry in the transverse and frontal plane on the spine and pelvis selected features is omnidirectional and varied. 2. The spine and pelvis features are most often significantly positively influenced by sex, then by body height, then body weight, environment, and age. The spine features are influenced by the pelvis asymmetry. The environment is most often significantly negative, then body height and weight, then sex and age. The features of the spine are occasionally influenced by pelvic asymmetry. 3. Sex, age, weight and body height, and the environment are most often influenced by the size of the angle tilt of the sacrolumbar segment of the spine (Alpha), length of thoracic kyphosis (DKP) and height of lumbar lordosis (RLL). Pelvic asymmetry in the frontal plane, which is less transverse affects majority of the spine features.

Grouped variable selection for generalized eigenvalue problems

Many problems require the selection of a subset of variables from a full set of optimization variables. The computational complexity of an exhaustive search over all possible subsets of variables is, however, prohibitively expensive, necessitating more efficient but potentially suboptimal search strategies. We focus on sparse variable selection for generalized Rayleigh quotient optimization and generalized eigenvalue problems. Such problems often arise in the signal processing field, e.g., in the design of optimal data-driven filters. We extend and generalize existing work on convex optimization-based variable selection using semidefinite relaxations toward group-sparse variable selection using the ℓ1,∞-norm. This group-sparsity allows, for instance, to perform sensor selection for spatio-temporal (instead of purely spatial) filters, and to select variables based on multiple generalized eigenvectors instead of only the dominant one. Furthermore, we extensively compare our method to state-of-the-art methods for sensor selection for spatio-temporal filter design in a simulated sensor network setting. The results show both the proposed algorithm and backward greedy selection method best approximate the exhaustive solution. However, the backward greedy selection has more specific failure cases, in particular for ill-conditioned covariance matrices. As such, the proposed algorithm is the most robust currently available method for group-sparse variable selection in generalized eigenvalue problems.

Dermatology disease prediction based on firefly optimization of ANFIS classifier

&lt;abstract&gt; &lt;p&gt;The rate of increase in skin cancer incidences has become worrying in recent decades. This is because of constraints like eventual draining of ozone levels, air's defensive channel capacity and progressive arrival of Sun-oriented UV radiation to the Earth's surface. The failure to diagnose skin cancer early is one of the leading causes of death from the disease. Manual detection processes consume more time well as not accurate, so the researchers focus on developing an automated disease classification method. In this paper, an automated skin cancer classification is achieved using an adaptive neuro-fuzzy inference system (ANFIS). A hybrid feature selection technique was developed to choose relevant feature subspace from the dermatology dataset. ANFIS analyses the dataset to give an effective outcome. ANFIS acts as both fuzzy and neural network operations. The input is converted into a fuzzy value using the Gaussian membership function. The optimal set of variables for the Membership Function (MF) is generated with the help of the firefly optimization algorithm (FA). FA is a new and strong meta-heuristic algorithm for solving nonlinear problems. The proposed method is designed and validated in the Python tool. The proposed method gives 99% accuracy and a 0.1% false-positive rate. In addition, the proposed method outcome is compared to other existing methods like improved fuzzy model (IFM), fuzzy model (FM), random forest (RF), and Naive Byes (NB).&lt;/p&gt; &lt;/abstract&gt;

Classifying Individual Shrub Species in UAV Images—A Case Study of the Gobi Region of Northwest China

Shrublands are the main vegetation component in the Gobi region and contribute considerably to its ecosystem. Accurately classifying individual shrub vegetation species to understand their spatial distributions and to effectively monitor species diversity in the Gobi ecosystem is essential. High-resolution remote sensing data create vegetation type inventories over large areas. However, high spectral similarity between shrublands and surrounding areas remains a challenge. In this study, we provide a case study that integrates object-based image analysis (OBIA) and the random forest (RF) model to classify shrubland species automatically. The Gobi region on the southern slope of the Tian Shan Mountains in Northwest China was analyzed using readily available unmanned aerial vehicle (UAV) RGB imagery (1.5 cm spatial resolution). Different spectral and texture index images were derived from UAV RGB images as variables for species classification. Principal component analysis (PCA) extracted features from different types of variable sets (original bands, original bands + spectral indices, and original bands + spectral indices + texture indices). We tested the ability of several non-parametric decision tree models and different types of variable sets to classify shrub species. Moreover, we analyzed three main shrubland areas comprising different shrub species and compared the prediction accuracies of the optimal model in combination with different types of variable sets. We found that the RF model could generate higher accuracy compared with the other two models. The best results were obtained using a combination of the optimal variable set and the RF model with an 88.63% overall accuracy and 0.82 kappa coefficient. Integrating OBIA and RF in the species classification process provides a promising method for automatic mapping of individual shrub species in the Gobi region and can reduce the workload of individual shrub species classification.

Variable selection for estimating individual tree height using genetic algorithm and random forest

Tree height is an important trait in forest science and is highly associated with the site quality from which the trees are measured. However, other factors, such as competition and species interaction, may yield better estimates for individual tree height when taken into account, but these variables have so far been challenging in model fitting. We propose a hybrid approach using genetic algorithms for variables selection and a machine learning algorithm (random forest) for fitting models of individual tree heights. We compare our proposed hybrid method with a mixed-effects model and random forest model using a dataset of 5,608 trees and 189 environmental variables (forest inventory-based variables, soil, topographic, climate, spectral, and geographic) from sites in southeastern Brazil. The tree height models were evaluated using the coefficient of determination, absolute bias, and root means square error (RMSE) based on the validation of dataset performance. The optimal set of variables of the proposed method include the ratio of diameter at breast height to quadratic mean diameter, distance independent competition index, dominant height, the soil silt and boron content. Our findings showed that the proposed hybrid method achieved an accuracy comparable with other methodologies in estimating the total height of the individual trees, and such a modelling approach could have broader applications in forestry and ecological science where a studied response trait has a large number of potential explanatory variables.

Investigation of the Effects of Nanoparticle Concentration and Cutting Parameters on Surface Roughness in MQL Hard Turning Using MoS2 Nanofluid

Minimum quantity lubrication (MQL) has gained significant attention in various research fields and industrial applications for its advantages of being environmentally friendly and suitable for sustainable production. The effectiveness of MQL is increasing significantly by using nano cutting fluid, which can be produced by suspending nanoparticles in the based cutting fluid. This study aims to investigate the effects of MoS2 nanoparticle concentration, cutting speed, and feed rate on MQL hard turning of 90CrSi steel in terms of surface roughness and surface microstructure. The Box–Behnken experimental design was used to analyze the influence of input parameters and their interaction effects as well as to find the optimal set of variables. The obtained results prove the improvement of the machinability of carbide tools due to higher cooling and lubricating performance created by MoS2 nanofluid MQL, which contributes to improve the surface quality and reduce the manufacturing cost. There is an interaction effect between nanoparticle concentration and feed rate which has a strong influence on surface roughness.

Novel Laboratory Index, Based on Fasting Blood Parameters, Accurately Reflects Insulin Sensitivity.

Simple and reliable measurement of insulin sensitivity may be important for the prevention of insulin-resistance-related diseases. Surrogate indices of insulin sensitivity are of limited utility in population without signs of metabolic syndrome. The aim of our study was to provide simple and accurate index of insulin sensitivity. The study group comprised 150 young healthy participants. Hyperinsulinemic-euglycemic clamp was performed. Regression models with different laboratory parameters were constructed. Validation cohort 1 comprised independent group of 110 subjects, including individuals with prediabetes and newly diagnosed type 2 diabetes. Validation cohort 2 comprised 38 obese subjects before and after diet-induced weight loss. Validation cohort 3 comprised 60 nondiabetic subjects from an independent center. The supervised principal component model established optimal set of variables correlated with insulin sensitivity. This model (Fasting Laboratory Assessment of Insulin Sensitivity [FLAIS]) used red blood cell count, alanine aminotransferase activity, serum C-peptide, SHBG, IGF-binding protein 1, and adiponectin concentrations. FLAIS exhibited strong correlation with clamp-derived insulin sensitivity. The sensitivity of the model was 90% and the specificity was 68%. In validation cohort 1, differences in FLAIS among the groups paralleled those observed with the clamp, with the lowest values in prediabetes and diabetes. In validation cohort 2, FLAIS reflected the change in insulin sensitivity after weight loss. The main findings were confirmed in validation cohort 3. We provide simple and accurate method of assessing insulin sensitivity, which allows to identify insulin resistance even in the population without overt metabolic disturbances.

NTCP Modeling of Late Effects for Head and Neck Cancer: A Systematic Review.

NTCP Modeling of Late Effects for Head and Neck Cancer: A Systematic Review.

Optimization by Means of Chemometric Tools of an Ultrasound-Assisted Method for the Extraction of Betacyanins from Red Dragon Fruit (Hylocereus polyrhizus)

Betacyanins have been reported to provide prominent health-promoting effects, in addition to contributing to the exotic color and pleasant palate of red dragon fruit that has been attracting universal interest for over a decade. An accurate determination of betacyanins in dragon fruit is important if we are to benefit from the nutraceutical features of these compounds. Seven betacyanin pigments have been identified and extracted by means of ultrasound-assisted techniques from red dragon fruit. Chemometric tools such as Box–Behnken Design (BBD) in conjunction with response surface methodology (RSM) have been successfully used to evaluate and optimize the relevant extraction variables i.e., temperature (A: 10, 35, 60 °C), solvent composition (B: 20, 50, 80% methanol in water), sample to solvent ratio (C: 0.1:10, 0.2:10, 0.3:10 g/mL), power (D: 20, 45, 70%), and cycle (E: 0.3, 0.5, 0.7 s−1). The results obtained from the analysis of variance (ANOVA) suggest that the solvent composition (p 0.0063), sample to solvent ratio (p 0.0126), and cycle (p 0.0302) are the most influential variables in betacyanin extraction. The optimal variable settings for ultrasound-assisted extraction (UAE) were a 54.6 °C extraction temperature, 0.3 s−1 cycle, 20% ultrasound power, 21.4% methanol in water solvent composition, 0.2:10 sample to solvent ratio, and a 5 min extraction time. The validation of the method for repeatability and intermediate precision provided excellent results at 1.56% and 2.94% respectively. Furthermore, the novel UAE method was successfully used for the determination of betacyanins content in red dragon fruit from different geographic origins.

Optimization of Connecting Rod Design Parameters for External Fixation System: A Biomechanical Study

The role of connecting rod in healing process of a fractured bone has always been of significant importance for surgeons. Adding a connecting rod to the fixator would be a secure option for increasing stability without increasing infection rate. The roles of 4 design parameters of the connecting rod (ie, connecting rod diameter, elevation, material, and configuration) were assessed by using finite element models to calculate axial stiffness and interfragmentary strain at the fracture gap. Taguchi method was used to achieve an optimal design set for maximizing stability with regard to connecting rod variables. Also, analysis of variance (ANOVA) approach was employed to determine contribution percentage of each design parameter on outputs. For optimizing connecting rod design parameters, an optimal set of variables consisting of 11 mm, 40 mm, 200 GPa, and Type 3 external fixator were determined by Taguchi for connecting rod diameter, elevation, modulus of elasticity, and configuration, respectively. However, as Type 3 external fixator stability is a little more than Type 2, it would be better if Type 3 external fixator in Taguchi suggestion be replaced by Type 2 external fixator to be as minimally invasive as possible. Furthermore, ANOVA results revealed that the connecting rod configuration is the most important parameter with 95% and 96% effectiveness on the interfragmentary strain and axial stiffness.