Prediction of the relationship between the mandibular and craniofacial bone and cervical vertebrae based on a genetic algorithm in patients with skeletal class Ⅱ malocclusion / 口腔疾病防治

Objective@#To explore the correlation between the parameters of the mandible and parameters of cervical vertebrae and craniofacial bone in class Ⅱ skeletal patients in Northeast China and to establish correlation equations expressing the relationship between the mandible and cervical vertebrae and craniofacial bone directly and quantitatively for the clinical diagnosis and treatment of orthodontics and orthognathics and for prediction. @*Methods @#The mandible, cranial facial bone and cervical vertebrae of 201 children and adolescents aged 8 to 20 years were measured using digital cranial lateral tablets. All of the cases were divided into male (n=75) and female (n=126) groups using a sensitivity analysis method based on genetic algorithms to select the craniofacial bone and cervical bone with strong sensitivity to mandible parameters and to establish relevant equations. @*Results @#Through sensitivity analysis, the parameters with the strongest correlation between the measured values of the mandible were H4 and SN, those with a strong correlation were SN-Ar, the anterior and posterior high ratio SGo/NGn, the Y axis angle and mandibular angle Ar-Go-Gn. The established equation was as follows: males: Ar-Pg=28.415+1.818×H4+0.746×SN(r2=0.056 8, P < 0.001); females: Ar-Pg=15.168+1.706×H4+0.675×SN+0.31×SN-Ar-0.29×Y axis angle (r2=0.611, P < 0.001). No significant difference was found between the predicted values obtained by the established equations and measured values (P > 0.05). @*Conclusion @#The mandibular length equation established by sensitivity analysis and genetic algorithms is statistically significant and can predict a certain degree of growth and development.

Prediction on vertical craniofacial bone relationship in patients withskeletal class Ⅱ malocclusion based on genetic algorithms method / 吉林大学学报(医学版)

Objective:To establish the quantitative relationship equation of the crantiofacial vertical points in the skeletal classⅡ malocclusion patients with various vertical types by using genetic algorithms method,and to express the measured values in the patients with different gender with the same formula.Methods:A total of 155 skeletal class Ⅱ malocclusion patients without treatment,aged from 10 to 18 years old,were selected and divided into high-angle group(n=50),average-angle group(n=58),low-angle group(n=47);5 samples were randomly selected in each group as the test samples,the rest as the experimental sample.The cephalometic radiographs were performed and measured.The relevant influencing factors of craniofacial structure were ensured.The genetic algorithm was used to optimize the equation parameters to obtain the correlation equation.The error between the predicted value and the measured value was compared.Results:The various parameters had no significant differences between different gender in high-angle,average-angle and low-angle groups(P>0.05);then the men and the women with same type were combined,most of the indicators had statistically significant differences between three groups (P0.05).Conclusion:The quantitative relationship equation of the crantiofacial vertical points in the skeletal class Ⅱmalocclusion patients with various vertical types established with genetic algorithms may show the vertical quantitative relationship and predict the growth to a certain degree.

Analysis on correlation of sagittal craniofacial structures with different classes of malocclusion based on genetic algorithms method / 吉林大学学报(医学版)

Objective:To optimize the parameters of the equation of sagittal craniofacial structures with different classes of malocclusion using genetic algorithms(GAS), and to explore the rules .Methods:A total of 240 patients with average angle malocclusion aged 8-18 years old were divided into three groups: Angle Class Ⅰ(n=79), Angle Class Ⅱ(n=76)and Angle Class Ⅲ(n=85) groups.In each group 10 cases were randomly selected as the test samples, the rest as the experimental samples.The cephalometric analysis was performed on all the patients'' cephalograms, and the results of Ba-N,Ba-A,Ba-S,S-Ptm,Ptm-A,Ba-Ar,Ar-Go,Go-PoG,Ba-PoG and N-S-Ar were analyzed by two independent samples t-test and One-Way ANOVA. The relevant influencing factors of craniofacial structures were found.The parameters of the equation was optimized to obtain the relevant equations using GAS.The predicted values of the optimized equation were compared with the measured values.Results:There were no significant differences in sex between Angle Class Ⅰ, Class Ⅱ and Class Ⅲ groups(P> 0.05);when the men and women with the same type were combined,the Ba-A,Ptm-A,Ar-Go,and Ba-PoG had statistically significant differences between Angle Class Ⅰ, Class Ⅱ, and Class Ⅲ groups (P 0.05), and the error was small.Conclusion: The optimal relation equation of craniofacial structure of sagittal malocclusion is established by GAS with the quantitative regularity.

Growth Characteristics Modeling of Mixed Culture of Bifidobacterium bifidum and Lactobacillus acidophilus using Response Surface Methodology and Artificial Neural Network

Different culture conditions viz. additional carbon and nitrogen content, inoculum size and age, temperature and pH of the mixed culture of Bifidobacterium bifidum and Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted for the cultivations using a Fractional Factorial (FF) design experiments for different variables. This novel concept of combining the optimization and modeling presented different optimal conditions for the mixture of B. bifidum and L. acidophilus growth from their one variable at-a-time (OVAT) optimization study. Through these statistical tools, the product yield (cell mass) of the mixture of B. bifidum and L. acidophilus was increased. Regression coefficients (R2) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.08 and 0.3%, respectively. The optimum conditions for the maximum biomass yield were at temperature 38°C, pH 6.5, inoculum volume 1.60 mL, inoculum age 30 h, carbon content 42.31% (w/v), and nitrogen content 14.20% (w/v). The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Growth characteristics modeling of Lactobacillus acidophilus using RSM and ANN

The culture conditions viz. additional carbon and nitrogen content, inoculum size, age, temperature and pH of Lactobacillus acidophilus were optimized using response surface methodology (RSM) and artificial neural network (ANN). Kinetic growth models were fitted to cultivations from a Box-Behnken Design (BBD) design experiments for different variables. This concept of combining the optimization and modeling presented different optimal conditions for L. acidophilus growth from their original optimization study. Through these statistical tools, the product yield (cell mass) of L. acidophilus was increased. Regression coefficients (R²) of both the statistical tools predicted that ANN was better than RSM and the regression equation was solved with the help of genetic algorithms (GA). The normalized percentage mean squared error obtained from the ANN and RSM models were 0.06 and 0.2%, respectively. The results demonstrated a higher prediction accuracy of ANN compared to RSM.

Prediction of the mesiodistal size of unerupted canines and premolars for a group of Romanian children: a comparative study

Objectives The aim of the present study was to develop an optimization method of multiple linear regression equation (MLRE), using a genetic algorithm to determine a set of coefficients that minimize the prediction error for the sum of permanent premolars and canine dimensions in a group of young people from a central area of Romania represented by a city called Sibiu. Material and Methods To test the proposed method, we used a multiple linear regression equation derived from the estimation method proposed by Mojers, to which we adjusted regression coefficients using the Breeder genetic algorithm. A total of 92 children were selected with complete permanent teeth with no clinically visible dental caries, proximal restorations or orthodontic treatment. A hard dental stone was made for each of these models, which was then measured with a digital calliper. The Dahlberg analyses of variance had been performed to determine the error of method, then the Correlation t Test was applied, and finally the MLRE equations were obtained using the version 16 for Windows of the SPSS program. Results The correlation coefficient of MLRE was between 51-67% and the significance level was set at α=0.05. Comparing predictions provided by the new and respectively old method, we can conclude that the Breeder genetic algorithm is capable of providing the best values for parameters of multiple linear regression equations, and thus our equations are optimized for the best performance. Conclusion The prediction error rates of the optimized equations using the Breeder genetic algorithm are smaller than those provided by the multiple linear regression equations proposed in the recent study. .

METODOLOGÍA PARA DIMENSIONAMIENTO DE MECANISMO POLICÉNTRICO DE RODILLA UTILIZANDO ANÁLISIS DE MARCHA Y ALGORITMOS GENÉTICOS / METHODOLOGY TO GAUGE A FOUR-BAR LINKAGE PROSTHETIC KNEE MECHANISM BASED ON GAIT ANALYSIS AND GENETIC ALGORITHMS

El objetivo de esta investigación es desarrollar una metodología para dimensionar un mecanismo policéntrico de rodilla de 4 barras para máxima estabilidad. Basado en el hecho de que la estabilidad del mecanismo durante la respuesta a la carga depende de la posición del centro instantáneo de rotación (CIR) respecto la fuerza de reacción del piso (FRP) durante la fase de apoyo, se desarrolló una plataforma de cómputo que representa el movimiento real de la pierna, el vector FRP y el mecanismo con su CIR. Para obtener los datos de entrada a la plataforma, se realizó un análisis de marcha a una paciente con amputación transfemoral unilateral, obteniendo la FRP, el ángulo de flexo-extensión de rodilla y la cinemática de los miembros inferiores. Por otra parte, a través de los algoritmos genéticos (AGs), se obtienen las dimensiones y configuración de los eslabones del mecanismo requeridas para iterar con la plataforma en la cual, comparando la ubicación de la FRP respecto al CIR en el plano sagital, se determinan las dimensiones funcionales adecuadas. El mecanismo se dimensionó exitosamente utilizando la metodología desarrollada, garantizando estabilidad de la rodilla después del contacto inicial y flexión voluntaria antes del despegue de punta.

This research was aimed to develop a methodology for establishing the proper dimensions of a four-bar linkage prosthetic knee mechanism for maximum stability. Based on the fact that the stability of a four-bar knee during load-bearing is determined by the location of the instantaneous center of rotation (ICR) with respect to the ground reaction force (GRF) vector, a computational platform was developed to simulate the movement of the leg, the GRF vector and the position of the ICR of the mechanism. On one hand, a gait analysis was carried out on a subject with unilateral transfemoral amputation, from which the GRF, the knee flexion-extension angle and the kinematics of the lower limbs were determined. On the other hand, genetic algorithms (GAs) technique provided the dimensions and mechanism links configuration required to iterate with the platform on which, comparing the location of the GRF and the ICR in the sagittal plane, the functional dimensions of the mechanism were obtained. The polycentric knee mechanism was gauged successfully by ensuring knee stability during the initial contact and load response as well as the ability to initiate voluntary flexion toward late stance before the toe-off.

Growth characteristics modeling of Bifidobacterium bifidum using RSM and ANN

The aim of this work was to optimize the biomass production by Bifidobacterium bifidum 255 using the response surface methodology (RSM) and artificial neural network (ANN) both coupled with GA. To develop the empirical model for the yield of probiotic bacteria, additional carbon and nitrogen content, inoculum size, age, temperature and pH were selected as the parameters. Models were developed using » fractional factorial design (FFD) of the experiments with the selected parameters. The normalized percentage mean squared error obtained from the ANN and RSM models were 0.05 and 0.1 percent, respectively. Regression coefficient (R²) of the ANN model showed higher prediction accuracy compared to that of the RSM model. The empirical yield model (for both ANN and RSM) obtained were utilized as the objective functions to be maximized with the help of genetic algorithm. The optimal conditions for the maximal biomass yield were 37.4 °C, pH 7.09, inoculum volume 1.97 ml, inoculum age 58.58 h, carbon content 41.74 percent (w/v), and nitrogen content 46.23 percent (w/v). The work reported is a novel concept of combining the statistical modeling and evolutionary optimization for an improved yield of cell mass of B. bifidum 255.

Algoritmos Genéticos aplicados a la Ingeniería biomédica / Genetic algorithms applied to Biomedical Engineering

En este trabajo se describe las aplicaciones y alcances del método de los algoritmos genéticos (AG) en la investigación en bioingeniería, mecanobiología y medicina. Para este fin, se ha desarrollado el trabajo sobre tres artículos recientes que describen las aplicaciones de los AG en problemas de ingeniería biomédica. Este trabajo pone de manifiesto la importancia del uso de nuevas metodologías de optimización en las investigaciones biomédicas.

In present paper are described the applications and scope of the genetic algorithms method (GA) in the case of the research in the bioengineering, mechanobiology and medicine. For this aim, the paper on three recent articles was developed describing the applications of the GA in problems related to biomedical engineering. Present paper emphasizes the significance of the use of new methodologies of optimization in the biomedical researches.

Linear versus non-linear noninvasive temperature prediction in a homogeneous medium subjected to physiotherapeutic ultrasound / Comparação entre predição linear e não linear não-invasiva de temperatura num meio homogêneo sujeito a ultra-som de terapia

The lack of accurate time-spatial temperature estimators/predictors conditions the safe application of thermal therapies, such as hyperthermia. In this paper, a comparison between a linear and a non-linear class of models for non-invasive temperature prediction in a homogeneous medium, subjected to ultrasound at physiotherapeutic levels is presented. The linear models used were autoregressive with exogenous inputs (ARX) and the non-linear models were radial basis functions neural networks (RBFNN). In order to create and validate the models, an experiment was build to extract in vitro ultrasound RF-lines, as well as its correspondent temperature values. Then, features were extracted from the measured RF-lines and the models were trained and validated. For both the models, the best-fitted structures were selected using the multi-objective genetic algorithm (MOGA), given the enormous number of possible structures. The best RBFNN model presented a maximum absolute predictive error in the validation set five times less than the value presented by the best ARX model. In this work, the best RBFNN reached a maximum absolute error of 0.42 ºC, which is bellow the value pointed as a borderline between an appropriate and an undesired temperature estimator, which is 0.5 ºC. The average error was one order of magnitude less in the RBFNN case, and a less biased estimation was met. In addition, the best RBFNN needed less environmental information(inputs), given the capacity to non-linearly relate the information. The results obtained are encouraging, considering that coherent results should be obtained in a time-spatial modelling schema using RBFNN models.

A falta de estimadores de temperatura espaço-temporais que sejam precisos impede a aplicação segura das terapias térmicas, como por exemplo a hipertermia. Neste artigo é apresentada uma comparação entre uma classe de modelos lineares e uma classe de modelos não lineares, na predição não invasiva de temperatura num meio homogêneo, quando o mesmo é aquecido por ultra-som em níveis usados em fisioterapia. Os modelos lineares considerados foram do tipo auto-regressivo com entradas exógenas (ARX); a nível não-linear foram considerados redes neuronais RBF (RBFNN). Para treinar e validar os modelos foram recolhidas os ecos provenientes do meio, bem como os correspondentes valores de temperatura. Após a colheita de informação, foram extraídas características dos ecos medidos e posteriormente os modelos foram treinados e validados. Para ambas as classes de modelos, as melhores estruturas foram seleccionadas usando um algoritmo genético multi-objectivo (MOGA), devido ao número elevado de estruturas possíveis. O melhor modelo RBFNN apresentou um erro máximo absoluto cinco vezes inferior ao erro máximo absoluto apresentado pelo melhor modelo ARX. Neste trabalho, o melhor modelo RBFNN apresentou um erro máximo absolutode 0,42 ºC, valor este que é inferior ao limite (0,5 ºC) apresentado como sendo a fronteira entre um estimador desejado e um estimador indesejado. O erro médio cometido pelo melhor modelo neuronal é uma ordem de grandeza inferior ao erro médio apresentado pelo melhor modelo linear, obtendo-se deste modo uma estimação menos enviesada no caso das redes neuronais, com menos informação do ambiente (menos entradas) devido ao processamento não-linear dos dados de entrada. Os resultados obtidos são encorajadores, apontando no sentido de se obter bons resultados numa estimação espaço-temporal.

Prediction of mesiodistal crown diameters of canine based on genetic algorithms method / 吉林大学学报(医学版)

Objective To establish an equation between canine and other permanent teeth,and probe a new method to predict the sum of the mesiodistal diameters of unerupted permanent canines.Methods Samples consisted of 118 dental casts were obtained from Chinese patients(50 males,68 females,respectively).Mesiodistal tooth diameters were measured by a vernier caliper.GAS was used to establish the equation between canine and other permanent teeth,and compared with stepwise regression analysis.Those were modelling samples that optimized equations coefficient(112 samples).Others were inspection samples(6 samples).Results The equation between canine and other permanent teeth was established.MU=0.0511U1+0.2164U2+0.2780U4+0.0407U5+0.2853U6+0.8321①;ML=0.2998U1+0.1294U2+0.3912U4+0.0088U5+0.0791U6+0.9839②;FU=0.1419U1+0.1741U2+0.3258U4+0.0412U5+0.093U6+1.7355③;FL=0.2796U1+0.3750U2+0.2968U4+0.0268U5+0.0043U6+0.6030④.Compared with stepwise regression analysis,the mean error of precision by GAS was less(P

Application of genetic algorithms to construction of immune-based detector / 医疗卫生装备

In recent years,immune-based intrusion detection has become a key research in computer security area,which has opened up a new conjecture for computer security.Genetic algorithms,which simulates the natural evolution,can optimize the searching process.Based on natural immune theory,this research simulates natural cognition and memory mechanism to form and analyze an algorithm of constructing detector.