Finite element analysis method and optimization of flexible ginger moxibustion based on Abaqus and Taguchi method / 生物医学工程学杂志

Ginger moxibustion has the effect of regulating zang-fu organs and activating qi and blood circulation. When used, ginger paste is required to be close to human skin. Currently, the ginger box used clinically in the hospital can't meet the requirement of large area fitting human skin, and the efficacy of ginger moxibustion is significantly reduced. In this study, a flexible ginger paste box was proposed, which was composed of flexible components polydimethylsiloxane (PDMS), spring and wire netting. The large flexibility of the structure made it fit well with human skin. Finite element method was used to study the fitting degree between ginger paste box and waist soft tissue. Finite element models of flexible ginger paste box and waist soft tissue were established based on Hypermesh and Abaqus software. The equivalent contact area between the flexible ginger paste box and waist was obtained by numerical simulation under different PDMS unilateral thickness, spring wire diameter, wire netting diameter and ginger paste layer thickness. The four parameters were taken as the influencing factors, and the equivalent contact area was taken as the optimization objective. The typical value analysis and variance analysis of S/N were performed by Taguchi method, and the results showed that among the four influencing factors, the wire netting diameter had the largest influence on equivalent contact area and its contribution rate reached 41.98%. The contribution rates of PDMS unilateral thickness, spring wire diameter and ginger paste layer thickness reached 36.48%, 13.97% and 6.50%, respectively. The optimized PDMS unilateral thickness, spring wire diameter, wire netting diameter and ginger paste layer thickness were 1.5, 0.4, 0.15, 35 mm, respectively, and the equivalent contact area was 95.60 cm 2. The optimized flexible ginger paste box with great fitting performance can improve the effect of ginger moxibustion.

Comportamiento ante el desgaste por deslizamiento en seco del acero inoxidable súper dúplex en un tribómetro bola sobre anillo / Dry sliding wear behavior of super duplex stainless steel in a ball on ring tribometer

RESUMEN En el presente trabajo se realiza la caracterización del comportamiento ante el desgaste por deslizamiento en seco de un acero inoxidable súper dúplex. Los ensayos fueron desarrollados en un tribómetro tipo bola sobre anillo. Como material del anillo se empleó el acero inoxidable dúplex tipo SAF 2507 sin tratamiento térmico y como material para la bola se usó el acero AISI 52100. Los ensayos se realizaron sin lubricante en condiciones de ambiente (aire), temperatura y humedad estándar de laboratorio. Los parámetros seleccionados, a fin de estudiar sus efectos en el coeficiente desgaste por deslizamiento, fueron: velocidad de deslizamiento (0,9 m/s y 2 m/s), carga normal (9 N, 19 N y 29 N) y distancias de deslizamiento (500 m, 1000 m y 2000 m). Se empleó un diseño experimental de Taguchi con nueve tratamientos y dos réplicas. En la caracterización del acero SAF 2507 se obtuvo valores del coeficiente de desgaste en el intervalo desde 0,19588 x 1012 m2/N hasta 0,72381 x 1012 m2/N, para las condiciones evaluadas. El factor que más afecta el coeficiente de desgaste es la velocidad de deslizamiento. El mecanismo de desgaste identificado para el SAF 2507 es de adhesión y delaminación de alta velocidad.

ABSTRACT In this paper the characterization of the behavior during dry sliding wear of a super duplex stainless steel was performed. The tests were developed in a ball on ring tribometer type. As material of the ring is used the duplex stainless steel type SAF 2507 without heat treatment and as material for the ball is used the steel AISI 52100. Tests were conducted without lubrication in ambient conditions (air), temperature and humidity laboratory standard was used. The parameters selected in order to study its effects on sliding wear coefficient were: sliding speed (0.9 m/s and 2 m/s), normal load (9 N, 19 N and 29 N) and distances slip (500 m, 1000 m and 2000 m). Taguchi experimental design with nine treatments and two replicates was used. In the characterization of steel SAF 2507 wear coefficient values was obtained in the range from 0.19588 x 10-12 m2/N to 0.72381 x 10-12 m2/N, for the conditions tested. The factor that most affects the wear coefficient is the sliding velocity. The wear mechanism identified for the SAF 2507 was adhesion and high speed delamination.

Optimum synthesis of CuO nanoparticles with the highest antifungal activity against oral pathogen Candida albicans

This research was aimed to optimize the synthesis of copper oxide (CuO) nanoparticles with the highest antifungalproperties against Candida albicans as an oral fungal pathogen. To this end, nine experiments involving differentsynthesis conditions were designed using the Taguchi method and the copper oxide nanoparticles synthesized bycoprecipitation method. The antifungal activity of synthesized nanoparticles against C. albicans was evaluatedusing the colony forming unit and disk diffusion methods. According to the results, the synthesized copper oxidenanoparticles under the five experimental conditions (CuCl2 0.1 M, NaOH 0.1 M, and a 75 minutes stirring time)showed the highest antifungal activity against C. albicans (71.72%). The optimization results demonstrated thatall three studied factors were effective in improving the antifungal activity of copper oxide nanoparticles and theantifungal activity in the proposed conditions can be improved by 77.85%. The synthesis of nanoparticles in optimalconditions confirmed the improved antifungal activity of the nanoparticles. The results of this study proved that CuOnanoparticles have a potential ability as an antifungal agent against oral fungal pathogens of C. albicans.

Improvement of anther cultures conditions using the Taguchi method in three cereal crops

Background: Plant tissue cultures have the potential to reprogram the development of microspores from normal gametophytic to sporophytic pathway resulting in the formation of androgenic embryos. The efficiency of this process depends on the genotype, media composition and external conditions. However, this process frequently results in the regeneration of albino instead of green plants. Successful regeneration of green plants is affected by the concentration of copper sulfate (CuSO4) and silver nitrate (AgNO3) and the length of induction step. In this study, we aimed at concurrent optimization of these three factors in barley (Hordeum vulgare L.), wheat (Triticum aestivum L.), and triticale (x Triticosecale spp. Wittmack ex A. Camus 1927) using the Taguchi method. We evaluated uniform donor plants under varying experimental conditions of in vitro anther culture using the Taguchi approach, and verified the optimized conditions. Results: Optimization of the regeneration conditions resulted in an increase in the number of green regenerants compared with the control. Statistic Taguchi method for optimization of the in vitro tissue culture plant regeneration via anther cultures allowed reduction of the number of experimental designs from 27 needed if full factorial analysis is used to 9. With the increase in the number of green regenerants, the number of spontaneous doubled haploids decreased. Moreover, in barley and triticale, the number of albino regenerants was reduced. Conclusion: The statistic Taguchi approach could be successfully used for various factors (here components of induction media, time of incubation on induction media) at a one time, that may impact on cereals anther cultures to improve the regeneration efficiency

Methodology to Weight Evaluation Areas from Autism Spectrum Disorder ADOS-G Test with Artificial Neural Networks and Taguchi Method / Metodología para ponderar áreas de evaluación de la herramienta ADOS-G en trastorno del espectro autista con redes neuronales artificiales y método de Taguchi

Autism diagnosis requires validated diagnostic tools employed by mental health professionals with expertise in autism spectrum disorders. This conventionally requires lengthy information processing and technical understanding of each of the areas evaluated in the tools. Classifying the impact of these areas and proposing a system that can aid experts in the diagnosis is a complex task. This paper presents the methodology used to find the most significant items from the ADOS-G tool to detect Autism Spectrum Disorders through Feed-forward Artificial Neural Networks with back-propagation training. The number of cases for the network training data was determined by using the Taguchi method with Orthogonal Arrays reducing the sample size from 531,441 to only 27. The trained network provides an accuracy of 100% with 11 different cases used only for validation, which provides a specificity and sensitivity of 1. The network was used to classify the 12 items from the ADOS-G tool algorithm into three levels of impact for Autism diagnosis: High, Medium and Low. It was found that the items "Showing", "Shared enjoyment in Interaction" and "Frequency of vocalization directed to others", are the areas of highest impact for Autism diagnosis. The methodology here presented can be replicated to different Autism diagnosis tests to classify their impact areas as well.

El diagnóstico del autismo requiere del uso de herramientas de diagnóstico validadas internacionalmente que son utilizadas por los profesionales de la salud expertos en trastornos del espectro autista, lo cual requiere de procesamiento de mucha información y un entendimiento técnico de cada una de las áreas evaluadas en ellas. La clasificación del impacto que tienen cada una de estas áreas, así como la propuesta de un sistema que pueda ayudar a los expertos en el diagnóstico, es una tarea compleja, por lo que en este artículo se presenta una metodología utilizada para encontrar los elementos más significativos de la herramienta de diagnóstico de autismo ADOS-G a través de redes neuronales artificiales entrenadas con retropropagación del error. El número de casos para entrenamiento de la red se seleccionó utilizando el método de Taguchi con arreglos ortogonales, reduciendo el tamaño de la muestra de 531,441 a solo 27 casos. La red entrenada tiene una exactitud del 100% validada con 11 casos diferentes de niños evaluados para diagnóstico de trastorno del espectro autista con lo que se obtuvo una especificidad y sensibilidad de 1. La red neuronal artificial se utilizó para clasificar los 12 elementos del algoritmo de la herramienta ADOS-G en tres niveles de impacto: Alto, Medio y Bajo. Se encontró que los elementos "Mostrar", "Placer compartido durante la interacción" y "Frecuencia de vocalizaciones dirigidas a otros" son las áreas de mayor impacto para el diagnóstico de autismo. La metodología presentada puede ser replicada para diferentes herramientas de diagnóstico de autismo para clasificar sus áreas de mayor impacto también.