Lightweight materials are in constant progress due to the new requirements of mobility. At the same time, it is mandatory to meet the internal standards of the original equipment manufacturers to guarantee product quality, and market regulations are necessary to reduce or eliminate pollution emissions. In order to reach these technical requirements, the design is optimized, and new materials and alloys are evaluated. The search for these new types of materials is long and expensive. For this search, new technologies have emerged, such as integrated computational materials engineering, which is a valuable tool to forecast through simulation alloy characteristics that meet specific requirements without fabrication. This research develops an artificial neural network to establish the chemical composition of a new aluminum alloy based on the desired manufacturing characteristics as well as fatigue strength. For this, the proposed artificial neural network was trained with the chemical composition of preexisting aluminum-based alloys and the resulting desired mechanical properties. The significant contribution of the proposed research consists not only of the neural network high-performance forecasting but also the fact that for to train and validate it, not only simulations of its responses to the different possibilities of alloys were tried but also validated through an experimental laboratory test performed by uniaxial machine. The proposed artificial neural network results show an average correlation of 99.33% between its forecasting and laboratory testing.

In the field of casting aluminum alloys, integrated computational materials engineering (ICME) is a relatively emerging trend. Through mathematical modeling, new alloys that meet the requirements of the industry can be generated. However, the development of a new alloy with particular attributes is still an enormous challenge; testing the individual possible combinations is numerous. There are several dozen different metals, and even if the alloys are binary, there are many alternatives. This makes ICME a suitable and practical tool to predict the behavior of new alloys. In this paper, the presented model when used to characterize the behavior of alloys shows a reliability level superior to 90%. The mathematical description of the model is also presented. It can be used as a reliable tool for the selection of the desired elements in its composition letting the selection of the most important properties according to the industrial needs and standards.

Body composition assessment using instruments such as dual X-ray densitometry (DXA) can be complex and their use is often limited to research. This cross-sectional study aimed to develop and validate a densitometric method for fat mass (FM) estimation using 3D cameras. Using two such cameras, stereographic images, and a mesh reconstruction algorithm, 3D models were obtained. The FM estimations were compared using DXA as a reference. In total, 28 adults, with a mean BMI of 24.5 (±3.7) kg/m² and mean FM (by DXA) of 19.6 (±5.8) kg, were enrolled. The intraclass correlation coefficient (ICC) for body volume (BV) was 0.98–0.99 (95% CI, 0.97–0.99) for intra-observer and 0.98 (95% CI, 0.96–0.99) for inter-observer reliability. The coefficient of variation for kinetic BV was 0.20 and the mean difference (bias) for BV (liter) between Bod Pod and Kinect was 0.16 (95% CI, −1.2 to 1.6), while the limits of agreement (LoA) were 7.1 to −7.5 L. The mean bias for FM (kg) between DXA and Kinect was −0.29 (95% CI, −2.7 to 2.1), and the LoA was 12.1 to −12.7 kg. The adjusted R² obtained using an FM regression model was 0.86. The measurements of this 3D camera-based system aligned with the reference measurements, showing the system’s feasibility as a simpler, more economical screening tool than current systems.

A inicios del nuevo milenio el desarrollo económico en favor de mejorar el nivel de vida de los habitantes del mundo continúa siendo un desafío complejo, por lo que ha llevado al entorno empresarial a reinventarse, a través de la generación de nuevos conocimientos, por medio de actividades de Investigación y Desarrollo (I+D), que han tenido como resultado innovaciones que permiten impactar dentro de los mercados regionales, nacionales e internacionales. Dentro de este contexto, la innovación tecnológica y la actividad creadora constituyen fuentes fundamentales de progreso y crecimiento, y, por lo tanto, la propiedad intelectual ha venido adquiriendo cada vez más importancia, es por cuanto que su protección se ha convertido en una herramienta para promover la creación del conocimiento y su respectiva transferencia internacional. Bajo la premisa anterior las figuras de protección de la propiedad industrial como son marcas, patentes, modelos de utilidad y diseños industriales toman una relevancia sustancial, ya que son mecanismos que aseguran la protección de las innovaciones y su explotación económica, dando como resultado el desarrollo y el crecimiento económico de los países que a su vez se ven favorecidos dentro de la balanza económica. Esta investigación se centra en el desarrollo de un modelo de pronóstico lineal que ayude a evaluar el impacto directo que tienen las figuras de la protección industrial con el crecimiento y desarrollo económico de países como Alemania, argentina, Brasil, China, España, Estados Unidos de América, Francia, India, Japón, agregando a México para compararlo con la actividad internacional.