Artificial intelligence (AI) systems to generate images from natural language are a game changer in graphic design. However, in visual persuasive communication, the image is not just a drawing, but is the message itself. It arises from the need for understanding how AI systems could be adopted in the design of visual communication. Thus, this article presents a model that describes the theoretical–methodological factors present in the design of persuasive visual discourses, which arises from a qualitative study with an analysis of grounded theory from the opinion of experts in the essential areas for visual communication. Thus, we propose a model to guide the design process of persuasive messages throughout seven stages. Additionally, an analysis for contributing to the orderly and informed incorporation of technologies, techniques, and methods relevant to each stage is presented.

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.

Nanofibers, which are formed by the electrospinning process, are used in a variety of applications. For this purpose, a specific diameter suited for each application is required, which is achieved by varying a set of parameters. This parameter adjustment process is empirical and works by trial and error, causing high input costs and wasting time and financial resources. In this work, an artificial neural network model is presented to predict the diameter of polyethylene nanofibers, based on the adjustment of 15 parameters. The model was trained from 105 records from data obtained from the literature and was then validated with nine nanofibers that were obtained and measured in the laboratory. The average error between the actual results was 2.29%. This result differs from those taken in an evaluation of the dataset. Therefore, the importance of increasing the dataset and the validation using independent data is highlighted.

The optimization is essential in the engineering area and, in conjunction with use of meta-heuristics, has had a great impact in recent years; this is because of its great precision in search of optimal parameters for the solution of problems. In this work, the use of the Artificial Bee Colony Algorithm (ABC) is presented to optimize the values for the variables of a proportional integral controller (PI) to observe the behavior of the controller with the optimized T_i and K_p values. It is proposed using a robot built using the MINDSTORMS version EV3 kit. The objective of this work is to demonstrate the improvement and efficiency of the controllers in conjunction with optimization meta-heuristics. In the results section, we observe that the results improve considerably compared to traditional methods. In this work, the main contribution is the implementation of an optimization algorithm (ABC) applied to a controller (PI), and the results are tested to control the movement of a robot. There are many papers where the kit is used in various domains such as education as well as research for science and technology tasks and some real-world problems by engineering scholars, showing the acceptable result.

La Trombosis Venosa Profunda (TVP) es una manifestación de una Enfermedad Tromboembólica (ET). Cuando en una TVP los trombos venosos se desprenden y viajan a través del torrente sanguíneo pueden ocasionar una Trombo Embolia Pulmonar (TEP). La existencia de Trombosis Venosa Profunda (TVP) en las extremidades inferiores se ha descrito como uno de los principales factores de riesgo para el desarrollo de la TEP. Se considera que hasta el 90% de los émbolos pulmonares proceden de trombos venosos de las extremidades inferiores. Las técnicas más utilizadas para la detección de TVP son los modelos de probabilidad clínica, el dímero D y las pruebas de imagen no invasivas, como la ecografía para la TVP y la angiotomografía computadorizada (TC) para el embolismo pulmonar. Sin embargo, debido a la inespecificidad de los síntomas de la TVP, el umbral para ordenar una ecografía es bajo, además de ser un proceso complicado que requiere la participación de un médico especialista para su interpretación. En las últimas décadas el aprendizaje automático ha surgido como apoyo en la toma de decisiones para el diagnóstico de diversas enfermedades, algunas de las tecnologías más utilizadas en el campo de la medicina incluyen Support Vector Machine (SVM), Árboles de decisión y las Redes Neuronales Artificiales (RNA). En el presente artículo se hace una revisión de las tecnologías existentes para la detección de la TVP así como de los principales algoritmos de aprendizaje automático comúnmente utilizados en aplicaciones biomédicas; se propone el diseño de un sistema computarizado que utilice técnicas de aprendizaje automático como herramienta de apoyo para la detección oportuna de un posible padecimiento de TVP.

Informal communication is a necessary resource to accomplish activities in hospital work. It is used for collaboration and task coordination, as well as to gather knowledge and human resources necessary for patient care. Results of a field study that we conducted in a hospital show that most informal interactions happen face to face due to opportunistic encounters; a significant amount of these interactions involve the use of information artifacts - such electronic health record, lab results among others - on discussions about patient care. However, some of these interactions present some breakdowns. For this reason, we present the concept of on-the-move collaborative environment, a design concept that provides pervasive support for informal colocated collaboration through the interaction of a set of electronic services and heterogeneous devices. Finally, we present our realization of an OCE based system through the implementation of a prototype that provides four main services that allow users to access persons, artifacts and devices every time/every where.