Soft Skills: A Comparative Analysis Between Online and Classroom Teaching

Currently the Spanish universities are making a great effort to effectively incorporate the development and assessment of generic skills in their training programs. Information and communications technologies (ICT) offer a wide range of possibilities but create uncertainty among teachers about the process and results. It is considered of interest to conduct a study to analyze the extent to which social skills like commitment, communication and teamwork are acquired by students and teachers. It seeks to ascertain the influence of the learning context, online or classroom training, in the development of these personal skills among the participants in the sample. For this study two universities have been chosen, Universidad a Distancia de Madrid (UDIMA) offering online training environment, and Universidad Politécnica de Madrid (UPM) with classroom training modality. A total of 257 individuals, 230 students and 27 teachers have answered the survey called Evalsoft. This instrument was designed in the project with the same name by a research team from Universidad Complutense of Madrid (UCM). Some interesting conclusions can be highlighted: it is in the online context where there are higher levels of commitment and teamwork than in the classroom modality; teachers have higher social skills that students and these improve with age. Sex and the training program appear to influence these social skills.

Age regression from soft aligned face images using low computational resources

The initial step in most facial age estimation systems consists of accurately aligning a model to the output of a face detector (e.g. an Active Appearance Model). This fitting process is very expensive in terms of computational resources and prone to get stuck in local minima. This makes it impractical for analysing faces in resource limited computing devices. In this paper we build a face age regressor that is able to work directly on faces cropped using a state-of-the-art face detector. Our procedure uses K nearest neighbours (K-NN) regression with a metric based on a properly tuned Fisher Linear Discriminant Analysis (LDA) projection matrix. On FG-NET we achieve a state-of-the-art Mean Absolute Error (MAE) of 5.72 years with manually aligned faces. Using face images cropped by a face detector we get a MAE of 6.87 years in the same database. Moreover, most of the algorithms presented in the literature have been evaluated on single database experiments and therefore, they report optimistically biased results. In our cross-database experiments we get a MAE of roughly 12 years, which would be the expected performance in a real world application.

A regularised continuum damage model based on the mesoscopic scale for soft tissue

Material properties of soft fibrous tissues are highly conditioned by the hierarchical structure of this kind of composites. Collagen based tissues present, at decreasing length scales, a complex framework of fibres, fibrils, tropocollagen molecules and amino-acids. Understanding the mechanical behaviour at nano-scale level is critical to accurately incorporate this structural information in phenomenological damage models. In this work we derive a relationship between the mechanical and geometrical properties of the fibril constituents and the soft tissue material parameters at macroscopic scale. A Hodge–Petruska two-dimensional model has been used to describe the fibrils as staggered arrays of tropocollagen molecules. After a mechanical characterisation of each of the fibril components, two fibril failures modes have been defined related with two planes of weakness. A phenomenological continuous damage model with regularised softening was presented along with meso-structurally based definitions for its material parameters. Finally, numerical analysis at fibril, fibre and tissue levels are presented to show the capabilities of the model