The lack of sunlight on mountain roads in the wintertime leads to an increase in accidents. In this paper, a methodology is presented for the use of sunny and shady areas to be included as a parameter in road design. The inclusion of this parameter allows for the design of an iterative method for the projected infrastructures. The parameterization of the road layout facilitates the possibility of applying an iterative process of modifying the geometric elements that constitute it, examining different layout alternatives until a layout is achieved in which the surface area in the shady area is minimized, increasing the road safety and minimizing environmental impact. The methodology has been defined, generating and analyzing the results of the solar lighting study using a file in IFC format capable of integrating with the rest of the design elements (platform, signaling, structures, etc.) and thus obtaining a BIM format which allows the model to be viewed in three dimensions and moves towards 4D and 5D. The model used for the study was a high mountain road located in the province of Teruel (Spain). It is a road section characterized by successive curves in which several traffic accidents have occurred due to running off the road, partly because of the presence of ice on the platform.

The number of infectious spots or pathological structures recorded on dermatological images is a tool to aid in the diagnosis and monitoring of disease progression. Dermatological images for the detection and monitoring of the evolution of acne infections are evaluated globally, comparing whether the increase or decrease in infectious lesions appearing on an image is significant. This evaluation method is only indicative since its accuracy is low. The accuracy problem could be improved by an exact count of the number of structures and spots appearing on the image. The mathematical function circular Hough transform (CHT) function implemented in MATLAB is here applied to develop a procedure for counting these structures. CHT has been used in the recognition of benign and distorted red blood cells, in the detection of pellet sizes in industrial processes and in the automated detection and morphological characterization of breast tumor masses from infrared images, as well as for the detection of brain aneurysms and use in magnetic resonance imaging. The sensitivity factor is one of the many parameters required to feed the CHT algorithm. Its choice is unclear as there is no proper methodology to select an optimum value suitable for each image. In this work, a procedure for determining the optimal value of the sensitivity factor is proposed The approach is validated by comparison with the results of the manual counting of the points (ground truth).

Acne vulgaris is an inflammatory chronic disease of pilosebaceous units-a pilosebaceous unit is formed by all the hair follicles related to the same sebaceous gland. The main affected regions are face, neck, chest and back. Clinical manifestations are seborrhoea or overproduction of sebum, the presence of non-inflammatory and inflammatory lesions on skin (open and closed commedos, and pustules and papules, respectively), and scars. 1 It is the most common dermatological pathology worldwide. 2 85% of adolescents suffer from acne. 3 In order to monitor and treat acne properly, a precise and reliable method to establish acne severity is needed. 4 Currently, there is a wide range of acne grading systems, which shows the lack of a global standard. These systems can be divided into two groups: those based on manual lesion counting and the ones that use a model photography. 3,4 Several studies have proven lesion counting to be an objective and reliable method. 4,5

Background: Acne vulgaris is one of the most common human pathologies worldwide. Its prevalence causes a high healthcare expenditure. Acne healthcare costs and effects on individuals' quality of life lead to the need of analysing current acne evaluation, treatment and monitoring methods. One of the most common ones is manual lesion counting by a dermatologist. However, this technique has several limitations, such as time spent. That is the reason why the development of new computer-assisted techniques is needed in order to automatically count the acne lesions. Materials and methods:Using the fluorescence images, a segmentation algorithm is implemented in MATLAB.Results: A new counting tool has been obtained that provides a form of objective evaluation of acne vulgaris disease. The effectiveness of the application of the segmentation method is more than 90%, being valid for the follow-up and diagnosis of injuries. Conclusion:Automated counting of acne lesions has been proposed to solve current limitations of evaluation and monitoring methods for acne vulgaris. It is clear that the use of machine learning algorithms such as k-means enables clinicians to objectively and quickly evaluate the severity of acne.

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