Photoplethysmographic Waveform in Hyperbaric Environment

The objective of this work is the identification of significant variations of morphological parameters of the photoplethysmographic (PPG) signal when the subjects are exposed to an increase in atmospheric pressure. To achieve this goal, the PPG signal of 26 subjects, exposed to a hyperbaric environment whose pressure increases up to 5 atm, has been recorded. From this record, segments of 4 minutes have been processed at 1 atm, 3 atm and 5 atm, both in the descending (D) and ascending (A) periods of the immersion. In total, four states (3D, 5, 3A and 1A) normalized to the basal state (1D) have been considered. In these segments, six morphological parameters of the PPG signal were studied. The width, the amplitude, the widths of the anacrotic and catacrotic phases, and the upward and downward slopes of each PPG pulse were extracted. The results showed significant increases in the three parameters related to the pulse width. This increase is significant in the four states analysed for the anacrotic phase width. Furthermore, a significant decrease in the amplitude and in both slopes (in the states 1A) was observed. These results show that the PPG width responds rapidly to the increase in pressure, indicating an activation of the sympathetic system, while amplitude and pulse slopes are decreased when the subjects are exposed to the hyperbaric environment for a considerable period of time

Using Data-mining Techniques for the prediction of the severity of road crashes in Cartagena, Colombia

Objective: Analyze the road crashes in Cartagena (Colombia) and the factors associated with the collision and severity. The aim is to establish a set of rules for defining countermeasures to improve road safety. Methods: Data mining and machine learning techniques were used in 7894 traffic accidents from 2016 to 2017. The severity was determined between low (84%) and high (16%). Five classification algorithms to predict the accident severity were applied with WEKA Software (Waikato Environment for Knowledge Analysis). Including Decision Tree (DT-J48), Rule Induction (PART), Support Vector Machines (SVMs), Naïve Bayes (NB), and Multilayer Perceptron (MLP). The effectiveness of each algorithm was implemented using cross-validation with 10-fold. Decision rules were defined from the results of the different methods. Results: The methods applied are consistent and similar in the overall results of precision, accuracy, recall, and area under the ROC curve. Conclusions: 12 decision rules were defined based on the methods applied. The rules defined show motorcyclists, cyclists, including pedestrians, as the most vulnerable road users. Men and women motorcyclists between 20–39 years are prone in accidents with high severity. When a motorcycle or cyclist is not involved in the accident, the probable severity is low

On the use of Process Mining and Machine Learning to support decision making in systems design

Research on process mining and machine learning techniques has recently received a significant amount of attention by product development and management communities. Indeed, these techniques allow both an automatic process and activity discovery and thus are high added value services that help reusing knowledge to support decision-making. This paper proposes a double layer framework aiming to identify the most significant process patterns to be executed depending on the design context. Simultaneously, it proposes the most significant parameters for each activity of the considered process pattern. The framework is applied on a specific design example and is partially implemented.FUI GONTRAN

Presencia de Ischnomera xanthoderes (Mulsant, 1858) (Coleoptera: Oedemeridae) en la provincia de Huelva (Suroeste de Andalucía, España)

Ischnomera xanthoderes (Mulsant, 1858) is recorded for the first time from Huelva province. It is the fourth Andalusian province where this species occurs.Se presenta la primera cita para la provincia de Huelva de Ischnomera xanthoderes (Mulsant, 1858), siendo la cuarta provincia andaluza que la contempla