Tensión superficial de las mezclas O2-Ar, N2-Ar y O2-N2-Ar

articulo de investigacionThe surface tension of some binary and ternary mixtures was calculated by means of molecular dynamics simulations in a canonical set. The analyzed mixtures were oxygen-argon, nitrogen-argon and oxygen-nitrogenargon. The force field for argon was recalculated in order to reproduce the experimental surface tension. The corresponding force fields for O2 and N2 were taken from a previous work [Mol. Simul. 45 (2019) 958-966], where it was shown that such force fields reproduce the experimental surface tension curves, as pure fluids. The nitrogen-argon surface tension was calculated for several mole fractions of argon. The obtained curve was compared with those experimental data and a good agreement was found. The standard Lorentz-Berthelot combining rules were employed. For the oxygen-argon mixture it was necessary to modify the cross term of the combining rules in order to reproduce theoretical and experimental data. The surface tension of the ternary mixture was also estimated varying the mole fraction of argon at a certain concentration of oxygen and nitrogen, previously adjusted. Several temperatures were used in order to show a tendency mostly at relatively low temperatures. After comparing the available experimental data, which are scarce, a good agreement was observed

Combining mobile-health (mHealth) and artificial intelligence (AI) methods to avoid suicide attempts: the Smartcrises study protocol

The screening of digital footprint for clinical purposes relies on the capacity of wearable technologies to collect data and extract relevant information’s for patient management. Artificial intelligence (AI) techniques allow processing of real-time observational information and continuously learning from data to build understanding. We designed a system able to get clinical sense from digital footprints based on the smartphone’s native sensors and advanced machine learning and signal processing techniques in order to identify suicide risk. Method/design: The Smartcrisis study is a cross-national comparative study. The study goal is to determine the relationship between suicide risk and changes in sleep quality and disturbed appetite. Outpatients from the Hospital Fundación Jiménez Díaz Psychiatry Department (Madrid, Spain) and the University Hospital of Nimes (France) will be proposed to participate to the study. Two smartphone applications and a wearable armband will be used to capture the data. In the intervention group, a smartphone application (MEmind) will allow for the ecological momentary assessment (EMA) data capture related with sleep, appetite and suicide ideations. Discussion: Some concerns regarding data security might be raised. Our system complies with the highest level of security regarding patients’ data. Several important ethical considerations related to EMA method must also be considered. EMA methods entails a non-negligible time commitment on behalf of the participants. EMA rely on daily, or sometimes more frequent, Smartphone notifications. Furthermore, recording participants’ daily experiences in a continuous manner is an integral part of EMA. This approach may be significantly more than asking a participant to complete a retrospective questionnaire but also more accurate in terms of symptoms monitoring. Overall, we believe that Smartcrises could participate to a paradigm shift from the traditional identification of risks factors to personalized prevention strategies tailored to characteristics for each patientThis study was partly funded by Fundación Jiménez Díaz Hospital, Instituto de Salud Carlos III (PI16/01852), Delegación del Gobierno para el Plan Nacional de Drogas (20151073), American Foundation for Suicide Prevention (AFSP) (LSRG-1-005-16), the Madrid Regional Government (B2017/BMD-3740 AGES-CM 2CM; Y2018/TCS-4705 PRACTICO-CM) and Structural Funds of the European Union. MINECO/FEDER (‘ADVENTURE’, id. TEC2015–69868-C2–1-R) and MCIU Explora Grant ‘aMBITION’ (id. TEC2017–92552-EXP), the French Embassy in Madrid, Spain, The foundation de l’avenir, and the Fondation de France. The work of D. Ramírez and A. Artés-Rodríguez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013–41718-R), AID (TEC2014–62194-EXP) and the COMONSENS Network (TEC2015–69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015– 69868-C2–1-R) and CAIMAN (TEC2017–86921-C2–2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of P. Moreno-Muñoz has been supported by FPI grant BES-2016-07762

A Novel Prediction Method for Early Recognition of Global Human Behaviour in Image Sequences

Human behaviour recognition has been, and still remains, a challenging problem that involves different areas of computational intelligence. The automated understanding of people activities from video sequences is an open research topic in which the computer vision and pattern recognition areas have made big efforts. In this paper, the problem is studied from a prediction point of view. We propose a novel method able to early detect behaviour using a small portion of the input, in addition to the capabilities of it to predict behaviour from new inputs. Specifically, we propose a predictive method based on a simple representation of trajectories of a person in the scene which allows a high level understanding of the global human behaviour. The representation of the trajectory is used as a descriptor of the activity of the individual. The descriptors are used as a cue of a classification stage for pattern recognition purposes. Classifiers are trained using the trajectory representation of the complete sequence. However, partial sequences are processed to evaluate the early prediction capabilities having a specific observation time of the scene. The experiments have been carried out using the three different dataset of the CAVIAR database taken into account the behaviour of an individual. Additionally, different classic classifiers have been used for experimentation in order to evaluate the robustness of the proposal. Results confirm the high accuracy of the proposal on the early recognition of people behaviours.This work was supported in part by the University of Alicante, Valencian Government and Spanish government under grants GRE11-01, GV/2013/005 and DPI2013-40534-R

Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC

Plane model extraction from three-dimensional point clouds is a necessary step in many different applications such as planar object reconstruction, indoor mapping and indoor localization. Different RANdom SAmple Consensus (RANSAC)-based methods have been proposed for this purpose in recent years. In this study, we propose a novel method-based on RANSAC called Multiplane Model Estimation, which can estimate multiple plane models simultaneously from a noisy point cloud using the knowledge extracted from a scene (or an object) in order to reconstruct it accurately. This method comprises two steps: first, it clusters the data into planar faces that preserve some constraints defined by knowledge related to the object (e.g., the angles between faces); and second, the models of the planes are estimated based on these data using a novel multi-constraint RANSAC. We performed experiments in the clustering and RANSAC stages, which showed that the proposed method performed better than state-of-the-art methods

Visual analysis of fatigue in Industry 4.0

The performance of manufacturing operations relies heavily on the operators’ performance. When operators begin to exhibit signs of fatigue, both their individual performance and the overall performance of the manufacturing plant tend to decline. This research presents a methodology for analyzing fatigue in assembly operations, considering indicators such as the EAR (Eye Aspect Ratio) indicator, operator pose, and elapsed operating time. To facilitate the analysis, a dataset of assembly operations was generated and recorded from three different perspectives: frontal, lateral, and top views. The top view enables the analysis of the operator’s face and posture to identify hand positions. By labeling the actions in our dataset, we train a deep learning system to recognize the sequence of operator actions required to complete the operation. Additionally, we propose a model for determining the level of fatigue by processing multimodal information acquired from various sources, including eye blink rate, operator pose, and task duration during assembly operations.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. “A way of making Europe” European Regional Development Fund (ERDF) and MCIN/AEI/10.13039/501100011033 for supporting this work under the MoDeaAS project (grant PID2019-104818RB-I00)

A Quantitative Comparison of Calibration Methods for RGB-D Sensors Using Different Technologies

RGB-D (Red Green Blue and Depth) sensors are devices that can provide color and depth information from a scene at the same time. Recently, they have been widely used in many solutions due to their commercial growth from the entertainment market to many diverse areas (e.g., robotics, CAD, etc.). In the research community, these devices have had good uptake due to their acceptable level of accuracy for many applications and their low cost, but in some cases, they work at the limit of their sensitivity, near to the minimum feature size that can be perceived. For this reason, calibration processes are critical in order to increase their accuracy and enable them to meet the requirements of such kinds of applications. To the best of our knowledge, there is not a comparative study of calibration algorithms evaluating its results in multiple RGB-D sensors. Specifically, in this paper, a comparison of the three most used calibration methods have been applied to three different RGB-D sensors based on structured light and time-of-flight. The comparison of methods has been carried out by a set of experiments to evaluate the accuracy of depth measurements. Additionally, an object reconstruction application has been used as example of an application for which the sensor works at the limit of its sensitivity. The obtained results of reconstruction have been evaluated through visual inspection and quantitative measurements

A Framework for Simulating Home Control Networks

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Mechanical properties of treadmill surfaces compared to other overground sport surfaces

The mechanical properties of the surfaces used for exercising can affect sports performance and injury risk. However, the mechanical properties of treadmill surfaces remain largely unknown. The aim of this study was, therefore, to assess the shock absorption (SA), vertical deformation (VD) and energy restitution (ER) of different treadmill models and to compare them with those of other sport surfaces. A total of 77 treadmills, 30 artificial turf pitches and 30 athletics tracks were assessed using an advanced artificial athlete device. Differences in the mechanical properties between the surfaces and treadmill models were evaluated using a repeated-measures ANOVA. The treadmills were found to exhibit the highest SA of all the surfaces (64.2 ± 2; p < 0.01; effect size (ES) = 0.96), while their VD (7.6 ± 1.3; p < 0.01; ES = 0.87) and ER (45 ± 11; p < 0.01; ES = 0.51) were between the VDs of the artificial turf and track. The SA (p < 0.01; ES = 0.69), VD (p < 0.01; ES = 0.90) and ER (p < 0.01; ES = 0.89) were also shown to differ between treadmill models. The differences between the treadmills commonly used in fitness centers were much lower than differences between the treadmills and track surfaces, but they were sometimes larger than the differences with artificial turf. The treadmills used in clinical practice and research were shown to exhibit widely varying mechanical properties. The results of this study demonstrate that the mechanical properties (SA, VD and ER) of treadmill surfaces differ significantly from those of overground sport surfaces such as artificial turf and athletics track surfaces but also asphalt or concrete. These different mechanical properties of treadmills may affect treadmill running performance, injury risk and the generalizability of research performed on treadmills to overground locomotion.Sin financiación3.275 JCR (2019) Q1, 15/64 Instruments & Instrumentation; Q2, 22/86 Chemistry, Analytical, 77/266 Engineering, Electrical & Electronic0.653 SJR (2019) Q1, 38/374 Instrumentation; Q2, 41/126 Analytical Chemistry, 107/683 Information Systems, 207/1409 Electrical and Electronic Engineering, 993/2754 Medicine (miscellaneous), 60/231 Atomic and Molecular Physics, and Optics; Q3, 239/456 BiochemistryNo data IDR 2019UE