The effect of national protest in Ecuador on PM pollution.

Particulate matter (PM) accounts for millions of premature deaths in the human population every year. Due to social and economic inequality, growing human dissatisfaction manifests in waves of strikes and protests all over the world, causing paralysis of institutions, services and circulation of transport. In this study, we aim to investigate air quality in Ecuador during the national protest of 2019, by studying the evolution of PM2.5 (PM ≤ 2.5 µm) concentrations in Ecuador and its capital city Quito using ground based and satellite data. Apart from analyzing the PM2.5 evolution over time to trace the pollution changes, we employ machine learning techniques to estimate these changes relative to the business-as-usual pollution scenario. In addition, we present a chemical analysis of plant samples from an urban park housing the strike. Positive impact on regional air quality was detected for Ecuador, and an overall - 10.75 ± 17.74% reduction of particulate pollution in the capital during the protest. However, barricade burning PM peaks may contribute to a release of harmful heavy metals (tire manufacture components such as Co, Cr, Zn, Al, Fe, Pb, Mg, Ba and Cu), which might be of short- and long-term health concerns.

Seasonal variations in PM10 inorganic composition in the Andean city.

Particulate matter (PM) is one of the key pollutants causing health risks worldwide. While the preoccupation for increased concentrations of these particles mainly depends on their sources and thus chemical composition, some regions are yet not well investigated. In this work the composition of chemical elements of atmospheric PM10 (particles with aerodynamic diameters ≤ 10 µm), collected at the urban and suburban sites in high elevation tropical city, were chemically analysed during the dry and wet seasons of 2017-2018. A large fraction (~ 68%) of PM10 composition in Quito, Ecuador is accounted for by water-soluble ions and 16 elements analysed using UV/VIS spectrophotometer and Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Hierarchical clustering analysis was performed to study a correlation between the chemical composition of urban pollution and meteorological parameters. The suburban area displays an increase in PM10 concentrations and natural elemental markers during the dry (increased wind intensity, resuspension of soil dust) season. Meanwhile, densely urbanized area shows increased total PM10 concentrations and anthropogenic elemental markers during the wet season, which may point to the worsened combustion and traffic conditions. This might indicate the prevalence of cardiovascular and respiratory problems in motorized areas of the cities in the developing world.

Bioinspired Implementation and Assessment of a Remote-Controlled Robot.

Daily activities are characterized by an increasing interaction with smart machines that present a certain level of autonomy. However, the intelligence of such electronic devices is not always transparent for the end user. This study is aimed at assessing the quality of the remote control of a mobile robot whether the artefact exhibits a human-like behavior or not. The bioinspired behavior implemented in the robot is the well-described two-thirds power law. The performance of participants who teleoperate the semiautonomous vehicle implementing the biological law is compared to a manual and nonbiological mode of control. The results show that the time required to complete the path and the number of collisions with obstacles are significantly lower in the biological condition than in the two other conditions. Also, the highest percentage of occurrences of curvilinear or smooth trajectories are obtained when the steering is assisted by an integration of the power law in the robot's way of working. This advanced analysis of the performance based on the naturalness of the movement kinematics provides a refined evaluation of the quality of the Human-Machine Interaction (HMI). This finding is consistent with the hypothesis of a relationship between the power law and jerk minimization. In addition, the outcome of this study supports the theory of a CNS origin of the power law. The discussion addresses the implications of the anthropocentric approach to enhance the HMI.

Effects of Local Ischemic Compression on Upper Limb Latent Myofascial Trigger Points: A Study of Subjective Pain and Linear Motor Performance.

OBJECTIVE: To analyse the effect of the manual ischemic compression (IC) on the upper limb motor performance (MP) in patients with LTrPs. MATERIALS AND METHODS: A quasiexperimental study was performed in twenty subjects allocated to either patients group with LTrPs (PG, n=10) or healthy group with no symptoms (HG, n=10). Subjective pain and linear MP (movement time and Fitts' Law) were assessed before and after a linear tapping task. Data were analysed with mixed factorial ANOVA for intergroup linear motor performance differences and dependent t-student test for intragroup pain differences. RESULTS: PG had a linear MP lower than the HG before treatment (p < 0.05). After IC, the PG showed a significant decrease of pain (4.07 ± 1.91 p < 0.001). Furthermore, the movement time (15.70 ± 2.05 p < 0.001) and the Fitts' Law coefficient (0.80 ± 0.53 p < 0.001) were significantly reduced. However, one IC session did not allow the PG to get the same MP than the HG (p < 0.05). CONCLUSION: The results suggest the IC effectiveness on pain and MP impairment in subjects with LTrPs. However, the MP of these patients is only partially improved after the IC application.

Smart Web-Based Platform to Support Physical Rehabilitation.

The enhancement of ubiquitous and pervasive computing brings new perspectives in medical rehabilitation. In that sense, the present study proposes a smart, web-based platform to promote the reeducation of patients after hip replacement surgery. This project focuses on two fundamental aspects in the development of a suitable tele-rehabilitation application, which are: (i) being based on an affordable technology, and (ii) providing the patients with a real-time assessment of the correctness of their movements. A probabilistic approach based on the development and training of ten Hidden Markov Models (HMMs) is used to discriminate in real time the main faults in the execution of the therapeutic exercises. Two experiments are designed to evaluate the precision of the algorithm for classifying movements performed in the laboratory and clinical settings, respectively. A comparative analysis of the data shows that the models are as reliable as the physiotherapists to discriminate and identify the motion errors. The results are discussed in terms of the required setup for a successful application in the field and further implementations to improve the accuracy and usability of the system.