Abstract: Addition of fibers to cement-based materials improve tensile and flexural strength, fracture toughness, abrasion resistance, delay cracking, and reduce crack widths. Natural fibers have recently become more popular in the construction materials community. This investigation addresses the characterization of a new animal fiber (pig hair), a massive food-industry waste worldwide, and its use in mortars. Morphological, physical and mechanical properties of pig hair are determined in order to be used as reinforcement in mortars. A sensitivity analysis on the volumes of fiber in mortars is developed. The results from this investigation showed that reinforced mortars significantly improve impact strength, abrasion resistance, plastic shrinkage cracking, age at cracking, and crack widths as fiber volume increases. Other properties such as compressive and flexural strength, density, porosity and modulus of elasticity of reinforced mortars are not significantly affected by the addition of pig hair. (C) 2017 Elsevier Ltd. All rights reserved.

Abstract: We explore strategic information transmission when there is noise at the observation stage, when an expert observes signals, before he advises a policymaker. That is, the expert might be inexpert. We account for the fact that his signals might be totally uninformative, which is commonly known by players. We find that this inexpertise translates into a greater preference misalignment between players and that this yields a less informative equilibrium. We show that our results follow from the fact that the strategic effect of noise – the welfare change exclusive due to changes in the equilibrium partition – is always negative. Numerical simulations show that noise might be beneficial if the policymaker openly disagrees about noise chances. This makes the point that whether noise is beneficial or not crucially depends on how early in the game it arises, and also whether noise chances are commonly known by players or not.

Abstract: Electric motors are among the most widely used electro-mechanical systems in the mining industry in Chile. As they are physically present in virtually all phases of the production process, they must be regularly inspected. In order to maintain a maximum level of operability and to avoid the occurrence of catastrophic failures and unscheduled stoppages, regular, planned reviews to measure the internal parameters of their components are performed manually. This part of the process presents serious shortcomings, such as significant inspection times, information input errors and, in some cases, measurement errors resulting from the technical difficulty of physically taking the measurement. This research presents the design, development and evaluation of a new sensor to support the inspection of brush-holder spring tensions as well as the integration of a digital gauge to control other critical dimensions of the brush-holder mounting. Its purpose is to provide the mining industry with a new system and a fully automated inspection protocol to ease maintenance and control tasks, particularly focusing on reducing inspection times. These products are expected to increase the equipment operating life, as well as delivering a maintenance report of the motor's key parameters which directly benefits the Key Performance Indicators (KPI) of mining management

Abstract: Point matching in multiple images is an open problem in computer vision because of the numerous geometric transformations and photometric conditions that a pixel or point might exhibit in the set of images. Over the last two decades, different techniques have been proposed to address this problem. The most relevant are those that explore the analysis of invariant features. Nonetheless, their main limitation is that invariant analysis all alone cannot reduce false alarms. This paper introduces an efficient point-matching method for two and three views, based on the combined use of two techniques: (1) the correspondence analysis extracted from the similarity of invariant features and (2) the integration of multiple partial solutions obtained from 2D and 3D geometry. The main strength and novelty of this method is the determination of the point-to-point geometric correspondence through the intersection of multiple geometrical hypotheses weighted by the maximum likelihood estimation sample consensus (MLESAC) algorithm. The proposal not only extends the methods based on invariant descriptors but also generalizes the correspondence problem to a perspective projection model in multiple views. The developed method has been evaluated on three types of image sequences: outdoor, indoor, and industrial. Our developed strategy discards most of the wrong matches and achieves remarkable F-scores of 97%, 87%, and 97% for the outdoor, indoor, and industrial sequences, respectively.

Abstract: The CO2 and water vapor exchange between leaf and atmosphere are relevant for plant physiology. This process is done through the stomata. These structures are fundamental in the study of plants since their properties are linked to the evolutionary process of the plant, as well as its environmental and phytohormonal conditions. Stomatal detection is a complex task due to the noise and morphology of the microscopic images. Although in recent years segmentation algorithms have been developed that automate this process, they all use techniques that explore chromatic characteristics. This research explores a unique feature in plants, which corresponds to the stomatal spatial distribution within the leaf structure. Unlike segmentation techniques based on deep learning tools, we emphasize the search for an optimal threshold level, so that a high percentage of stomata can be detected, independent of the size and shape of the stomata. This last feature has not been reported in the literature, except for those results of geometric structure formation in the salt formation and other biological formations.

Abstract: Until a safe and effective vaccine to fight the SARS-CoV-2 virus is developed and available for the global population, preventive measures, such as wearable tracking and monitoring systems supported by Internet of Things (IoT) infrastructures, are valuable tools for containing the pandemic. In this review paper we analyze innovative wearable systems for limiting the virus spread, early detection of the first symptoms of the coronavirus disease COVID-19 infection, and remote monitoring of the health conditions of infected patients during the quarantine. The attention is focused on systems allowing quick user screening through ready-to-use hardware and software components. Such sensor-based systems monitor the principal vital signs, detect symptoms related to COVID-19 early, and alert patients and medical staff. Novel wearable devices for complying with social distancing rules and limiting interpersonal contagion (such as smart masks) are investigated and analyzed. In addition, an overview of implantable devices for monitoring the effects of COVID-19 on the cardiovascular system is presented. Then we report an overview of tracing strategies and technologies for containing the COVID-19 pandemic based on IoT technologies, wearable devices, and cloud computing. In detail, we demonstrate the potential of radio frequency based signal technology, including Bluetooth Low Energy (BLE), Wi-Fi, and radio frequency identification (RFID), often combined with Apps and cloud technology. Finally, critical analysis and comparisons of the different discussed solutions are presented, highlighting their potential and providing new insights for developing innovative tools for facing future pandemics.

Abstract: Color is a complex communicative element. At the level of artistic creation, this component influences both formal aspects and symbolic weight, directly affecting the construction of the message, and its associated emotion. During the COVID-19 pandemic, people generated countless images transmitting the subjective experiences of this event, and the social network Instagram was used to share this visual material. Using the repository of images created in the Instagram account CAM (The COVID Art Museum), we propose a methodology to understand the use of color and its emotional relationship in this context. The proposed methodology consists of creating a model that learns to recognize emotions via a convolutional neural network using the ArtEmis database. This model will subsequently be applied to recognize emotions in the CAM dataset, also extracting color attributes and their harmonies. Once both processes are completed, we combine the results, generating an expanded discussion on the usage of color and emotion. The results indicate that warm colors and analog compositions prevail in the sample. The relationship between emotions and composition shows a trend in positive emotions, reinforced by the results of the emotional relationship analysis of color attributes (hue, saturation, and lighting).

Abstract: Spontaneous colonization of mine tailing dams by plants is a potential tool for phytostabilization of such reservoirs. However, the physical and chemical properties of each mine tailings deposit determine the success of natural plant establishment. The plant Baccharis linearis is the main native nanophanerophyte species (evergreen sclerophyllous shrub) that naturally colonizes abandoned copper tailings dams in arid to semiarid north-central Chile. This study compare growth of B. linearis against the physical and chemical properties of a Technosol derived from copper mine tailings. Five sites inside the deposit were selected based on B. linearis vegetation density (VD), at two soil sampling depths under the canopy of adult individuals. Physical and chemical properties of tailings samples and nutrient concentrations in tailings and plants were each determined. Some morphological features of the plants (roots and aerial parts) were also quantified. There were significant differences in soil available water capacity (AW) and relative density (Rd) at different VD. Sites with low AW and high Rd had lower nutrient concentrations and higher Zn content in tailings, decreased infection by arbuscular mycorrhizal fungi, and increased fine root abundance and root hair length in individual plants. In contrast, higher AW, which was positively correlated with fine particles and organic matter content, had a positive effect on vegetation coverage, increased N and P contents in tailings, and increased N contents in leaf tissues, even when available N and P levels in tailings were low. Multiple constraints, such as low AW, N, P, and B contents and high Zn concentrations in the tailings restricted vegetation coverage, but no phenotypic differences were observed between individuals. Thus, in order to promote dense coverage by B. linearis, water retention in these tailings must be improved by increasing colloidal particles (organic and/or inorganic) contents, which have a positive effect on colonization by this species.

Abstract: Horizontal displacements of a multiple-anchor pile wall in a 28.5 m deep excavation using the top-down construction method have been monitored using optical fiber (Brillouin optical time-domain reflectometry (BOTDR)), strain gauges, inclinometers, and a topographic survey. This work presents a comparison between these different techniques to measure horizontal displacements in the pile at several stages of the soil excavation process. It was observed that displacements can be separated into two components: Rigid body motion and pile flexural deformation. Measurements using optical fiber and inclinometers are considered the most adequate and easy to install. A numerical model allows us to evaluate the influence of earth pressure on the estimated horizontal displacements. It is shown that using soil pressure on the wall given by p = 0.65Ka gamma h, on a simplified modeled wall, provides a close deduction of horizontal displacements compared to observed values on the field.