ALBERT

Description: Objective: The use of high-performance sports technology to describe the physiological load of stress and the quality of recovery in a population of executives during the workday. Methodology: Heart rate variability values were recorded during 48 h from which the relationship between stress/recovery quality (stress balance) was obtained for three differentiated time slots: work, after work, and night in a workday. Results: We observed a negative stress balance during the 24 h of measurement in the course of a workday, being negative at work and after work, and positive at night. The stress generated or maintained outside working hours correlates significantly with a lower quality of recovery during the 24 h workday. Conclusions: It is necessary to prioritize strategies that help improve stress management in executives through the improvement of tools and strategies that mainly promote greater relaxation outside working hours.

Description: This work presents an integral methodological approach to assess the environmental potential hazards posed by metals and metalloids hosted by spolic technosols derived from old tailings from a mining operation for galena (PbS, with high Ag contents)-sphalerite (ZnS, with a varied cohort of trace elements contents) in central Spain. We studied the total and soluble concentrations and spatial distribution of Pb, Zn, Cd, As, and Fe and the mineralogy of these soils, as well as an ecotoxicological evaluation by means of bioassays. The indices assessing soil contamination such as pollution load index (PI) and natural mobility index (NMI) have been calculated. Furthermore, the phytotoxic effect of the soil samples has been determined and a chronic sediment toxicity test using the benthic ostracod Heterocypris incongruens was applied. The geochemical study of 33 spolic technosols samples indicates large to extremely large metal and metalloid total contents: up to 48,600 mg kg−1 Pb, 34,000 mg kg−1 Zn, 500 mg kg−1 Cd, and 1000 mg kg−1 As. Given that sphalerite is usually the most important host mineral for cadmium in hydrothermal mineral deposits, there is a high correlation (R = 0.75) between this element and Zn. On the other hand, despite being two metallogenically intertwined elements in ore deposits, Pb and Zn show a less significant relationship, which can be attributed both to heterogeneities in the mineralogical composition of the veins, and to the complex history of the mineral concentration process: In the older process, the interest was only for Pb, meanwhile in the late period, the interest was focused in Zn. The Phytotoxkit® bioassay showed that soils with high PTEs presented very high toxicity, particularly the inhibition germination is related to Pb, As, and Cd content and root inhibition with Pb content. Both indexes were correlated with pH and electrical conductivity; samples with lower pH and higher soluble salt content are those with higher seed germination inhibition and root growth inhibition. On the other hand, the Ostracodtoxkit® bioassay showed very high sensitivity, with 100% mortality. The applied bioassays confirmed the soil toxicity and it is highly recommended to complement the results from environmental chemistry with results from bioassays, in order to provide a more complete and relevant information on the bioavailability of contaminants and to characterize the risk of contaminated areas.

Description: The future climate is projected to change rapidly with potentially severe consequences for global food security. This study aims to improve the understanding of future changes in the suitability of crop growth conditions. It proposes a definition of crop realization, of the climate departure from recent historical variability, or crop–climate departure. Four statistically downscaled and bias-corrected Global Climate Models (GCMs): CCCMA, CNRM5, NOAA-GFDL, and MIROC5 performed simulations for the period 1960–2100 under the Representative Concentration Pathway RCP8.5 scenario to compute 20 year moving averages at 5-year increments. These were used to drive a crop suitability model, Ecocrop, for eight different crops across the three Food and Agriculture Organizations (FAO) AgroEcological Zones (AEZs) of West Africa (Guinea, Sahel, and Savanna). Simulations using historical climate data found that all crops except maize had a suitability index value (SIV) ≥0.50 outside the Sahel region, equivalent to conditions being suitable or strongly suitable. Simulations of future climate reveal that warming is projected to constrain crop growth suitability for cassava and pineapple in the Guinea zone. A potential for the northward expansion of maize is projected by the end of the century, suggesting a future opportunity for its growth in the southern Sahel zone. Crop growth conditions for mango and pearl millet remain suitable across all three AEZs. In general, crops in the Savanna AEZ are the most sensitive to the projected changes in climate. The changes in the crop–climate relationship suggests a future constraint in crop suitability, which could be detrimental to future food security in West Africa. Further studies to explore associated short- and long-term adaptation options are recommended.

Description: Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.

Description: Immersive techniques such as augmented reality through devices such as the AR-Sandbox and deep learning through convolutional neural networks (CNN) provide an environment that is potentially applicable for motor rehabilitation and early education. However, given the orientation towards the creation of topographic models and the form of representation of the AR-Sandbox, the classification of images is complicated by the amount of noise that is generated in each capture. For this reason, this research has the purpose of establishing a model of a CNN for the classification of geometric figures by optimizing hyperparameters using Random Search, evaluating the impact of the implementation of a previous phase of color–space segmentation to a set of tests captured from the AR-Sandbox, and evaluating this type of segmentation using similarity indexes such as Jaccard and Sørensen–Dice. The aim of the proposed scheme is to improve the identification and extraction of characteristics of the geometric figures. Using the proposed method, an average decrease of 39.45% to a function of loss and an increase of 14.83% on average in the percentage of correct answers is presented, concluding that the selected CNN model increased its performance by applying color–space segmentation in a phase that was prior to the prediction, given the nature of multiple pigmentation of the AR-Sandbox.

Description: The objectives this study were to examine the integrated use of oil–coagulant for the direct extraction of coagulant from Moringa oleifera (MO) with 5% and 10% (NH4)2SO4 extractor solution to harvest Scenedesmus obliquus cultivated in urban wastewater and to analyze the oil extracted from MO and S. obliquus. An average content of 0.47 g of coagulant and 0.5 g of oil per gram of MO was obtained. Highly efficient algal harvest, 80.33% and 72.13%, was achieved at a dose of 0.38 g L−1 and pH 8–9 for 5% and 10% extractor solutions, respectively. For values above pH 9, the harvest efficiency decreases, producing a whitish water with 10% (NH4)2SO4 solution. The oil profile (MO and S. obliquus) showed contents of SFA of 36.24–36.54%, monounsaturated fatty acids of 32.78–36.13%, and polyunsaturated fatty acids of 27.63–30.67%. The biodiesel obtained by S. obliquus and MO has poor cold flow properties, indicating possible applications limited to warm climates. For both biodiesels, good fuel ignition was observed according to the high cetane number and positive correlation with SFA and negative correlation with the degree of saturation. This supports the use of MO as a potentially harmless bioflocculant for microalgal harvest in wastewater, contributing to its treatment, and a possible source of low-cost biodiesel.

Description: This paper presents an online economic re-dispatch scheme based on the generation cost optimization with security constraints to mitigate line overloads before and after line and generation contingencies. The proposed optimization model considers simplification of mathematical expressions calculated from online variables as the power transfer distribution factor (PTDF) and line outage distribution factor (LODF). Thus, a first algorithm that calculates economic re-dispatch for online operation to avoid overloads during the normal operation and a second algorithm that calculates online emergency economic re-dispatch when overloads occur due to line and generator contingencies are proposed in this paper. The results show that the proposed algorithms avoid overload before and after contingencies, improving power system security, and at the same time reducing operational costs. This scheme allows a reduction of power generation units in the electricity market during online operation that considers line overloads in the power system.

Description: This article provides a deep analysis of the water quality at the upper basin of the Bogota River (Colombia) between 2008 and 2017. The Water Quality Index has been the indicator employed to determine the ecological status of the river. This index was chosen in order to normalize the analysis, given that it is commonly used by the Institute of Hydrology, Meteorology and Environmental Studies, a government agency of the Ministry of Environment and Sustainable Development of Colombia, to determine the state of surface effluents. The results obtained were organized in a double-entry matrix in order to relate the variables of the sample period and the sampling station. The research revealed an insufficient quality of water, demonstrating that the high stretch of the Bogota River basin has, in general, regular or acceptable water quality, while only five stations showed an acceptable status. Surprisingly, the stations located close to the wastewater treatment plants of the municipalities of Choconta, Suesca, Gachancipa, and Tocancipa, as well as Rio Negro, have a poor water quality, discharging a high load of contaminants into the river. Although great efforts have been made by Colombian authorities to restore the critical state of the majority of their aquatic ecosystems, recent implementation of policies and instruments have not shown significant achievements yet. For this reason, this study aims to present a powerful decision-tool for the monitoring and evaluation of correction measures implemented on this river basin. The data used in this research were provided by the Regional Autonomous Corporation of Cundinamarca.

Description: Convective hydrothermal systems have been extensively studied using electrical and electromagnetic methods given the strong correlation between low conductivity anomalies associated with hydrothermal brines and high temperature areas. However, studies addressing the application of similar geophysical methods to hot dry rock geothermal systems are very limited in the literature. The Timanfaya volcanic area, located on Lanzarote Island (Canary Islands), comprises one of these hot dry rock systems, where ground temperatures ranging from 250 to 605 °C have been recorded in pyroclastic deposits at shallow (〈70 m) depths. With the aim of characterizing the geophysical signature of the high ground temperature areas, three different geophysical techniques (ground penetrating radar, electromagnetic induction and magnetic prospecting) were applied in a well-known geothermal area located inside Timanfaya National Park. The area with the highest ground temperatures was correlated with the location that exhibited strong ground penetrating radar reflections, high resistivity values and low magnetic anomalies. Moreover, the high ground temperature imaging results depicted a shallow, bowl-shaped body that narrowed and deepened vertically to a depth greater than 45 m. The ground penetrating radar survey was repeated three years later and exhibited subtle variations of the signal reflection patterns, or signatures, suggesting a certain temporal variation of the ground temperature. By identifying similar areas with the same geophysical signature, up to four additional geothermal areas were revealed. We conclude that the combined use of ground penetrating radar, electromagnetic induction and magnetic methods constitutes a valuable tool to locate and study both the geometry at depth and seasonal variability of geothermal areas associated with hot dry rock systems.

Description: The adoption of both Cyber–Physical Systems (CPSs) and the Internet-of-Things (IoT) has enabled the evolution towards the so-called Industry 4.0. These technologies, together with cloud computing and artificial intelligence, foster new business opportunities. Besides, several industrial applications need immediate decision making and fog computing is emerging as a promising solution to address such requirement. In order to achieve a cost-efficient system, we propose taking advantage from spot instances, a new service offered by cloud providers, which provide resources at lower prices. The main downside of these instances is that they do not ensure service continuity and they might suffer from interruptions. An architecture that combines fog and multi-cloud deployments along with Network Coding (NC) techniques, guarantees the needed fault-tolerance for the cloud environment, and also reduces the required amount of redundant data to provide reliable services. In this paper we analyze how NC can actually help to reduce the storage cost and improve the resource efficiency for industrial applications, based on a multi-cloud infrastructure. The cost analysis has been carried out using both real AWS EC2 spot instance prices and, to complement them, prices obtained from a model based on a finite Markov chain, derived from real measurements. We have analyzed the overall system cost, depending on different parameters, showing that configurations that seek to minimize the storage yield a higher cost reduction, due to the strong impact of storage cost.