Search results for: J. V. Gonzalez Fernandez

Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: María Luisa Gonzalez-SanJose, Mihaela Mihnea, Vicente Gomez-Miguel

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The zoning methodology currently recommended by the OIVV as official methodology to carry out viticulture zoning studies and to define and delimit the ‘terroirs’ has been applied in this study. This methodology has been successfully applied on the most significant an important Spanish Oenological D.O. regions, such as Ribera de Duero, Rioja, Rueda and Toro, but also it have been applied around the world in Portugal, different countries of South America, and so on. This is a complex methodology that uses edaphoclimatic data but also other corresponding to vineyards and other soils’ uses The methodology is useful to determine Homogeneous Soil Units (HSU) to different scale depending on the interest of each study, and has been applied from viticulture regions to particular vineyards. It seems that this methodology is an appropriate method to delimit correctly the medium in order to enhance its uses and to obtain the best viticulture and oenological products. The present work is focused on the comparison of volatile composition of wines made from grapes grown in different HSU that coexist in a particular viticulture region of Castile-Lion cited near to Burgos. Three different HSU were selected for this study. They represented around of 50% of the global area of vineyards of the studied region. Five different vineyards on each HSU under study were chosen. To reduce variability factors, other criteria were also considered as grape variety, clone, rootstocks, vineyard’s age, training systems and cultural practices. This study was carried out during three consecutive years, then wine from three different vintage were made and analysed. Different red wines were made from grapes harvested in the different vineyards under study. Grapes were harvested to ‘Technological maturity’, which are correlated with adequate levels of sugar, acidity, phenolic content (nowadays named phenolic maturity), good sanitary stages and adequate levels of aroma precursors. Results of the volatile profile of the wines produced from grapes of each HSU showed significant differences among them pointing out a direct effect of the edaphoclimatic characteristic of each UHT on the composition of the grapes and then on the volatile composition of the wines. Variability induced by HSU co-existed with the well-known inter-annual variability correlated mainly with the specific climatic conditions of each vintage, however was most intense, so the wine of each HSU were perfectly differenced. A discriminant analysis allowed to define the volatiles with discriminant capacities which were 21 of the 74 volatiles analysed. Detected discriminant volatiles were chemical different, although .most of them were esters, followed by were superior alcohols and fatty acid of short chain. Only one lactone and two aldehydes were selected as discriminant variable, and no varietal aroma compounds were selected, which agree with the fact that all the wine were made from the same grape variety.

Keywords: viticulture zoning, terroir, wine, volatile profile

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Authors: Britt Erni, Aldara Vazquez Fernandez, Roland Maurer

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Visuospatial perspective taking (VSPT) is a process that allows to integrate spatial information from different points of view, and to transform the mental images we have of the environment to properly orient our movements and anticipate the location of landmarks during navigation. VSPT is also related to egocentric perspective transformations (imagined rotations or translations of one's point of view) and to infer the visuospatial experiences of another person (e.g. if and how another person sees objects). This process is deeply related to a wide-ranging capacity called the theory of mind (ToM), an essential cognitive function that allows us to regulate our social behaviour by attributing mental representations to individuals in order to make behavioural predictions. VSPT is often considered in the literature as the starting point of the development of the theory of mind. VSPT and ToM include several levels of knowledge that have to be assessed by specific tasks. Unfortunately, the lack of tasks assessing these functions in clinical neuropsychology leads to underestimate, in brain-damaged patients, deficits of these functions which are essential, in everyday life, to regulate our social behaviour (ToM) and to navigate in known and unknown environments (VSPT). Therefore, this study aims to create and standardize a VSPT task in order to explore the cognitive requirements of VSPT and ToM, and to specify their relationship in healthy adults and thereafter in brain-damaged patients. Two versions of a computerized VSPT task were administered to healthy participants (M = 28.18, SD = 4.8 years). In both versions the environment was a 3D representation of 10 different geometric shapes placed on a circular base. Two sets of eight pictures were generated from this: of the environment with an avatar somewhere on its periphery (locations) and of what the avatar sees from that place (views). Two types of questions were asked: a) identify the location from the view, and b) identify the view from the location. Twenty participants completed version 1 of the task and 20 completed the second version, where the views were offset by ±15° (i.e., clockwise or counterclockwise) and participants were asked to choose the closest location or the closest view. The preliminary findings revealed that version 1 is significantly easier than version 2 for accuracy (with ceiling scores for version 1). In version 2, participants responded significantly slower when they had to infer the avatar's view from the latter's location, probably because they spent more time visually exploring the different views (responses). Furthermore, men significantly performed better than women in version 1 but not in version 2. Most importantly, a sensitive task (version 2) has been created for which the participants do not seem to easily and automatically compute what someone is looking at yet which does not involve more heavily other cognitive functions. This study is further completed by including analysis on non-clinical participants with low and high degrees of schizotypy, different socio-educational status, and with a range of older adults to examine age-related and other differences in VSPT processing.

Keywords: mental transformation, spatial cognition, theory of mind, visuospatial perspective taking

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Authors: Alvarez M., Chapela M. J., Balboa E., Rubianes D., Sinde E., Fernandez de Ana C., Rodríguez-Blanco A.

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The gut microbiota plays an important role as gut inflammation could contribute to colorectal cancer development; however, this role is still not fully understood, and tools able to prevent this progression are yet to be developed. The main objective of this study was to monitor the effects of a mushroom extracts formulation in gut microbial community composition of an Azoxymethane (AOM)/Dextran sodium sulfate (DSS) mice model of inflammation-linked carcinogenesis. For the in vivo study, 41 adult male mice of the C57BL / 6 strain were obtained. 36 of them have been induced in a state of colon carcinogenesis by a single intraperitoneal administration of AOM at a dose of 12.5 mg/kg; the control group animals received instead of the same volume of 0.9% saline. DSS is an extremely toxic polysaccharide sulfate that causes chronic inflammation of the colon mucosa, favoring the appearance of severe colitis and the production of tumors induced by AOM. Induction by AOM/DSS is an interesting platform for chemopreventive intervention studies. This time the model was used to monitor gut microbiota changes as a result of supplementation with a specific mushroom extracts formulation previously shown to have prebiotic activity. The animals have been divided into three groups: (i) Cancer + mushroom extracts formulation experimental group: to which the MicoDigest2.0 mushroom extracts formulation developed by Hifas da Terra S.L has been administered dissolved in drinking water at an estimated concentration of 100 mg / ml. (ii) Control group of animals with Cancer: to which normal water has been administered without any type of treatment. (iii) Control group of healthy animals: these are the animals that have not been induced cancer or have not received any treatment in drinking water. This treatment has been maintained for a period of 3 months, after which the animals were sacrificed to obtain tissues that were subsequently analyzed to verify the effects of the mushroom extract formulation. A microbiological analysis has been carried out to compare the microbial communities present in the intestines of the mice belonging to each of the study groups. For this, the methodology of massive sequencing by molecular analysis of the 16S gene has been used (Ion Torrent technology). Initially, DNA extraction and metagenomics libraries were prepared using the 16S Metagenomics kit, always following the manufacturer's instructions. This kit amplifies 7 of the 9 hypervariable regions of the 16S gene that will then be sequenced. Finally, the data obtained will be compared with a database that makes it possible to determine the degree of similarity of the sequences obtained with a wide range of bacterial genomes. Results obtained showed that, similarly to certain natural compounds preventing colorectal tumorigenesis, a mushroom formulation enriched the Firmicutes and Proteobacteria phyla and depleted Bacteroidetes. Therefore, it was demonstrated that the consumption of the mushroom extracts’ formulation developed could promote the recovery of the microbial balance that is disrupted in the mice model of carcinogenesis. More preclinical and clinical studies are needed to validate this promising approach.

Keywords: carcinogenesis, microbiota, mushroom extracts, inflammation

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Authors: Olga Hernán-Vega, Patricia López-Rodríguez, David Escot-Bocanegra, Raúl Fernández-Recio, Ignacio Bravo

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Non-Cooperative Target Identification has become a key research domain in the Defense industry since it provides the ability to recognize targets at long distance and under any weather condition. High Resolution Range Profiles, one-dimensional radar images where the reflectivity of a target is projected onto the radar line of sight, are widely used for identification of flying targets. According to that, to face this problem, an approach to Non-Cooperative Target Identification based on the exploitation of Singular Value Decomposition to a matrix of range profiles is presented. Target Identification based on one-dimensional radar images compares a collection of profiles of a given target, namely test set, with the profiles included in a pre-loaded database, namely training set. The classification is improved by using Singular Value Decomposition since it allows to model each aircraft as a subspace and to accomplish recognition in a transformed domain where the main features are easier to extract hence, reducing unwanted information such as noise. Singular Value Decomposition permits to define a signal subspace which contain the highest percentage of the energy, and a noise subspace which will be discarded. This way, only the valuable information of each target is used in the recognition process. The identification algorithm is based on finding the target that minimizes the angle between subspaces and takes place in a transformed domain. Two metrics, F1 and F2, based on Singular Value Decomposition are accomplished in the identification process. In the case of F2, the angle is weighted, since the top vectors set the importance in the contribution to the formation of a target signal, on the contrary F1 simply shows the evolution of the unweighted angle. In order to have a wide database or radar signatures and evaluate the performance, range profiles are obtained through numerical simulation of seven civil aircraft at defined trajectories taken from an actual measurement. Taking into account the nature of the datasets, the main drawback of using simulated profiles instead of actual measured profiles is that the former implies an ideal identification scenario, since measured profiles suffer from noise, clutter and other unwanted information and simulated profiles don't. In this case, the test and training samples have similar nature and usually a similar high signal-to-noise ratio, so as to assess the feasibility of the approach, the addition of noise has been considered before the creation of the test set. The identification results applying the unweighted and weighted metrics are analysed for demonstrating which algorithm provides the best robustness against noise in an actual possible scenario. So as to confirm the validity of the methodology, identification experiments of profiles coming from electromagnetic simulations are conducted, revealing promising results. Considering the dissimilarities between the test and training sets when noise is added, the recognition performance has been improved when weighting is applied. Future experiments with larger sets are expected to be conducted with the aim of finally using actual profiles as test sets in a real hostile situation.

Keywords: HRRP, NCTI, simulated/synthetic database, SVD

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Authors: Laura Gonzalez, Santiago Benavides, Martha Elena Londono, Ana Elisa Casas, Adriana Restrepo-Osorio

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Cotton products are sensitive to microorganisms due to its ability to retain moisture, which might cause change into the coloration, mechanical properties reduction or foul odor generation; consequently, this represents risks to the health of users. Nowadays, have been carried out researches to give antibacterial properties to textiles using different strategies, which included the use of silver nanoparticles (AgNPs). The antibacterial behavior can be affected by laundering process reducing its effectiveness. In the other way, the environmental impact generated for the synthetic antibacterial agents has motivated to seek new and more ecological ways for produce AgNPs. The aims of this work are to determine the antibacterial activity of cotton fabric functionalized with green (G) and commercial (C) AgNPs after twenty washing cycles, also to evaluate morphological and color changes. A plain weave cotton fabric suitable for dyeing and two AgNPs solutions were use. C a commercial product and G produced using an ecological method, both solutions with 0.5 mM concentration were impregnated on cotton fabric without stabilizer, at a liquor to fabric ratio of 1:20 in constant agitation during 30min and then dried at 70 °C by 10 min. After that the samples were subjected to twenty washing cycles using phosphate-free detergent simulated on agitated flask at 150 rpm, then were centrifuged and dried on a tumble. The samples were characterized using Kirby-Bauer test determine antibacterial activity against E. coli y S. aureus microorganisms, the results were registered by photographs establishing the inhibition halo before and after the washing cycles, the tests were conducted in triplicate. Scanning electron microscope (SEM) was used to observe the morphologies of cotton fabric and treated samples. The color changes of cotton fabrics in relation to the untreated samples were obtained by spectrophotometer analysis. The images, reveals the presence of inhibition halo in the samples treated with C and G AgNPs solutions, even after twenty washing cycles, which indicated a good antibacterial activity and washing durability, with a tendency to better results against to S. aureus bacteria. The presence of AgNPs on the surface of cotton fiber and morphological changes were observed through SEM, after and before washing cycles. The own color of the cotton fiber has been significantly altered with both antibacterial solutions. According to the colorimetric results, the samples treated with C lead to yellowing while the samples modified with G to red yellowing Cotton fabrics treated AgNPs C and G from 0.5 mM solutions exhibited excellent antimicrobial activity against E. coli and S. aureus with good laundering durability effects. The surface of the cotton fibers was modified with the presence of AgNPs C and G due to the presence of NPs and its agglomerates. There are significant changes in the natural color of cotton fabric due to deposition of AgNPs C and G which were maintained after laundering process.

Keywords: antibacterial property, cotton fabric, fastness to wash, Kirby-Bauer test, silver nanoparticles

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Authors: Antonio Ruiz Gonzalez, Kwang-Leong Choy

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The measurement of electrolytes has a high value in the clinical routine. Ions are present in all body fluids with variable concentrations and are involved in multiple pathologies such as heart failures and chronic kidney disease. In the case of dissolved potassium, although a high concentration in the blood (hyperkalemia) is relatively uncommon in the general population, it is one of the most frequent acute electrolyte abnormalities. In recent years, the integration of thin films technologies in this field has allowed the development of highly sensitive biosensors with ultra-low limits of detection for the assessment of metals in liquid samples. However, despite the current efforts in the miniaturization of sensitive devices and their integration into portable systems, only a limited number of successful examples used commercially can be found. This fact can be attributed to a high cost involved in their production and the sustained degradation of the electrodes over time, which causes a signal drift in the measurements. Thus, there is an unmet necessity for the development of low-cost and robust sensors for the real-time monitoring of analyte concentrations in patients to allow the early detection and diagnosis of diseases. This paper reports a thin film ion-selective sensor for the evaluation of potassium ions in aqueous samples. As an alternative for this fabrication method, aerosol assisted chemical vapor deposition (AACVD), was applied due to cost-effectivity and fine control over the film deposition. Such a technique does not require vacuum and is suitable for the coating of large surface areas and structures with complex geometries. This approach allowed the fabrication of highly homogeneous surfaces with well-defined microstructures onto 50 nm thin gold layers. The degradative processes of the ubiquitously employed poly (vinyl chloride) membranes in contact with an electrolyte solution were studied, including the polymer leaching process, mechanical desorption of nanoparticles and chemical degradation over time. Rational design of a protective coating based on an organosilicon material in combination with cellulose to improve the long-term stability of the sensors was then carried out, showing an improvement in the performance after 5 weeks. The antifouling properties of such coating were assessed using a cutting-edge quartz microbalance sensor, allowing the quantification of the adsorbed proteins in the nanogram range. A correlation between the microstructural properties of the films with the surface energy and biomolecules adhesion was then found and used to optimize the protective film.

Keywords: hyperkalemia, drift, AACVD, organosilicon

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Authors: Ernest Moles, Patricia Urbán, María Belén Jiménez-Díaz, Sara Viera-Morilla, Iñigo Angulo-Barturen, Maria Antònia Busquets, Xavier Fernàndez-Busquets

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Bearing in mind the absence of an effective vaccine against malaria and its severe clinical manifestations causing nearly half a million deaths every year, this disease represents nowadays a major threat to life. Besides, the basic rationale followed by currently marketed antimalarial approaches is based on the administration of drugs on their own, promoting the emergence of drug-resistant parasites owing to the limitation in delivering drug payloads into the parasitized erythrocyte high enough to kill the intracellular pathogen while minimizing the risk of causing toxic side effects to the patient. Such dichotomy has been successfully addressed through the specific delivery of immunoliposome (iLP)-encapsulated antimalarials to Plasmodium falciparum-infected red blood cells (pRBCs). Unfortunately, this strategy has not progressed towards clinical applications, whereas in vitro assays rarely reach drug efficacy improvements above 10-fold. Here, we show that encapsulation efficiencies reaching >96% can be achieved for the weakly basic drugs chloroquine (CQ) and primaquine using the pH gradient active loading method in liposomes composed of neutrally charged, saturated phospholipids. Targeting antibodies are best conjugated through their primary amino groups, adjusting chemical crosslinker concentration to retain significant antigen recognition. Antigens from non-parasitized RBCs have also been considered as targets for the intracellular delivery of drugs not affecting the erythrocytic metabolism. Using this strategy, we have obtained unprecedented nanocarrier targeting to early intraerythrocytic stages of the malaria parasite for which there is a lack of specific extracellular molecular tags. Polyethylene glycol-coated liposomes conjugated with monoclonal antibodies specific for the erythrocyte surface protein glycophorin A (anti-GPA iLP) were capable of targeting 100% RBCs and pRBCs at the low concentration of 0.5 μM total lipid in the culture, with >95% of added iLPs retained into the cells. When exposed for only 15 min to P. falciparum in vitro cultures synchronized at early stages, free CQ had no significant effect over parasite viability up to 200 nM drug, whereas iLP-encapsulated 50 nM CQ completely arrested its growth. Furthermore, when assayed in vivo in P. falciparum-infected humanized mice, anti-GPA iLPs cleared the pathogen below detectable levels at a CQ dose of 0.5 mg/kg. In comparison, free CQ administered at 1.75 mg/kg was, at most, 40-fold less efficient. Our data suggest that this significant improvement in drug antimalarial efficacy is in part due to a prophylactic effect of CQ found by the pathogen in its host cell right at the very moment of invasion.

Keywords: immunoliposomal nanoparticles, malaria, prophylactic-therapeutic polyvalent activity, targeted drug delivery

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Authors: María Luisa Gonzalez-SanJose, Javier Garcia-Lomillo, Raquel Del Pino, Miriam Ortega-Heras, Maria Dolores Rivero-Perez, Pilar Muñiz-Rodriguez

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One of the most preoccupant problems related to the diet of the occidental societies is the high salt intake. In Spain, salt intake is almost twice as recommended by the World Health Organization (WHO). A lot of negative health effects of high sodium intake have been described being the hypertension, cardiovascular and coronary diseases ones of the most important. Due to this fact, government and other institutions are working on the gradual reduction of this consumption. Intake of meat products have been described as the main processed products that bring salt to the diet, followed by snacks and savory crackers. However, fortunately, the food industry has also raised awareness of this problem and is working intensely, and in recent years attempts to reduce the salt content in processed products, and is developing special lines with low sodium content. It is important to consider that processed food are the main source of sodium in occidental countries. One of the possible strategies to reduce the salt content in food is to find substitutes that can emulate their taste properties without adding much sodium or products that mask or substitute salty sensations with other flavors and aromas. In this sense, multiple products have been proposed and used until now. Potassium salts produce similar salty sensations without bring sodium, however their intake should be also limited, by healthy reasons. Furthermore, some potassium salts shows some better notes. Other alternatives are the use of flavor enhancers, spices, aromatic herbs, sea-plant derivate products, etc. The wine pomace is rich in potassium salts, content organic acid and other flavored substances, therefore it could be an interesting raw material to obtain derived products that could be useful as alternative ‘seasonings’. Considering previous comments, the main aim of this study was to evaluate the possible use of a natural seasoning, made from red wine pomace, in two different foods, crackers and burgers. The seasoning was made in the pilot plant of food technology of the University of Burgos, where the studied crackers and patties were also made. Different members of the University, students, docent and administrative personal, taste the products, and a trained panel evaluated salty intensity. The seasoning in addition to potassium contain significant levels of dietary fiber and phenolic compounds, which also makes it interesting as a functional ingredient. Both burgers and crackers made with the seasoning showed better taste that those without salt. Obviously, they showed lower sodium content than normal formulation, and were richer in potassium, antioxidant and fiber. Then, they showed lower values of the relation Na/K. All these facts are correlated with more ‘healthy’ products especially to that people with hypertension and other coronary dysfunctions.

Keywords: healthy foods, low salt, seasoning, wine pomace

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Authors: Andrés Lara Guillén, José M. Soriano Disla, Gemma Castejón Martínez, David Fernández-Gutiérrez

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The worldwide population is exponentially increasing, which causes a rising demand for food, energy and non-renewable resources. These demands must be attended to from a circular economy point of view. Under this approach, the obtention of strategic products from biowaste is crucial for the society to keep the current lifestyle reducing the environmental and social issues linked to the lineal economy. This is the main objective of the VALUEWASTE project. VALUEWASTE is about valorizing urban biowaste into proteins for food and feed and biofertilizers, closing the loop of this waste stream. In order to achieve this objective, the project validates three value chains, which begin with the anaerobic digestion of the biowaste. From the anaerobic digestion, three by-products are obtained: i) methane that is used by microorganisms, which will be transformed into microbial proteins; ii) digestate that is used by black soldier fly, producing insect proteins; and iii) a nutrient-rich effluent, which will be transformed into biofertilizers. VALUEWASTE is an innovative solution, which combines different technologies to valorize entirely the biowaste. However, it is also required to demonstrate that the solution is greener than other traditional technologies (baseline systems). On one hand, the proteins from microorganisms and insects will be compared with other reference protein production systems (gluten, whey and soybean). On the other hand, the biofertilizers will be compared to the production of mineral fertilizers (ammonium sulphate and synthetic struvite). Therefore, the aim of this study is to provide that biowaste valorization can reduce the environmental impacts linked to both traditional proteins manufacturing processes and mineral fertilizers, not only at a pilot-scale but also at an industrial one. In the present study, both baseline system and VALUEWASTE solution are evaluated through the Environmental Life Cycle Assessment (E-LCA). The E-LCA is based on the standards ISO 14040 and 14044. The Environmental Footprint methodology was the one used in this study to evaluate the environmental impacts. The results for the baseline cases show that the food proteins coming from whey have the highest environmental impact on ecosystems compared to the other proteins sources: 7.5 and 15.9 folds higher than soybean and gluten, respectively. Comparing feed soybean and gluten, soybean has an environmental impact on human health 195.1 folds higher. In the case of biofertilizers, synthetic struvite has higher impacts than ammonium sulfate: 15.3 (ecosystems) and 11.8 (human health) fold, respectively. The results shown in the present study will be used as a reference to demonstrate the better environmental performance of the bio-based products obtained through the VALUEWASTE solution. Other originalities that the E-LCA performed in the VALUEWASTE project provides are the diverse direct implications on investment and policies. On one hand, better environmental performance will serve to remove the barriers linked to these kinds of technologies, boosting the investment that is backed by the E-LCA. On the other hand, it will be a germ to design new policies fostering these types of solutions to achieve two of the key targets of the European Community: being self-sustainable and carbon neutral.

Keywords: anaerobic digestion, biofertilizers, circular economy, nutrients recovery

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Authors: Natalia Alvarado, Myldred Hernandez-Gonzalez, Mary Laird, Madeline Phillips, Elizabeth Miller, Luis Mendez, Teresa Satterfield Linares

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Objectives: Focusing on the reported levels of depressive symptoms from Hispanic individuals in the U.S. during the ongoing COVID-19 pandemic, we analyze the psychological effects of being labeled an ‘essential worker/trabajador(a) esencial.’ We situate this attribute within the complex context of how an individual’s mental health is linked to work status and his/her community’s attitude toward such a status. Method: 336 Spanish-speaking adults (Mage = 34.90; SD = 11.00; 46% female) living in the U.S. participated in a mixed-method study. Participants completed a self-report Spanish-language survey consisting of COVID-19 prompts (e.g., Soy un trabajador esencial durante la pandemia. I am an ‘essential worker’ during the pandemic), civic engagement scale (CES) attitudes (e.g., Me siento responsable de mi comunidad. I feel responsible for my community) and behaviors (e.g., Ayudo a los miembros de mi comunidad. I help members of my community), and the Center for Epidemiological Studies Depression Scale (e.g., Me sentía deprimido/a. I felt depressed). The survey was conducted several months into the pandemic and before the vaccine distribution. Results: Regression analyses show that being labeled an essential worker was correlated to CES attitudes (b= .28, p < .001) and higher CES behaviors (b= .32, p < .001). Essential worker status also reported higher levels of depressive symptoms (b= .17, p < .05). In addition, we found that CES attitudes and CES behaviors were related to higher levels of depressive symptoms (b= .11, p <.05, b = .22, p < .001, respectively). These findings suggest that those who are on the frontlines during the COVID-19 pandemic suffer higher levels of depressive symptoms, despite their affirming community attitudes and behaviors. Discussion: Hispanics/Latinxs make up 53% of the high-proximity employees who must work in person and in close contact with others; this is the highest rate of any racial or ethnic category. Moreover, 31% of Hispanics are classified as essential workers. Our outcomes show that those labeled as trabajadores esenciales convey attitudes of remaining strong and resilient for COVID-19 victims. They also express community attitudes and behaviors reflecting a sense of responsibility to continue working to help others during these unprecedented times. However, we also find that the pressure of maintaining basic needs for others exacerbates mental health challenges and stressors, as many essential workers are anxious and stressed about their physical and economic security. As a result, community attitudes do not protect from depressive symptoms as Hispanic essential workers are failing to balance everyone’s needs, including their own (e.g., physical exhaustion and psychological distress). We conclude with a discussion on alternatives to the phrase ‘essential worker’ and of incremental steps that can be taken to address pandemic-related mental health issues targeting US Hispanic workers.

Keywords: COVID-19, essential worker, mental health, race and ethnicity

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Authors: T. N. Tran Thi, J. Morse, C. Detlefs, P. K. Cook, C. Yıldırım, A. C. Jakobsen, T. Zhou, J. Hartwig, V. Zurbig, D. Caliste, B. Fernandez, D. Eon, O. Loto, M. L. Hicks, A. Pakpour-Tabrizi, J. Baruchel

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The ability to grow boron-doped diamond epilayers of high crystalline quality is a prerequisite for the fabrication of diamond power electronic devices, in particular high voltage diodes and metal-oxide-semiconductor (MOS) transistors. Boron and intrinsic diamond layers are homoepitaxially overgrown by microwave assisted chemical vapour deposition (MWCVD) on single crystal high pressure, high temperature (HPHT) grown bulk diamond substrates. Various epilayer thicknesses were grown, with dopant concentrations ranging from 1021 atom/cm³ at nanometer thickness in the case of 'delta doping', up 1016 atom/cm³ and 50µm thickness or high electric field drift regions. The crystalline quality of these overgrown layers as regards defects, strain, distortion… is critical for the device performance through its relation to the final electrical properties (Hall mobility, breakdown voltage...). In addition to the optimization of the epilayer growth conditions in the MWCVD reactor, other important questions related to the crystalline quality of the overgrown layer(s) are: 1) what is the dependence on the bulk quality and surface preparation methods of the HPHT diamond substrate? 2) how do defects already present in the substrate crystal propagate into the overgrown layer; 3) what types of new defects are created during overgrowth, what are their growth mechanisms, and how can these defects be avoided? 4) how can we relate in a quantitative manner parameters related to the measured crystalline quality of the boron doped layer to the electronic properties of final processed devices? We describe synchrotron-based techniques developed to address these questions. These techniques allow the visualization of local defects and crystal distortion which complements the data obtained by other well-established analysis methods such as AFM, SIMS, Hall conductivity…. We have used Grazing Incidence X-ray Diffraction (GIXRD) at the ID01 beamline of the ESRF to study lattice parameters and damage (strain, tilt and mosaic spread) both in diamond substrate near surface layers and in thick (10–50 µm) overgrown boron doped diamond epi-layers. Micro- and nano-section topography have been carried out at both the BM05 and ID06-ESRF) beamlines using rocking curve imaging techniques to study defects which have propagated from the substrate into the overgrown layer(s) and their influence on final electronic device performance. These studies were performed using various commercially sourced HPHT grown diamond substrates, with the MWCVD overgrowth carried out at the Fraunhofer IAF-Germany. The synchrotron results are in good agreement with low-temperature (5°K) cathodoluminescence spectroscopy carried out on the grown samples using an Inspect F5O FESEM fitted with an IHR spectrometer.

Keywords: synchrotron X-ray diffaction, crystalline quality, defects, diamond overgrowth, rocking curve imaging

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Authors: Álvaro Ramírez Vidal, Martín Muñoz Morales, Francisco Jesús Fernández Morales, Luis Rodríguez Romero, José Villaseñor Camacho, Javier Llanos López

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In recent times, environmental problems have led to the extensive use of biological systems to solve them. Among the different types of biological systems, the use of plants such as aquatic macrophytes in constructed wetlands and terrestrial plant species for treating polluted soils and sludge has gained importance. Though the use of constructed wetlands for wastewater treatment is a well-researched domain, the slowness of pollutant degradation and high biomass production pose some challenges. Plants used in CW participate in different mechanisms for the capture and degradation of pollutants that also can retain some pharmaceutical and personal care products (PPCPs) that are very persistent in the environment. Thus, these systems present advantages in line with the guidelines published for the transition towards friendly and ecological procedures as they are environmentally friendly systems, consume low energy, or capture atmospheric CO₂. However, the use of CW presents some drawbacks, as the slowness of pollutant degradation or the production of important amounts of plant biomass, which need to be harvested and managed periodically. Taking this opportunity in mind, it is important to highlight that this residual biomass (of lignocellulosic nature) could be used as the feedstock for the generation of carbonaceous materials using thermochemical transformations such as slow pyrolysis or hydrothermal carbonization to produce high-value biomass-derived carbons through sustainable processes as adsorbents, catalysts…, thereby improving the circular carbon economy. Thus, this work carried out the analysis of some PPCPs commonly found in urban wastewater, as salicylic acid or ibuprofen, to evaluate the remediation carried out for the Phragmites Australis. Then, after the harvesting, this biomass can be used to synthesize electrodes through hydrothermal carbonization (HTC) and produce high-value biomass-derived carbons with electrocatalytic activity to remove heavy metals and persistent pollutants, promoting circular economy concepts. To do this, it was chosen biomass derived from the natural environment in high environmental risk as the Daimiel Wetlands National Park in the center of Spain, and the rest of the biomass developed in a CW specifically designed to remove pollutants. The research emphasizes the impact of the composition of the biomass waste and the synthetic parameters applied during HTC on the electrocatalytic activity. Additionally, this parameter can be related to the physicochemical properties, as porosity, surface functionalization, conductivity, and mass transfer of the electrodes lytic inks. Data revealed that carbon materials synthesized have good surface properties (good conductivities and high specific surface area) that enhance the electro-oxidants generated and promote the removal of PPCPs and the chemical oxygen demand of polluted waters.

Keywords: constructed wetlands, carbon materials, heavy metals, pharmaceutical and personal care products, hydrothermal carbonization

Procedia PDF Downloads 50

Authors: Diego F. Carrillo-González, Milena Osorio, Natalia M. Cerro, Yasser Y. Lenis

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Introduction: mortality during the implantation and early embryonic development periods reach around 30% in different mammalian species. It has been described that progesterone (P4) and Arginine (Arg) play a beneficial role in establishing and maintaining early pregnancy in mammals. The combined effect between Arg and P4 on reproductive parameters in the rabbit species is not yet elucidated, to our best knowledge. Objective: to assess the effect of L-arginine and progesterone during the post-breeding period in female rabbits on the composition of the amniotic fluid, the placental structure, and the bone growth in their fetuses. Methods: crossbred female rabbits (n=16) were randomly distributed into four experimental groups (Ctrl, Arg, P4, and Arg+P4). In the control group, 0.9% saline solution was administered as a placebo, the Arg group was administered arginine (50 mg/kg BW) from day 4.5 to day 19 post-breeding, the P4 group was administered progesterone (Gestavec®, 1.5 mg/kg BW) from 24 hours to day 4 post-breeding and for the Arg+P4 group, an administration was performed under the same time and dose guidelines as the Arg and P4 treatments. Four females were sacrificed, and the amniotic fluid was collected and analyzed with rapid urine test strips, while the placenta and fetuses were processed in the laboratory to obtain histological plates. The percentage of deciduous, labyrinthine, and junctional zones was determined, and the length of the femur for each fetus was measured as an indicator of growth. Descriptive statistics were applied to identify the success rates for each of the tests. Afterwards, A one-way analysis of variance (ANOVA) was performed, and a comparison of means was conducted by Tukey's test. Results: a higher density (p<0.05) was observed in the amniotic fluid for fetuses in the control group (1022±2.5g/mL) compared to the P4 (1015±5.3g/mL) and Arg+P4 (1016±4,9g/mL) groups. Additionally, the density of amniotic fluid in the Arg group (1021±2.5g/mL) was higher (p<0.05) than in the P4 group. The concentration of protein, glucose, and ascorbic acid had no statistical difference between treatments (p>0.05). The histological analysis of the uteroplacental regions, a statistical difference (p<0,05) in the proportion of deciduous zone was found between the P4 group (9.6±2.6%) when compared with the Ctrl (28.15±12.3%), and Arg+P4 (26.3±4.9) groups. In the analysis of the fetuses, the weight was higher for the Arg group (2.69±0.18), compared to the other groups (p<0.05), while a shorter length was observed (p<0.05) in the fetuses for the Arg+P4 group (25.97±1.17). However, no difference (p>0.05) was found when comparing the length of the developing femurs between the experimental groups. Conclusion: the combination of L-arginine and progesterone allows a reduction in the density of amniotic fluid, without affecting the protein, energy, and antioxidant components. However, the use of L-arginine stimulates weight gain in fetuses, without affecting size, which could be used to improve production parameters in rabbit production systems. In addition, the modification in the deciduous zone could show a placental adaptation based on the fetal growth process, however more specific studies on the placentation process are required.

Keywords: arginine, progesterone, rabbits, reproduction

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Authors: M. Cardenas-Garcia, P. P. Gonzalez-Perez

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Intercellular communication is a necessary condition for cellular functions and it allows a group of cells to survive as a population. Throughout this interaction, the cells work in a coordinated and collaborative way which facilitates their survival. In the case of cancerous cells, these take advantage of intercellular communication to preserve their malignancy, since through these physical unions they can send signs of malignancy. The Wnt/β-catenin signaling pathway plays an important role in the formation of intercellular communications, being also involved in a large number of cellular processes such as proliferation, differentiation, adhesion, cell survival, and cell death. The modeling and simulation of cellular signaling systems have found valuable support in a wide range of modeling approaches, which cover a wide spectrum ranging from mathematical models; e.g., ordinary differential equations, statistical methods, and numerical methods– to computational models; e.g., process algebra for modeling behavior and variation in molecular systems. Based on these models, different simulation tools have been developed from mathematical ones to computational ones. Regarding cellular and molecular processes in cancer, its study has also found a valuable support in different simulation tools that, covering a spectrum as mentioned above, have allowed the in silico experimentation of this phenomenon at the cellular and molecular level. In this work, we simulate and explore the complex interaction patterns of intercellular communication in cancer cells using the Cellulat bioinformatics tool, a computational simulation tool developed by us and motivated by two key elements: 1) a biochemically inspired model of self-organizing coordination in tuple spaces, and 2) the Gillespie’s algorithm, a stochastic simulation algorithm typically used to mimic systems of chemical/biochemical reactions in an efficient and accurate way. The main idea behind the Cellulat simulation tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation in intra and intercellular signaling networks. Unlike most of the cell signaling simulation tools, such as E-Cell, BetaWB and Cell Illustrator which provides abstractions to model only intracellular behavior, Cellulat is appropriate for modeling both intracellular signaling and intercellular communication, providing the abstractions required to model –and as a result, simulate– the interaction mechanisms that involve two or more cells, that is essential in the scenario discussed in this work. During the development of this work we made evident the application of our computational simulation tool (Cellulat) for the modeling and simulation of intercellular communication between normal and cancerous cells, and in this way, propose key molecules that may prevent the arrival of malignant signals to the cells that surround the tumor cells. In this manner, we could identify the significant role that has the Wnt/β-catenin signaling pathway in cellular communication, and therefore, in the dissemination of cancer cells. We verified, using in silico experiments, how the inhibition of this signaling pathway prevents that the cells that surround a cancerous cell are transformed.

Keywords: cancer cells, in silico approach, intercellular communication, key molecules, modeling and simulation

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Authors: B. R. Campomanes-Alvarez, P. Quiros, B. Fernandez

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Automatic Speech Recognition (ASR) is a machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker or an audio file, analyzes it using algorithms, and produces an output in the form of a text. Some speech recognition systems use Hidden Markov Models (HMMs) to deal with the temporal variability of speech and Gaussian Mixture Models (GMMs) to determine how well each state of each HMM fits a short window of frames of coefficients that represents the acoustic input. Another way to evaluate the fit is to use a feed-forward neural network that takes several frames of coefficients as input and produces posterior probabilities over HMM states as output. Deep neural networks (DNNs) that have many hidden layers and are trained using new methods have been shown to outperform GMMs on a variety of speech recognition systems. Acoustic models for state-of-the-art ASR systems are usually training on massive amounts of data. However, audio files with their corresponding transcriptions can be difficult to obtain, especially in the Spanish language. Hence, in the case of these low-resource scenarios, building an ASR model is considered as a complex task due to the lack of labeled data, resulting in an under-trained system. Semi-supervised learning approaches arise as necessary tasks given the high cost of transcribing audio data. The main goal of this proposal is to develop a procedure based on acoustic semi-supervised learning for Spanish ASR systems by using DNNs. This semi-supervised learning approach consists of: (a) Training a seed ASR model with a DNN using a set of audios and their respective transcriptions. A DNN with a one-hidden-layer network was initialized; increasing the number of hidden layers in training, to a five. A refinement, which consisted of the weight matrix plus bias term and a Stochastic Gradient Descent (SGD) training were also performed. The objective function was the cross-entropy criterion. (b) Decoding/testing a set of unlabeled data with the obtained seed model. (c) Selecting a suitable subset of the validated data to retrain the seed model, thereby improving its performance on the target test set. To choose the most precise transcriptions, three confidence scores or metrics, regarding the lattice concept (based on the graph cost, the acoustic cost and a combination of both), was performed as selection technique. The performance of the ASR system will be calculated by means of the Word Error Rate (WER). The test dataset was renewed in order to extract the new transcriptions added to the training dataset. Some experiments were carried out in order to select the best ASR results. A comparison between a GMM-based model without retraining and the DNN proposed system was also made under the same conditions. Results showed that the semi-supervised ASR-model based on DNNs outperformed the GMM-model, in terms of WER, in all tested cases. The best result obtained an improvement of 6% relative WER. Hence, these promising results suggest that the proposed technique could be suitable for building ASR models in low-resource environments.

Keywords: automatic speech recognition, deep neural networks, machine learning, semi-supervised learning

Procedia PDF Downloads 319

Authors: C. Fernandez-Plaza, I. Abad, E. Diaz, I. Diaz

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Births occurring before the 37th week of gestation are considered preterm births. A threat of preterm is defined as the beginning of regular uterine contractions, dilation and cervical effacement between 23 and 36 gestation weeks. To author's best knowledge, the factors that determine the beginning of the birth are not completely defined yet. In particular, the incidence of sleep habits on preterm births is weekly studied. The aim of this study is to develop a model to predict the factors affecting premature delivery on pregnancy, based on the above potential risk factors, including those derived from sleep habits and light exposure at night (introduced as 12 variables obtained by a telephone survey using two questionnaires previously used by other authors). Thus, three groups of variables were included in the study (maternal, fetal and sleep habits). The study was approved by Research Ethics Committee of the Principado of Asturias (Spain). An observational, retrospective and descriptive study was performed with 481 births between January 1, 2015 and May 10, 2016 in the University Central Hospital of Asturias (Spain). A statistical analysis using SPSS was carried out to compare qualitative and quantitative variables between preterm and term delivery. Chi-square test qualitative variable and t-test for quantitative variables were applied. Statistically significant differences (p < 0.05) between preterm vs. term births were found for primiparity, multi-parity, kind of conception, place of residence or premature rupture of membranes and interruption during nights. In addition to the statistical analysis, machine learning methods to look for a prediction model were tested. In particular, tree based models were applied as the trade-off between performance and interpretability is especially suitable for this study. C5.0, recursive partitioning, random forest and tree bag models were analysed using caret R-package. Cross validation with 10-folds and parameter tuning to optimize the methods were applied. In addition, different noise reduction methods were applied to the initial data using NoiseFiltersR package. The best performance was obtained by C5.0 method with Accuracy 0.91, Sensitivity 0.93, Specificity 0.89 and Precision 0.91. Some well known preterm birth factors were identified: Cervix Dilation, maternal BMI, Premature rupture of membranes or nuchal translucency analysis in the first trimester. The model also identifies other new factors related to sleep habits such as light through window, bedtime on working days, usage of electronic devices before sleeping from Mondays to Fridays or change of sleeping habits reflected in the number of hours, in the depth of sleep or in the lighting of the room. IF dilation < = 2.95 AND usage of electronic devices before sleeping from Mondays to Friday = YES and change of sleeping habits = YES, then preterm is one of the predicting rules obtained by C5.0. In this work a model for predicting preterm births is developed. It is based on machine learning together with noise reduction techniques. The method maximizing the performance is the one selected. This model shows the influence of variables related to sleep habits in preterm prediction.

Keywords: machine learning, noise reduction, preterm birth, sleep habit

Procedia PDF Downloads 111

Authors: E. Mas de les Valls, M. Castells-Sanabra, R. Capdevila, N. Pla, Rosa M. Fernandez-Canti, V. de Medina, A. Mujal, C. Barahona, E. Velo, M. Vigo, M. A. Santos, T. Soto

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Absenteeism in universities, including polytechnical universities, is influenced by a variety of factors. Some factors overlap with those causing absenteeism in schools, while others are specific to the university and work-related environments. Indeed, these factors may stem from various sources, including students, educators, the institution itself, or even the alignment of degree curricula with professional requirements. In Spain, there has been an increase in absenteeism in polytechnical university studies, especially after the Covid crisis, posing a significant challenge for institutions to address. This study focuses on Universitat Politècnica de Catalunya• BarcelonaTech (UPC) and aims to quantify the current level of absenteeism and identify its main causes. The study is part of the teaching innovation project ASAP-UPC, which aims to minimize absenteeism through the redesign of teaching methodologies. By understanding the factors contributing to absenteeism, the study seeks to inform the subsequent phases of the ASAP-UPC project, which involve implementing methodologies to minimize absenteeism and evaluating their effectiveness. The study utilizes surveys conducted among students and polytechnical companies. Students' perspectives are gathered through both online surveys and in-person interviews. The surveys inquire about students' interest in attending classes, skill development throughout their UPC experience, and their perception of the skills required for a career in a polytechnical field. Additionally, polytechnical companies are surveyed regarding the skills they seek in prospective employees. The collected data is then analyzed to identify patterns and trends. This analysis involves organizing and categorizing the data, identifying common themes, and drawing conclusions based on the findings. This mixed-method approach has revealed that higher levels of absenteeism are observed in large student groups at both the Bachelor's and Master's degree levels. However, the main causes of absenteeism differ between these two levels. At the Bachelor's level, many students express dissatisfaction with in-person classes, perceiving them as overly theoretical and lacking a balance between theory, experimental practice, and problem-solving components. They also find a lack of relevance to professional needs. Consequently, they resort to using online available materials developed during the Covid crisis and attending private academies for exam preparation instead. On the other hand, at the Master's level, absenteeism primarily arises from schedule incompatibility between university and professional work. There is a discrepancy between the skills highly valued by companies and the skills emphasized during the studies, aligning partially with students' perceptions. These findings are of theoretical importance as they shed light on areas that can be improved to offer a more beneficial educational experience to students at UPC. The study also has potential applicability to other polytechnic universities, allowing them to adapt the surveys and apply the findings to their specific contexts. By addressing the identified causes of absenteeism, universities can enhance the educational experience and better prepare students for successful careers in polytechnical fields.

Keywords: absenteeism, polytechnical studies, professional skills, university challenges

Procedia PDF Downloads 42

Authors: Idoia Landa Oregi, Itsaso Gonzalez Ochoantesana, Olatz Nicolas Buxens, Carlo Ferretti

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The impact of citizens in urban transformations has become increasingly important in the pursuit of creating citizen-centered cities. Citizens at the forefront of the urban transformation process are key to establishing resilient, sustainable, and inclusive cities that cater to the needs of all residents. Therefore, collecting data and information directly from citizens is crucial for the sustainable development of cities. Within this context, public participation becomes a pillar for acquiring the necessary information from citizens. Public participation in urban transformation processes establishes a more responsive, equitable, and resilient urban environment. This approach cultivates a sense of shared responsibility and collective progress in building cities that truly serve the well-being of all residents. However, the implementation of public participation practices often overlooks strategies to effectively engage citizens in the processes, resulting in non-successful participatory outcomes. Therefore, this research focuses on identifying and analyzing the critical aspects of citizen engagement during the same participatory urban transformation process in different European contexts: Ermua (Spain), Elva (Estonia) and Matera (Italy). The participatory neighborhood regeneration process is divided into three main stages, to turn social districts into inclusive and smart neighborhoods: (i) the strategic level, (ii) the design level, and (iii) the implementation level. In the initial stage, the focus is on diagnosing the neighborhood and creating a shared vision with the community. The second stage centers around collaboratively designing various action plans to foster inclusivity and intelligence while pushing local economic development within the district. Finally, the third stage ensures the proper co-implementation of the designed actions in the neighborhood. To this date, the presented results critically analyze the key aspects of engagement in the first stage of the methodology, the strategic plan, in the three above-mentioned contexts. It is a multifaceted study that incorporates three case studies to shed light on the various perspectives and strategies adopted by each city. The results indicate that despite of the various cultural contexts, all cities face similar barriers when seeking to enhance engagement. Accordingly, the study identifies specific challenges within the participatory approach across the three cities such as the existence of discontented citizens, communication gaps, inconsistent participation, or administration resistance. Consequently, key learnings of the process indicate that a collaborative sphere needs to be cultivated, educating both citizens and administrations in the aspects of co-governance, giving these practices the appropriate space and their own communication channels. This study is part of the DROP project, funded by the European Union, which aims to develop a citizen-centered urban renewal methodology to transform the social districts into smart and inclusive neighborhoods.

Keywords: citizen-centred cities, engagement, public participation, urban transformation

Procedia PDF Downloads 25

Authors: Brayan S. Pabón, R. Ricardo Moreno, Edith Gonzalez

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Inside the wide Colombian natural fiber set is the banana stem leaf, known as Calceta de Plátano, which is a material present in several regions of the country and is a fiber extracted from the pseudo stem of the banana plant (Musa paradisiaca) as a regular maintenance process. Colombia had a production of 2.8 million tons in 2007 and 2008 corresponding to 8.2% of the international production, number that is growing. This material was selected to be studied because it is not being used by farmers due to it being perceived as a waste from the banana harvest and a propagation pest agent inside the planting. In addition, the Calceta does not have industrial applications in Colombia since there is not enough concrete knowledge that informs us about the properties of the material and the possible applications it could have. Based on this situation the industrial design is used as a link between the properties of the material and the need to transform it into industrial products for the market. Therefore, the project identifies potential design attributes that the banana stem leaf can have for product development. The methodology was divided into 2 main chapters: Methodology for the material recognition: -Data Collection, inquiring the craftsmen experience and bibliography. -Knowledge in practice, with controlled experiments and validation tests. -Creation of design attributes and material profile according to the knowledge developed. Moreover, the Design methodology: -Application fields selection, exploring the use of the attributes and the relation with product functions. -Evaluating the possible fields and selection of the optimum application. -Design Process with sketching, ideation, and product development. Different protocols were elaborated to qualitatively determine some material properties of the Calceta, and if they could be designated as design attributes. Once defined, performed and analyzed the validation protocols, 25 design attributes were identified and classified into 4 attribute categories (Environmental, Functional, Aesthetics and Technical) forming the material profile. Then, 15 application fields were defined based on the relation between functions of product and the use of the Calceta attributes. Those fields were evaluated to measure how much are being used the functional attributes. After fields evaluation, a final field was defined , influenced by traditional use of the fiber for packing food. As final result, two products were designed for this application field. The first one is the Multiple Container, which works to contain small or large-thin pieces of food, like potatoes chips or small sausages; it allows the consumption of food with sauces or dressings. The second is the Chorizo container, specifically designed for this food due to the long shape and the consumption mode. Natural fiber research allows the generation of a solider and a more complete knowledge about natural fibers. In addition, the research is a way to strengthen the identity through the investigation of the proper and autochthonous, allowing the use of national resources in a sustainable and creative way. Using divergent thinking and the design as a tool, this investigation can achieve advances in the natural fiber handling.

Keywords: banana stem leaf, Calceta de Plátano, design attributes, natural fibers, product design

Procedia PDF Downloads 226

Authors: Raquel Dafouz Neus Cáceres, Javier Fernandez-Rubio, Belinda Huerta José Luis Rodríguez-Gil, Nicola Mastroianni, Miren López de Alda, Damià Barceló, Yolanda Valcárcel

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The region of Galicia, found in north-western (NW) Spain, is a national and world leader in shellfish, especially mussel production, and recognized for its fishing industry. Few studies have evaluated the presence of emerging contaminants in NW Spain, with those published mainly concerning the continental aquatic environment. The objective of this study was to identify the environmental risk posed by the presence of pharmaceuticals and drugs of abuse in this important coastal region. The presence of sixteen pharmaceuticals (benzodiazepines, anxiolytics, and caffeine), and 19 drugs of abuse (cocainics, amphetamine-like compounds, opiates and opioids, lysergic compounds, and cannabinoids) was assessed in 23 sites located in the Rías (Coastal inlets) of Muros, Arousa, and Pontevedra (NW Spain). Twenty-two of these locations were affected by waste-water treatment plant (WWTP) effluents, and one represented the effluent of one of these WWTPs. Venlafaxine was the pharmaceutical compound detected at higher concentration in the three Rías, with a maximum value of 291 ng/L at the site Porto do Son (Ría de Muros). Total concentration in the three Rías was 819,26 ng/L. Next, citalopram and lorazepam were the most prevalent compounds detected. Metabolite of cocaine benzoylecgonine was the drug of abuse with the highest concentration, measured at 972 ng/L in the Ría of Noia WWTP (no dilution). This compound was also detected at 142 ng/L in the site La Isla de Aros, Ría of Pontevedra. Total concentration for the three Rías was 1210 ng/L. Ephedrine was also detected at high level in the three Rías, with a total concentration of 579,28 ng/L. The results obtained for caffeine show maximum and average concentrations of 857 ng/L Isla de Arosa, Ría de Pontevedra the highest measured in seawater in Spain. A preliminary hazard assessment was carried out by comparing these measured environmental concentrations (MEC) to predicted no-effect concentrations (PNECs) for aquatic organisms. Six out of the 22 seawater samples resulted in a Hazard Quotient (HQ) from chronic exposure higher than 1 with the highest being 17.14, indicating a high probability of adverse effects in the aquatic environment. In addition, the risk was assessed on the basis of persistence, bioaccumulation, and toxicity (PBT). This work was financially supported by the Spanish Ministry of Economy and Competitiveness through the Carlos III Health Institute and the program 'Proyectos de Investigacion en Salud 2015-2017' FIS (PI14/00516), the European Regional Development Fund (ERDF), the Catalan Government (Consolidated Research Groups '2014 SGR 418 - Water and Soil Quality Unit' and 2014 SGR 291 - ICRA), and the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 603437. The poster entitled 'Environmental Risk of Pharmaceuticals, Drugs of Abuse and Stimulant Caffeine in Marine Water: A Case Study in the North-Western of Spain'.

Keywords: drug of abuse, pharmaceuticals, caffeine, environmental risk, seawater

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Authors: Marlon Gallo, Eduardo Miguel, Laura Oca, Eneko Gonzalez, Unai Iraola

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The heat generation of an energy storage system is an essential topic when designing a battery pack and its cooling system. Heat generation estimation is used together with thermal models to predict battery temperature in operation and adapt the design of the battery pack and the cooling system to these thermal needs guaranteeing its safety and correct operation. In the present work, a comparison between the use of a heat flux sensor (HFS) for indirect measurement of heat losses in a cell and the widely used and simplified version of Bernardi’s equation for estimation is presented. First, a Li-ion cell is thermally characterized with an HFS to measure the thermal parameters that are used in a first-order lumped thermal model. These parameters are the equivalent thermal capacity and the thermal equivalent resistance of a single Li-ion cell. Static (when no current is flowing through the cell) and dynamic (making current flow through the cell) tests are conducted in which HFS is used to measure heat between the cell and the ambient, so thermal capacity and resistances respectively can be calculated. An experimental platform records current, voltage, ambient temperature, surface temperature, and HFS output voltage. Second, an equivalent circuit model is built in a Matlab-Simulink environment. This allows the comparison between the generated heat predicted by Bernardi’s equation and the HFS measurements. Data post-processing is required to extrapolate the heat generation from the HFS measurements, as the sensor records the heat released to the ambient and not the one generated within the cell. Finally, the cell temperature evolution is estimated with the lumped thermal model (using both HFS and Bernardi’s equation total heat generation) and compared towards experimental temperature data (measured with a T-type thermocouple). At the end of this work, a critical review of the results obtained and the possible mismatch reasons are reported. The results show that indirectly measuring the heat generation with HFS gives a more precise estimation than Bernardi’s simplified equation. On the one hand, when using Bernardi’s simplified equation, estimated heat generation differs from cell temperature measurements during charges at high current rates. Additionally, for low capacity cells where a small change in capacity has a great influence on the terminal voltage, the estimated heat generation shows high dependency on the State of Charge (SoC) estimation, and therefore open circuit voltage calculation (as it is SoC dependent). On the other hand, with indirect measuring the heat generation with HFS, the resulting error is a maximum of 0.28ºC in the temperature prediction, in contrast with 1.38ºC with Bernardi’s simplified equation. This illustrates the limitations of Bernardi’s simplified equation for applications where precise heat monitoring is required. For higher current rates, Bernardi’s equation estimates more heat generation and consequently, a higher predicted temperature. Bernardi´s equation accounts for no losses after cutting the charging or discharging current. However, HFS measurement shows that after cutting the current the cell continues generating heat for some time, increasing the error of Bernardi´s equation.

Keywords: lithium-ion battery, heat flux sensor, heat generation, thermal characterization

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Authors: F. Uriel, M. M. Fernandez Liporace

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In the last decade, academic achievement in higher education has become a topic of agenda in Argentina, regarding the high figures of adjustment problems, academic failure and dropout, and the low graduation rates in the context of massive classes and traditional teaching methods. Psychological variables, such as perceived social support, academic motivation and learning styles and strategies have much to offer since their measurement by tests allows a proper diagnose of their influence on academic achievement. Framed in a major research, several studies analysed multiple samples, totalizing 5135 students attending Argentinean public universities. The first goal was aimed at the identification of statistically significant differences in psychological variables -perceived social support, learning styles, learning strategies, and academic motivation- by age, gender, and degree of academic advance (freshmen versus sophomores). Thus, an inferential group differences study for each psychological dependent variable was developed by means of student’s T tests, given the features of data distribution. The second goal, aimed at examining associations between the four psychological variables on the one hand, and academic achievement on the other, was responded by correlational studies, calculating Pearson’s coefficients, employing grades as the quantitative indicator of academic achievement. The positive and significant results that were obtained led to the formulation of different predictive models of academic achievement which had to be tested in terms of adjustment and predictive power. These models took the four psychological variables above mentioned as predictors, using regression equations, examining predictors individually, in groups of two, and together, analysing indirect effects as well, and adding the degree of academic advance and gender, which had shown their importance within the first goal’s findings. The most relevant results were: first, gender showed no influence on any dependent variable. Second, only good achievers perceived high social support from teachers, and male students were prone to perceive less social support. Third, freshmen exhibited a pragmatic learning style, preferring unstructured environments, the use of examples and simultaneous-visual processing in learning, whereas sophomores manifest an assimilative learning style, choosing sequential and analytic processing modes. Despite these features, freshmen have to deal with abstract contents and sophomores, with practical learning situations due to study programs in force. Fifth, no differences in academic motivation were found between freshmen and sophomores. However, the latter employ a higher number of more efficient learning strategies. Sixth, freshmen low achievers lack intrinsic motivation. Seventh, models testing showed that social support, learning styles and academic motivation influence learning strategies, which affect academic achievement in freshmen, particularly males; only learning styles influence achievement in sophomores of both genders with direct effects. These findings led to conclude that educational psychologists, education specialists, teachers, and universities must plan urgent and major changes. These must be applied in renewed and better study programs, syllabi and classes, as well as tutoring and training systems. Such developments should be targeted to the support and empowerment of students in their academic pathways, and therefore to the upgrade of learning quality, especially in the case of freshmen, male freshmen, and low achievers.

Keywords: academic achievement, academic motivation, coping, learning strategies, learning styles, perceived social support

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Authors: Bello Gonzalez T. D. J., Setten Van M., Essen Van A., Brouwer M., Veldman K. T.

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Resistance to fluoroquinolones (FQ) has evolved increasingly over the years, posing a significant challenge for the treatment of human infections, particularly gastrointestinal tract infections caused by zoonotic bacteria transmitted through the food chain and environment. In broiler chickens, a relatively high proportion of FQ resistance has been observed in Escherichia coli indicator, Salmonella and Campylobacter isolates. We hypothesize that flumequine (Flu), used as a secondary choice for the treatment of poultry infections, could potentially be associated with a high proportion of FQ resistance. To evaluate this hypothesis, we used an in vitro fermentation chicken caeca model. Two continuous single-stage fermenters were used to simulate in real time the physiological conditions of the chicken caeca microbial content (temperature, pH, caecal content mixing, and anoxic environment). A pool of chicken caecal content containing FQ-resistant E. coli obtained from chickens at slaughter age was used as inoculum along with a spiked FQ-susceptible Campylobacter jejuni strain isolated from broilers. Flu was added to one of the fermenters (Flu-fermenter) every 24 hours for two days to evaluate the selection and maintenance of FQ resistance over time, while the other served as a control (C-Fermenter). The experiment duration was 5 days. Samples were collected at three different time points: before, during and after Flu administration. Serial dilutions were plated on Butzler culture media with and without Flu (8mg/L) and enrofloxacin (4mg/L) and on MacConkey culture media with and without Flu (4mg/L) and enrofloxacin (1mg/L) to determine the proportion of resistant strains over time. Positive cultures were identified by mass spectrometry and matrix-assisted laser desorption/ionization (MALDI). A subset of the obtained isolates were used for Whole Genome Sequencing analysis. Over time, E. coli exhibited positive growth in both fermenters, while C. jejuni growth was detected up to day 3. The proportion of Flu-resistant E. coli strains recovered remained consistent over time after antibiotic selective pressure, while in the C-fermenter, a decrease was observed at day 5; a similar pattern was observed in the enrofloxacin-resistant E. coli strains. This suggests that Flu might play a role in the selection and persistence of enrofloxacin resistance, compared to C-fermenter, where enrofloxacin-resistant E. coli strains appear at a later time. Furthermore, positive growth was detected from both fermenters only on Butzler plates without antibiotics. A subset of C. jejuni strains from the Flu-fermenter revealed that those strains were susceptible to ciprofloxacin (MIC < 0.12 μg/mL). A selection of E. coli strains from both fermenters revealed the presence of plasmid-mediated quinolone resistance (PMQR) (qnr-B19) in only one strain from the C-fermenter belonging to sequence type (ST) 48, and in all from Flu-fermenter belonged to ST189. Our results showed that Flu selective impact on PMQR-positive E. coli strains, while no effect was observed in C. jejuni. Maintenance of Flu-resistance was correlated with antibiotic selective pressure. Further studies into antibiotic resistance gene transfer among commensal and zoonotic bacteria in the chicken caeca content may help to elucidate the resistance spread mechanisms.

Keywords: fluoroquinolone-resistance, escherichia coli, campylobacter jejuni, in vitro model

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Authors: Pau Rodriguez, Jordi Gonzalez, Josep M. Gonfaus, Xavier Roca

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Image sharing in social networks has increased exponentially in the past years. Officially, there are 600 million Instagrammers uploading around 100 million photos and videos per day. Consequently, there is a need for developing new tools to understand the content expressed in shared images, which will greatly benefit social media communication and will enable broad and promising applications in education, advertisement, entertainment, and also psychology. Following these trends, our work aims to take advantage of the existing relationship between text and personality, already demonstrated by multiple researchers, so that we can prove that there exists a relationship between images and personality as well. To achieve this goal, we consider that images posted on social networks are typically conditioned on specific words, or hashtags, therefore any relationship between text and personality can also be observed with those posted images. Our proposal makes use of the most recent image understanding models based on neural networks to process the vast amount of data generated by social users to determine those images most correlated with personality traits. The final aim is to train a weakly-supervised image-based model for personality assessment that can be used even when textual data is not available, which is an increasing trend. The procedure is described next: we explore the images directly publicly shared by users based on those accompanying texts or hashtags most strongly related to personality traits as described by the OCEAN model. These images will be used for personality prediction since they have the potential to convey more complex ideas, concepts, and emotions. As a result, the use of images in personality questionnaires will provide a deeper understanding of respondents than through words alone. In other words, from the images posted with specific tags, we train a deep learning model based on neural networks, that learns to extract a personality representation from a picture and use it to automatically find the personality that best explains such a picture. Subsequently, a deep neural network model is learned from thousands of images associated with hashtags correlated to OCEAN traits. We then analyze the network activations to identify those pictures that maximally activate the neurons: the most characteristic visual features per personality trait will thus emerge since the filters of the convolutional layers of the neural model are learned to be optimally activated depending on each personality trait. For example, among the pictures that maximally activate the high Openness trait, we can see pictures of books, the moon, and the sky. For high Conscientiousness, most of the images are photographs of food, especially healthy food. The high Extraversion output is mostly activated by pictures of a lot of people. In high Agreeableness images, we mostly see flower pictures. Lastly, in the Neuroticism trait, we observe that the high score is maximally activated by animal pets like cats or dogs. In summary, despite the huge intra-class and inter-class variabilities of the images associated to each OCEAN traits, we found that there are consistencies between visual patterns of those images whose hashtags are most correlated to each trait.

Keywords: emotions and effects of mood, social impact theory in social psychology, social influence, social structure and social networks

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Authors: Jose A. Alvarez Aldegunde, Adrian Fernandez Sanchez, Matthew D. Menden, Bernardo Vila Rodriguez

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Up to five statistical pre-processings have been carried out considering the pollutant records of the stations present in Cartagena de Indias, Colombia, also taking into account the childhood asthma incidence surveys conducted in hospitals in the city by the Health Ministry of Colombia for this study. These pre-processings have consisted of different techniques such as the determination of the quality of data collection, determination of the quality of the registration network, identification and debugging of errors in data collection, completion of missing data and purified data, as well as the improvement of the time scale of records. The characterization of the quality of the data has been conducted by means of density analysis of the pollutant registration stations using ArcGis Software and through mass balance techniques, making it possible to determine inconsistencies in the records relating the registration data between stations following the linear regression. The results obtained in this process have highlighted the positive quality in the pollutant registration process. Consequently, debugging of errors has allowed us to identify certain data as statistically non-significant in the incidence and series of contamination. This data, together with certain missing records in the series recorded by the measuring stations, have been completed by statistical imputation equations. Following the application of these prior processes, the basic series of incidence data for respiratory disease and pollutant records have allowed the characterization of the influence of pollutants on respiratory diseases such as, for example, childhood asthma. This characterization has been carried out using statistical correlation methods, including visual correlation, simple linear regression correlation and spectral analysis with PAST Software which identifies maximum periodicity cycles and minimums under the formula of the Lomb periodgram. In relation to part of the results obtained, up to eleven maximums and minimums considered contemporary between the incidence records and the particles have been identified taking into account the visual comparison. The spectral analyses that have been performed on the incidence and the PM2.5 have returned a series of similar maximum periods in both registers, which are at a maximum during a period of one year and another every 25 days (0.9 and 0.07 years). The bivariate analysis has managed to characterize the variable "Daily Vehicular Flow" in the ninth position of importance of a total of 55 variables. However, the statistical correlation has not obtained a favorable result, having obtained a low value of the R2 coefficient. The series of analyses conducted has demonstrated the importance of the influence of pollutants such as PM2.5 in the development of childhood asthma in Cartagena. The quantification of the influence of the variables has been able to determine that there is a 56% probability of dependence between PM2.5 and childhood respiratory asthma in Cartagena. Considering this justification, the study could be completed through the application of the BenMap Software, throwing a series of spatial results of interpolated values of the pollutant contamination records that exceeded the established legal limits (represented by homogeneous units up to the neighborhood level) and results of the impact on the exacerbation of pediatric asthma. As a final result, an economic estimate (in Colombian Pesos) of the monthly and individual savings derived from the percentage reduction of the influence of pollutants in relation to visits to the Hospital Emergency Room due to asthma exacerbation in pediatric patients has been granted.

Keywords: Asthma Incidence, BenMap, PM2.5, Statistical Analysis

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Authors: Sylvana A. Vega, Cesar E. Huilinir, Carlos J. Gonzalez

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Co-digestion in anaerobic biodigesters is a technology improving hydrolysis by increasing methane generation. In the present study, the dimensional computational fluid dynamics (CFD) is numerically analyzed using Ansys Fluent software for agitation in a full-scale Continuous Stirred Tank Reactor (CSTR) biodigester during the co-digestion process. For this, a rheological study of the substrate is carried out, establishing rotation speeds of the stirrers depending on the microbial activity and energy ranges. The substrate is organic waste from industrial sources of sanitary water, butcher, fishmonger, and dairy. Once the rheological behavior curves have been obtained, it is obtained that it is a non-Newtonian fluid of the pseudoplastic type, with a solids rate of 12%. In the simulation, the rheological results of the fluid are considered, and the full-scale CSTR biodigester is modeled. It was coupling the second-order continuity differential equations, the three-dimensional Navier Stokes, the power-law model for non-Newtonian fluids, and three turbulence models: k-ε RNG, k-ε Realizable, and RMS (Reynolds Stress Model), for a 45° tilt vane impeller. It is simulated for three minutes since it is desired to study an intermittent mixture with a saving benefit of energy consumed. The results show that the absolute errors of the power number associated with the k-ε RNG, k-ε Realizable, and RMS models were 7.62%, 1.85%, and 5.05%, respectively, the numbers of power obtained from the analytical-experimental equation of Nagata. The results of the generalized Reynolds number show that the fluid dynamics have a transition-turbulent flow regime. Concerning the Froude number, the result indicates there is no need to implement baffles in the biodigester design, and the power number provides a steady trend close to 1.5. It is observed that the levels of design speeds within the biodigester are approximately 0.1 m/s, which are speeds suitable for the microbial community, where they can coexist and feed on the substrate in co-digestion. It is concluded that the model that more accurately predicts the behavior of fluid dynamics within the reactor is the k-ε Realizable model. The flow paths obtained are consistent with what is stated in the referenced literature, where the 45° inclination PBT impeller is the right type of agitator to keep particles in suspension and, in turn, increase the dispersion of gas in the liquid phase. If a 24/7 complete mix is considered under stirred agitation, with a plant factor of 80%, 51,840 kWh/year are estimated. On the contrary, if intermittent agitations of 3 min every 15 min are used under the same design conditions, reduce almost 80% of energy costs. It is a feasible solution to predict the energy expenditure of an anaerobic biodigester CSTR. It is recommended to use high mixing intensities, at the beginning and end of the joint phase acetogenesis/methanogenesis. This high intensity of mixing, in the beginning, produces the activation of the bacteria, and once reaching the end of the Hydraulic Retention Time period, it produces another increase in the mixing agitations, favoring the final dispersion of the biogas that may be trapped in the biodigester bottom.

Keywords: anaerobic co-digestion, computational fluid dynamics, CFD, net power, organic waste

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Authors: J. Chamorro-Servent, S. Tassani, M. A. Gonzalez-Ballester, L. J. Roca, J. Romeu, O. Camara

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Electromagnetic and microwave imaging (MWI) have been used in medical imaging in the last years, being the most common applications of breast cancer and stroke detection or monitoring. In those applications, the subject or zone to observe is surrounded by a number of antennas, and the Nyquist criterium can be satisfied. Additionally, the space between the antennas (transmitting and receiving the electromagnetic fields) and the zone to study can be prepared in a homogeneous scenario. However, this may differ in other cases as could be intracardiac catheters, stomach monitoring devices, pelvic organ systems, liver ablation monitoring devices, or uterine fibroids’ ablation systems. In this work, we analyzed different MWI algorithms to find the most suitable method for dealing with an intrabody scenario. Due to the space limitations usually confronted on those applications, the device would have a cylindrical configuration of a maximum of eight transmitters and eight receiver antennas. This together with the positioning of the supposed device inside a body tract impose additional constraints in order to choose a reconstruction method; for instance, it inhabitants the use of well-known algorithms such as filtered backpropagation for diffraction tomography (due to the unusual configuration with probes enclosed by the imaging region). Finally, the difficulty of simulating a realistic non-homogeneous background inside the body (due to the incomplete knowledge of the dielectric properties of other tissues between the antennas’ position and the zone to observe), also prevents the use of Born and Rytov algorithms due to their limitations with a heterogeneous background. Instead, we decided to use a time-reversed algorithm (mostly used in geophysics) due to its characteristics of ignoring heterogeneities in the background medium, and of focusing its generated field onto the scatters. Therefore, a 2D time-reversed finite difference time domain was developed based on the time-reversed approach for microwave breast cancer detection. Simultaneously an in-silico testbed was also developed to compare ground-truth dielectric properties with corresponding microwave imaging reconstruction. Forward and inverse problems were computed varying: the frequency used related to a small zone to observe (7, 7.5 and 8 GHz); a small polyp diameter (5, 7 and 10 mm); two polyp positions with respect to the closest antenna (aligned or disaligned); and the (transmitters-to-receivers) antenna combination used for the reconstruction (1-1, 8-1, 8-8 or 8-3). Results indicate that when using the existent time-reversed method for breast cancer here for the different combinations of transmitters and receivers, we found false positives due to the high degrees of freedom and unusual configuration (and the possible violation of Nyquist criterium). Those false positives founded in 8-1 and 8-8 combinations, highly reduced with the 1-1 and 8-3 combination, being the 8-3 configuration de most suitable (three neighboring receivers at each time). The 8-3 configuration creates a region-of-interest reduced problem, decreasing the ill-posedness of the inverse problem. To conclude, the proposed algorithm solves the main limitations of the described intrabody application, successfully detecting the angular position of targets inside the body tract.

Keywords: FDTD, time-reversed, medical imaging, microwave imaging

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Authors: Carolina Cuéllar, Paz González

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Research has shown that school leadership has a significant indirect effect on students’ achievements. In Chile, evidence has also revealed that this impact is stronger in vulnerable schools. With the aim of strengthening school leadership, public policy has taken up the challenge of enhancing capabilities of novice principals through the implementation of induction programs, which include a mentoring component, entrusting the task of delivering these programs to universities. The importance of using mentoring or coaching models in the preparation of novice school leaders has been emphasized in the international literature. Thus, it can be affirmed that building leadership capacity through partnership is crucial to facilitate cognitive and affective support required in the initial phase of the principal career, gain role clarification and socialization in context, stimulate reflective leadership practice, among others. In Chile, mentoring is a recent phenomenon in the field of school leadership and it is even more new in the preparation of new principals who work in public schools. This study, funded by the Chilean Ministry of Education, sought to explore the challenges and lessons arising from the design and implementation of mentoring processes which are part of the induction programs, according to the perception of the different actors involved: ministerial agents, university coordinators, mentors and novice principals. The investigation used a qualitative design, based on a study of three cases (three induction programs). The sources of information were 46 semi-structured interviews, applied in two moments (at the beginning and end of mentoring). Content analysis technique was employed. Data focused on the uniqueness of each case and the commonalities within the cases. Five main challenges and lessons emerged in the design and implementation of mentoring within the induction programs for new principals from Chilean public schools. They comprised the need of (i) developing a shared conceptual framework on mentoring among the institutions and actors involved, which helps align the expectations for the mentoring component within the induction programs, along with assisting in establishing a theory of action of mentoring that is relevant to the public school context; (ii) recognizing trough actions and decisions at different levels that the role of a mentor differs from the role of a principal, which challenge the idea that an effective principal will always be an effective mentor; iii) improving mentors’ selection and preparation processes trough the definition of common guiding criteria to ensure that a mentor takes responsibility for developing critical judgment of novice principals, which implies not limiting the mentor’s actions to assist in the compliance of prescriptive practices and standards; (iv) generating common evaluative models with goals, instruments and indicators consistent with the characteristics of mentoring processes, which helps to assess expected results and impact; and (v) including the design of a mentoring structure as an outcome of the induction programs, which helps sustain mentoring within schools as a collective professional development practice. Results showcased interwoven elements that entail continuous negotiations at different levels. Taking action will contribute to policy efforts aimed at professionalizing the leadership role in public schools.

Keywords: induction programs, mentoring, novice principals, school leadership preparation

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Authors: Beatriz González-rodrigo, Diego Hidalgo-leiva, Omar Flores, Claudia Germoso, Maribel Jiménez-martínez, Laura Navas-sánchez, Belén Orta, Nicola Tarque, Orlando Hernández- Rubio, Miguel Marchamalo, Juan Gregorio Rejas, Belén Benito-oterino

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Cities facing seismic hazard necessitate detailed risk assessments for effective urban planning and vulnerability identification, ensuring the safety and sustainability of urban infrastructure. Comprehensive studies involving seismic hazard, vulnerability, and exposure evaluations are pivotal for estimating potential losses and guiding proactive measures against seismic events. However, broad-scale traditional risk studies limit consideration of specific local threats and identify vulnerable housing within a structural typology. Achieving precise results at neighbourhood levels demands higher resolution seismic hazard exposure, and vulnerability studies. This research aims to bolster sustainability and safety against seismic disasters in three Central American and Caribbean capitals. It integrates geospatial techniques and artificial intelligence into seismic risk studies, proposing cost-effective methods for exposure data collection and damage prediction. The methodology relies on prior seismic threat studies in pilot zones, utilizing existing exposure and vulnerability data in the region. Emphasizing detailed building attributes enables the consideration of behaviour modifiers affecting seismic response. The approach aims to generate detailed risk scenarios, facilitating prioritization of preventive actions pre-, during, and post-seismic events, enhancing decision-making certainty. Detailed risk scenarios necessitate substantial investment in fieldwork, training, research, and methodology development. Regional cooperation becomes crucial given similar seismic threats, urban planning, and construction systems among involved countries. The outcomes hold significance for emergency planning and national and regional construction regulations. The success of this methodology depends on cooperation, investment, and innovative approaches, offering insights and lessons applicable to regions facing moderate seismic threats with vulnerable constructions. Thus, this framework aims to fortify resilience in seismic-prone areas and serves as a reference for global urban planning and disaster management strategies. In conclusion, this research proposes a comprehensive framework for seismic risk assessment in high-risk urban areas, emphasizing detailed studies at finer resolutions for precise vulnerability evaluations. The approach integrates regional cooperation, geospatial technologies, and adaptive fragility curve adjustments to enhance risk assessment accuracy, guiding effective mitigation strategies and emergency management plans.

Keywords: assessment, behaviour modifiers, emergency management, mitigation strategies, resilience, vulnerability

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