Search results for: Verónica Díaz

Authors: Mohammed Hakim Jafferali, Balaje Vijayaraghavan, Ricardo A. Figueroa, Ellinor Crafoord, Veronica J. Larsson, Einar Hallberg, Santhosh Gudise

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The nuclear envelope which surrounds the chromatin of eukaryotic cells contains more than a hundred transmembrane proteins. Mutations in some genes encoding nuclear envelope proteins give rise to human diseases including neurological disorders. The function of many nuclear envelope proteins is not well established. This is partly because nuclear envelope proteins and their interactions are difficult to study due to the inherent resistance to extraction of nuclear envelope proteins. We have developed a novel method called MCLIP, to identify interacting partners of nuclear envelope proteins in live cells. Using MCLIP, we found three new binding partners of the inner nuclear membrane protein Samp1: the intermediate filament protein Lamin B1, the LINC complex protein Sun1 and the G-protein Ran. Furthermore, using in vitro studies, we show that Samp1 binds both Emerin and Ran directly. We have also studied the interaction between Samp1 and Ran in detail. The results show that the Samp1 binds stronger to RanGTP than RanGDP. Samp1 is the first transmembrane protein known to bind Ran and it is tempting to speculate that Samp1 may provide local binding sites for RanGTP at membranes.

Keywords: MCLIP, nuclear envelope, ran, Samp1

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Authors: L. J. Díaz, M. A. Hernández, A. K. Molina, A. Bermúdez, C. Crane, V. M. Pabón

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One of the most important biological indicators that show the exposure to the radiation is the micronuclei (MN). This technique is using to determinate the radiation effects in blood cultures as a biological control and a complement to the physics dosimetry. In Colombia the necessity to apply this analysis has emerged due to the current biological indicator most used is the chromosomal aberrations (CA), that is why it is essential the MN technique’s standardization and validation to have enough tools to improve the radioprotection topic in the country. Besides, this technique will be applied on the construction of a dose-response curve, that allow measure an approximately dose to irradiated people according to MN frequency found. Inside the steps that carried out to accomplish the standardization and validation is the statistic analysis from the lectures of “in vitro” peripheral blood cultures with different analysts, also it was determinate the best culture medium and conditions for the MN can be detected easily.

Keywords: micronuclei, radioprotection, standardization, validation

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Authors: Hector A. Tinoco, Cesar Garcia-Diaz, Olga L. Ocampo-Lopez

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In this study, an experimental approach is established to assess the performance of different beams coupled to a Voice Coil Motor (VCM) with the aim to maximize mechanically the energy harvesting in the inductive transducer that is included on it. The VCM is extracted from a recycled hard disk drive (HDD) and it is adapted for carrying out experimental tests of energy harvesting. Two individuals were selected for walking with the VCM-beam device as well as to evaluate the performance varying two parameters in the beam; length of the beams and a mass addition. Results show that the energy harvesting is maximized with specific beams; however, the harvesting efficiency is improved when a mass is added to the end of the beams.

Keywords: hard disk drive, energy harvesting, voice coil motor, energy harvester, gait motions

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Authors: J. Fernandez, N. Aguilar, R. Fernandez de Canete, J. C. Ramos-Diaz

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In this paper, a simulation model of the glucose-insulin system for a patient undergoing diabetes Type 1 is developed by using a causal modeling approach under system dynamics. The OpenModelica simulation environment has been employed to build the so called causal model, while the glucose-insulin model parameters were adjusted to fit recorded mean data of a diabetic patient database. Model results under different conditions of a three-meal glucose and exogenous insulin ingestion patterns have been obtained. This simulation model can be useful to evaluate glucose-insulin performance in several circumstances, including insulin infusion algorithms in open-loop and decision support systems in closed-loop.

Keywords: causal modeling, diabetes, glucose-insulin system, diabetes, causal modeling, OpenModelica software

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Authors: Mabel Ortiz, Claudio Díaz

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This paper explores Chilean pre-service teachers' perceptions about the provision of corrective feedback in a wiki environment during the collaborative writing of an argumentative essay. After conducting a semi-structured interview on 22 participants, the data were processed through the content analysis technique. The results show that students have positive perceptions about corrective feedback, provided through a wiki virtual environment, which in turn facilitates feedback provision and impacts language learning effectively. Some of the positive perceptions about virtual feedback refer to permanent access, efficiency, simultaneous revision and immediacy. It would then be advisable to integrate wiki-based feedback as a methodology for the language classroom and collaborative writing tasks.

Keywords: argumentative essay, focused corrective feedback, perception, wiki environment

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Authors: J. Fernandez, R. Fernandez de Canete, J. Perea-Paizal, J. C. Ramos-Diaz

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The high prevalence of cardiovascular diseases has provoked a raising interest in the development of mathematical models in order to evaluate the cardiovascular function both under physiological and pathological conditions. In this paper, a physical model of the cardiovascular system with intrinsic regulation is presented and implemented by using the object-oriented Modelica simulation software tools.  For this task, a multi-compartmental system previously validated with physiological data has been built, based on the interconnection of cardiovascular elements such as resistances, capacitances and pumping among others, by following an electrohydraulic analogy. The results obtained under both physiological and pathological scenarios provide an easy interpretative key to analyze the hemodynamic behavior of the patient. The described approach represents a valuable tool in the teaching of physiology for graduate medical and nursing students among others.

Keywords: cardiovascular system, MODELICA simulation software, physical modelling, teaching tool

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Authors: Diego Santibañez Oyarce, Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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The advent of high-throughput sequencing technologies has revolutionized genomics, generating vast amounts of genetic data that challenge traditional bioinformatics methods. Machine learning addresses these challenges by leveraging computational power to identify patterns and extract information from large datasets. However, biological sequence data, being symbolic and non-numeric, must be converted into numerical formats for machine learning algorithms to process effectively. So far, some encoding methods, such as one-hot encoding or k-mers, have been explored. This work proposes additional approaches for encoding DNA sequences in order to compare them with existing techniques and determine if they can provide improvements or if current methods offer superior results. Data from the 16S rRNA gene, a universal marker, was used to analyze eight bacterial groups that are significant in the pulmonary environment and have clinical implications. The bacterial genes included in this analysis are Prevotella, Abiotrophia, Acidovorax, Streptococcus, Neisseria, Veillonella, Mycobacterium, and Megasphaera. These data were downloaded from the NCBI database in Genbank file format, followed by a syntactic analysis to selectively extract relevant information from each file. For data encoding, a sequence normalization process was carried out as the first step. From approximately 22,000 initial data points, a subset was generated for testing purposes. Specifically, 55 sequences from each bacterial group met the length criteria, resulting in an initial sample of approximately 440 sequences. The sequences were encoded using different methods, including one-hot encoding, k-mers, Fourier transform, and Wavelet transform. Various machine learning algorithms, such as support vector machines, random forests, and neural networks, were trained to evaluate these encoding methods. The performance of these models was assessed using multiple metrics, including the confusion matrix, ROC curve, and F1 Score, providing a comprehensive evaluation of their classification capabilities. The results show that accuracies between encoding methods vary by up to approximately 15%, with the Fourier transform obtaining the best results for the evaluated machine learning algorithms. These findings, supported by the detailed analysis using the confusion matrix, ROC curve, and F1 Score, provide valuable insights into the effectiveness of different encoding methods and machine learning algorithms for genomic data analysis, potentially improving the accuracy and efficiency of bacterial classification and related genomic studies.

Keywords: DNA encoding, machine learning, Fourier transform, Fourier transformation

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Authors: Michael R. Phangtriastu, Herriyandi Herriyandi, Diaz D. Santika

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This paper presents a comparison of the implementation of metaheuristic algorithms to train the antecedent parameters and consequence parameters in the adaptive network-based fuzzy inference system (ANFIS). The algorithms compared are genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The objective of this paper is to benchmark well-known metaheuristic algorithms. The algorithms are applied to several data set with different nature. The combinations of the algorithms' parameters are tested. In all algorithms, a different number of populations are tested. In PSO, combinations of velocity are tested. In ABC, a different number of limit abandonment are tested. Experiments find out that ABC is more reliable than other algorithms, ABC manages to get better mean square error (MSE) than other algorithms in all data set.

Keywords: ANFIS, artificial bee colony, genetic algorithm, metaheuristic algorithm, particle swarm optimization

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Authors: Sharon Diaz Ruiz

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The population of Latinx adult English language learners (ELLs) in the United States will increase in the next few years and become even more racially and linguistically diverse. Our classrooms reflect these demographic changes; therefore, there will always be the need to identify language teaching practices that would allow educators to meet this linguistic diversity. This qualitative study explores Latinx adult English language learners' perceptions of culturally responsive teaching strategies. Participants in this study will be enrolled in an English developmental course for the Fall of 2022. The data collection process will consist of overt observation during five presentations/activities, including culturally inclusive readings and student reflections. The teaching materials selected will align with the course module's goals and objectives. The result of this investigation will shed light on the gap in the literature documenting the application of culturally responsive pedagogy to Latino adult language learners.

Keywords: emergent bilinguals, adult learners, Latinx learners, ELL

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Authors: Juan Ignacio Arango, Andres Diaz, Giacomo Barbieri

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In soilless culture, the definition of efficient fertigation strategies is fundamental for the growth of crops. Flexible test-benches able to independently manage groups of crops are key for investigating efficient fertigation practices through experimentation. These test-benches must be able to provide nutrient solution (NS) in a precise, uniform and repeatable way in order to effectively implement and compare different fertigation strategies. This article describes a distribution module for investigating fertigation practices able to control the fertigation dose and frequency. The proposed solution is characterized in terms of precision, uniformity and repeatability since these parameters are fundamental in the implementation of effective experiments for the investigation of fertigation practices. After a calibration process, the implemented system reaches a precision of 1mL, a uniformity of 98.5% at a total cost of 735USD.

Keywords: recision horticulture, test-bench, fertigation strategy, automation, flexibility

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Authors: Veronica Sorgato, Jeremy Belhassen, Philippe Chartier, Roddy Sihanath, Nicolas Docquiere, Jean-Yves Giraud

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We analyzed the image-guided radiotherapy method used by the TomoTherapy® System (Accuray Corp.) for patient repositioning in clinical routine. The TomoTherapy® System computes X, Y, Z and roll displacements to match the reference CT, on which the dosimetry has been performed, with the pre-treatment MV CT. The accuracy of the repositioning method has been studied according to the treatment localization. For this, a database of 18774 treatment sessions, performed during 2 consecutive years (2016-2017 period) has been used. The database includes the X, Y, Z and roll displacements proposed by TomoTherapy® System as well as the manual correction of these proposals applied by the radiation therapist. This manual correction aims to further improve the repositioning based on the clinical situation and depends on the structures surrounding the target tumor tissue. The statistical analysis performed on the database aims to define repositioning limits to be used as security and guiding tool for the manual adjustment implemented by the radiation therapist. This tool will participate not only to notify potential repositioning errors but also to further improve patient positioning for optimal treatment.

Keywords: accuracy, IGRT MVCT, image-guided radiotherapy megavoltage computed tomography, statistical analysis, tomotherapy, localization

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Authors: Valerie Alexander, Amanda Gutierrez, Veronica Campbell, Dara Schwartz, B. Charles Tatum

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It has long been assumed that personality disorders (PD) originate in adolescence or early adulthood and that the maladaptive behaviors significantly attenuate over time. The Diagnostic and Statistical Manual of Mental Disorders-5 supports early onset of PD and views the pattern of behaviors as enduring and stable. The premise of this study is that PD may not always begin early in life, that behaviors may change over the lifespan, and that current treatment modalities may be beneficial in seniors. Self-injurious behaviors (SIB) exhibited earlier in life may, in older adults, be manifested in less overt high-risk behaviors but by refusal to take medication and get necessary medical treatment. Dialectical Behavioral Therapy is a well-known treatment modality for teaching emotional regulation and distress tolerance and thus reducing self-injurious behaviors yet very little has been studied about SIB and treatment in older adults. The population for this study was older adults, with a history of SIB, a PD, and depression and/or anxiety. Participants learned an adapted version of Dialectical Behavioral Therapy (DBT) as developed by DBT trained therapists. The results provided clinical potentials for the efficacy of DBT to reduce SIB, decrease depression and anxiety in the older adult population.

Keywords: anxiety, depression, dialectical behavioral therapy, personality disorders, self-harm behavior, treatment in older adults

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Authors: Veronica Kah Jo Wong, Yoke Mui Lim, Nurul Sakina Mokhtar Azizi

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The impact on the construction industry in Malaysia is unprecedented, as the government implemented a lockdown to restrict human movement in an effort to stop COVID-19 from spreading. Quantity surveyor (QS), as one of the key construction professionals, found that the working practices and environments for quantity surveyors today have changed due to the current pandemic. The QS profession must deal not only with changes in project issues but also with a different working environment in which most people are required to work from home and follow the standard operating procedures. Therefore, QS should be flexible, agile, and have the capability to adapt to the current working practices by strengthening their competencies. Adapting to the current and recovering environment of COVID-19 may result in the emergence of a new competence such as skill and knowledge for QS in order to maintain the quality of performance in the delivery of their professional services. Thus, this paper's objective is to investigate the changes in knowledge and skills in quantity surveyors. The data will be collected through interviews with registered professional QS to gain better insights that are specific in this industry, and the findings will be verified using the Delphi method. It is hoped that new knowledge and skill will be found from the study and will not only contribute to the betterment of the professional QS but also in guiding higher learning institutions to incorporate the new competencies into their curriculum.

Keywords: competency, COVID-19 pandemic, Malaysia, quantity surveying

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Authors: Clarissa D. M. O. Guimaraes, Mariza C. M. Fernandes, Francisco R. V. Diaz, Juliana R. Souza

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The use of mineral fillers in the preparation of organic matrix composites can be an efficient alternative in minimizing the environmental damage generated in passive mineral beneficiation processes. In addition, it may represent a new material option for wind, construction, and aeronautical industries, for example. In this sense, epoxy resin composites with Tailings of Scheelite (TS) were developed. The composites were manufactured with 5%, 10% and 20% of TS in volume percentage, homogenized by mechanical mixing and molded in a silicon mold. In order to make the tribological evaluation, pin on disk tests were performed to analyze coefficient of friction and wear. The wear mechanisms were identified by SEM (scanning electron microscope) images. The coefficient of friction had a tendency to decrease with increasing amount of filler. The wear tends to increase with increasing amount of filler, although it exhibits a similar wear behavior. The results suggest characteristics that are potential used in many tribological applications.

Keywords: composites, mineral filler, tailings of scheelite, tribology

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Authors: Fernanda Bravo Cornejo, Camilo Cerda Sarabia, Belén Díaz Díaz, Diego Santibañez Oyarce, Esteban Gómez Terán, Hugo Osses Prado, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Streptococcus pyogenes is a gram-positive bacteria involved in a wide range of diseases and is a major-human-specific bacterial pathogen. In Chile, this year the 'Ministerio de Salud' declared an alert due to the increase in strains throughout the year. This increase can be attributed to the multitude of factors including antimicrobial resistance (AMR) and Virulence Factors (VF). Understanding these VF and AMR is crucial for developing effective strategies and improving public health responses. Moreover, experimental identification and characterization of these pathogenic mechanisms are labor-intensive and time-consuming. Therefore, new computational methods are required to provide robust techniques for accelerating this identification. Advances in Machine Learning (ML) algorithms represent the opportunity to refine and accelerate the discovery of VF associated with Streptococcus pyogenes. In this work, we evaluate the accuracy of various machine learning models in predicting the virulence factors and antimicrobial resistance of Streptococcus pyogenes, with the objective of providing new methods for identifying the pathogenic mechanisms of this organism.Our comprehensive approach involved the download of 32,798 genbank files of S. pyogenes from NCBI dataset, coupled with the incorporation of data from Virulence Factor Database (VFDB) and Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. These datasets provided labeled examples of both virulent and non-virulent genes, enabling a robust foundation for feature extraction and model training. We employed preprocessing, characterization and feature extraction techniques on primary nucleotide/amino acid sequences and selected the optimal more for model training. The feature set was constructed using sequence-based descriptors (e.g., k-mers and One-hot encoding), and functional annotations based on database prediction. The ML models compared are logistic regression, decision trees, support vector machines, neural networks among others. The results of this work show some differences in accuracy between the algorithms, these differences allow us to identify different aspects that represent unique opportunities for a more precise and efficient characterization and identification of VF and AMR. This comparative analysis underscores the value of integrating machine learning techniques in predicting S. pyogenes virulence and AMR, offering potential pathways for more effective diagnostic and therapeutic strategies. Future work will focus on incorporating additional omics data, such as transcriptomics, and exploring advanced deep learning models to further enhance predictive capabilities.

Keywords: antibiotic resistance, streptococcus pyogenes, virulence factors., machine learning

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Authors: Verónica Arredondo, Miguel Martínez-Panero, Teresa Peña, Victoriano Ramírez

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The Mexican Chamber of Deputies is composed of 500 representatives: 300 of them elected by relative majority and another 200 ones elected through proportional representation in five electoral clusters (constituencies) with 40 representatives each. In this mixed-member electoral system, the seats distribution of proportional representation is not independent of the election by relative majority, as it attempts to correct representation imbalances produced in single-member districts. This two-fold structure has been maintained in the successive electoral reforms carried out along the last three decades (eight from 1986 to 2014). In all of them, the election process of 200 seats becomes complex: Formulas in the Law are difficult to understand and to be interpreted. This paper analyzes the Mexican electoral system after the electoral reform of 2014, which was applied for the first time in 2015. The research focuses on contradictions and issues of applicability, in particular situations where seats allocation is affected by ambiguity in the law and where asymmetrical treatment of political parties arises. Due to these facts, a proposal of electoral reform will be presented. It is intended to be simpler, clearer, and more enduring than the current system. Furthermore, this model is more suitable for producing electoral outcomes free of contradictions and paradoxes. This approach would allow a fair treatment of political parties and as a result an improved opportunity to exercise democracy.

Keywords: electoral mathematics, electoral reform, Mexican electoral system, political asymmetry, proportional representation

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Authors: Odigie Veronica

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The paper focused on the application of Information and Communication Technology (ICT) in effective service delivery in view of the ongoing COVID-19 experience. It adopted the exploratory research method with three research objectives captured. Consequently, the objectives were to ascertain the meaning of online education, understand the concept of COVID-19 and to determine the relevance of online education in effective service delivery in institutions of learning. It is evident from the findings that through ICT, online mode of learning can be adopted in schools which helps greatly in promoting continual education. Online mode of education is practiced online; it brings both the teacher and learners from different places together, without any physical boundary/contact (at least 75%); and has helped greatly in human development in countries where it has been practiced. It is also a welcome development owing to its many benefits such as exposure to digital learning, having access to works of great teachers and educationists such as Socrates, Plato, Dewey, R.S. Peters, J. J. Rosseau, Nnamdi Azikwe, Carol Gilligan, J. I. Omoregbe, Jane Roland Martin, Jean Piaget, among others; and the facilitation of uninterrupted learning for class promotion and graduation of students. Developing the learners all round is part of human development which helps in developing a nation. These and many more are some benefits online education offers which make ICT very relevant in our contemporary society

Keywords: online education, COVID-19 pandemic, effective service delivery, human development

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Authors: Javier Fernandez, Mayte Medina, Rafael Fernandez de Canete, Nuria Alcain, Juan Carlos Ramos-Diaz

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At present, the experimentation of anesthetic drugs on patients requires a regulation protocol, and the response of each patient to several doses of entry drug must be well known. Therefore, the development of pharmacological dose control systems is a promising field of research in anesthesiology. In this paper, it has been developed a non-linear compartmental the pharmacokinetic-pharmacodynamical model which describes the anesthesia depth effect in a sufficiently reliable way over a set of patients with the depth effect quantified by the Bi-Spectral Index. Afterwards, an Artificial Neural Network (ANN) predictive controller has been designed based on the depth of anesthesia model so as to keep the patient in the optimum condition while he undergoes surgical treatment. For the purpose of quantifying the efficiency of the neural predictive controller, a classical proportional-integral-derivative controller has also been developed to compare both strategies. Results show the superior performance of predictive neural controller during BiSpectral Index reference tracking.

Keywords: anesthesia, bi-spectral index, neural network control, pharmacokinetic-pharmacodynamical model

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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

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Authors: Hsiao-Tseng Lin, Nguyen Ngoc Dat, Szu-Mei Hsiao, R. J. Hernández-Díaz

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The practitioner system has become an important tool for universities working to shrink the gap between industry and vocational education. Beginning in 2015, Meiho University conducted a consecutive three-year program for teaching excellence, funded in part by Taiwan’s Ministry of Education, with a total project funding of over $2.5 million USD. One of the highlights of this program is the recruitment of 300 industry practitioners to participate in collaborative teaching, a dual-mentor system, and curriculum planning. More than 60% of the practitioners boast more than 10 years of practical industry experience, and 52% of them have earned master's degree or higher. Students rated their overall program satisfaction over 4.5(out of 5.0) on average. This study explores the perspectives of academics and industry practitioners using in-depth interviews and surveys, along with an examination of the challenges of the practitioner system. The paper enables the framing of practitioner system policies by vocational education institutions and industry to facilitate more effective and efficient transfer of knowledge between academics and practitioners, leading to enhanced university competitive advantage, which would ultimately benefit society.

Keywords: collaborative teaching, industry practitioners, practitioner system, vocational education

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Authors: Diego Diaz H., Cristian Larroulet

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Although a large earthquake has clear and immediate consequences such as deaths, destruction of infrastructure and displacement (at least temporary) of part of the population, scientific research about the impact of a geological disaster in economic activity is inconclusive, especially when looking beyond the very short term. Estimating the economic impact years after a disaster strike is non-trivial since there is an unavoidable difficulty in attributing the observed effect to the disaster and not to other economic shocks. Case studies are performed that determine the impact of earthquakes in Chile, Japan, and New Zealand at a regional level by applying the synthetic control method, using the natural disaster as treatment. This consisted in constructing a counterfactual from every region in the same country that is not affected (or is slightly affected) by the earthquake. The results show that the economies of Canterbury and Tohoku achieved greater levels of GDP per capita in the years after the disaster than they would have in the absence of the disaster. For the case of Chile, however, the region of Maule experiences a decline in GDP per capita because of the earthquake. All the results are robust according to the placebo tests. Also, the results suggest that national institutional quality improve the growth process after the disaster.

Keywords: earthquake, economic growth, institutional quality, synthetic control

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Authors: Luis Miguel Méndez Díaz

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In this article, a mathematics teaching-learning strategy will be presented, specifically differential calculus in one variable, in a fun and competitive space in which the action on the part of the student is manifested and not only the repetition of information on the part of the teacher. Said action refers to motivating, problematizing, summarizing, and coordinating a game of dominoes whose thematic cards are designed around the basic and main contents of differential calculus. The strategies for teaching this area are diverse and precisely the game of dominoes is one of the most used strategies in the practice of mathematics because it stimulates logical reasoning and mental abilities. The objective on this investigation is to identify the way in which the game of dominoes affects the learning and understanding of fundamentals concepts of differential calculus in one variable through experimentation carried out on students of the first semester of the School of Engineering and Sciences of the Technological Institute of Monterrey Campus Querétaro. Finally, the results of this study will be presented and the use of this strategy in other topics around mathematics will be recommended to facilitate logical and meaningful learning in students.

Keywords: collaborative learning, logical-mathematical intelligence, mathematical games, multiple intelligences

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Authors: Mihai-Alexandru Florea, Veronica Drumea, Roxana Nita, Cerasela Gird, Laura Olariu

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The aim of this study was to investigate the residues of pesticide found in tea water infusions. A multi-residues method to determine 147 pesticides has been developed using the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) procedure and dispersive solid phase extraction (d-SPE) for the cleanup the pesticides from complex matrices such as plants and tea. Sample preparation was carefully optimized for the efficient removal of coextracted matrix components by testing more solvent systems. Determination of pesticides was performed using GC-MS/MS (100 of pesticides) and LC-MS/MS (47 of pesticides). The selected reaction monitoring (SRM) mode was chosen to achieve low detection limits and high compounds selectivity and sensitivity. Overall performance was evaluated and validated according to DG-SANTE Guidelines. To assess the pesticide residue transfer rate (qualitative) from dried tea in infusions the samples (tea) were spiked with a mixture of pesticides at the maximum residues level accepted for teas and herbal infusions. In order to investigate the release of the pesticides in tea preparations, the medicinal plants were prepared in four ways by variation of water temperature and the infusion time. The pesticides from infusions were extracted using two methods: QuEChERS versus solid-phase extraction (SPE). More that 90 % of the pesticides studied was identified in infusion.

Keywords: tea, solid-phase extraction (SPE), selected reaction monitoring (SRM), QuEChERS

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Authors: Camilo Cerda Sarabia, Fernanda Bravo Cornejo, Diego Santibanez Oyarce, Hugo Osses Prado, Esteban Gómez Terán, Belén Diaz Diaz, Raúl Caulier-Cisterna, Jorge Vergara-Quezada, Ana Moya-Beltrán

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Antimicrobial resistance (AMR) represents a significant and rapidly escalating global health threat. Projections estimate that by 2050, AMR infections could claim up to 10 million lives annually. Respiratory infections, in particular, pose a severe risk not only to individual patients but also to the broader public health system. Despite the alarming rise in resistant respiratory infections, AMR within the lung microbiome (microbial community) remains underexplored and poorly characterized. The lungs, as a complex and dynamic microbial environment, host diverse communities of microorganisms whose interactions and resistance mechanisms are not fully understood. Unlike studies that focus on individual genomes, analyzing the entire microbiome provides a comprehensive perspective on microbial interactions, resistance gene transfer, and community dynamics, which are crucial for understanding AMR. However, this holistic approach introduces significant computational challenges and exposes the limitations of traditional analytical methods such as the difficulty of identifying the AMR. Machine learning has emerged as a powerful tool to overcome these challenges, offering the ability to analyze complex genomic data and uncover novel insights into AMR that might be overlooked by conventional approaches. This study investigates microbial resistance within the lung microbiome using unsupervised machine learning approaches to uncover resistance patterns and potential clinical associations. it downloaded and selected lung microbiome data from HumanMetagenomeDB based on metadata characteristics such as relevant clinical information, patient demographics, environmental factors, and sample collection methods. The metadata was further complemented by details on antibiotic usage, disease status, and other relevant descriptions. The sequencing data underwent stringent quality control, followed by a functional profiling focus on identifying resistance genes through specialized databases like Antibiotic Resistance Database (CARD) which contains sequences of AMR gene sequence and resistance profiles. Subsequent analyses employed unsupervised machine learning techniques to unravel the structure and diversity of resistomes in the microbial community. Some of the methods employed were clustering methods such as K-Means and Hierarchical Clustering enabled the identification of sample groups based on their resistance gene profiles. The work was implemented in python, leveraging a range of libraries such as biopython for biological sequence manipulation, NumPy for numerical operations, Scikit-learn for machine learning, Matplotlib for data visualization and Pandas for data manipulation. The findings from this study provide insights into the distribution and dynamics of antimicrobial resistance within the lung microbiome. By leveraging unsupervised machine learning, we identified novel resistance patterns and potential drivers within the microbial community.

Keywords: antibiotic resistance, microbial community, unsupervised machine learning., sequences of AMR gene

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Authors: S. Bayona, J. Nunez, D. Paez, C. Diaz

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The application of Lean manufacturing tools continues to be an effective solution for increasing productivity, reducing costs and eliminating waste in the manufacture of goods and services. This article analyzes the production process of a hot asphalt manufacturing company from an administrative and technical perspective. Three main phases were analyzed, the first phase was related to the determination of the risk priority number of the main operations in asphalt mix production process by an FMEA (Failure Mode Effects Analysis), in the second phase the Value Stream Mapping (VSM) of the production line was performed and in the third phase a SWOT (Strengths, Weaknesses Opportunities, Threats) matrix was constructed. Among the most valued failure modes were the lack training of workers in occupational safety and health issues, the lack of signaling and classification of granulated material, and the overweight of vehicles loaded. The analysis of the results in the three phases agree on the importance of training operational workers, improve communication with external actors in order to minimize delays in material orders and strengthen control suppliers.

Keywords: asphalt, lean manufacturing, productivity, process

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Authors: Chema Segovia, Pau Díaz-Solano, Tony Ramos Murphy

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The PALIMPSEST project, funded by Horizon 2020 and associated with the New European Bauhaus, aims to revitalize sustainability practices in heritage landscapes through co-creation processes led by creative agents. Specifically, PALIMPSEST focuses on the pivotal roles of architecture, design, and art in addressing sustainability challenges. The project aims to demonstrate that these creative disciplines can generate a distinctive kind of value while addressing environmental needs, enhancing societal engagement, supporting foresighting activities, and increasing awareness. In the summer of 2023, Palimpsest launched an open call to select the teams that will lead the development of three creativity-based sustainability processes in three different pilot cities: Jerez de la Frontera (Spain), Lodz (Poland), and Milan (Italy). The call received 141 applications. Through a survey conducted among the candidates and an in-depth analysis of their proposals, we assessed the level of engagement that European creative agents have in tackling sustainability challenges, as well as their alignment with the principles advocated by the New European Bauhaus.

Keywords: arts, architecture, co-creation, design, new European Bauhaus, sustainability

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Authors: Christian Gabriel Mariveles, Darelle Jay Gallardo, Leslie Dayaoen, Laurenz Paul Diaz

Abstract:

True experimental research with descriptive analysis was conducted. Utilizing Quicklime (Calcium Oxide) for self-healing properties of coconut husk fibre concrete brick. There are 2 setups established: the first one has the 1:1:2 ratio of calcium oxide, cement and sand, and the second one has a 2:1:2 ratio of the same variables. The bricks are made from the residences along Barangay Greater Lagro. The mixture of sand and cement is mixed with coconut husk fibers and then molded with different ratios in the molder. After the drying of cement, the researchers tested the bricks in the laboratory for compressive strength. The brick with the highest PSI is picked by the researchers to drop into freefall testing, and it makes remarkable remarks as it is deformed after dropping to different heights with a maximum of 20 feet. Unfortunately, the self-healing capabilities were not observed during the 12 weeks of monitoring. However, the brick was weighed after 12 weeks of monitoring, and it increased in weight by 0.030 kg. from 1.833 kg. to 1.863 kg. meaning that this ratio 2 has the potential to self-heal, but 12 weeks of monitoring by the researchers is not enough to conclude that it has a significant difference.

Keywords: self healing, coconut husk bricks, research, calcium oxide, utilizing quicklime

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Authors: Jorge A. Ruiz-Vanoye, Ocotlán Díaz-Parra, Alejandro Fuentes-Penna, José J. Hernández-Flores, Edith Olaco Garcia

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The Project Scheduling Problem (PSP) is a generic name given to a whole class of problems in which the best form, time, resources and costs for project scheduling are necessary. The PSP is an application area related to the project management. This paper aims at being a guide to understand PSP by presenting a survey of the general parameters of PSP: the Resources (those elements that realize the activities of a project), and the Activities (set of operations or own tasks of a person or organization); the mathematical models of the main variants of PSP and the algorithms used to solve the variants of the PSP. The project scheduling is an important task in project management. This paper contains mathematical models, resources, activities, and algorithms of project scheduling problems. The project scheduling problem has attracted researchers of the automotive industry, steel manufacturer, medical research, pharmaceutical research, telecommunication, industry, aviation industry, development of the software, manufacturing management, innovation and technology management, construction industry, government project management, financial services, machine scheduling, transportation management, and others. The project managers need to finish a project with the minimum cost and the maximum quality.

Keywords: PSP, Combinatorial Optimization Problems, Project Management; Manufacturing Management, Technology Management.

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Authors: Eduardo Cabrera-Rode, Janet Rodriguez, Aimee Alvarez, Ragmila Echevarria, Antonio D. Reyes, Ileana Cubas-Duenas, Silvia E. Turcios, Oscar Diaz-Diaz

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Obex is a nutritional supplement to help weight loss naturally. In addition, this supplement has a satiating effect that helps control the craving to eat between meals. The purpose of this study was to evaluate the effect of Obex in the metabolic syndrome (MS). This was an open label pilot study conducted in 30 patients with MS and ages between 29 and 60 years old. Participants received Obex, at a dose of one sachet before (30 to 45 minutes) the two main meals (lunch and dinner) daily (mean two sachets per day) for 3 months. The content of the sachets was dissolved in a glass of water or fruit juice. Obex ingredients: Açai (Euterpe oleracea Mart.) berry, inulin, Phaseolus vulgaris, Caralluma fimbriata, inositol, choline, arginine, ornitine, zinc sulfate, carnitine fumarate, methionine, calcium pantothenate, pyridoxine and folic acid. In addition to anthropometric measures and blood pressure, fasting plasma glucose, total cholesterol, triglycerides and HDL-cholesterol and insulin were determined. Insulin resistance was assessed by HOMA-IR index. Three indirect indexes were used to calculate insulin sensitivity [QUICKI index (Quantitative insulin sensitivity check index), Bennett index and Raynaud index]. Metabolic syndrome was defined according to the Joint Interim Statement (JIS) criteria. The JIS criteria require at least three of the following components: (1) abdominal obesity (waist circumference major or equal major or equal 94 cm for men or 80 cm for women), (2) triglycerides major or equal 1.7 mmol/L, (3) HDL cholesterol minor 1.03 mmol/L for men or minor 1.30 mmol/L for women, (4) systolic/diastolic blood pressure major or equal 130/85mmHg or use antihypertensive drugs, and (5) fasting plasma glucose major or equal 5.6 mmol/L or known treatment for diabetes. This study was approved by the Ethical and Research Committee of the National Institute of Endocrinology, Cuba and conducted according to the Declaration of Helsinki. Obex is registered as a food supplement in the National Institute of Nutrition and Food, Havana, Cuba. Written consent was obtained from all patients before the study. The clinical trial had been registered at ClinicalTrials.gov. After three months of treatment, 43.3% (13/30) of participants decreased the frequency of MS. Compared to baseline, Obex significantly reduced body weight, BMI, waist circumference, and waist/hip ratio and improved HDL-c (p<0.0001) and in addition to lowering blood pressure (p<0.05). After Obex intake, subjects also have shown a reduction in fasting plasma glucose (p<0.0001) and insulin sensitivity was enhanced (p=0.001). No adverse effects were seen in any of the participants during the study. In this pilot study, consumption of Obex decreased the prevalence of MS due to the improved selected components of the metabolic syndrome, indicating that further studies are warranted. Obex emerges as an effective and well tolerated treatment for preventing or delaying MS and therefore potential reduction of cardiovascular risk.

Keywords: nutritional supplement, metabolic syndrome, weight loss, insulin resistance

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Authors: Angie Quevedo, Juan Bussi, Nestor Tancredi, Juan Fajardo-Díaz, Florentino López-Urías, Emilio Muñóz-Sandoval

Abstract:

In this work, we study the carbon nanotubes (CNTs) formation by catalytic chemical vapor deposition (CCVD) over a catalyst with 20 % of Ni supported over La₂Zr₂O₇ (Ni20LZO). The high C solubility of Ni made it one of the most used in CNTs synthesis. Nevertheless, Ni presents also sintering and coalescence at high temperature. These troubles can be reduced by choosing a suitable support. We propose La₂Zr₂O₇ as for this matter since the incorporation of Ni by co-precipitation and calcination at 900 °C allows a good dispersion and interaction of the active metal (in the oxidized form, NiO) with this support. The CCVD was performed using 1 g of Ni20LZO at 950 °C during 30 min in Ar:H₂ atmosphere (2.5 L/min). The precursor, benzylamine, was added by a nebulizer-sprayer. X ray diffraction study shows the phase separation of NiO and La₂Zr₂O₇ after the calcination and the reduction to Ni after the synthesis. Raman spectra show D and G bands with a ID/IG ratio of 0.75. Elemental study verifies the incorporation of 1% of N. Thermogravimetric analysis shows the oxidation process start at around 450 °C. Future studies will determine the application potential of the samples.

Keywords: N doped carbon nanotubes, catalytic chemical vapor deposition, nickel catalyst, bimetallic oxide

Procedia PDF Downloads 163