Universidad Tecnológica de Panamá
Recent publications
Study region Panama faces seasonal floods and droughts, and rising freshwater demand for domestic consumption, hydropower, and the operation of the Panama Canal. A process-based hydrological model of the country would complement the existing national water security plan as a scenario planning tool. Study focus In Panama, as in much of the Global South, sufficient observed data do not exist for all watersheds to calibrate complex hydrological models. Understanding and improving the performance of uncalibrated hydrological models could greatly expand their utility in such regions. In this study, we build and validate an uncalibrated Soil and Water Assessment Tool (SWAT) model for Panama. We extend the default precipitation submodel and demonstrate the importance of accounting for spatial autocorrelation patterns in precipitation inputs: we found large improvements over the default model, not only for monthly means (NSE = 0.88, from 0.69 for default SWAT), but especially for standard deviations (NSE = 0.59, from 0.27) and maxima (NSE = 0.51, from 0.21) of discharge across locations and months. New hydrological insights for region We found a strong seasonal trend and regional differences in the spatial autocorrelation of rainfall, suggesting that this phenomenon should not be modeled statically. The resulting precipitation and hydrology models provide important baseline information for Panama, especially on variability and extremes, and could serve as a template for other regions with limited data.
We explored the optical properties of silver and gold nanospheres by light and electron spectroscopy techniques for applications in light trapping. We first studied the nanoparticles by transmission electron microscopy (TEM) and UV–visible which showed the redshift of surface plasmon frequency with the increase in particle size. The plasmonic properties of isolated nanospheres were probed with high spatial resolution using scanning transmission electron microscope (STEM) fitted with an electron energy loss spectrometer (EELS). The particles were then deposited on bismuth ferrite (BiFeO3) thin films via the drop-casting technique and the light trapping effect was studied by specular reflectance. Two configurations of incident light were used, frontal and rear light incidence. In the former configuration, the light impinges frontally on the nanoparticles and in the latter configuration the light passes first through the BiFeO3 film before hitting the particles. In the frontal configuration, the reflectance showed a significant decrease compared to the case without nanoparticles which was associated with two phenomena: absorption and scattering toward the BiFeO3 film. In order to elucidate which phenomenon predominates, the rear measurement was performed where the scattered light inside the substrate is captured by the detector. In rear reflectance, there was an increase in intensity for large silver nanoparticles compared to the case without nanoparticles revealing that more light was directed inside the substrate. These observations were interpreted by simulations which showed that the best particles for light trapping are silver spheres larger than 60 nm. The results are of potential interest for emerging solar cells considering light trapping.
Intense competition in goods transportation has highlighted the importance of understanding customers’ interests in order to design successful relationship strategies. This study proposes, through a segmentation approach, to identify customer groups based on their perceptions of sustainable practices and relational variables about their main transport supplier. From a sample of 122 companies, a multiple correspondence analysis was carried out. The results show that there are three groups of customer companies, which correspond to a high, low, and medium relational and sustainability approach. The identified segments are also significantly different in terms of time of operation in the maritime sector, type of activity, size, and age. This proposal provides valuable information at the managerial level on the most influential attributes in the generation of loyalty in the B2B context of the maritime transport sector.
This work aims to establish Sb mobility, its transfer to biota and its effect on soil health in a semi-arid climate. The results show the presence of stibnite (Sb2S3) as the main primary Sb compound, bindhemite (Pb2Sb2O6(O,OH)), and minor proportions of stibiconite (Sb³⁺(Sb⁵⁺)2O6(OH)) as oxidised Sb species. This research also observes very high total Sb contents in mining materials (max: 20,000 mg kg⁻¹) and soils (400–3000 mg kg⁻¹), with physical dispersion around mining materials restricted to 450 m. The soil-to-plant transfer is very low, (bioaccumulation factor: 0.0002–0.1520). Most Sb remains in a residual fraction (99.9%), a very low fraction is bound to Fe and Mn oxy-hydroxides or organic matter, and a negligible proportion of Sb is leachable. The higher Sb mobility rates has been found under oxidising conditions with a long contact time between solids and water. The main factors that explain the poor Sb mobility and dispersion in the mining area are the low annual rainfall rates that slow down the Sb mobilisation process and the scarce formation of oxidised Sb compounds. All these data suggest poor SbIII formation and a low toxicological risk in the area associated with past mining activities. The low mobility of Sb suggests advantages for future sustainable mining of such ore deposits in a semi-arid climate and is also indicative of the limitations of geochemical exploration in the search for new Sb deposits.
Statistical fault injection is widely used to estimate the reliability of mission-critical microprocessor-based systems when exposed to radiation and to evaluate the performance of fault mitigation strategies. However, further research is needed to gain a better understanding of the accuracy of the results and the feasibility of their application under realistic radiation conditions. In this paper, an understanding of scenarios in which Instruction Set Architecture simulators or emulators may be relied upon for realistic statistical fault injection campaigns is advanced. An analysis is presented of the results from two simulation-based fault injection tools versus a set of fault emulation results on a real processor. The conclusions of the analysis assist the selection of the most efficient tool and method for testing many different software-based fault mitigation techniques within reasonable time periods and at affordable costs throughout an irradiation campaign. In particular, it was established that a partially ordered set of relations could be defined on the basis of statistical fault injection in relation to the effects of different versions of an application and a given simulator that remained unaltered during the irradiation experiments. The tests were conducted with a Texas Instruments MSP430 microcontroller to perform both fault injection campaigns and irradiation experiments using neutrons at the Los Alamos Neutron Science Center (LANSCE) Weapons Neutron Research Facility at Los Alamos, USA.
Thin films of amorphous BaTiO3 (a-BTO) were grown by spin coating technique on a glass substrate and annealed at different temperatures, from 300 to 500 °C, with a step of 50 °C. The samples were characterized structurally by X-ray diffraction (XRD), where broadband around 25° indicates the amorphous formation in all the thin films. The optical properties of the a-BTO thin films were determined by transmittance and reflectance UV-Visible spectroscopy. The optical responses were fit using Lorentz and Lorentz-Tauc classical models to determine the complex dielectric function. In addition, the photoluminescence (PL) of the glass and the a-BTO/glass system was also measured to observe the antireflective capacity of the a-BTO thin film as a function of the calcination temperature. To understand the experimental structural results ab-initio molecular dynamic calculation of 2x2x2 BTO supercell was utilized, while the electronic density of state and the complex dielectric function were obtained using Density Functional Theory (DFT). A comparison between the experimental and theoretical results is presented and discussed.
This article describes key material science/technology issues to implement polycrystalline diamond scaffolds to enable processes for biological cells growth relevant for using cells grown in the laboratory for the treatment of human biological conditions. Issues investigated include 1. Synthesis/characterization of microcrystalline diamond (MCD), nanocrystalline diamond (NCD) and transformational ultrananocrystalline diamond (UNCD) coating-based scaffolds. Diamond films were grown on silicon substrates using the hot filament chemical vapor deposition (HFCVD) technique, by which filaments heated to ∼2,300 °C induce cracking of CH4 molecules into C atoms/CHx (x = 1, 2, 3) radicals, growing diamond films via chemical reaction on the substrates’ surface. MCD and NCD + films were grown flowing the H2/CH4 gas mixture. NCD- and UNCD films were grown using the Ar/CH4 gas mixture plus H2 flux (73.5%, 49%, and 9.8%), which for high fluxes, induced increased the concentration of H-containing trans-polyacetylene (T-PA) molecules in UNCD films’ grain boundaries, impacting biological performance. 2. Studies of viability and proliferation of human lung carcinoma cell line (A549) grown on surfaces of MCD, NCD, and UNCD films, using 3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl tetrazolium bromide (MTT) assay, which showed no significant difference in cell proliferation among the MCD, NCD, and UNCD films.
Resonant wireless power transfer (WPT) systems have been evolving and improving their designs over the last few years, looking to efficiently charge electric vehicles, cellphones, and biomedical devices. In this article, we present to the scientific community the data obtained from the optimization of a resonant WPT prototype, operating at different vertical misalignments and load conditions, known to have an impact on the behavior of these type of systems. To maximize the power transferred to the load, we developed a proportional-integral frequency control algorithm that employs the phase-shift between the voltage and current waveforms in the transmitting antenna (resonance indicator) as a setpoint. Data on the performance and control optimization process of the prototype during laboratory tests were acquired using a LabVIEW interface, which was designed to capture information such as the evolution of the frequency, the phase-shift, and the load voltage, from multiple devices (a microcontroller, an oscilloscope, a digital multimeter, and a controllable power supply). The data were organized and presented in tables and graphs using MATLAB. The importance of the dataset relies on the opportunity to utilize the information as a basis for the improvement of the associated electronics by using different transmission topologies, higher speed components, new-generation microcontrollers, and to modelling novel intelligent control algorithms, such as adaptative neuro-fuzzy inference systems.
Surface Dielectric Barrier Discharge (SDBD) is a well-known technology for active aerodynamic flow control with low power consumption. It is a type of plasma actuation for flow control with no moving parts and very fast response times. Research on SDBD flow control over the years has shown great potential for flow separation, boundary layer transition, drag reductions and suppression of local heating. A major area of research on SDBD flow control lies in increasing the effectiveness of SDBD actuators with new electrode configurations, surface materials, and plasma array designs. This review aims to provide a comprehensive report of research performed on SDBD flow control over the last 2 decades with a focus on SDBD reactor designs. Aspects of SDBD flow control including discharge morphology and actuation mechanism through momentum and energy transfer have been discussed in depth. Additionally, the future of research in SDBD actuated flow control has been explored. This review can serve as the baseline to develop new SDBD reactor designs for specific applications with improved effectiveness and advanced systems.
Background: A growing number of mobile applications have been designed for the chronic disease patient as the primary user. Mobile health applications for self-care have the potential to help patients living with chronic conditions such as kidney disease, and can be used to manage aspects such as the consumption of substances that are harmful to health. Chronic kidney disease causes significant morbidity throughout Panama, and is also responsible for an increase in cardiovascular disease. Objective: In this paper, we present a review of the applications offered by the Android store, based on a search and selection of the most efficient options that fulfill a set of criteria and functionalities. Methods: We evaluate a big health data model in terms of its usefulness for studies, research and projections of Panamanian patients with this chronic disease. Results and discusion: We present a mobile application based on the most important standards and functionalities for the Panamanian population affected by this disease. Our analysis also highlights the importance of mobile applications for the self-care of chronic diseases and their usefulness to both patients and health care providers, since they can support better health habits and give good results in terms of following a diet, promoting a healthy lifestyle, and encouraging physical activity. The analysis presented here will form the basis for the development of an application that will be simple, user-friendly and powerful, in the sense that it will empower patients with the resources they need for self-care. . Conclusion: Mobile applications are found to show promise for the self-care of chronic conditions, and can improve the quality of life of Panamanian patients. In addition, we intend to develop a data repository for scientific research within Central America.
Context Social debt describes the accumulation of unforeseen project costs (or potential costs) from sub-optimal software development processes. Community smells are sociotechnical anti-patterns and one source of social debt. Because community smells impact software teams, development processes, outcomes, and organizations, we to understand their impact on software engineering. Objective To provide an overview of community smells in social debt, based on published literature, and describe future research. Method We conducted a systematic literature review (SLR) to identify properties, understand origins and evolution, and describe the emergence of community smells. This SLR explains the impact of community smells on teamwork and team performance. Results We include 25 studies. Social debt describes the impacts of poor socio-technical decisions on work environments, people, software products, and society. For each of the 30 community smells identified as sources of social debt, we provide a detailed description, management approaches, organizational strategies, and mitigation effectiveness. We identify five groups of management approaches: organizational strategies, frameworks, models, tools, and guidelines. We describe 11 common properties of community smells. We develop the Community Smell Stages Framework to concisely describe the origin and evolution of community smells. We then describe the causes and effects for each community smell. We identify and describe 8 types of causes and 11 types of effects related to the community smells. Finally, we provide 8 comprehensive Sankey diagrams that offer insights into threats the community smells pose to teamwork factors and team performance. Conclusion Community smells explain the influence work conditions have on software developers. The literature is scarce and focuses on a small number of community smells. Thus, the community smells still need more research. This review helps by organizing the state of the art about community smells. Our contributions provide motivations for future research and provide educational material for software engineering professionals.
As one of the main consumers of primary energy globally, buildings have been among the main targets for implementing energy efficiency solutions, such as building control strategies that maintain occupant comfort and reduce operating costs. The design of such control schemes relies on a thermal model of the building to predict indoor temperature. The model should be sufficiently accurate to describe building dynamics but simple enough to remain optimal for control purposes. This paper proposes a methodology to identify thermal RC networks to model building thermal dynamics of a residential buildings located in humid and rainy climates, a topic not widely covered in current literature. The candidate models for the methodology are determined through a parameter dispersion study, which consists of training the models multiple times and checking if the parameters converge to a single value regardless of their initial value. Then the effect of the training dataset characteristics on model performance is studied. The methodology is established and then tested in a residential case study in Panama from these conclusions. Results show that a linear model with few parameters and trained with only 10 days of data can successfully represent a system with prominent nonlinear phenomena. The model with the best performance during active operation has a validation root mean square error of 0.36°C, which is satisfactory for controller design purposes. The model is then used to tune a proportional integral derivative controller, successfully employed to maintain the desired indoor temperature. Using the identified model to calibrate the controller avoids tedious trial and error procedures.
The cost impact of implementing additive manufacturing (AM) in the fabrication process is nowadays an issue. The scope of this research is to establish a cost model framework that can predict the cost of a piece in a low to medium batch considering fused deposition modelling (FDM) printing parameters. Every enterprise wants to increase its internal capabilities for tools, prototypes, and the production of pieces for maintenance purposes. FDM is an AM technology increasingly used in aerospace, automotive, and many other sectors. The research methodology consists of developing a cost model based on the extrusion-type additive manufacturing process for any given machine characteristics and comparing the cost per piece based on diverse lot sizes and raw materials. The proposed cost model framework is capable to calculate the cost per piece for any given extrusion type machine characteristic for low to medium production batches. The framework could be used to predict the best machine size and material type that could be suitable for a certain situation. The strength of our approach lies in the energy cost calculus, which is dependent on machine capabilities and size.
Over the past decade, an increase in global connectivity and social media users has changed the way in which opinions and sentiments are shared. Platforms such as Twitter can act as public forums for expressing opinions on non-personal matters, but often also as an outlet for individuals to share their feelings and personal thoughts. This becomes especially evident during times of crisis, such as a massive civil disorder or a pandemic. This study proposes the estimation and analysis of sentiments expressed by Twitter users of the Republic of Panama during the years 2019 and 2020. The proposed workflow is comprised of the extraction, quantification, processing and analysis of Spanish- language Twitter data based on Sentiment Analysis. This case of study highlights the importance of developing natural language processing resources explicitly devised for supporting opinion mining applications in Latin American countries, where language regionalisms can drastically change the lexicon on each country. A comparative analysis performed between popular machine learning algorithms demonstrated that a version of a distributed gradient boosting algorithm could infer sentiment polarity contained in Spanish text in an accurate and time-effective manner. This algorithm is the tool used to analyze over 20 million tweets produced between the years of 2019 and 2020 by residents of the Republic of Panama, accurately displaying strong sentiment responses to events occurred in the country over the two years that the analysis performed spanned. The obtained results highlight the potential that methodologies such as the one proposed in this study could have for transparent government monitoring of responses to public policies on a population scale.
The thermal comfort of an individual is known as the mental satisfaction they possess in a medium. This depends on several ambient factors such as air temperature, mean radiant temperature , relative humidity, air velocity, and personal factors such as cloth and metabolic activity. In buildings, occupants interact with different systems and equipment such as air conditioning, ventilation , lighting, and other appliances to influence these factors or demonstrate adaptive tendencies with the systems to reach comfort. Within the last two decades, preference-based occupant-centered control systems have been incorporated into buildings, generally validated with comfort indexes. A frequently found challenge is the formulation of the method used to create a system that considers the stochastic characteristics of the occupant's portrait. Here, a method that links the advantages of both probabilistic and schedule-based methods and satisfactorily integrates it with comfort indexes through a controller is proposed. It is intended to compare the controller's effect on thermal comfort through comfort indexes and energy consumption when implementing different occupant models applied in Panama. Sensibility analysis, gray-box building modeling, and thermal indexes were used in the controller's design. Results showed that the best controller is the probability-based model providing low power consumption and PMV levels.
The Covid-19 pandemic has greatly impacted Latin America, the continent with the highest number of cases and Covid-related deaths. Strict confinement conditions at the beginning of the pandemic put to a halt recycling activities and augmented the consumption of plastic as a barrier to stop the spread of the virus. In Latin America the lack of data to understand the waste management dynamics difficult the adjustment of waste management strategies to cope with the Covid-19. As a novel contribution to the waste management data gap for Latin America, this study uses a virtual and participatory methodology that collects and generates information on household solid waste generation and composition. Data was collected between June and November 2021 in six countries in the Latin America region, with a total of 503 participants. Participants indicated that the pandemic motivated them to initiate or increase waste reduction (41%), waste separation (40%) and waste recovery (33%) activities. 43% of participants perceived and increase on their total volume of waste; however, the quantitative data showed a decrease on household waste generation in Peru (-31%), Honduras (-25%) and Venezuela (-82%). No changes in waste composition were observed. Despite the limited sample size, this data provides a much-needed approximation of household waste generation and composition in a pandemic situation during 2021.
Este artículo describe el análisis realizado a la capacidad de las lentejas de agua (Lemna minor) de fitorremediar concentraciones de hierro, comparándolas con la capacidad de la hydrilla (Hydrilla verticillata) durante siete días. Para realizar este estudio se utilizó agua declorada, a la cual se le agregó sulfato ferroso (II) heptahidratado (FeSO4•7H2O) y se analizó las diferentes características químicas y físicas del agua. Tanto las lentejas de agua como la hydrilla presentaron una capacidad inicial de absorción similar durante las primeras 48 horas del experimento, presentando un porcentaje de reducción de la concentración de hierro en el agua de 49.07% por parte de la lenteja de agua y un 42.90% para la hydrilla. Al incrementar el intervalo de tiempo (t > 1 día) se ralentizó el proceso de absorción gradualmente hasta obtener un porcentaje de reducción mínimo de 1.10% para las lentejas de agua y un 15.93% para la hydrilla antes de presentar una desorción de 4.44% y 22.73% respectivamente al final del experimento. La hydrilla, con un porcentaje de reducción de hierro del 53.02%, en comparación al 51.44% de las lentejas de agua. Ambas especies demostraron una gran capacidad para remover hierro en agua, con potencial a ser un método económicamente viable para la fitorremediación del hierro en un medio acuático.
Con la llegada de la pandemia de COVID-19, para los estudiantes universitarios fue un reto adquirir conocimiento y potenciar sus habilidades blandas y duras en medio de una educación a distancia y/o virtual, donde el uso de las tecnologías de la información y la comunicación (TIC), tecnologías del aprendizaje y el conocimiento (TAC), tecnologías del empoderamiento y la participación (TEP) son apremiantes, es decir la didáctica ludificada, ahora gamificada. El deber docente conllevó un proceso decisivo de adaptación para el desarrollo de su labor como promotor de conocimiento. La experiencia virtual estudiante - docente y viceversa durante el primer semestre 2021 en Ecología General tuvo como objetivo conocer áreas protegidas de Panamá y presentar alguna iniciativa que desde su conocimiento previamente adquirido o creado ayude a otros en la generación de nuevo aprendizaje. La práctica virtual demostró que los estudiantes tienen la capacidad de responsabilizarse por aquello que desean aprender haciendo, sin necesidad de que el docente domine las mismas herramientas tecnológicas; que el desempeño docente seguirá siendo evaluado constantemente por sus estudiantes para moldearnos a sus necesidades actuales. El docente en el aula presencial o virtual es el principal alentador en lo que habilidades duras se refiere, sin menoscabo de las blandas.
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2,472 members
Jessica Guevara
  • Facultad de Ingeniería Eléctrica
Vladimir Villarreal
  • Facultad de Ingeniería de Sistemas Computacionales
Miguel Vargas-Lombardo
  • Centro de Investigación, Desarrollo e Inovación en Tecnologías de la Información y las Comunicaciones
Elida De Obaldia
  • Departamento de Ciencias Naturales
Panamá, Panama