Universidad Tecnológica de Panamá

Currently, cities face the challenges of achieving net-zero emissions, sustainable resource usage, and occupational safety. Sustainable manufacturing processes (SMP) in the architecture, engineering, and construction industry (AEC) could help to master such challenges if non-digitized or insufficiently networked processes did not repeatedly hinder it. The smart combination of additive manufacturing (AM) and nature-based design (NbD) could lead to an economic breakthrough in SMP. AM quickens process fulfillment and automation, offering potential to reduce expenditures in resources, costs, and associated risks while ensuring sustainability, and if early integrated into the design, it allows defining lightweight, sustainable, and material as objectives. NbD approaches in AM for AEC lead to complex structures with superior performance, minimizing material usage, and fostering regenerative, inclusive, and climate-adapted designs that shape contemporary architecture. Thus, this chapter comprehensively reviews NbD for AM projects, analyzing (i) geometry-focused design strategies, (ii) modeling approaches with performance criteria, and (iii) challenges of implementing NbD approaches in AEC.

Natural products (NPs) are secondary metabolites of natural origin with broad applications across various human activities, particularly discovering bioactive compounds. Structural elucidation of new NPs entails significant cost and effort. On the other hand, the dereplication of known compounds is crucial for the early exclusion of irrelevant compounds in contemporary pharmaceutical research. NAPROC-13 stands out as a publicly accessible database, providing structural and 13 C NMR spectroscopic information for over 25,000 compounds, rendering it a pivotal resource in natural product (NP) research, favoring open science. This study seeks to quantitatively analyze the chemical content, structural diversity, and chemical space coverage of NPs within NAPROC-13, compared to FDA-approved drugs and a very diverse subset of NPs, UNPD-A. Findings indicated that NPs in NAPROC-13 exhibit comparable properties to those in UNPD-A, albeit showcasing a notably diverse array of structural content, scaffolds, ring systems of pharmaceutical interest, and molecular fragments. NAPROC-13 covers a specific region of the chemical multiverse regarding physicochemical properties and a region as broad as UNPD-A in terms of structural features represented by fingerprints.

The soil–plant transfer of trace elements is a complex system in which many factors are involved such as the availability and bioavailability of elements in the soil, climate, pedological parameters, and the essential or toxic character of the elements. The present study proposes the evaluation of the use of multielement contents in vascular plants for prospecting ore deposits of trace elements of strategic interest for Europe. To accomplish this general goal, a study of the soil–plant transfer of major and trace elements using Quercus ilex as a study plant has been developed in the context of two geological domains with very different characteristics in geological terms and in the presence of ore deposits: the Almadén syncline for Hg and the Guadalmez syncline for Sb. The results have made it possible to differentiate geological domains not only in terms of individual elements, but also as a combination of major and trace elements using Factor Analysis. The bioconcentration factors have demonstrated the uptake of macronutrients and micronutrients in very high concentrations but these were barely dependent, or even independent of the concentrations in the soil, in addition to high values of this factor for Sb. The Factor Analysis allowed for the differentiation of geogenic elements from other linked to stibnite ore deposits (Sb, S, and Cu). This element (Sb) can be uptake by Quercus ilex via the root and from there translocating it to the leaves, showing a direct relation between concentrations in soil and plants. This finding opens the possibility of using Quercus ilex leaves for biogeochemical prospecting of geological domains or lithological types of interest to prospect for Sb deposits.

The current reality places technology at the forefront of the educational communication process, especially considering the health emergency brought on by COVID-19. Creating mechanisms to facilitate the continuity of teaching represented one of the greatest challenges during the pandemic. A collaborative, innovative ecosystem model targeted towards the teaching-learning process is developed. An exploratory, descriptive study with a mixed-methods approach is conducted; cases from universities in Colombia, Panama, Peru, and the Dominican Republic are examined, surveys are implemented, and classroom and university experiences are recounted. Each study experience generates a space for teachers to discuss and reflect on the students’ progress and the methodologies applied for flexibility and adaptability to available information technologies. In this way, the foundation for a collaborative innovation ecosystem is proposed to organically strengthen student knowledge through articulated structures, functional relationships, and supportive methods and procedures. An active control system is also proposed to ensure meaningful learning. This experience leaves alternative options for all universities and the education system for health emergencies that prevent face-to-face interaction, taking advantage of new information technologies and the era of artificial intelligence.

This article presents the implementation of hardware tools such as Raspberry Pi, cameras, sensors, motors, and controllers, along with software components like convolutional neural networks and a mobile application for waste classification. In the future, the proposed waste collector and classifier implementation will contribute to environmental care and environmental education. The project’s innovation lies in the automation of waste classification using neural networks, automatic notifications generated by the prototype when a container is full and transmitted to the mobile application via a web server, and the flexibility of the prototype for various environments, including educational, office, and industrial settings. The advancements in the project include the creation of a mobile application to monitor container levels, the construction of the prototype, training results of selected neural networks, and the evaluation of the final network with test images.

This study aims to evaluate three neural network models to identify a base model that will be used for welding defect detection. A methodology is proposed that involves evaluating three different models: EfficientNet, MobilNet, and You Only Look Once, using a dataset of images of dogs and cats for training. The analysis includes training, monitoring, and tuning crucial parameters to achieve an optimal model. The final model selection will be based on accuracy, complexity and training time, ensuring the most accurate and efficient detection of the database (cats and dogs). You Only Look Once is identified as an attractive model due to its high object detection accuracy, crucial for accurate defect identification. Challenges include variability in conditions and defect types, necessitating greater diversity of data sets and model refinement to improve visual presentation. The impact of this study lies in its ability to evaluate the application of convolutional neural networks model for welding defect detection, which can have a great impact on the quality and safety of welded structures in various industrial fields. By achieving more accurate and efficient defect detection, potentially dangerous and costly structural failures can be avoided. Furthermore, the developed methodology and approaches may be applicable to other domains requiring visual anomaly detection, thus extending the impact beyond the realm of welding.

We report the inactivation of SARS CoV-2 and its surrogate—Human coronavirus OC43 (HCoV-OC43), on representative porous (KN95 mask material) and nonporous materials (aluminum and polycarbonate) using a Compact Portable Plasma Reactor (CPPR). The CPPR is a compact (48 cm³), lightweight, portable and scalable device that forms Dielectric Barrier Discharge which generates ozone using surrounding atmosphere as input gas, eliminating the need of source gas tanks. Iterative CPPR exposure time experiments were performed on inoculated material samples in 3 operating volumes. Minimum CPPR exposure times of 5–15 min resulted in 4–5 log reduction of SARS CoV-2 and its surrogate on representative material samples. Ozone concentration and CPPR energy requirements for virus inactivation are documented. Difference in disinfection requirements in porous and non-porous material samples is discussed along with initial scaling studies using the CPPR in 3 operating volumes. The results of this feasibility study, along with existing literature on ozone and CPPR decontamination, show the potential of the CPPR as a powerful technology to reduce fomite transmission of enveloped respiratory virus-induced infectious diseases such as COVID-19. The CPPR can overcome limitations of high temperatures, long exposure times, bulky equipment, and toxic residuals related to conventional decontamination technologies.

The optimal conditions for laccase production and vinasse biotreatment with a native strain of Trametes villosa were determined by a screening-optimization approach. Eleven factors including nutrient concentration, vinasse dilution (%v/v), inoculum volume, carbon to nitrogen ratio and initial pH, were investigated for their effects on laccase activity applying the Plackett-Burman screening design. The selected factors were optimized using a central composite design, and then evaluated on a vinasse biotreatment experiment. The factors that contributed the most to the enzymatic activity were the concentrations of MgSO4∙7H2O (A), FeSO4∙7H2O (B) and CuSO4∙5H2O (C), alongside initial pH. After 10 days, laccase activity was 544.038 U L-1 for the following concentrations of A, B and C: 0.250 g L-1, 0.020 mg L-1, and 0.100 g L-1, respectively. Vinasse biotreatment under optimized conditions resulted in 82.74%, 78% and 75.97% of phenol, color, and COD removal respectively, while final pH value was 6.90. These results showed that the native strain of T. villosa has a good potential for further research on laccase production and vinasse sustainable management.

The use of prioritization analysis techniques allows identifying the level of criticality of physical assets and helps to manage resources: human, economic and technological in a more efficient way. In other words, the process of criticality analysis helps to determine the importance and consequences of the failures of productive equipment in the operational context in which they perform. This article explains the basic theoretical aspects of the equipment prioritization analysis process based on risk matrices (failure frequency and consequences); and the development of the model named Risk Qualitative Criticality Matrix (RQCM). Finally, are presented and analysed the results of a case of application of the RQCM in the sector of ophthalmic lenses (new factory built in Costa Rica – PRATS Laboratory).

Quality inspection of buildings is a task that can be tedious and prone to human errors, so the use of autonomous mobile robots is very attractive. In this work, a swerve drive mobile robot is proposed for tile hollowness inspection using an active thermographic approach. It integrates a hot air blower as heating source and an infrared thermal camera to observe the hollow region contour, which is the result of the characteristics of the thermal diffusion process in different media. In addition, image processing techniques were used to segment the hollow region contour. As a result, an automatic, fast and effective inspection strategy is proposed.

The analysis of quality attributes is a vital phase that determines the functionalities and properties a software system must have from its conception and development, an aspect from which current systems do not escape. Let’s add the paradigm of the Internet of Things (IoT) to the current systems, and as a result, the requirements, and capabilities of the components to be automated in the most common systems and services increase exponentially. This leads to the linking of services in the computational cloud and other contexts and domains of scientific and business interests. In this research work, a literature review supported by the ISO/IEC 25010 standard is carried out to identify the quality attributes for two application domains: Health sector and Commerce sector; the associated and attended aspects for each domain is pointed out according to the quality characteristics of the software product. This research is the basis for identifying other application domains in sectors yet to be studied, in which IOT technologies and software engineering are immersed and are necessary for the development of new experiences in the context of requirements, planning, complex processes, project development and service for software development with higher quality.

When developing models or frameworks in electronic health, known as eHealth, they must consider a variety of essential characteristics for their optimal functioning; however, identifying these characteristics is complicated because health processes, especially those directed at health care, are very diverse. Therefore, the objective of the research is to identify the most used characteristics in the development of frameworks or models for eHealth, to determine which are the ones that have had the greatest relevance and thus offer a general extract of the essential components in the design of eHealth-oriented models or frameworks. The set of evidence shows as a result the essential characteristics to develop models and frameworks in the eHealth field, such as: optimal management of documentation based on semantic terminology, adoption or management of ontologies, application of IT governance standards, quality and health, safety and privacy, functional and quality requirements, among others. Based on what has been observed, it is concluded that by identifying the main characteristics that any model or framework developed for eHealth must have, it allows the researcher or developer to have a clearer vision of what these types of systems should consider creating an architectural structure. More solid, formal and quality in the context of eHealth.

Presents information and current topics of interest to the global communications industry.

Background: SARS-CoV-2 is an infectious disease caused by the coronavirus that was first reported in December 2019 in China and immediately spread around the world causing a pandemic, which has caused countless deaths and cases in global health. Mental health has not gone untouched by this pandemic; due to the lockdown and the vast amounts of information disseminated, the Panamanian population has begun to feel the collateral effects. Objective: We propose classifying tweets using a machine learning (ML) and deep learning (DL) approach and pattern search to make recommendations to the emotional and psychological reactions of the Panamanian population. Methods: Our study has been carried out with a corpus in spanish extracted from X for the automatic classification of texts, from which we have categorized, through the ML&DL approach, the tweets about Covid-19 in Panama, in order to know if the population has suffered any mental health effects. Results: We can say that the ML models provide competitive results in terms of automatic identification of texts with an accuracy of 90%. Conclusion: X is a social network and an important information channel where you can explore, analyze and organize opinions to make better decisions. Text mining and patron search are a natural language processing (NLP) task that, using ML&DL algorithms, can integrate innovative strategies into information and communication technologies.

Concerns about climate change have boosted the massive introduction of renewable energy into electric power systems. Renewable generators can mitigate pollution by replacing fuel-based energy with clean energy. However, these generators displace conventional synchronous ones. This changes power system dynamics and can lead to a less stable operation. To face this emerging problem, many approaches have been proposed in order to enable renewable generators and consumers to provide ancillary services. Nevertheless, if renewable generators adjust their production to support grid ancillary services, all the primary energy resources cannot be optimally used. This work proposes a way for railway systems to contribute to primary and supplementary frequency control and to relieve renewable generation from frequency control tasks. Furthermore, a tuning control method is introduced. Due to the high stochasticity of railway system operation, a method for selecting local optimum parameters is proposed. The frequency control scheme is set and tested in simulation, and experimentally validated by means of Hardware In the Loop tests with a real micro controller. The results demonstrate, first, the capacity of railway systems to manage a certain quantity of frequency deviations and, second, the feasibility of implementing the proposed control strategy in real time. This work broadens the framework of railway-based demand response approaches.