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Introducción y objetivos: La fibrilación auricular es la arritmia cardiaca más frecuente y una de las complicaciones más temidas es la embolia sistémica y/o el accidente cerebrovascular (ACV). En la actualidad se cuenta con diversas terapias antitrombóticas para prevención del primero, sin embargo, hay temor por complicaciones relacionadas con la anticoagulación, y aunque existen recomendaciones de diferentes guías de prácticas clínicas, continúa el uso subóptimo de la misma. El objetivo del estudio es describir datos clínicos, diagnósticos y terapéuticos de pacientes con fibrilación auricular que ingresan al servicio de urgencias, además, se realizó una correlación entre el riesgo de embolia sistémica y de sangrado con el tipo de terapia recibida. Métodos: Estudio descriptivo retrospectivo tipo serie de casos de pacientes con diagnóstico de la fibrilación auricular no valvular durante un año. Se reportan las frecuencias de comorbilidades, diagnósticos de ingreso y tipo de terapia específica. Resultados: Se incluyeron 98 pacientes, la media de edad fue 72 años, la falla cardiaca y la hipertensión arterial fueron las principales comorbilidades; el 92% tenían indicación de anticoagulación por la escala CHA2DS2VASc, 54% de estos pacientes no la venían recibiendo a pesar de que se calculó en la mayoría bajo riesgo de sangrado. El 5% de los pacientes ingresaron con diagnóstico de ACV isquémico y el 5% presentaban sobreanticogulación. Conclusión: Las características de nuestra población con la fibrilación auricular es similar a la descrita en la literatura, pero existe baja adherencia a las recomendaciones terapéuticas en anticoagulación.

The rise of smart learning environments and the transformative potential of the Metaverse are increasingly evident across various sectors, particularly in education. While substantial attention has been directed toward student engagement within the Metaverse, the evolution of teaching methodologies enabled by this technology remains under-examined. This study investigates the influence of teachers’ prior experience with technology on their willingness to adopt the Metaverse in diverse educational contexts. Utilizing a sample of 311 educators from the Brazilian Amazon, partial least squares structural equation modeling (PLS-SEM) was employed to validate the theoretical model. The findings emphasize the significant relationship between previous technological experiences, the mediating role of social influence in adoption, and performance expectations. Furthermore, the results underscore the pivotal role of technological engagement in shaping educators’ perceptions and their readiness to adopt emerging innovations, such as the Metaverse.

Image segmentation is a fundamental process in image processing, aiming to transform the image into a more comprehensible or simplified representation. This process groups pixels under common characteristics, facilitating the unique identification of regions or elements of interest. The quality of segmentation is crucial as it significantly influences the subsequent stages of image processing. Image segmentation plays a vital role in various advanced applications, including computer vision and the analysis of medical, topographical, and astronomical images. Given that there is no universal segmentation method guaranteeing optimal performance for all cases, selecting an appropriate technique for a specific type of image or application represents a complex and demanding challenge. In this work, we propose the use of Multi-Objective Evolutionary Algorithms (MOEAs) as a training tool that integrates operations representative of the usual techniques and strategies in image segmentation. This allows for the generation of operation sequences adapted to specific applications or types of images. The objective functions used to guide the evolutionary process are the maximization of sensitivity (TPR) and specificity (TNR), fundamental components of ROC analysis. Experiments were conducted with multiple images sharing common characteristics from image databases, specifically: i ) benign and malignant melanoma images, ii ) ophthalmoscopic retinal images, and iii ) binary cell form images. We compared the segmentation generated by our proposed algorithm with the ideal segmentation. The results are quite promising and demonstrate the feasibility of using MOEAs to generate sequences of segmentation operations valid for specific applications.

Abstract Leishmaniasis are neglected diseases transmitted by vectors that affect domestic and wild animals, including humans. Due to its incidence and lethality, this zoonosis is a worrying public health problem, making it essential to identify all links in the transmission chain. Infection of wild mammals by Leishmania spp. remains poorly understood, especially in southern Brazil. Therefore, the objective was to research, using the PCR technique, the presence of Leishmania spp. DNA in road-killed wild mammals in Southern Brazil. Carcasses of 96 animals were collected from highways in the Pelotas microregion, Rio Grande do Sul, southern Brazil and subjected to necropsies. Tissue fragments (spleen, skin, liver, kidney, heart, lung, lymph nodes, bone marrow and blood) were collected and genomic DNA was extracted. PCR protocols targeting the ITS1, kDNA and 18S genes were tested. We found no evidence of Leishmania spp. circulation in the studied population. However, epidemiological studies like this one are of great relevance, as they allow monitoring of the occurrence of pathogens and help identify possible risk areas. As these animals act as epidemiological markers for the presence of the microorganism, studies must be carried out continuously to understand whether there are sources of infection in the region.

Background: This work is part of the regional research project PIPER—Intelligent Platform for the Optimization of Recycling Operations, which aims to develop a network design model for waste collection in the metropolitan city of Rome, Italy. The goal is to enhance the strategic planning of treatment and disposal facilities by incorporating transshipment locations to improve the efficiency of waste collection. The motivation for this study stems from the stringent targets set by the European Green Deal to achieve near-zero emissions and the critical role of waste management in realizing circular economy practices. Methods: The problem is formulated as a mixed integer linear program (MILP) that includes constraints on vehicle allocation to shifts and driver requirements. An additional feature of the model is its ability to account for vehicle maintenance issues when deployed on consecutive shifts. Results: The model was tested against a real-world case study in Rome and demonstrated significant potential savings, with a reduction in total traveled distance exceeding 10%. The mathematical model was also used for evaluating strategic scenarios in a “what-if” analysis, allowing the municipal collection company to assess location options for depots, waste treatment facilities, and transshipment points, as well as to optimize the fleet composition and driver allocation during work shifts. Conclusions: This study presents a robust tool for strategic planning in waste collection operations, highlighting the benefits of using transshipment locations to improve efficiency. The findings indicate substantial potential for cost savings and operational improvements, making the model a valuable asset for municipal waste management planning.

Despite the recent developments in AI, ethical questions arise when consumers contemplate how their data is being treated. This paper develops a conceptual model building on the theory of acceptance, risk, trust, and attitudes towards AI to understand the drivers that lead consumers to accept AI, considering consumers' ethical concerns. The model was empirically tested with 200 consumers of AI marketing services. The findings reveal that perceived risk significantly impacts attitudes toward AI, ethical concerns, and perceived trust and suggest a significant association between perceived risk, ethical concerns, and social norms. This research provides important theoretical and managerial implications for the ethical aspects of AI in marketing by highlighting the ethical and moral questions surrounding AI's acceptance. Doi: 10.28991/ESJ-2023-07-02-01 Full Text: PDF

Machine learning approaches may establish a complex and non-linear relationship among input and response variables for the assessment of the Basic Education Development Index (IDEB) database and show indicators that may contribute to monitoring the quality of education. This paper uses extensive experimental databases from public schools, consisting of a case study in Brazil, to analyze data such as the physical and technological structure of schools and teacher profiles. The research proposes decision tree-based machine learning models for predictions of the best attributes to positively contribute to IDEB. It employs a newly developed SHapley Additive exPlanations (SHAP) approach to classify input variables, so to identify variables that impact the most the final model; a non-probabilistic sample was used, composed from three official databases of 450 schools, and 617 teachers. Results show that the number of computers per student, teachers’ service time, broadband internet access, investments in technology training for teachers, and computer labs in schools are the variables that have the greatest effect on IDEB. The model applied shows high prediction accuracy for test data (MSE = 0.2094 and R² = 0.8991). This article contributes to improving efficiency when monitoring parameters used to measure the quality of a teaching-learning process. Doi: 10.28991/ESJ-2022-SIED-020 Full Text: PDF

Internet energy consumption has increased rapidly, and energy conservation has become a significant issue that requires focused research efforts. The most promising solution is to identify the minimum power subsets within the network and shut down unnecessary network devices and links to satisfy traffic loads. Due to their distributed network control, implementing a centralized and coordinated strategy in traditional networks is challenging. Software-Defined Networking (SDN) is an emerging technology with dynamic, manageable, cost-effective, and adaptable solutions. SDN decouples network control and forwarding functions, allowing network control to be directly programmable, centralizing control with a global network view to manage power states. Nevertheless, it is crucial to develop efficient algorithms that leverage the centralized control of SDN to achieve maximum energy savings and consider peak traffic times. Traffic demand usually cannot be satisfied, even when all network devices are active. This work jointly addresses the routing of traffic flows and the assignment of SDN devices to these flows, called the Routing and Device Assignment (RDA) problem. It simultaneously seeks to minimize the network’s energy consumption and blocked traffic flows. For this approach, we develop an exact solution based on Mixed-Integer Linear Programming (MILP) as well as a metaheuristic based on a Genetic Algorithm (GA) that seeks to optimize both criteria by routing flows efficiently and suspending devices not used by the flows. Conducted simulations on traffic environment scenarios show up to 34% savings in overall energy consumption for the MILP and 33% savings achieved by the GA. These values are better than those obtained using competitive state-of-the-art strategies.

The manuscript presents a tool to estimate and predict data accuracy in hospitality by means of automated machine learning (AutoML). It uses a tree-based pipeline optimization tool (TPOT) as a methodological framework. The TPOT is an AutoML framework based on genetic programming, and it is particularly useful to generate classification models, for regression analysis, and to determine the most accurate algorithms and hyperparameters in hospitality. To demonstrate the presented tool’s real usefulness, we show that the TPOT findings provide further improvement, using a real-world dataset to convert key hospitality variables (customer satisfaction, loyalty) to revenue, with up to 93% prediction accuracy on unseen data. Doi: 10.28991/ESJ-2022-06-06-02 Full Text: PDF

Extensive damage to peripheral nerves is a health problem with few therapeutic alternatives. In this context, the development of tissue engineering seeks to obtain materials that can help recreate environments conducive to cellular development and functional repair of peripheral nerves. Different hydrogels have been studied and presented as alternatives for future treatments to emulate the morphological characteristics of nerves. Along with this, other research proposes the need to incorporate electrical stimuli into treatments as agents that promote cell growth and differentiation; however, no precedent correlates the simultaneous effects of the types of hydrogel and electrical stimuli. This research evaluates the neural differentiation of PC12 cells, relating the effect of collagen, alginate, GelMA, and PEGDA hydrogels with electrical stimulation modulated in four different ways. Our results show significant correlations for different cultivation conditions. Electrical stimuli significantly increase neural differentiation for specific experimental conditions dependent on electrical frequency, not voltage. These backgrounds allow new material treatment schemes to be formulated through electrical stimulation in peripheral nerve tissue engineering.

This paper proposes a two-stage model to tackle a problem arising in Waste Management. The decision-maker (a regional authority) is interested in locating sorting facilities in a regional area and defining the corresponding capacities. The decision-maker is aware that waste will be collected and brought to the installed facilities by independent private companies. Therefore, the authority wants to foresee the behaviour of these companies in order to avoid shortsighted decisions. In the first stage, the regional authority divides the clients into clusters, further assigning facilities to these clusters. In the second stage, an effective route is defined to serve client pickup demand. The main idea behind the model is that the authority aims to find the best location–allocation solution by clustering clients and assigning facilities to these clusters without generating overlaps. In doing so, the authority tries to (i) assign the demand of clients to the facilities by considering a safety stock within their capacities to avoid shortages during the operational phase, (ii) minimise Greenhouse Gases emissions, (iii) be as compliant as possible with the solution found by the second stage problem, the latter aiming at optimising vehicle tour lengths. After properly modelling the problem, we propose a matheuristic solution algorithm and conduct extensive computational analysis on a real-case scenario of an Italian region. Validation of the approach is achieved with promising results.

Culicoides have medical and veterinary importance, as they play a role as vectors of viruses, protozoa, and nematodes that cause diseases. Despite the relevance to public health, greater attention has been given to other insect vectors. Therefore, the objective of this study was to evaluate the efficiency of the Captor® light trap in capturing Culicoides that could be examined for the presence of Leishmania DNA. The insects were captured in a rural area of Santa Maria, Rio Grande do Sul, Brazil, where canine and human visceral leishmaniasis have been diagnosed. Adult insects were collected weekly, from 6:00 pm to 6:00 am, for a 12 month period using a Captor® brand suction light trap. All Culicoides were identified at species level. Pools of Culicoides were tested using the Polymerase Chain Reaction (PCR) technique for the detection of Leishmania DNA. A total of 16,016 specimens were collected (71.54% females and 28.39% males), divided among seven species. In the DNA analysis, none of the pools showed a positive result for Leishmania. The data presented demonstrate that the trap is efficient and can be an alternative for use in entomological research. They also demonstrate that, despite the females having hematophagous habits, similar to other vector insects, they did not have contact with Leishmania in the studied locality.

This paper addresses serialized approaches of the routing, modulation level, and spectrum assignment (RMLSA) problem in elastic optical networks, using multiple sequential sub-sets of requests, under Integer Linear Programming (ILP). The literature has reported two-stage serial optimization methods referred to as RML+SA, which retain computational efficiency when the problem grows, compared to the classical one-stage RMLSA optimization approach. However, there still remain numerous issues in terms of the spectrum used that can be improved when compared to the RMLSA solution. Consequently, this paper proposes RML+SA solutions considering multiple sequential sub-sets of requests, split traffic flow, as well as path-oriented and link-oriented routing models. Simulation results on different test scenarios determine that: (a) the multiple sequential sub-sets of request-based models improve computation time without worsening the spectrum usage when compared to just one set of requests optimization approaches, (b) divisible traffic flow approaches show promise in cases where the number of request sub-sets is low compared to the non-divisible counterpart, and (c) path-oriented routing succeeds in improving the used spectrum by increasing the number of candidate routes compared to link-oriented routing.