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El presente artículo examina la relación emergente entre el ser humano y la inteligencia artificial (IA) en la fase de preproducción en la industria de los medios audiovisuales, adoptando una perspectiva interdisciplinaria fundamentada en la ecología de los medios y la teoría del actor red. Por un lado, se presenta el mutualismo, en el que humanos y especies artificiales colaboran para mejorar la eficiencia y calidad del proceso creativo, por medio del análisis de ScriptBook y Cinelytic, dos de las herramientas de IA más importantes e utilizadas en la industria cinematográfica. Por otro lado, se plantea la amenaza distópica en que la IA podría suplantar a los guionistas humanos en el ecosistema mediático audiovisual, como se ilustró en la huelga de los sindicatos de productores y guionistas de Hollywood en el año 2023. Estos escenarios plantean interrogantes sobre el futuro de la industria y la coevolución entre humanos e IA a través del concepto de mutación. Se enfatiza la necesidad de adaptarse a estas transformaciones, reconociendo el papel fundamental de los investigadores y la sociedad en monitorizar la relación entre ambas especies para comprender su potencial cocreativo y coevolutivo. Por último, se subraya la importancia de adaptarse ágilmente a un ecosistema mediático en constante cambio como una tarea esencial para aprovechar plenamente el potencial de la inteligencia híbrida y garantizar la supervivencia de la especie humana en este nuevo paradigma tecnológico.

Integrating artificial intelligence in education can revolutionize how educational resources are generated, and assessments are conducted. However, current automated systems often struggle with precision, relevance, and usability issues, particularly in adapting to the specific needs of diverse educational contexts. This study addresses these challenges by developing and refining an automated syllabus generation and academic evaluation system, focusing on continuous user feedback and iterative adjustments. Our approach involved optimizing text processing algorithms to improve the system’s contextual understanding and incorporating customization options to align the generated content with course-specific objectives. The results were significant: the system’s evaluation precision increased from 78.5% to 89.7% over six months, and the relevance of the generated syllabi improved from 82.0% to 90.5%. Usability also saw a notable enhancement, with user satisfaction scores rising by 21.1%. These findings demonstrate that an adaptive, user-centered approach to education automation can effectively overcome current systems’ limitations, leading to more accurate, relevant, and user-friendly tools. By focusing on the iterative improvement of the system based on continuous feedback, we have developed a solution that not only meets the immediate needs of educators and students but also has the potential to scale and adapt to future educational challenges.

Colchicine is an alkaloid traditionally used to treat inflammatory conditions such as gout and familial Mediterranean fever. Currently, there are proposals for the use of this drug in several other situations, such as cardiovascular and liver diseases and diabetes. In this study, the current literature on the potential of colchicine in the treatment of obesity and metabolic syndrome (MS) was evaluated. The inhibitory action of the NLRP3 inflammasome and other processes, such as reductions in the migration and activation of immune system cells, are effects observed in both in vitro studies and animal models related to colchicine, as well as the promotion of mechanisms of the intensification of lipid metabolism, the reduction of tissue fibrosis, and the reduction of serum glucose and triglycerides. These factors are associated with changes in the prognoses of patients with MS, which, together with obesity, has a high association with inflammatory mechanisms for its maintenance and secondary impairments to homeostasis. In humans, clinical research has rarely addressed the use of colchicine in obesity and MS, with only one pilot randomized clinical trial having been conducted, which identified a beneficial anti-inflammatory effect on endothelial function and the process of insulin resistance in this population. However, it is not yet possible to extrapolate its findings and apply its results to a broader context. Given the potential of this “ancient drug” in various pathological contexts and its good tolerability, it is important that its properties continue to be investigated and that more clinical studies be conducted to expand the therapeutic applications of this low-cost substance in patients with obesity and MS.

Bearings are one of the most essential elements in an induction motor, and they are built with different materials and constructions according to their application. These components are usually one of the most failure-prone parts of an electric motor, so correct and accurate measurements, instrumentation, and processing methods are required to prevent and detect the presence of different failures. This work develops a methodology based on the fusion of current and magnetic stray flux signals, calculation of statistical and non-statistical indicators, genetic algorithms (GAs), linear discriminant analysis (LDA), and neural networks. The proposed approach achieves a diagnostic effectiveness of 99.8% for detecting various damages in the outer race at 50 Hz frequency and 96.6% at 60 Hz. It also demonstrates 99.8% effectiveness for detecting damages in the presence of contaminants in lubrication at 50 Hz and 97% at 60 Hz. These results apply across metallic, ceramic, and hybrid bearings.

We investigate a new event of accelerated environmental change that was recorded during the late Barremian in the pelagic Subbetic Basin (Western Tethys). Two pelagic sections have been studied using a multi-proxy approach based on C-isotope stratigraphy and a high-resolution quantitative study of nannofossil assemblages, along with major and trace elements and biomarkers. Our results provide a detailed biostratigraphy and C-isotope stratigraphy, and outline the paleoenvironmental conditions recorded during the early stages of the Taxy Episode. A disturbance has been identified in the C-isotope record, called the IFeNE (Intra-Feradianus negative C-excursion), which is coeval with environmental and biotic changes that predate the well-known ISNE (Intra-Sarasini negative C-excursion). The combined analysis of nannofossil associations, C-isotopes, major and trace elements, and biomarker distributions indicates a separate episode of warming heralding the ISNE, resulting in the acceleration of the hydrological cycle and a consequent increase in continental inputs and the fertilization of surface waters. The origin of the Taxy Episode (the IFeNE and ISNE) has been related to orbital factors (high-eccentricity cycles), and to a global increase in volcanism, probably related to the early phases of the Ontong Java Plateau.

El objetivo del trabajo es valorizar una propuesta quirúrgica para el tratamiento del glaucoma que tiene como fin evitar las recidivas y complicaciones. La técnica consiste en la realización de una ampolla filtrante (cámara de derivación tensional) utilizando un pilar de trabeculado córneoescleral juntamente con un pilar de iris colocados iritraescleralmente por medio de un colgajo escleral. Se seleccionaron, al azar, 70 pacientes (125 ojos) con más de 7 años de intervención por esta técnica. Se evaluó campo visual, tensión ocular y se realizó biornicroscopía y gofioscopía. La eficiencia fue del 95%, observándose que pacientes con un campo visual entre 10 y 20 grados y con tensiones superiores a 40 mmHg antes de la cirugía mostraron mejoría de 5 grados en promedio. Los resultados se fundamentan en el logro de una endotelización de la cámara accesoria lograda y en la construcción de una bisagra que impide el cierre del canal entre las cámaras anterior y accesoria. Los resultados del seguimiento a largo plazo, sugieren que la técnica analizada ofrece una buena alternativa para la cirugía del glaucoma.

Bearings are one of the main components of induction motors, machines widely employed in today’s industries, making their monitoring a primordial task; however, most systems focus on measuring one physical magnitude to detect one kind of fault at a time. This research tackles the combination of two common faults, grease contamination and outer race damage, as lubricant contamination significantly impacts the life of the bearing and the emergence of other defects; as a contribution, this paper proposes a methodology for the diagnosis of this combination of faults based on a proprietary data acquisition system measuring vibration and current signals, from which time domain statistical and fractal features are computed and then fused using LDA for dimensionality reduction, ending with an SVM model for classification, achieving 97.1% accuracy, correctly diagnosing the combination of the contamination with different severities of the outer race damage, improving the classification results achieved when using vibration and current signals individually by 7.8% and 27.2%, respectively.

Induction motors (IM) play a fundamental role in the industrial sector because they are robust, efficient, and low-cost machines. Changes in the environment, installation errors, or modifications to working conditions can generate faults in induction motors. The trend on IM fault detection is focused on the design techniques and sensors capable of evaluating multiple faults with various signals using non-invasive analysis. The methodology is based on processing electric current signals by applying the short-time Fourier transform (STFT). Additionally, the computation of the mean and standard deviation of infrared thermograms is proposed as main indicators. The proposed system combines both parameters by means of Support Vector Machine and k-nearest-neighbor classifiers. The development of the diagnostic system was done with digital hardware implementations using a Xilinx PYNQ Z2 card that integrates an FPGA with a microprocessor, thus taking advantage of the acquisition and processing of digital signals and images in hardware. The proposed method has proved to be effective for the classification of healthy (HLT), misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB) faults with an accuracy close to 99%.

Background: A high incidence of acute kidney injury (AKI) has been reported in coronavirus disease 2019 (COVID-19) patients in critical care units and those undergoing invasive mechanical ventilation (IMV). The introduction of dexamethasone (DXM) as treatment for severe COVID-19 has improved mortality, but its effects in other organs remain under study. Objective: The objective of this study was to evaluate the association between DXM and AKI in COVID-19. Methods: In this prospective observational cohort study, we evaluated the incidence of AKI in critically ill COVID-19 patients undergoing mechanical ventilation, and the association of DXM treatment with the incidence, severity, and outcomes of AKI. The association between DXM treatment and AKI was evaluated by multivariable logistic regression. The association of the combination of DXM treatment and AKI on mortality was evaluated by Cox-regression analysis. Results: We included 552 patients. AKI was diagnosed in 311 (56%), of which 196 (63%) corresponded to severe (stage 2 or 3) AKI, and 46 (14.8%) received kidney replacement therapy. Two hundred and sixty-seven (48%) patients were treated with DXM. This treatment was associated to lower incidence of AKI (Odds Radio 0.34, 95% Confidence intervals [CI] 0.22-0.52, p < 0.001) after adjusting for age, body mass index, laboratory parameters, SOFA score, and vasopressor use. DXM treatment significantly reduced mortality in patients with severe AKI (HR 0.63, 95%CI 0.41-0.96, p = 0.032). Conclusions: The incidence of AKI is high in COVID-19 patients under IMV. DXM treatment is associated with a lower incidence of AKI and a lower mortality in the group with severe AKI.

This paper analyses and compares the operation of the criminal governance that conditions the routine and activities of the residents and the justice and security system officials of three border cities. Ethnographic strategies, such as observation, interviews, and informal conversations identified the actors, the goods, and services they exploit, as well as the mechanisms of institutional and social interaction. They act with impunity in these territories due to the control of political commodities, institutional infiltration and different mechanisms of coercion based on symbolic and brutal violence, and the engagement of criminal and disciplinary sanctioning processes, which generate terror and build social fear.

Resumo Fundamento vários estudos avaliam alterações ecocardiográficas como preditores de risco cardiovascular; entretanto, nenhum associa risco cardiovascular global com alterações ecocardiográficas em brasileiros. Objetivo Este estudo avalia a associação entre risco cardiovascular global (ASCVD) e achados ecocardiográficos como hipertrofia ventricular esquerda (HVE), disfunção diastólica (DDVE) e aumento do volume do átrio esquerdo (AE). Métodos A população foi composta por participantes do ELSA-Brasil que realizaram ecocardiografia entre 2008 e 2010 (n = 2.973). Eram assintomáticos e não tinham história de doença cardiovascular (DCV). O escore ASCVD foi calculado em dois períodos: 2008-2010 e 2012-2014. Razões de prevalência (RP) foram estimadas com intervalos de confiança (IC) de 95%. Resultados Evidenciou-se associação entre alterações ecocardiográficas e alto risco cardiovascular global (escore ASCVD ≥ 7,5) nos dois períodos do estudo, separadamente. O risco global combinado (baixo risco no primeiro período e alto risco no segundo período) teve associação significativa apenas com DDVE (RP = 3,68; IC 95%: 2,63-5,15) e HVE (RP = 2,20; IC 95%: 1,62–3,00). Conclusão Alterações ecocardiográficas (DDVE, HVE e aumento do volume do AE) são preditores independentes de risco cardiovascular em adultos brasileiros sem DCV prévias.

Resumo Fundamento A hipertensão arterial é considerada um importante fator de risco de morbidade e mortalidade cardiovascular em mulheres na pós-menopausa. Embora a terapia hormonal da menopausa (THM) seja um tratamento muito eficiente para sintomas vasomotores nesse período, a influência dessa terapia na pressão arterial ainda não está clara. Objetivo Avaliar a relação entre o uso de THM e a hipertensão em participantes do ELSA-Brasil. Métodos Um estudo transversal usando dados da linha de base da coorte ELSA-Brasil, com 2.138 mulheres que passaram por menopausa natural. Neste estudo, foi analisado a hipertensão, definida como pressão arterial ≥140/90 mmHg ou uso anterior de anti-hipertensivo, e o uso da THM, com participantes sendo classificadas em grupos daquelas que nunca usaram, que já usaram e que estavam em uso atual. As associações foram avaliadas usando-se um modelo de regressão logística multivariada com uma significância estatística definida em p

Heart Failure (HF) has been identified as an important public health problem, with high morbidity and mortality, despite advances in current therapy. New strategies are demanded to reduce the number of hospitalizations and deaths. Telemedicine approaches could improve the management of patients with cardiovascular conditions. Sixty patients with heart failure with reduced ejection fraction (HFrEF) were randomized to this pilot study. Weekly electronic messages were sent for 1 year. The use of telemedicine was effective instrument for the evolutionary follow-up of patients with HFrEF during the COVID-19 pandemic, but did not demonstrate an impact on the reduction of cardiovascular outcomes or hospitalization for HF. REBEC - Brazilian Registry of Clinical Trials identifier RBR-5q6x56k. Monitoring heart disease patients via WhatsApp during the COVID-19 pandemic. Available from http://www.ensaiosclinicos.gov.br/rg/RBR-5q6x56k/

This paper adopts the form of a log book of experiences, observations, interpretations, ideas and resources obtained as a result of using digital information models to address the needs of the institutions and managers of the Archaeological Site of Itálica (CAI, after the Spanish initials) between 2006 and 2021. The problems related to the knowledge and management of the site vary enormously both in scale and complexity. We discuss them here in chronological order to provide a vision of how digital geometric registration and information models have played an increasingly important role at this type of heritage site. The significance of the case study resides both in the importance of the remains, recognised since the 18th century, and in the difficulty of applying systematic processes to manage the site today.

El síndrome de Marfan es una enfermedad hereditaria autosómica dominante que compromete diversos sistemas que habitualmente requieren un enfoque multidisciplinario. La causa del síndrome de Marfan es desconocida, pero recientes estudios genéticos han relacionado esta enfermedad a un defecto microfibrilar extracelular localizado en el cromosoma 15q15-q21,3. Debido a la severidad de los signos y síntomas el diagnóstico suele ser a edad muy temprana. Reportamos un paciente con síndrome de Marfan extremo con todas las particulares que éste síndrome ofrece.

Industrial processes involving manipulator robots require accurate positioning and orienting for high-quality results. Any decrease in positional accuracy can result in resource wastage. Machine learning methodologies have been proposed to analyze failures and wear in electronic and mechanical components, affecting positional accuracy. These methods are typically implemented in software for offline analysis. In this regard, this work proposes a methodology for detecting a positional deviation in the robot’s joints and its implementation in a digital system of proprietary design based on a field-programmable gate array (FPGA) equipped with several developed intellectual property cores (IPcores). The method implemented in FPGA consists of the analysis of current signals from a UR5 robot using discrete wavelet transform (DWT), statistical indicators, and a neural network classifier. IPcores are developed and tested with synthetic current signals, and their effectiveness is validated using a real robot dataset. The results show that the system can classify the synthetic robot signals for joints two and three with 97% accuracy and the real robot signals for joints five and six with 100% accuracy. This system aims to be a high-speed reconfigurable tool to help detect robot precision degradation and implement timely maintenance strategies.

Nowadays, energy decarbonization due to integrating renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical system networks, especially in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method for improving the low-pass filter and moving average for grid-connected photovoltaic systems. This novel method includes state-of-charge monitoring control of the supercapacitor’s energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. This research aims to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration. The results demonstrate significant power quality and stability improvements achieved through the proposed method. For instance, the power smoothing method effectively reduced power fluctuations by 16.7% with the low-pass filter, 14.05% with the ramp-rate filter, and 9.7% with the moving average filter.

Resumo A doença de Chagas (DC) é causada pelo Trypanosoma Cruzi. Esse parasita pode infectar vários órgãos do corpo humano, especialmente o coração, causando inflamação, fibrose, arritmias e remodelação cardíaca, e promovendo a cardiomiopatia chagásica crônica (CCC) no longo prazo. Entretanto, poucas evidências científicas elucidaram os mecanismos moleculares que regulam os processos fisiopatológicos nessa doença. Os microRNAs (miRNAs) são reguladores de expressão gênica pós-transcricional que modulam a sinalização celular, participando de mecanismos fisiopatológicos da DC, mas o entendimento dos miRNAs nessa doença é limitado. Por outro lado, há muitas evidências científicas demonstrando que o treinamento com exercício físico (TEF) modula a expressão de miRNAs, modificando a sinalização celular em indivíduos saudáveis. Alguns estudos também demonstram que o TEF traz benefícios para indivíduos com DC, porém esses não avaliaram as expressões de miRNA. Dessa forma, não há evidências demonstrando o papel do TEF na expressão dos miRNAs na DC. Portanto, essa revisão teve o objetivo de identificar os miRNAs expressos na DC que poderiam ser modificados pelo TEF.

Resumo Fundamento A síndrome do PRKAG2 é uma rara doença genética autossômico dominante, fenocópia da miocardiopatia hipertrófica, caracterizada pelo acúmulo intracelular de glicogênio. Manifestações clínicas incluem pré-excitação ventricular, hipertrofia ventricular, distúrbio de condução cardíaca e arritmias atriais. Objetivo Comparar características clínicas e eletrofisiológicas observadas em pacientes com flutter atrial, com e sem síndrome do PRKAG2. Métodos Estudo observacional, comparativo de pacientes com flutter atrial: grupo A, cinco pacientes de família com síndrome do PRKAG2; e grupo B, 25 pacientes sem fenótipo da síndrome. O nível de significância foi de 5%. Resultados Todos os pacientes do grupo A apresentaram pré-excitação ventricular e bloqueio de ramo direito; quatro tinham marca-passo (80%). Pacientes do grupo A tinham menor idade (39±5,4 vs. 58,6±17,6 anos, p=0,021), e maior espessura de septo interventricular (mediana=18 vs. 10 mm; p

Resumo Fundamento A variabilidade da pressão arterial (VPA) tem valor prognóstico para desfechos cardiovasculares fatais e não fatais. Objetivos Este estudo teve como objetivo avaliar a associação entre a VPA em uma única visita e o risco cardiovascular em participantes do Estudo Longitudinal de Saúde do Adulto (ELSA-Brasil). Métodos O presente estudo transversal foi conduzido com dados basais (2008-2010) de 14.357 participantes do ELSA-Brasil, sem história de doença cardiovascular. A VPA foi quantificada pelo coeficiente de variação de três medidas padronizadas da pressão arterial sistólica (PAS) realizadas com um oscilômetro. Medidas antropométricas e exames laboratoriais também foram realizados. O risco cardiovascular foi avaliado pelo estimador de risco de doença cardiovascular aterosclerótica (ASCVD), e se empregou a análise de regressão logística multivariada com nível de significância de 5%. Resultados Um risco cardiovascular significativamente maior foi determinado por uma VPA elevada para ambos os sexos. Uma prevalência significativamente maior de alto risco foi observada mais em homens que em mulheres em todos os quartis, com a maior diferença observada no quarto quartil de variabilidade (48,3% vs. 17,1%). Comparações entre quartis por sexo revelaram um risco significativamente mais alto para homens no terceiro (OR=1,20; IC95%: 1,02 - 1,40) e no quarto quartis OR=1,46; IC95%: 1,25 -1,71), e para mulheres no quarto quartil (OR=1,27; IC95%: 1,03 - 1,57). Conclusão Análises de dados basais de participantes do ELSA-Brasil revelaram que a variabilidade da pressão arterial se associou com risco cardiovascular aumentado, especialmente nos homens.

Photovoice involves respondents taking photographs of their environment to promote critical discussions and reflect on their experiences. Photovoice empowers marginalized communities and serves to reach policymakers. The Arizona Youth Identity Project (AZYIP) used photovoice with an innovative approach in a multisite research design with a large sample size and completely online research implementation using video conferencing, mobile phones, and video messages. We outline our process for other researchers interested in utilizing this dynamic method. We also reflect on the challenges and opportunities of engaging in this research design for future projects.

Outrunner brushless DC motors (BLDC) are a type of permanent magnet synchronous motor (PMSM) widely used in electric micro-mobility vehicles, such as scooters, electric bicycles, wheelchairs, and segways, among others. Those vehicles have many operational constraints because they are driven directly by the user with light protective wearing. Therefore, to improve control strategies to make the drive safer, it is essential to model the traction system over a wide range of operating conditions in a street environment. In this work, we developed an electro-mechanical model based on the Hardware-in-the-Loop (HIL) structure for a two-wheeler electric scooter, using the BLDC motor to explore its response and to test linear controllers for speed and torque management under variable operating conditions. The proposed model includes motor parameters, power electronics component characteristics, mechanical structure, and external operating conditions. Meanwhile the linear controllers will be adjusted or tuned though a heuristic approach based on Genetic Algorithms (GAs) to optimize the system’s response. The HIL scheme will be able to simulate a wide range of conditions such as user weight, slopes, wind speed changes, and combined conditions. The designed model can be used to improve the design of the controller and estimate mechanical and electrical loads. Finally, the results of the controller tests show how the proposed cascade scheme, tuned through the GA, improves the system behavior and reduces the mean square error with respect to a classical tuning approach between 20% and 60%.

A substantial number of off-grid olive oil mills in Morocco are powered by diesel-fired generators, which hugely contribute to air pollution and greenhouse gas emissions. In this research work, a biomass gasification combined heat and power (CHP) plant fueled with local by-products was explored as a renewable alternative to electrify off-grid olive oil mills in this country. The case study considered a gasification CHP plant with a rated power of 80 kWe, in order to enable adaptation of the producer gas flow rate to abrupt changes in the power generation unit under dynamic operation. A downdraft gasifier and a producer gas conditioning unit were modeled under steady state operation using Cycle-Tempo, while the power generation unit was modeled in the Thermoflex simulation environment under partial and full load operation. Olive cake pellets and olive pruning chips were evaluated as biomass feedstock, with moisture contents ranging from 5% to 20% (wet basis). The results from the simulation of the gasification CHP plant showed net electrical efficiencies and CHP efficiencies around 18% and 35%, respectively. Finally, a profitability assessment of the gasification CHP plant was developed for 2 months of continuous operation, together with a sensitivity analysis. The results for the baseline scenario reveal a payback period of 7–8 years and a 68.5% accumulated profit based on the capital investment, which suggest that biomass gasification CHP plants can represent an economically feasible and sustainable solution for the electrification of off-grid areas in Morocco.

In the manufacturing industry, computer numerical control (CNC) machine tools are of great importance since the processes in which they are used allow the creation of elements used in multiple sectors. Likewise, the condition of the cutting tools used is paramount due to the effect they have on the process and the quality of the supplies produced. For decades, methodologies have been developed that employ various signals and sensors for wear detection, prediction and monitoring; however, this field is constantly evolving, with new technologies and methods that have allowed the development of non-invasive, efficient and robust systems. This paper proposes the use of magnetic stray flux and motor current signals from a CNC lathe and the analysis of images of machined parts for wear detection using online and offline information under the variation in cutting speed and tool feed rate. The information obtained is processed through statistical and non-statistical indicators and dimensionally reduced by linear discriminant analysis (LDA) and a feed-forward neural network (FFNN) for wear classification. The results obtained show a good performance in wear detection using the individual signals, achieving efficiencies of 77.5%, 73% and 89.78% for the analysis of images, current and stray flux signals, respectively, under the variation in cutting speed, and 76.34%, 73% and 63.12% for the analysis of images, current and stray flux signals, respectively, under the variation of feed rate. Significant improvements were observed when the signals are fused, increasing the efficiency up to 95% for the cutting speed variations and 82.84% for the feed rate variations, achieving a system that allows detecting the wear present in the tools according to the needs of the process (online/offline) under different machining parameters.

Shadows are likely to cause flaws in color interpretation, loss of image information or deformation of objects; this is a relevant issue owing to the fact that with the development of unmanned aerial vehicles and satellite devices, object detection by aerial images has become an essential aim to research works. The main contribution of this article is the development of an algorithm that processes shadow detection by detaching the channels red, green, blue and an additional low saturation channel on individual masks; having created those masks, the task of shadow detection is accomplished by conducting a pixel-by-pixel basis with the aid of a decision tree and statistical descriptors extracted out of the image, where the low saturation channel information leads to improvements on shadow detection accuracy in regions that contain asphalt and concrete. Thus, experimental results show the good behavior of shadow detection algorithm in terms of accuracy, over-segmentation, and under-segmentation, in which the accuracy of obtained shadow detection approaches a 94%.

AimPrevious studies showed that granulocyte-colony stimulating factor (G-CSF) improved heart function in a mice model of Chronic Chagas Cardiomyopathy (CCC). Herein, we report the interim results of the safety and efficacy of G-CSF therapy vs. placebo in adults with Chagas cardiomyopathy.MethodsPatients with CCC, New York Heart Association (NYHA) functional class II to IV and left ventricular ejection fraction (LVEF) 50% or below were included. A randomization list using blocks of 2 and 4 and an allocation rate of 1:1 was generated by R software which was stratified by functional class. Double blinding was done to both arms and assessors were masked to allocations. All patients received standard heart failure treatment for 2 months before 1:1 randomization to either the G-CSF (10 mcg/kg/day subcutaneously) or placebo group (1 mL of 0.9% saline subcutaneously). The primary endpoint was either maintenance or improvement of NYHA class from baseline to 6–12 months after treatment, and intention-to-treat analysis was used.ResultsWe screened 535 patients with CCC in Salvador, Brazil, of whom 37 were randomized. Overall, baseline characteristics were well-balanced between groups. Most patients had NYHA class II heart failure (86.4%); low mean LVEF was 32 ± 7% in the G-CSF group and 33 ± 10% in the placebo group. Frequency of primary endpoint was 78% (95% CI 0.60–0.97) vs. 66% (95% CI 0.40–0.86), p = 0.47, at 6 months and 68% (95% CI 0.43–0.87) vs. 72% (95% CI 0.46–0.90), p = 0.80, at 12 months in placebo and G-CSF groups, respectively. G-CSF treatment was safe, without any related serious adverse events. There was no difference in mortality between both arms, with five deaths (18.5%) in treatment vs. four (12.5%) in the placebo arm. Exploratory analysis demonstrated that the maximum rate of oxygen consumption during exercise (VO2 max) showed an improving trend in the G-CSF group.ConclusionG-CSF therapy was safe and well-tolerated in 12 months of follow-up. Although prevention of symptom progression could not be demonstrated in the present study, our results support further investigation of G-CSF therapy in Chagas cardiomyopathy patients.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT02154269].

This work investigates the possibility of reusing water treatment sludge (WTS) at as-collected water content mixed with lateritic soil in geotechnical works to avoid waste disposal and reduce the exploitation of natural soils. Three mixtures with different proportions of soil to WTS (5:1, 4:1, 3:1) were prepared. Leaching and solubilization tests were conducted for environmental characterization of the WTS. Particle size distribution, consistency limits and specific gravity of solids of the soil and the three mixtures were determined for geotechnical characterization. Compaction, permeability and triaxial compression tests were carried out to investigate the geotechnical behaviour of the materials. The sludge was classified as non-hazardous and non-inert by Brazilian standards. The addition of sludge to the soil did not significantly change its granulometry but led to a reduction in the specific gravity of solids and an increase in the plasticity index. In the compaction tests, the maximum dry unit weight decreased, and the optimum moisture content increased with WTS addition. The hydraulic conductivity of the mixtures was between 10 and 100 times that of the soil. In triaxial compressive tests, there was an increase in strain and a reduction in strength during shear, as well as an increase in the friction angle (between 1º and 4º) and reduction in cohesion (between 9 kPa and 16 kPa). Despite causing changes in soil behaviour, the addition of sludge does not preclude the use of the resulting mixtures in geotechnical works, since the parameters remain as established in the literature and technical. However, a case-by-case analysis must be carried out before using the mixtures.

Resumo Fundamento Episódios de alta frequência atrial (EAFAs) estão associados a um risco elevado de eventos isquêmicos cerebrais, porém não existem estudos relacionados com a presença de EAFAs e eventos isquêmicos cerebrais em pacientes chagásicos. Objetivo Investigar a associação entre a presença de EAFAs ≥ 6 minutos e eventos isquêmicos cerebrais em pacientes chagásicos. Métodos Estudo de coorte com pacientes chagásicos, portadores de dispositivos cardíacos eletrônicos implantáveis (DCEIs), acompanhados no ambulatório de arritmias de um hospital universitário, na cidade de Salvador/BA, entre maio de 2016 e junho de 2017. Pacientes com diagnóstico de flutter atrial/fibrilação atrial, com DCEI unicameral e em uso de anticoagulação oral foram excluídos. Foram considerados EAFAs com frequência atrial ≥ 190 batimentos por minuto e duração ≥ 6 minutos (min), e os eventos isquêmicos cerebrais foram identificados por meio de tomografia computadorizada (TC) de crânio. Resultados Os 67 participantes da pesquisa (67,2% do sexo feminino, com idade média de 63,6 ± 9,2 anos) foram acompanhados por 98 ± 28,8 dias e 11,9% dos pacientes apresentaram EAFAs ≥ 6 minutos. A TC de crânio evidenciou eventos isquêmicos cerebrais silenciosos em 16,4% dos pacientes, sendo que, destes, 63,6% haviam apresentado os EAFAs ≥ 6 minutos na análise dos DCEIs. A idade avançada (OR 1,12 [IC 95% 1,03-1,21; p=0,009) e a presença de EAFAs ≥ 6 minutos (OR 96,2 [IC 95% 9,4-987,4; p

ABSTRACT Introduction and objectives: The Lotus Valve device (Boston Scientific) is a second-generation fully-retrievable and repositionable transcatheter aortic valve. We report the initial multicenter experience with the Lotus valve in the management of patients with severe aortic stenosis. Methods: Observational study that described the short and long-term results of implanting the Lotus valve in 8 Spanish and Portuguese centers from March 2014 through April 2016. Results: The study included 102 patients (mean age 80.4 ± 6.1 years; STS score 5.2% ± 3.3%) with severe symptomatic aortic stenosis (mean aortic valve area 0.66 ± 0.17 cm2, aortic gradients 74.3 / 45.6 mmHg). The valve was successfully implanted in 100 patients (98%), with significant improvement in both the peak and mean aortic valve gradients and with only one patient showing moderate paravalvular regurgitation. Upon hospital discharge, mortality rate was 3.9% while the stroke rate was 2.9%. No cases of valve embolization, ectopic valve deployment or additional valve implantation (valve-in-valve) were seen. Thirty-three patients (32.3%) received a permanent pacemaker. Conclusions: The Lotus Valve System is effective and safe for the management of patients with severe symptomatic aortic stenosis. In particular, considering the low rate of periprosthetic regurgitation and lack of complications like embolization or ectopic valve deployment; however at the expense of a high pacemaker implantation rate.

RESUMEN Introducción y objetivos: El dispositivo Lotus (Boston Scientific, Estados Unidos) es una prótesis valvular aórtica transcatéter de segunda generación, completamente recuperable y reposicionable. Se presenta la experiencia inicial con la prótesis Lotus en un registro multicéntrico. Métodos: Estudio observacional que reporta los resultados a corto y largo plazo del implante transfemoral de prótesis Lotus entre marzo de 2014 y abril de 2016 en 8 centros de España y Portugal. Resultados: Se incluyeron 102 pacientes (edad media 80,4 ± 6,1 años, índice STS medio 5,2% ± 3,3%) con estenosis aórtica grave sintomática (área valvular media 0,66 ± 0,17 cm2, gradientes 74,3/45,6 mmHg). Se implantó con éxito el dispositivo en 100 pacientes (98%), con mejoría significativa de los gradientes máximo y medio valvular, y un solo caso de regurgitación periprotésica moderada. No hubo ninguna embolización ni necesidad de implante de una nueva prótesis intravalvular. Hasta el alta hospitalaria, la mortalidad fue del 3,9% y la tasa de ictus fue del 2,9%. En 33 pacientes (32,3%) fue necesario el implante de marcapasos definitivo. Conclusiones: La válvula Lotus es eficaz y segura para el tratamiento de pacientes con estenosis aórtica grave sintomática. Destacan la escasa tasa de insuficiencia periprotésica y la ausencia de complicaciones derivadas del mal posicionamiento o la embolización de la prótesis, a costa de un alta incidencia de implante de marcapasos.

Novelty detection is a statistical method that verifies new or unknown data, determines whether these data are inliers (within the norm) or outliers (outside the norm), and can be used, for example, in developing classification strategies in machine learning systems for industrial applications. To this end, two types of energy that have evolved over time are solar photovoltaic and wind power generation. Some organizations around the world have developed energy quality standards to avoid known electric disturbances; however, their detection is still a challenge. In this work, several techniques for novelty detection are implemented to detect different electric anomalies (disturbances), which are k-nearest neighbors, Gaussian mixture models, one-class support vector machines, self-organizing maps, stacked autoencoders, and isolation forests. These techniques are applied to signals from real power quality environments of renewable energy systems such as solar photovoltaic and wind power generation. The power disturbances that will be analyzed are considered in the standard IEEE-1159, such as sag, oscillatory transient, flicker, and a condition outside the standard attributed to meteorological conditions. The contribution of the work consists of the development of a methodology based on six techniques for novelty detection of power disturbances, under known and unknown conditions, over real signals in the power quality assessment. The merit of the methodology is a set of techniques that allow to obtain the best performance of each one under different conditions, which constitutes an important contribution to the renewable energy systems.

Gears are reliable and robust elements that are found in any power transmission system. However, gears are prone to present incipient faults, such as wear, since they are constantly subjected to contact forces. Due to gears playing a key role in many industrial processes, it is important to develop condition monitoring strategies that ensure the proper functioning of the related power transmission system and the overall components. In this regard, the data on entropy provide relevant information that allow us to identify and quantify the effect of different wear levels in gears. Therefore, in this work, we proposed the use of seven entropy-related features to perform the identification of different wear severities in a gearbox. The novelty of this proposal lies in the use of the entropy features to carry out a high-performance characterization of the available vibration signals that are acquired from experimental tests. The novelty of this proposal lies in the fusion of three different techniques: entropy features, linear discriminant analysis, and artificial neural networks to obtain a machine learning approach for improving the detection of different wear severities in gears compared to other reported methodologies. This situation is achieved due to the high-performance characterization of the available vibration signals that are acquired from experimental tests. Additionally, the entropy features are subjected to a feature space transformation by means of linear discriminant analysis to obtain a 2D representation and, finally, the set of features extracted by linear discriminant analysis are used as inputs of a neural network-based classifier to determine the severity of wear that is present in the gears. The proposed methodology is validated and compared with a conventional statistical approach to show the improvement in the classification.

Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque–velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical features and genetic algorithms to find out those features that can best be used for detecting the gradual gear wear of a gearbox by using the signals, measured directly in the motor, from current and vibration sensors at different frequencies. The methodology also makes use of linear discriminant analysis to generate a bidimensional representation of the system conditions that are fed to a neural network with a simple structure for performing the classification of the condition. Four uniform gear wear conditions were tested, including the healthy state and three gradual conditions: 25%, 50%, and 75% wear in the gear teeth. Because of the sampling frequency, the number of sensors, the time for data acquisition, the different operation frequencies analyzed, and the computation of the different statistical features, meant that a large amount of data were generated that needed to be fused and reduced. Therefore, the proposed methodology provides an excellent generalized solution for data fusion and for minimizing the computational burden required. The obtained results demonstrate the effectiveness of fault gradualism detection for the proposed approach.

Due to their robustness, versatility and performance, induction motors (IMs) have been widely used in many industrial applications. Despite their characteristics, these machines are not immune to failures. In this sense, breakage of the rotor bars (BRB) is a common fault, which is mainly related to the high currents flowing along those bars during start-up. In order to reduce the stresses that could lead to the appearance of these faults, the use of soft starters is becoming usual. However, these devices introduce additional components in the current and flux signals, affecting the evolution of the fault-related patterns and so making the fault diagnosis process more difficult. This paper proposes a new method to automatically classify the rotor health state in IMs driven by soft starters. The proposed method relies on obtaining the Persistence Spectrum (PS) of the start-up stray-flux signals. To obtain a proper dataset, Data Augmentation Techniques (DAT) are applied, adding Gaussian noise to the original signals. Then, these PS images are used to train a Convolutional Neural Network (CNN), in order to automatically classify the rotor health state, depending on the severity of the fault, namely: healthy motor, one broken bar and two broken bars. This method has been validated by means of a test bench consisting of a 1.1 kW IM driven by four different soft starters coupled to a DC motor. The results confirm the reliability of the proposed method, obtaining a classification rate of 100.00% when analyzing each model separately and 99.89% when all the models are analyzed at a time.

En este trabajo se evaluó el efecto de la arcilla de desecho de un proceso de producción de polioles, como sustituto parcial del cemento en concreto armado, en concentraciones del 0%, 5% y 10%. Se determinaron las características físico-mecánicas del concreto y electroquímicas del acero durante un periodo de 356 días (ISO 11474), en probetas de 15x10x5 cm, con dos barras de acero embebidas en el concreto. Los resultados indican que la resistencia a la compresión disminuyó proporcionalmente según el contenido de arcilla; incrementándose para los 90 días de curado. Sin embargo, la sorción capilar del concreto disminuyó, lo cual permitió que el acero en ambiente marino mantuviera su estado pasivo por más tiempo que sus blancos, para las relaciones a/c evaluadas (0,45 y 0,60).

Abstract Background Gender diversity in health teams is associated with better productivity. As women’s participation in surgery has been growing, it is important to improve knowledge about the elements that guide their professional development. Objectives The aim of this study was to outline the demographic and professional features of female vascular surgeons in Brazil. Methods A cross-sectional study was designed, in which a questionnaire was made available online for 60 days. Invitations to participate were distributed by institutional e-mail sent from the Brazilian Society of Angiology and Vascular Surgery (SBACV) to associate women surgeons. Results are presented as numbers and percentages. Odds ratios and chi-square tests were used for analysis. Results From a total of 810 invitations sent out, 281 questionnaires were completed. The most prevalent age groups were 25-35 years (n = 115) and 36-45 years (n = 114). Among those who worked exclusively in the private sector, 79.8% had at least one board certification (OR: 0.76, 95% CI: 0.65-0.89; p = 0.001). Regarding workload distribution, 64.4% and 34.2% reported that they spend more time in the clinic and hospital, respectively. Respondents with more years of experience reported a predominance of office practice (p = 0.002). Although 67.3% (n = 189) had published scientific papers, 68% (n = 191) had never held leadership roles. Conclusions The study respondents consisted of highly qualified women surgeons with respect to training, certification, and scientific engagement, but they remain underrepresented in professional management positions. Surgical societies and health institutions should act to promote inclusive and diverse leadership.

Introduction: Dentin permeability is defined as the passage of fluids, ions, particles, molecules and bacteria through the dentin under certain conditions. Objective: Determine the effect of the active application of a dentin adhesive with an improved applicator on human dentin hydraulic conductance in vitro. Methods: An in vitro experimental study was conducted of 40 caries-free third molars with an indication of exodontia from healthy patients aged 15-30 years, prior informed consent. The molars were cleaned, disinfected and preserved to be included in epoxy resin blocks. Cross-sectional cuts were then made to obtain dentin discs 1.5 mm thick and 25 mm in diameter. These were divided into two 20-unit groups, were subjected to etching with 37 % orthophosphoric acid, and were washed and dried. On a precision scale photopolymerizable dentin adhesive was applied, recording the force exerted. Application was performed with a Microbrush applicator in Group 1 and with an improved dedicated applicator in Group 2. The samples were then placed in a diffusion chamber to determine flow rate and subsequently estimate hydraulic conductance, thus obtaining information about dentin permeability. Results: Results were slightly lower in Group 2, but they displayed normal distribution. Conclusions: Statistical analysis with various tests did not find any significant differences in hydraulic conductance values, nor could an inverse relation be established between the force of active application of an adhesive with an improved applicator and hydraulic conductance. Therefore, use of that instrument is not advised.

The monitoring of machine conditions is very important from the viewpoints of productivity, economic benefits, and maintenance. Several techniques have been proposed in which sensors are the key to providing relevant information to verify the system. Recently, the smart sensor concept is common, in which the sensors are integrated with a data processing unit executing dedicated algorithms used to generate meaningful information about the system in situ. Additionally, infrared thermography has gained relevance in monitoring processes, since the new infrared cameras have more resolution, smaller dimensions, reliability, functionality, and lower costs. These units were firstly used as secondary elements in the condition monitoring of machines, but thanks to modern techniques for data processing, the infrared sensors can be used to give a first, or even a direct, diagnosis in a nonintrusive way in industrial applications. Therefore, in this manuscript, the structure and development of an infrared-thermography-based smart sensor for diagnosing faults in the elements associated with induction motors, such as rolling bearings and the gearbox, is described. The smart sensor structure includes five main parts: an infrared primary sensor, a preprocessing module, an image processing module, classification of faults, and a user interface. The infrared primary sensor considers a low-cost micro thermal camera for acquiring the thermal images. The processing modules and the classification module implement the data processing algorithms into digital development boards, enabling smart system characteristics. Finally, the interface module allows the final users to require the smart sensor to perform processing actions and data visualization, with the additional feature that the diagnosis report can be provided by the system. The smart sensor is validated in a real experimental test bench, demonstrating its capabilities in different case studies.

Renewable energy-based power generation technologies are becoming more and more popular since they represent alternative solutions to the recent economic and environmental problems that modern society is facing. In this sense, the most widely spread applications for renewable energy generation are the solar photovoltaic and wind generation. Once installed, typically outside, the wind generators and photovoltaic panels suffer the environmental effects due to the weather conditions in the geographical location where they are placed. This situation, along with the normal operation of the systems, cause failures in their components, and on some occasions such problems could be difficult to identify and hence to fix. Thus, there are generated energy production stops bringing as consequence economical losses for investors. Therefore, it is important to develop strategies, schemes, and techniques that allow to perform a proper identification of faults in systems that introduce renewable generation, keeping energy production. In this work, an analysis of the most common faults that appear in wind and photovoltaic generation systems is presented. Moreover, the main techniques and strategies developed for the identification of such faults are discussed in order to address the advantages, drawbacks, and trends in the field of detection and classification of specific and combined faults. Due to the role played by wind and photovoltaic generation, this work aims to serve as a guide to properly select a monitoring strategy for a more reliable and efficient power grid. Additionally, this work will propose some prospective with views toward the existing areas of opportunity, e.g., system improvements, lacks in the fault detection, and tendency techniques that could be useful in solving them.