Assessment of co-contaminated soil amended by graphene oxide: Effects on pollutants, microbial communities and soil health.

Graphene oxide (GOx) is a nanomaterial with demonstrated capacity to remove metals from water. However, its effects on organic pollutants and metal(loid)s present in polluted soils when used for remediation purposes have not been extensively addressed. Likewise, few studies describe the effects of GOx on edaphic properties and soil biology. In this context, here we assessed the potential of GOx for remediating polluted soil focusing also on different unexplored effects of GOx in soil. To achieve this, we treated soil contaminated with concurrent inorganic (As and metals) and organic pollution (TPH and PAHs), using GOx alone and in combination with nutrients (N and P sources). In both cases increased availability of As and Zn was observed after 90 days, whereas Cu and Hg availability was reduced and the availability of Pb and the concentration of organic pollutants were not significantly affected. The application of GOx on the soil induced a significant and rapid change (within 1 week) in microbial populations, leading to a transient reduction in biodiversity, consistent with the alteration of several soil properties. Concurrently, the combination with nutrients exhibited a distinct behaviour, manifesting a more pronounced and persistent shift in microbial populations without a decrease in biodiversity. On the basis of these findings, GOx emerges as a versatile amendment for soil remediation approaches.

Impact of wildfire recurrence on soil properties and organic carbon fractions.

The recurrence and severity of wildfire is on the rise due to factors like global warming and human activities. Mediterranean regions are prone to significant wildfire events, which cause extensive damage to ecosystems and soil properties. This study focuses on the municipality of Allande in south-western Asturias (Spain), a region highly affected by recurrent wildfires. In this regard, we sought to examine how the recurrence of such fires influences soil organic carbon fractionation and other soil parameters, such as nitrogen fractionation, pH, and cation exchange capacity. The study involved six sampling plots with between varying fire recurrence levels, from 0 to 4 events between 2005 and 2022. The results revealed some significant effects of wildfires recurrence on soil texture, inorganic elemental composition and CEC, but not on pH and CE. In soil affected by recurrent fires, labile carbon fractions (cold-water extractable & hot-water extractable), and fulvic acid concentrations decreased by up to 36%, 5%, and 45%, respectively in comparison with undisturbed soil. In contrast, humic acid concentration remained stable or increased in soils damaged by fire. Additionally, nitrogen species in soil were observed to decrease significantly in high recurrence scenarios, especially nitrate. On the basis of our findings, we conclude that wildfires impact the distinct fractions of organic carbon and nitrogen in soils and that this effect is aggravated by increasing recurrence.

A novel algorithm for optimizing hydrocyclone operations in the decontamination of potentially toxic elements in soils.

We present the Three-Parameter Penalized Attributive Analysis for Upgrading (3PPAA-U) method as a tool for selecting the Best Upgrading Condition (BUC) in process engineering. Conventional approaches tend to consider only maximizing recovery (ε) and minimizing yield (γc); in contrast, the proposed 3PPAA-U introduces and seeks to maximize a third parameter, the grade (λ). This multi-parameter approach has not yet been explored in existing literature. In addition to controlling multiple parameters, the method is also superior to others as it includes inverse standard deviation weighting to avoid the distortion of results due to data dispersion. This reduces the possibility of drawing conclusions based on extreme values. Furthermore, the method can be used with a target-to-distance correction to optimize separation for multi-component feeds. To illustrate our method, we present a practical application of 3PPAA-U. Soil contaminated with potentially toxic elements (PTEs) was subject to hydrocycloning under 12 different experimental conditions. Results of these 12 experiments were compared using 3PPAA-U and conventional methods to identify the best upgrading conditions (BUC). Analysis reveals that the 3PPAA-U approach offers a simple and effective criterion for selecting BUC. Furthermore, 3PPAA-U has uses beyond soil remediation. It offers a versatile tool for optimizing operations across various processing and manufacturing environments offering a way to manage factors such as concentration, temperature, pressure, pH, Eh, grain size, and even broader environmental and economic considerations.

Valorization of steelmaking slag and coal fly ash as amendments in combination with Betula pubescens for the remediation of a highly As- and Hg-polluted mining soil.

Soil pollution by As and Hg is a pressing environmental issue given their persistence. The intricate removal processes and subsequent accumulation of these elements in soil adversely impact plant growth and pose risks to other organisms in the food chain and to underground aquifers. Here we assessed the effectiveness of non-toxic industrial byproducts, namely coal fly ash and steelmaking slag, as soil amendments, both independently and in conjunction with an organic fertilizer. This approach was coupled with a phytoremediation technique involving Betula pubescens to tackle soil highly contaminated. Greenhouse experiments were conducted to evaluate amendments' impact on the growth, physiology, and biochemistry of the plant. Additionally, a permeable barrier made of byproducts was placed beneath the soil to treat leachates. The application of the byproducts reduced pollutant availability, the production of contaminated leachates, and pollutant accumulation in plants, thereby promoting plant development and survival. Conversely, the addition of the fertilizer alone led to an increase in As accumulation in plants and induced the production of antioxidant compounds such as carotenoids and free proline. Notably, all amendments led to increased thiolic compound production without affecting chlorophyll synthesis. While fertilizer application significantly decreased parameters associated with oxidative stress, such as hydrogen peroxide and malondialdehyde, no substantial reduction was observed after byproduct application. Thermal desorption analysis of the byproducts revealed Hg immobilization mechanisms, thereby indicating retention of this metalloid in the form of Hg chloride. In summary, the revalorization of industrial byproducts in the context of the circular economy holds promise for effectively immobilizing metal(loid)s in heavily polluted soils. Additionally, this approach can be enhanced through synergies with phytoremediation.

A novel heuristic tool for selecting the best upgrading conditions for the removal of potentially toxic elements by soil washing.

Here, we propose two-parameter penalized attributive analysis, PPAA-U, a novel heuristic tool for selecting the best upgrading conditions (BUCs) for soil washing. Given a multi-component feed and a specific set of operating conditions, PPAA-U generates a quality index based on how well recoveries for key components are maximized while minimizing the yield. We demonstrate, through the calculation of families of curves, that this quality index is related linearly to recovery and to the inverse of the yield, meaning that reducing yield values is more important than maximizing recovery. To evaluate our method, electrostatic separation at 12 different voltages was carried out on soil samples from an ex-industrial site in Spain. Values of recovery, yield, and grade were analyzed using basic attributive analysis and PPAA-U with and without target-to-distance correction. Both methods identified the same optimal separation voltage, and the power of PPAA-U to correct for high variation in yields and recoveries was observed as a divergence between results produced by each method at low voltages where variation in these values was greatest. PPAA-U thus offers a convenient tool for soil washing optimization, and we suggest that it could be applied successfully to other industrial processes.

Terahertz Detection by Asymmetric Dual Grating Gate Bilayer Graphene FETs with Integrated Bowtie Antenna.

An asymmetric dual-grating gate bilayer graphene-based field effect transistor (ADGG-GFET) with an integrated bowtie antenna was fabricated and its response as a Terahertz (THz) detector was experimentally investigated. The device was cooled down to 4.5 K, and excited at different frequencies (0.15, 0.3 and 0.6 THz) using a THz solid-state source. The integration of the bowtie antenna allowed to obtain a substantial increase in the photocurrent response (up to 8 nA) of the device at the three studied frequencies as compared to similar transistors lacking the integrated antenna (1 nA). The photocurrent increase was observed for all the studied values of the bias voltage applied to both the top and back gates. Besides the action of the antenna that helps the coupling of THz radiation to the transistor channel, the observed enhancement by nearly one order of magnitude of the photoresponse is also related to the modulation of the hole and electron concentration profiles inside the transistor channel by the bias voltages imposed to the top and back gates. The creation of local n and p regions leads to the formation of homojuctions (np, pn or pp+) along the channel that strongly affects the overall photoresponse of the detector. Additionally, the bias of both back and top gates could induce an opening of the gap of the bilayer graphene channel that would also contribute to the photocurrent.

CLASIFICACIÓN DE LATIDOS DE CONTRACCIÓN VENTRICULAR PREMATURA BASADOS EN MÉTODOS DE APRENDIZAJE NO SUPERVISADO / CLASSIFICATION OF PREMATURE VENTRICULAR CONTRACTION BEATS BASED ON UNSUPERVISED LEARNING METHODS / CLASSIFICAÇÃO DE PREMATUROS BATIDAS CONTRAÇÃO VENTRICULAR BASEADO SUPERVISIONADO MÉTODOS DE APRENDIZAGEM

Las enfermedades cardiovasculares son la principal causa de mortalidad en el mundo, por lo que el desarrollo de algoritmos que detecten arritmias cardíacas en tiempo real se ha convertido en un campo de investigación importante. El desarrollo de estos algoritmos ha conllevado a la mejora de dispositivos cardiacos portátiles. Este artículo presenta el desempeño de dos algoritmos basados en aprendizaje de máquina no supervisado para la detección de latidos de contracción ventricular prematura en la señal ECG. Los latidos se extraen de las bases de datos del MIT-BIH, los cuales fueron pre-procesados y segmentados por el grupo de investigación de Dinámica Cardiovascular de la UPB. La Transformada Wavelet Discreta, el Análisis de Componentes Principales y un método híbrido propuesto son implementados para la extracción de características y reducción de dimensiones, a partir de los cuales se generan 8 espacios de características para la evaluación de los algoritmos. Kmeans y Mapas auto-organizados son desarrollados y comparados en términos de precisión y costo computacional. Se logró una especificidad del 96.22 % y una sensibilidad del 95.04 % con un tiempo de ejecución de 79.41µs por latido. Los resultados permiten concluir que estos métodos pueden implementarse en aplicaciones de detección de arritmias en tiempo real debido a su bajo costo computacional.

Cardiovascular diseases are the principal cause of mortality in the world, so that the development of algorithms that detect cardiac arrhythmias in real time has become an important field of research. The development of these algorithms has led to the improvement of wearable cardiac devices. This paper presents the performance of two algorithms based in unsupervised learning methods for the detection of Premature Ventricular Contraction in the ECG signal. The beats are extracted from MIT-BIH databases, which were preprocessed and segmented by the UPB’s Dynamic Cardiovascular research group. The Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA) and a proposed hybrid method are implemented for the feature extraction and dimension reduction, from which 8 feature spaces are generated and tested. Kmeans and Self Organizing Maps are developed and compared in terms of accuracy and computational cost. Specificity of 96.22 % and sensitivity of 95.94% with 79.41µs per beat are accomplished. The results show that these methods can be implemented in applications of real time arrhythmia detection because of their low computational cost.

A doença cardiovascular é a principal causa de morte em todo o mundo, de modo que o desenvolvimento de algoritmos para detectar arritmias cardíacas, em tempo real, tornou-se um importante campo de pesquisa. O desenvolvimento desses algoritmos tem levado a melhores dispositivos cardíacos portáteis. Este artigo apresenta o desempenho dos dois com base na aprendizagem de máquina sem supervisão para detecção de batidas de contração ventriculares prematuras nos algoritmos de sinais de ECG. As batidas são extraídos das bases de dados do MIT-BIH, que foram pré-processados e segmentado pelo grupo da UPB Cardiovasculares Dynamics pesquisa. A Transformada Wavelet Discreta, Análise de Componentes Principais e uma abordagem híbrida proposta são implementadas para extração de características e redução de dimensão, a partir do qual 8 espaços de recursos para a avaliação dos algoritmos são gerados. Kmeans e mapas de auto-organização são desenvolvidos e comparados em termos de precisão e custo computacional. A especificidade de 96,22% e uma sensibilidade de 95,04% com um tempo de execução de 79.41µs por batida foi alcançado. Os resultados mostram que estes métodos podem ser implementados em aplicações de detecção de arritmia em tempo real, devido ao seu baixo custo computacional.

Una experiencia de acompañamiento con doula a adolescentes en trabajo de parto / Doula support during labor: experience in a sample of pregnant teenagers

Objetivo: Comparar la percepción del dolor-temor durante el trabajo de parto y parto, y el grado de dificultad-satisfacción con la experiencia de parto, en un grupo de adolescentes acompañadas por doula y otro con atención habitual. Método: El grupo estudio comprendió 80 adolescentes del "Programa Emprende Mamá" que controlaron sus embarazos en centros de salud de la comuna de La Florida y con parto en el hospital Dr. Sótero del Río, acompañadas por una mujer familiar o amiga, elegida por la joven, con capacitación previa de 2 horas. El grupo control estuvo formado por 80 adolescentes no participantes del programa, que controlaron sus embarazos en los mismos centros de salud, con parto en el mismo período. Resultados: El grupo estudio refirió menor percepción de dolor durante el trabajo de parto y parto, menor temor durante el parto y lo percibió como más fácil. No se encontraron diferencias significativas en el nivel de satisfacción respecto de la experiencia total. Sin embargo, las adolescentes con doula señalaron estar satisfechas por aspectos positivos de la vivencia, mientras que el grupo control centra su satisfacción en la ausencia de expectativas negativas temidas. Conclusión: El acompañamiento por doula en el parto de la embarazada adolescente, es una intervención de bajo costo que permite mejorar cualitativamente su vivencia de parto.

Objective: To compare the effects of continuous doula support and standard hospital care on pain and fear perception, during labor and delivery, as well as perception of difficulty and satisfaction with the birth experience, in a sample of pregnant teenagers. Method: The study group was conformed by eighty pregnant teenagers participants in "Emprende Mama Program" attended in the outpatient clinics of La Florida county for their prenatal care and delivery at a public hospital. They received emotional support during labor by a 2 hour trained female relative or friend, previously chosen by them. The control group was conformed by eighty non participants teenagers attended at the same outpatient clinics and delivery in the same period. Results: Teenagers with doula support reported less pain and fear during labor and delivery and perceived their birth experience as less difficult. No significant differences were reported in relation to satisfaction with birth experience. Although teenagers with doula support reported satisfaction due to positive aspects with birth experience, meanwhile the control group felt mostly satisfied due the absence of negative feared expectations. Conclusion: Labor support given by doula, is a low cost intervention which improves quality perception of birth experience in the adolescent pregnant.