A Tool for the Assessment of Forest Biomass as a Source of Rural Sustainable Energy in Natural Areas in Honduras

Forest biomass as a rural sustainable energy source has received much attention in recent years due to its major economic, social, and environmental benefits. This research focuses on an adapted methodology based on parameters of the Evaluation of Ecological Integrity for using sitespecific information as a tool for the assessment of forest biomass as a source of rural sustainable energy in Honduras, focusing on the Central American Pine–Oak Forests. The parameters used were Percentage of Forest Cover (FC), Patch Area (AREA), Fractal Dimension Index (FRAC), and Proximity Index (PROX). The goal was an average index rating of 5 for an ecosystem which is intact or in its natural state. The findings showed an ecosystem degradation that was outside the range of acceptable variation with a simple average of 1.75, which is far lower than the target rating of five (5.0); the forest cover loss was 40% of the total area. This surprising finding shows that immediate intervention is required to maintain this ecosystem, and that if action is not taken, the ecosystem will suffer severe degradation. Decision makers must consider this methodology for using site-specific information and ensure that local communities are involved in restoring the ecosystem

Automatic segmentation of a meningioma using a computational technique in magnetic resonance imaging

Through this work we propose a computational techniquefor the segmentation of a brain tumor, identified as meningioma(MGT), which is present in magnetic resonance images(MRI). This technique consists of 3 stages developed inthe three-dimensional domain: pre-processing, segmentationand post-processing. The percent relative error (PrE) is consideredto compare the segmentations of the MGT, generatedby a neuro-oncologist manually, with the dilated segmentationsof the MGT, obtained automatically. The combination ofparameters linked to the lowest PrE, provides the optimal parametersof each computational algorithm that makes up theproposed computational technique. Results allow reporting aPrE of 1.44%, showing an excellent correlation between themanual segmentations and those produced by the computationaltechnique developed

Segmentación automática de un meningioma usando una técnica computacional en imágenes de resonancia magnética

Through this work we propose a computational technique for the segmentation of a brain tumor, identified as meningioma (MGT), which is present in magnetic resonance images (MRI). This technique consists of 3 stages developed in the three-dimensional domain: pre-processing, segmentation and post-processing. The percent relative error (PrE) is considered to compare the segmentations of the MGT, generated by a neuro-oncologist manually, with the dilated segmentations of the MGT, obtained automatically. The combination of parameters linked to the lowest PrE, provides the optimal parameters of each computational algorithm that makes up the proposed computational technique. Results allow reporting a PrE of 1.44%, showing an excellent correlation between the manual segmentations and those produced by the computational technique developed.Este trabajo propone una técnica computacional para la segmentación de un tumor cerebral, identificado como meningioma (MGT), que está presente en imágenes de resonancia magnética (MRI). Esta técnica consta de 3 etapas desarrolladas en el dominio tridimensional: preprocesamiento, segmentación y postprocesamiento. El porcentaje de error relativo (PrE) se considera para comparar las segmentaciones de la MGT, generadas por un neurooncólogo de forma manual, con las segmentaciones dilatadas de la MGT, obtenidas automáticamente. La combinación de parámetros vinculados al PrE más bajo proporciona los parámetros óptimos de cada algoritmo computacional que conforma la técnica de cálculo propuesta. Los resultados permiten informar un PrE de 1.44%, mostrando una excelente correlación entre las segmentaciones manuales y las producidas por la técnica computacional desarrollada

Filtros suavizadores en imágenes sintéticas de resonancia magnética cerebral: un estudio comparativo

This paper presents the evaluation of two computational techniques for smoothing noise that might be present in synthetic images or numerical phantoms of magnetic resonance (MRI). The images that will serve as the databases (DB) during the course of this evaluation are available freely on the Internet and are reported in specialized literature as synthetic images called BrainWeb. The images that belong to this DB were contaminated with Rician noise, this being the most frequent type of noise in real MRI images. Also, the techniques that are usually considered to minimize the impact of Rician noise on the quality of BrainWeb images are matched with the Gaussian filter (GF) and an anisotropic diffusion filter, based on the gradient of the image (GADF). Each of these filters has 2 parameters that control their operation and, therefore, undergo a rigorous tuning process to identify the optimal values that guarantee the best performance of both the GF and the GADF. The peak of the signal-to-noise ratio (PSNR) and the computation time are considered as key elements to analyze the behavior of each of the filtering techniques applied. The results indicate that: a) both filters generate PSNR values comparable to each other. b) The GF requires a significantly shorter computation time to soften the Rician noise present in the considered DB. Keywords: Synthetic Cerebral images, Magnetic resonance, Rician noise, Gaussian filter, Anisotropic diffusion filter, PSNR.Este artículo presenta la evaluación de dos técnicas computacionales para el suavizado de ruido, que puede estar presente en imágenes sintéticas o phantoms numéricos de resonancia magnética (MRI). Las imágenes que servirán como bases de datos (DB) para el desarrollo de la mencionada evaluación están disponibles, de manera libre, en la Internet y se reportan, en la literatura especializada, como imágenes sintéticas denominadas BrainWeb. Las imágenes pertenecientes a esta DB fueron contaminadas con ruido Riciano debido a que este es el tipo de ruido más frecuente en imágenes de MRI reales. Por otra parte, las técnicas consideradas para minimizar el impacto de este ruido, en la calidad de las imágenes de la BrainWeb, se hacen coincidir con el filtro Gausiano (GF) y un filtro de difusión anisotrópica, basado en el gradiente de la imagen (GADF). Cada uno de estos filtros posee 2 parámetros que controlan su funcionamiento y, por ende, deben someterse a un proceso de entonación riguroso para identificar los valores óptimos que garanticen el mejor desempeño tanto del GF como del GADF. El pico de la relación señal a ruido (PSNR) y el tiempo de cómputo son considerados como elementos clave para analizar el comportamiento de cada una de las técnicas de filtrado aplicadas. Los resultados indican que: a) Ambos filtros generan valores de PSNR comparables entre sí. b) El GF requiere de un tiempo de cómputo, significativamente, menor para suavizar el ruido Riciano presente en la DB considerada. Palabras clave: Imágenes sintéticas cerebrales, Resonancia magnética, Ruido Riciano, Filtro Gausiano, Filtro de difusión anisotrópica, PSNR

Urban Waste: Visualizing the Academic Literature through Bibliometric Analysis and Systematic Review

The management of solid urban waste is one of the biggest problems currently faced by society and the economy. It can be considered a negative externality that arises as a consequence of the production and consumption processes of industry and society. This study consists of a bibliometric analysis to recognize the articles published and included in high-impact scientific journals, as well as a systematic review of the literature. We have collected 1897 research articles from the Scopus database that have been published between 1981 and 2021. We have identified the main subject areas, authors, institutions, and countries of these publications, as well as research trends in terms of resource management. Our findings show that since the 20th century, there has been quantitative and qualitative growth in this line of research, especially since 2006, and that four main trends have been defined: environment, society, technical aspects, and economic aspects. The economic field makes reference to the circular economy and its link to the objectives and sustainable development goals of the 2030 agenda, in which there is an important need to provide solutions to the problems generated as a consequence of the inadequate management of solid waste

Experimental Analysis and Application of a Multivariable Regression Technique to Define the Optimal Drilling Conditions for Carbon Fiber Reinforced Polymer (CFRP) Composites

Carbon fiber reinforced polymers (CFRPs) are interesting materials due to their excellent properties, such as their high strength-to-weight ratio, low thermal expansion, and high fatigue resistance. However, to meet the requirements for their assembly, the drilling processes involved should be optimized. Defects such as delamination, dimensional errors and poor internal surface finish can lead to the premature failure of parts when bolt-joined or rivet-connected. In addition, the characteristic anisotropy and heterogeneity of these materials, and the issues related to the temperature reached during drilling, make it difficult to obtain optimal cutting parameters or to achieve high material removal rates. This research focuses on the optimization of the CFRPs drilling process by means of experimental analysis—varying the feed and spindle speed—for two different types of commercial drills—a twist tool and a dagger tool. An automatic image processing methodology was developed for the evaluation of the dimensional accuracy and delamination of the holes. The optimization was carried out using a multi-objective regression technique based on the dimensional deviations, delamination and surface finish. The areas with favorable machining conditions have been delimited for both tools and the results indicate that the twist tool allows one to achieve more productive cutting conditions than the dagger tool, when the combination of low feeds and high spindle speeds are the conditions to be avoided

Urban Waste: Visualizing the Academic Literature through Bibliometric Analysis and Systematic Review

The management of solid urban waste is one of the biggest problems currently faced by society and the economy. It can be considered a negative externality that arises as a consequence of the production and consumption processes of industry and society. This study consists of a bibliometric analysis to recognize the articles published and included in high-impact scientific journals, as well as a systematic review of the literature. We have collected 1897 research articles from the Scopus database that have been published between 1981 and 2021. We have identified the main subject areas, authors, institutions, and countries of these publications, as well as research trends in terms of resource management. Our findings show that since the 20th century, there has been quantitative and qualitative growth in this line of research, especially since 2006, and that four main trends have been defined: environment, society, technical aspects, and economic aspects. The economic field makes reference to the circular economy and its link to the objectives and sustainable development goals of the 2030 agenda, in which there is an important need to provide solutions to the problems generated as a consequence of the inadequate management of solid waste

A Tool for the Assessment of Forest Biomass as a Source of Rural Sustainable Energy in Natural Areas in Honduras

Forest biomass as a rural sustainable energy source has received much attention in recent years due to its major economic, social, and environmental benefits. This research focuses on an adapted methodology based on parameters of the Evaluation of Ecological Integrity for using site-specific information as a tool for the assessment of forest biomass as a source of rural sustainable energy in Honduras, focusing on the Central American Pine–Oak Forests. The parameters used were Percentage of Forest Cover (FC), Patch Area (AREA), Fractal Dimension Index (FRAC), and Proximity Index (PROX). The goal was an average index rating of 5 for an ecosystem which is intact or in its natural state. The findings showed an ecosystem degradation that was outside the range of acceptable variation with a simple average of 1.75, which is far lower than the target rating of five (5.0); the forest cover loss was 40% of the total area. This surprising finding shows that immediate intervention is required to maintain this ecosystem, and that if action is not taken, the ecosystem will suffer severe degradation. Decision makers must consider this methodology for using site-specific information and ensure that local communities are involved in restoring the ecosystem

Volumetry of subdural hematomas in computed tomography images: ABC methods versus an intelligent computational technique

This work evaluates the performance of some methods orientedtowards the generation of the volume of four subduralhematomas (SDH), present in multi-layer computed tomographyimages. To do this, firstly, a reference volume is specified:the volume obtained by a neurosurgeon using the manualplanimetric method (MPM); which allows the generation ofmanual segmentations of space-occupying lesions. In thiscase, these volumes are matched with the SDH. In parallel,the volumetry of the 4 SDHs is obtained, considering both theoriginal version of the ABC/2 method and two of its variants,identified in this paper as ABC/3 method and 2ABC/3 method.The ABC methods allow the calculation of the volume ofthe hematoma under the assumption that the SDH has anellipsoidal shape. In third place, SDH’s are studied throughan intelligent automatic technique (SAT) that generates thethree-dimensional segmentation of each SDH. Finally, thepercentage relative error is calculated as a metric to evaluatethe methodologies considered. The results show that the SATmethod exhibits the best performance generating an averagepercentage error of less than 5%

Volumetry of epidural hematomas in computed tomography images: Comparative study between linear and volumetric methods

This work evaluates the performance of somemethods employed for assessing the volume ofseven subdural hematomas (EDH), present inmulti-layer computed tomography images. Firstly, a referencevolume is considered to be that obtained by a neurosurgeonusing the manual planimetric method (MPM).Secondly, the volume of the 7 EDHs is obtained consideringboth the original version of the ABC/2 method and two ofits variants, identified in this paper as ABC/3 method and2ABC/3 method. The ABC methods allow for calculationof the volume of the hematoma under the assumptionthat the EDH has an ellipsoidal shape. In third place, anintelligent automatic technique (SAT) is implemented thatgenerates the three-dimensional segmentation of eachEDH and from it the volume of the hematoma is calculated.The SAT consists of the pre-processing, segmentationand post-processing stages. In order to make judgmentsabout the performance of the SAT, the Dice coefficient(Dc) is used to compare the dilated segmentations of theEDH with the EDH segmentations generated manually. Finally,the percentage relative error is calculated as a metricto evaluate the methodologies considered. The resultsshow that the SAT method exhibits the best performancegenerating an average percentage error of less than 2%