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Biochar is a carbonaceous and porous material with limited adsorption capacity, which increases by modifying its surface. Many of the biochars modified with magnetic nanoparticles reported previously were obtained in two steps: first, the biomass was pyrolyzed, and then the modification was performed. In this research, a biochar with Fe3O4 particles was obtained during the pyrolysis process. Corn cob residues were used to obtain the biochar (i.e., BCM) and the magnetic one (i.e., BCMFe). The BCMFe biochar was synthesized by a chemical coprecipitation technique prior to the pyrolysis process. The biochars obtained were characterized to determine their physicochemical, surface, and structural properties. The characterization revealed a porous surface with a 1013.52 m2/g area for BCM and 903.67 m2/g for BCMFe. The pores were uniformly distributed, as observed in SEM images. BCMFe showed Fe3O4 particles on the surface with a spherical shape and a uniform distribution. According to FTIR analysis, the functional groups formed on the surface were aliphatic and carbonyl functional groups. Ash content in the biochar was 4.0% in BCM and 8.0% in BCMFe; the difference corresponded to the presence of inorganic elements. The TGA showed that BCM lost 93.8 wt% while BCMFe was more thermally stable due to the inorganic species on the biochar surface, with a weight loss of 78.6%. Both biochars were tested as adsorbent materials for methylene blue. BCM and BCMFe obtained a maximum adsorption capacity (qm) of 23.17 mg/g and 39.66 mg/g, respectively. The obtained biochars are promising materials for the efficient removal of organic pollutants. [ABSTRACT FROM AUTHOR]

This paper focuses on the probabilistic analysis of Intensity Measures (IMs) and Engineering Demand Parameters (EDPs) in the context of earthquake-induced ground motions. Several statistical properties, which are desirable in IMs when they are used to predict EDPs, have been analysed. Specifically, efficiency, sufficiency and steadfastness have been quantified for a set of IMs with respect to two EDPs: the maximum inter-storey drift ratio, MIDR, and the maximum floor acceleration, MFA. Steadfastness is a new statistical property proposed in this article, which is related to the ability of IMs to forecast EDPs for large building suites. In other words, this means that efficiency does not significantly vary when different types of buildings are simultanously considered in the statistical analyses. This property allows reducing the number of calculations when performing seismic risk estimations at urban level since, for instance, a large variety of fragility curves, representing specific building typologies, can be grouped together within a more generic one. The main sources of uncertainty involved in the calculation of the seismic risk have been considered in the analysis. To do so, the nonlinear dynamic responses of probabilistic multi-degree-of-freedom building models, subjected to a large data set of ground motion records, have been calculated. These models have been generated to simulate the dynamic behavior of reinforced concrete buildings whose number of stories vary from 3 to 13. 18 spectrum-, energy- and direct-accelerogram-based IMs have been considered herein. Then, from clouds of IM-EDP points, efficiency, sufficiency and steadfastness have been quantified. For MIDR, results show that IMs based on spectral velocity are more efficient and steadfast than the ones based on spectral acceleration; spectral velocity averaged in a range of periods, AvSv, has shown to be the most efficient IM with an adequate level of steadfastness. For MFA, spectral acceleration-based-IMs are more efficient than velocity-based ones. A comparison is also presented on the use of linear vs quadratic regression models, and their implications on the derivation of fragility functions. Concerning sufficiency, most of the 18 IMs analysed do not have this property. Nonetheless, multi-regression models have been employed to address this lack of sufficiency allowing to obtain a so-called 'ideal' IM. [ABSTRACT FROM AUTHOR]

The context of conceiving, designing, implementing, and operating real-world systems and products, namely the CDIO initiative, is a framework for engineering education. It considers 12 standards, which are the reference for curriculum design and assessment. A good implementation of the CDIO standards can be considered as a multi-dimensional complex problem. In order to propose strategies for implementing the CDIO initiative in the Electronic Engineering curriculum at Universidad del Quindío, the General Morphological Analysis iGMA) was used. Some relevant dimensions of the curriculum and their values were contrasted in a cross-consistency assessment (CCA), where 8 dimensions were established, and a total of34,560 combinations were obtained in the problem space. Through the CCA, the number of coherent combinations was significantly reduced. Finally, these combinations were analyzed to propose the corresponding strategies that are the input for the implementation of the CDIO curriculum in the Electronic Engineering program. [ABSTRACT FROM AUTHOR]

Antecedentes: el bienestar animal de los bovinos de lechería especializada puede mejorarse a través de la estimulación sonora durante el ordeño, esta alternativa no se ha evaluado en los sistemas de la sabana de Bogotá. Objetivo: evaluar el efecto de la implementación de la música clásica en la calidad y producción láctea de bovinos en dos tipos de ordeño. Métodos: se realizaron tres visitas en cada tipo de ordeño mecánico (fijo y móvil), la primera fue con el objetivo de establecer la ubicación de los parlantes (Cabinas QSC2), sonómetro (Svantek svan873), grabadora (zoom H6n) y micrófonos Golden TEF. En una misma semana se realizaron las visitas 2 y 3, donde se tomaron muestras de leche por animal; en la segunda se realizaron las grabaciones del ruido (fondo y equipo de ordeño) y muestreo a 10 animales al azar (primeras en entrar al ordeño); y en la tercera visita a los mismos 10 animales se estimularon con música clásica durante 20 minutos previos al ordeño y durante el ordeño. Las muestras se analizaron para la determinación de proteína, grasa, sólidos totales y lactosa (Milkoscan- espectrofotometría infrarrojo), como también células somáticas y mesófilos (Fossomatic - citometría de flujo). Se midió la producción diaria por animal (l/día/animal). Para el análisis estadístico se usó Wilcoxon o t student. Resultados: el nivel sonoro promedio previo a la estimulación fue de 69,5 dB y durante la estimulación de 74,1 dB en el ordeño fijo, para el caso del ordeño móvil fue 93,9 preestimulación y durante el estímulo 94,3 dB en el ordeño móvil. Las variables composicionales de proteína, grasa, solidos totales y lactosa no mostraron diferencias estadísticamente significativas (p > 0,05) en ninguno de los sistemas de ordeño evaluados. En relación con la producción láctea no se presentaron diferencias en los promedios del ordeño fijo (Preestimulación 16,2 vs Post 18,3 l) pero en el caso del ordeño móvil (19,2 y 23,4 l) se obtuvo un cambio significativo (p < 0,05). Conclusiones: la música clásica no influenció la calidad composicional de la leche en los sistemas de ordeño mecánico y móvil pero estos resultados preliminares demuestran que pueden aumentar la producción láctea. [ABSTRACT FROM AUTHOR]

Random set theory is a generalization of Dempster-Shafer evidence theory, that employs an infinite number of focal elements. It can be used for the estimation of the bounds of the probability of failure of structural systems when there is both aleatory and epistemic uncertainty in the representation of the input variables. Indeed, this framework allows to model basic variables as cumulative distribution functions, distribution-free probability boxes, possibility distributions or families of intervals provided by experts, while representing the dependence of the implied variables by means of copulas. This paper reviews another method, which poses the calculation of the bounds of the probability of failure as a reliability-based-design-optimization problem. It is proved theoretically and by means of numerical experiments, that the latter method provides tighter bounds on the probability of failure than those estimated by random set theory. We also theoretically show some interesting relationships between the random set-based method and the optimization approach. [ABSTRACT FROM AUTHOR]

Structural reliability analysis should often face the problem that there is uncertainty about the probabilistic specification of the input random variables implied in a specific problem. In the poorest information case only the knowledge of intervals of fluctuation is available, while the opposite situation is that of a full specification of the joint distribution function of the variables. Parametric or non parametric probability boxes, arising from basic statistical tools such as confidence intervals or Kolmogorov–Smirnov distance, are intermediate situations. In any case, random or fixed intervals emerge for describing eitheistribution parameters under epistemic uncertainty. A crude Monte Carlo simulation for assessing the corresponding interval of the failure probability requires the solution of a double optimization problem for each probability level of the limit state function. This, obviously, implies a prohibitive computational labor. In this paper a method for drastically simplifying this task is proposed. The method exploits the ordering statistics representation property of the reliability plot, which is shown to approximately obey an orthogonal hyperbolic pattern. Accordingly a two-level FORM approach with relaxed accuracy constraints is used to derive the polar vectors for building two plots, one for the input variable space and the second to the epistemic uncertainty space. Using a variety of examples, it is demonstrated that the extrema of the failure probability are contained amongst the samples located in specific sectors of the upper level plot as indicated by the hyperbolae. This means that, after solving the two-level FORM problem, it suffices to calculate the failure probability (or the reliability index) for a small number of mean samples thus selected. Obviously, the method yields the same reliability interval estimates as the crude two-level Monte Carlo because its samples are underlying in it. [ABSTRACT FROM AUTHOR]

A general method for estimating the bounds of the reliability of a system in which the input variables are described by random sets (probability distributions, probability boxes, or possibility distributions), with dependence modeling is proposed. The method is based on an analytical property of the so-called design point vector; this property is exploited by constructing a nonlinear projection of Monte Carlo samples of the input variables in a two-dimensional diagram from which the analyst can easily extract the relevant samples for computing both the lower and upper bounds of the failure probability using random set theory. The method, which is illustrated with some examples, represents a dramatic reduction in the number of focal element evaluations performed when applying the Monte Carlo method to random set theory. [ABSTRACT FROM AUTHOR]

The main objective of this article is to propose a simplified methodology to assess the expected seismic damage in reinforced concrete buildings from a probabilistic point of view by using Monte Carlo simulation. In order to do so, the seismic behaviour of the building was studied by using random capacity obtained by considering the mechanical properties of the materials as random variables. From the capacity curves, the damage states and fragility curves can be obtained, and curves describing the expected seismic damage to the structure as a function of a seismic hazard characteristic can be developed. The latter can be calculated using the capacity spectrum and the demand spectrum according to the methodology proposed by the Risk-UE project. In order to define the seismic demand as a random variable, a set of real accelerograms were obtained from European and Spanish databases in such a way that the mean of their elastic response spectra was similar to an elastic response spectrum selected from Eurocode 8. In order to combine the uncertainties associated with the seismic action and the mechanical properties of materials, two procedures are considered to obtain functions relating the peak ground acceleration to the maximum spectral displacements. The first method is based on a series of non-linear dynamic analyses, while the second is based on the well-known ATC-40 procedure called equal displacement approximation. After applying both procedures, the probability density functions of the maximum displacement at the roof of the building are gathered and compared. The expected structural damage is finally obtained by replacing the spectral displacement calculated using ATC-40 and the incremental dynamic procedure. In the damage functions, the results obtained from incremental static and dynamic analyses are compared and discussed from a probabilistic point of view. [ABSTRACT FROM AUTHOR]

Abstract: This paper introduces a new regard and a powerful method for estimating small failure probabilities. It consists in considering the reliability problem as a false constrained optimization of a function. The optimization is called false because the minimum of the function is known beforehand. However, the process of computing such a minimum yields the samples located in the failure domain as a by-product, thus allowing the computation of the failure probability in a very simple manner. An algorithm based on an ad-hoc modification of the well-known Particle Swarm Optimization technique is proposed. It is characterized by the fact that it may deliver the same value of the failure probability as simple Monte Carlo simulation. In addition, the algorithm yields a visualization of all the computed samples in bidimensional plot, from which the critical realizations of the random variables can be drawn. These are the samples that mark the boundary between the safety and failure domains and therefore constitute a highly valuable information for design and diagnosis. The excellent accuracy and low computational cost of the proposed approach are illustrated with several examples. [Copyright &y& Elsevier]

Abstract: Structural and mechanical reliability analysis often face the problem that probability distributions of the input variables are known with imprecision. This latter is normally specified by intervals of variation of their parameters. Leaving aside a crude Monte Carlo simulation consisting this case in estimating the failure probability for several sets of random realizations of the input distributions, there are no parsimonious methods for solving this problem in the general case of several interval parameters per distribution. In this paper a method intended to fill this gap is proposed. It is based on a property of the reliability plot recently proposed by the author [Hurtado, Dimensionality reduction and visualization of structural reliability problems using polar features. Probabilistic Engineering Mechanics, 29 (2012) 16–31], namely the fact that the order statistics of any function of the input random variables, used for building a limit state function, is concealed in the plot. This property, which is demonstrated herein, is used for the development of numerical methods for interval or reliability analysis, as well as for their combination for the estimation of the reliability interval. The ordering property of the plot assures that the lowest and largest values of the failure probability derives from samples contained in two small sets of realizations of the input distribution parameters located in specific plot sectors. The application of the proposed methodology is illustrated with examples that demonstrate its rigorousness, simplicity and accuracy. [Copyright &y& Elsevier]

Abstract: The First-Order Reliability Method (FORM) is by far the most widely employed method for structural reliability computation. Its accuracy, however, is fair only when the curvature of the limit state function is very mild. In this paper, a reliability method built on the design point vector given by FORM is proposed. It is based on the computation of the limit state function of a relatively small selected set of Monte Carlo samples that have the highest similarity to the design point in a statistical sense. The similarity is measured by two nonlinear features extracted from the mass of input variable realizations. The bi-dimensionality of the mapping allows a simple visual selection of the highly relevant samples and the relevant sample selection is facilitated by the fact that the failure domain possesses a standard shape in the space spanned by those nonlinear features and also by the feasibility of mapping several possibilities of a second-order approximation of the limit state function onto the plot, which embrace the most relevant samples. The method yields the same estimate of the failure probability as the simple Monte Carlo, with a computational cost limited to the evaluation of a subset of the selected samples. Besides, the reduction of the variability of the probability estimates is easily accomplished. Its application is illustrated with some numerical examples derived from actual structural engineering practice. They show that the method is simple, efficient and elegant, as it allows visualizing the reliability problem in the plot constituted by the two nonlinear features. [Copyright &y& Elsevier]

Abstract: Fuzzy structural analysis is oriented to estimate the membership function of an output structural variable given the membership functions of the input ones, which are discretized in so-called α-cut levels. Such an estimation requires the solution of two optimization problems for each of them. In this paper an alternative approach is presented. It consists in solving a single optimization problem for the entire problem, which is that of FORM (first-order reliability analysis). It is shown that FORM solution has a valuable property, namely that it allows organizing the order statistics of the output variable along the design point vector. This property is exploited by a nonlinear projection of a large amount of standard Gaussian numbers, truncated according to the membership functions, onto a bi-dimensional space. From this mapping the samples necessary for directly solving the optimization problems are easily drawn using two curves, whose equations are derived from a similar transformation of the second-order approximation of the limit state function. A detailed structural example shows that the desired membership function of the response can be accurately estimated by the proposed method. [Copyright &y& Elsevier]

Abstract: A method for reducing the dimensionality of a structural reliability problem of many dimensions to only two independent dimensions is presented. Such a drastic reduction is achieved by means of a polar representation of a set of unclassified random numbers in the standard normal space. The most important feature of the proposed approach is that, due to the probabilistic properties of the nonlinear transformation applied, the safe and failure classes of samples are clearly distinguishable and occupy a standard position in a plot. On this basis it is possible to solve the reliability problem by means of a simple visually-aided selection of the relevant samples and discarding the rest. Also, the method permits to identify the samples in the safe domain that are on the verge of the failure domain, which constitute the so-called critical realizations or worst-case scenarios. Several benchmark examples demonstrate the simplicity and versatility of the proposed approach. Finally, some classical reliability methods are critically examined from the point of view of the proposed reliability plot. [Copyright &y& Elsevier]

Despite technological advances in seismic instrumentation, the assessment of the intensity of an earthquake using an observational scale as given, for example, by the modified Mercalli intensity scale is highly useful for practical purposes. In order to link the qualitative numbers extracted from the acceleration record of an earthquake and other instrumental data such as peak ground velocity, epicentral distance, and moment magnitude on the one hand and the modified Mercalli intensity scale on the other, simple statistical regression has been generally employed. In this paper, we will employ three methods of nonlinear regression, namely support vector regression, multilayer perceptrons, and genetic programming in order to find a functional dependence between the instrumental records and the modified Mercalli intensity scale. The proposed methods predict the intensity of an earthquake while dealing with nonlinearity and the noise inherent to the data. The nonlinear regressions with good estimation results have been performed using the 'Did You Feel It?' database of the US Geological Survey and the database of the Center for Engineering Strong Motion Data for the California region. [ABSTRACT FROM AUTHOR]

Abstract: The consideration of uncertainties in structural analysis has traditionally been made in the field of structural reliability within the framework of classical probability theory. In many cases, however, there is no sufficient probabilistic information to substantiate such an approach. In recent years several non-probabilistic methods have been proposed as alternatives. One of them is the interval analysis, oriented to estimate the range of variation of output structural variables given the ranges of the input ones. The method, however, requires the use of special arithmetics which renders its application unpractical for structural analysis based on finite elements or other numerical techniques. In this paper a Monte Carlo method that can be applied to probabilistic as well as to interval approaches to reliability analysis is proposed. It is based on the optimal properties of the classical design point of probabilistic structural reliability. In particular, it is shown that the order statistics of the output variable is concealed in a plot defined by the design point vector. This facilitates the selection of the relevant samples for either interval or probability-based analysis. The examples show that the desired interval of the response or the failure probability can be accurately estimated on such a basis. The paper also discusses the use of Monte Carlo methods for both reliability and interval analysis from the point of view of the proposed representation. [Copyright &y& Elsevier]

Analytical and experimental research for determining the seismic vulnerability of ferrocement houses, which is a low-cost system using precast thin panels, is presented. The research comprises the following tasks: (a) a field survey on the ferrocement dwelling houses actually built in seismic regions in Colombia; (b) a cyclic load test on 1:1 scale models; (c) identification of the parameters of the Bouc-Wen hysteretic model for the observed panel behaviour; and (d) Monte Carlo simulations of models intended to estimate the fragility curves of the houses. Despite the models exhibiting stiffness degradation, it was concluded that the system is adequate for regions of moderate to high seismicity. [ABSTRACT FROM AUTHOR]

Abstract: Monte Carlo simulation is a general and robust method for structural reliability analysis, affected by the serious efficiency problem consisting in the need of computing the limit state function a very large number of times. In order to reduce this computational effort the use of several kinds of solver surrogates has been proposed in the recent past. Proposals include the Response Surface Method (RSM), Neural Networks (NN), Support Vector Machines (SVM) and several other methods developed in the burgeoning field of Statistical Learning (SL). Many of these techniques can be employed either for function approximation (regression approach) or for pattern recognition (classification approach). This paper concerns the use of these devices for discriminating samples into safe and failure classes using the classification approach, because it constitutes the core of Monte Carlo simulation as applied to reliability analysis as such. Due to the flexibility of most SL methods, a critical step in their use is the generation of the learning population, as it affects the generalization capacity of the surrogate. To this end it is first demonstrated that the optimal population from the information viewpoint lies around in the vicinity of the limit state function. Next, an optimization method assuring a small as well as highly informative learning population is proposed on this basis. It consists in generating a small initial quasi-random population using Sobol sequence for triggering a Particle Swarm Optimization (PSO) performed over an iteration-dependent cost function defined in terms of the limit state function. The method is evaluated using SVM classifiers, but it can be readily applied also to other statistical classification techniques because the distinctive feature of the SVM, i.e. the margin band, is not actively used in the algorithm. The results show that the method yields results for the probability of failure that are in very close agreement with Monte Carlo simulation performed on the original limit state function and requiring a small number of learning samples. [Copyright &y& Elsevier]

Correction to: Bulletin of Earthquake Engineering https://doi.org/10.1007/s10518-021-01283-x This corrections is published as vendor overlooked several corrections related with Table 2. Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. [Extracted from the article]

The project "Artificial Beaches for Antofagasta" is part of the Bicentenary Program that is taken place in several cities aiming at improving their habitability standards by the year 2010. This project in Antofagasta seeks to recuperate the coastal border of the city through a series of interventions that tries to improve the accessibility of the inhabitants to the sea; to decontaminate various places of the coast, and to create a new coastal avenue. Due to its rocky nature, the coast of the city lacks of natural beaches, so the local authorities in charge of urban development started in 2000 with the idea of providing the city with three large artificial beaches. In order to achieve this task, the project included the construction of coastal defenses and the filling of some parts with sand, with interesting environmental benefits. [ABSTRACT FROM AUTHOR]

Abstract: In structural reliability, simulation methods are oriented to the estimation of the probability integral over the failure domain, while solver-surrogate methods are intended to approximate such a domain before carrying out the simulation. A method combining these two purposes at a time and intended to obtain a drastic reduction of the computational labor implied by simulation techniques is proposed. The method is based on the concept that linear or nonlinear transformations of the performance function that do not affect the boundary between safe and failure classes lead to the same failure probability than the original function. Useful transformations that imply reducing the number of performance function calls can be built with several kinds of squashing functions. A most practical of them is provided by the pattern recognition technique known as support vector machines. An algorithm for estimating the failure probability combining this method with importance sampling is developed. The method takes advantage of the guidance offered by the main principles of each of these techniques to assist the other. The illustrative examples show that the method is very powerful. For instance, a classical series problem solved with O(1000) importance sampling solver calls by several authors is solved in this paper with less than 40 calls with similar accuracy. [Copyright &y& Elsevier]

The reliability analysis of complex structures is hindered by the implicit nature of the limit-state function. For their approximation use has been made of the Response Surface Method (RSM) and, more recently, of Neural Networks. From the statistical viewpoint this corresponds to a regression approach. In the structural reliability literature little attention has been paid, however, to the possibility of treating the problem as a classification task. This enlarges the list of methods that are eventually useful to the purpose at hand and justifies an overall examination of their distinguishing features. This task is performed in this paper from the point of view of the Theory of Statistical Learning, which provides a unified framework for all regression, classification and probability density estimation. The classification methods are grouped into three categories and it is shown that only one group is useful for structural reliability, according to some specific criteria. In this category are the Multi-Layer Perceptrons and the Support Vector Machines, which are the recommended methods because (a) they can estimate the function on the basis of a few samples, (b) they use flexible and adaptive models and (c) they can overcome the curse of dimensionality. The paper also includes an in-depth analysis of the RSM from the point of view of statistical learning. It is shown that the empirically found instability of this method is explained with statistical learning concepts. [Copyright &y& Elsevier]

The assessment of the reliability of structural systems is increasingly being estimated with regard to the spatial fluctuation of the mechanical properties as well as loads. This leads to a detailed probabilistic modeling known as stochastic finite elements (SFE). In this paper an approach that departs from the main stream of methods for the reliability analysis of SFE models is proposed. The difference lies in that the reliability problem is treated as a classification task and not as the computation of an integral. To this purpose use is made of a kernel method for classification, which is the object of intensive research in pattern recognition, image analysis, and other fields. A greedy sequential procedure requiring a minimal number of limit state evaluations is developed. The algorithm is based on the key concept of support vectors, which guarantee that only the points closest to the decision rule need to be evaluated. The numerical examples show that this algorithm allows obtaining a highly accurate approximation of the failure probability of SFE models with a minimal number of calls of the finite element solver and also a fast computation. [ABSTRACT FROM AUTHOR]

Reanalysis of structures is a common task in two important fields of structural engineering, namely optimization and reliability. It implies the solution of the kinematic equations for each new set of data. In order to simplify this task several approximate methods have been developed. In this paper a simple method for reanalysis, applicable to either linear or nonlinear structures, is proposed. It is based on the improvement of the Neumann resolvent series, which diverges beyond a certain radius, by means of the iterated Shanks transformation. The method is compared with two relevant proposals intended to improve the convergence of the Neumann series, namely the Pade´ series and the scaled approximation methods. The accuracy and simplicity of the proposed method is demonstrated through some examples concerning linear and nonlinear structures. [Copyright &y& Elsevier]

The presence of calcium sulfate in the process water during the coal flotation greatly influences the recovery and selectivity of the separation. The concentrations of calcium and sulfate ions modify mineral hydrophobicity by altering surface properties resulting in depression or activation of the mineral species. An investigation to evaluate the statistical significance of the effect of the pH and concentration of calcium and sulfate ions on coal flotation was carried out; for this purpose, a 23 factorial design was implemented. A p-value < 0.05 was determined for the effect of calcium and sulfate ion concentrations, indicating that it is statistically significant. The interactions between factors (pH × calcium, pH × sulfate, calcium × sulfate and pH × calcium × sulfate) are also statistically significant, but the interaction between the concentration of calcium and sulfate ions has a notable influence according to the F statistic value. Employing 800 and 1920 mg/L of calcium and sulfate ions as experimental conditions yields a recovery of 90.4% with a concentrate containing 13% ash. [ABSTRACT FROM AUTHOR]

The use of cellular electrostimulation in medicine has been an increasingly growing area of interest, and graphene has emerged as a promising material in this field. This article explores how cellular electrostimulation can influence key biological processes and how graphene, with its unique properties, can enhance this technique. The electrochemical aspects of the graphene-cell interaction and its impact on cellular activity regulation were investigated. Additionally, various applications of graphene in cellular electrostimulation, from tissue engineering to disease treatment were examined. This article provides a comprehensive insight into how the combination of electrochemistry and graphene is transforming the field of regenerative medicine. [ABSTRACT FROM AUTHOR]

Activated carbons (AC) are of special interest due to their exceptional physical and chemical properties, these materials are in the form of granules or powders, but recently a new form of AC has been commercialized which is also known as Activated Carbon Fiber (ACF), and is manufactured in two presentations, cloth and felt. Activated carbon cloths (ACC) are materials that have excellent properties making them superior compared to traditional forms and are produced from precursors, through various processes that include physical and chemical activation, among the most commonly used impregnating agents are KOH, H3PO4, ZnCl2, AlCl3, NH4Cl, Na2CO3 and K2CO3, whose main function is to serve as dehydrators while preventing tars production. The characteristics and properties of the ACC depend on the nature of the material that was used to produce it, these characteristics have been used in a large number of applications, such as medicine, catalyst support systems, in the industry for the absorption of pollutants, water purification and waste water treatment, among others. This review shows the generalities and applications in recent studies and summarizes TCA applications from the different investigations carried out, as well as the process for obtaining them. [ABSTRACT FROM AUTHOR]

Highlights • Hysteretic and seismic behaviors of the Recycled Plastic Lumber walls are presented. • Structural parameters of the Recycled Plastic Lumber walls are determined. • Recycled Plastic Lumber walls do not exhibit fragile failure modes under cyclic test. • Multilinear hysteretic model fits well the nonlinear dynamical behavior of the walls. • Recycled Plastic Lumber walls have a satisfactory performance under seismic loads. Abstract Recycled Plastic Lumber (RPL) is a wood-like material made from recycled plastics that aims to diminish the environmental pollution resulting from plastic wastes. This material is used in different kinds of nonstructural and structural applications. Recently, RPL has been proposed as a suitable material to develop structural walls that comprise the seismic resistant system of housings, in order to lessen the housing deficiency. This article presents the results drawn from an experimental campaign carried out over three full-scale RPL walls, which were tested under cycling loading conditions to determine structural parameters such as strength, hysteretic behavior, ductility, energy dissipation, equivalent damping and characteristic failure modes of the RPL walls, which are necessary to design and to assess seismically the housings. Finally, a multilinear hysteretic model capable of simulating the nonlinear dynamic behavior exhibited by RPL walls was implemented, in order to simulate and to assess the seismic behavior of them under strong and destructive real earthquakes. [ABSTRACT FROM AUTHOR]

Random set theory is a general framework which comprises uncertainty in the form of probability boxes, possibility distributions, cumulative distribution functions, Dempster-Shafer structures or intervals; in addition, the dependence between the input variables can be expressed using copulas. In this paper, the lower and upper bounds on the probability of failure are calculated by means of random set theory. In order to accelerate the calculation, a well-known and efficient probability-based reliability method known as subset simulation is employed. This method is especially useful for finding small failure probabilities in both low- and high-dimensional spaces, disjoint failure domains and nonlinear limit state functions. The proposed methodology represents a drastic reduction of the computational labor implied by plain Monte Carlo simulation for problems defined with a mixture of representations for the input variables, while delivering similar results. Numerical examples illustrate the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Highlights: [•] Carbons surface charge contributes in a greater extend to arsenic (V) uptake. [•] Iron increases the As(V) adsorption capacity and promotes strong bonds Fe–O–As. [•] Adsorption capacity correlates with pHPZC and iron content. [•] As(V) adsorption is governed by OH-ligand exchange and electrostatic interactions. [ABSTRACT FROM AUTHOR]

Computational human phantoms are computer models used to obtain dose distributions within the human body exposed to internal or external radiation sources. In addition, they are increasingly used to develop detector efficiencies for in vivo whole-body counters. Two classes of computational human phantoms have been widely utilized for dosimetry calculation: stylized and voxel phantoms that describe human anatomy through mathematical surface equations and 3D voxel matrices, respectively. Stylized phantoms are flexible in that changes to organ position and shape are possible given avoidance of region overlap, while voxel phantoms are typically fixed to a given patient anatomy, yet can be proportionally scaled to match individuals of larger or smaller stature, but of equivalent organ anatomy. Voxel phantoms provide much better anatomical realism as compared to stylized phantoms which are intrinsically limited by mathematical surface equations. To address the drawbacks of these phantoms, hybrid phantoms based on non-uniform rational B-spline (NURBS) surfaces have been introduced wherein anthropomorphic flexibility and anatomic realism are both preserved. Researchers at the University of Florida have introduced a series of hybrid phantoms representing the ICRP Publication 89 reference newborn, 15 year, and adult male and female. In this study, six additional phantoms are added to the UF family of hybrid phantoms--those of the reference 1 year, 5 year and 10 year child. Head and torso CT images of patients whose ages were close to the targeted ages were obtained under approved protocols. Major organs and tissues were segmented from these images using an image processing software, 3D-DOCTOR. NURBS and polygon mesh surfaces were then used to model individual organs and tissues after importing the segmented organ models to the 3D NURBS modeling software, Rhinoceros. The phantoms were matched to four reference datasets: (1) standard anthropometric data, (2) reference organ masses from ICRP Publication 89, (3) reference elemental compositions provided in ICRP 89 as well as ICRU Report 46, and (4) reference data on the alimentary tract organs given in ICRP Publications 89 and 100. Various adjustments and refinements to the organ systems of the previously described newborn, 15 year and adult phantoms are also presented. The UF series of hybrid phantoms retain the non-uniform scalability of stylized phantoms while maintaining the anatomical realism of patient-specific voxel phantoms with respect to organ shape, depth and inter-organ distance. While the final versions of these phantoms are in a voxelized format for radiation transport simulation, their primary format is given as NURBS and polygon mesh surfaces, thus permitting one to sculpt non-reference phantoms using the reference phantoms as an anatomic template. [ABSTRACT FROM AUTHOR]

• Experimental hysteretic behaviors of the precast ferrocement walls are presented. • Structural parameters and damage limit states are obtained from cyclic tests. • Mostaghel's multilinear hysteretic model fits well the nonlinear behavior of the walls. • The walls' performance points for the DBE and MCE levels fall within the IO range. • The studied walls have a good seismic performance for one and two-story housing. In recent years, innovative structural systems based on Precast Ferrocement Walls (PFW) have been proposed for one and two-storey housing so as to lessen the housing deficiency. This article presents the results drawn from cycling loading tests carried out over two types of PFWs, in order to determine their strength, hysteretic behavior, ductility, energy dissipation capacity, equivalent viscous damping, and damage limit states. These walls have similar structural systems that differ only in the type of reinforcement. To assess their seismic performances for two earthquake hazard levels, a nonlinear dynamic analysis methodology based on the Performance-Based Seismic Design philosophy was developed, which uses the Mostaghel's multilinear hysteretic model to simulate accurately the actual inelastic behavior and energy dissipation capacity of the PFWs, together with the incremental dynamic analysis (IDA) technique. The Immediate Occupancy (IO), Life Safety (LS), and Collapse Prevention (CP) structural performance levels were considered and related to the damage limit states. Finally, the performance points of both PFWs fell within the IO range with a 100% probability of incurring no structural damage state for both earthquake hazard levels, which demonstrates their good seismic performance. [ABSTRACT FROM AUTHOR]

The main goals of this article are to analyze the use of simplified deterministic nonlinear static procedures for assessing the seismic response of buildings and to evaluate the influence that the uncertainties in the mechanical properties of the materials and in the features of the seismic actions have in the uncertainties of the structural response. A reinforced concrete building is used as a guiding case study. In the calculation of the expected spectral displacement, deterministic nonlinear static methods are simple and straightforward. For not severe earthquakes these approaches lead to somewhat conservative but adequate results when compared to more sophisticated procedures involving nonlinear dynamic analyses. Concerning the probabilistic assessment, the strength properties of the materials, concrete and steel, and the seismic action are considered as random variables. The Monte Carlo method is then used to analyze the structural response of the building. The obtained results show that significant uncertainties are expected; uncertainties in the structural response increase with the severity of the seismic actions. The major influence in the randomness of the structural response comes from the randomness of the seismic action. A useful example for selected earthquake scenarios is used to illustrate the applicability of the probabilistic approach for assessing expected damage and risk. An important conclusion of this work is the need of broaching the fragility of the buildings and expected damage assessment issues from a probabilistic perspective. [ABSTRACT FROM AUTHOR]

Background Dermatologic diseases vary widely as a result of geographic location and may be influenced by environmental factors. Aim To determine the epidemiology of dermatological diseases in the Peruvian Amazonia. Methods Transversal and multicentric study, which were carried out during February of 2006, 2007, and 2008 in three regional hospitals in the Peruvian Amazonia. All new patients who were looking for dermatological consultation were included. Univariate/bivariate analysis, chi square, and multinomial logistic regression were used with a confidence level of 95%. Results A total of 1602 patients were included. The infectious and parasitic dermatoses were the most prevalent (31.5%). There was a statiscally significant association between infections of the skin and subcutaneous tissue in children ( P < 0.001). The parasitic dermatoses such as scabiosis, pediculosis, and myiasis were associated with an altitude less than 700 meters above sea level (m.a.s.l.) ( P = 0.003, OR = 3.1, CI: 1.5–6.7). On the other hand, radiation-related disorders of the skin and subcutaneous tissue were associated with more than 700 m.a.s.l. ( P < 0.01, OR = 2.9, CI: 1.7–4.9). Conclusions Infectious dermatological diseases were the most common diagnoses in the Peruvian Amazonia. In addition, radiation-related disorders of the skin should be addressed for people living/traveling in the rainforest area. These findings may assist in the training of general doctors in diagnosis and treatment of the most common dermatoses in tropical areas. Moreover, this study would be helpful for physicians from developed countries when giving medical advice/attention to travelers or immigrants of tropical areas. [ABSTRACT FROM AUTHOR]

Background Endemic pemphigus foliaceus or fogo selvagem is an autoimmune skin disease characterized by the presence of subcorneal superficial blisters and antibodies of the immunoglobulin G4 (IgG4) class specific for the desmosomal glycoprotein, desmoglein 1. In Peru, no studies have been published on the seroprevalence of antibodies against desmoglein 1 in healthy subjects from endemic foci. Subjects and methods This was a cross-sectional study. The sample included 82 healthy subjects, 41 from the Pueblo Libre community, a focus of endemic pemphigus foliaceus, and 41 from a nonendemic urban area in Pucallpa City. Enzyme-linked immunosorbent assay (ELISA) was used to determine the presence of antibodies against desmoglein 1. Samples were processed and tested at the Department of Dermatology and Cutaneous Surgery, School of Medicine, University of Miami, Miami, Florida. Results It was found that 31.7% of healthy individuals (13 subjects) from the endemic focus had anti-desmoglein 1 antibodies. A statistically significant association was found between the distance from the endemic focus and the presence of antibodies against desmoglein 1 in subjects living within the endemic focus [Mantel–Haenszel odds ratio (OR), 3.34; P = 0.03; 95% confidence interval (CI), 1.06–10.48]. Agriculture as an occupation showed a statistically significant association with the presence of antibodies against desmoglein 1 (Mantel–Haenszel OR, 7.84; P < 0.001; 95% CI, 2.47–24.87). Conclusions Antibodies against desmoglein 1 are present in healthy subjects exposed to an endemic focus of pemphigus foliaceus (fogo selvagem). Agriculture is associated with a high risk of development of antibodies against desmoglein 1 in the endemic focus of the Pueblo Libre community. [ABSTRACT FROM AUTHOR]