A Finite Element‑Based Methodology for the Thermo‑mechanical Analysis of Early Age Behavior in Concrete Structures

This paper presents a general procedure based on fracture mechanics models in order to analyze the level of cracking and structural safety in reinforced concrete elements at early ages, depending on the stripping time. Our procedure involves the development of a thermo-mechanical numerical model based on the finite element method that accounts for the change in the mechanical properties of concrete with time. Moreover, fracture mechanisms are analyzed by means of a material damage model, which is characterized via specific experimental results obtained for standard specimens and notched beams under three-point bending testing. The loading conditions are both thermal and mechanical, and are obtained from the hydration process for a given concrete dosage. The presented methodology allows for the determination of the optimal stripping time, whereas it helps assessing the analysis of the cracking and the stress states of the elements under consideration. A practical application, namely the analysis of a retaining wall, is used to validate our methodology, showing its suitability in engineering practice.Ministerio de Economía y Competitividad BIA2016-75431-

The Digital Competence of Pre-Service Educators: The Influence of Personal Variables

[EN] Currently, 21st century students need competences that enable them to adapt to a new type of individual information and individual knowledge relationship, and, therefore, the education system should contemplate new ways for learners to develop in accordance with this so-called information and knowledge society. One of special importance is so-called digital competency. This article presents the results of a research study to determine the influence that the variables of gender, age, and academic degree have on the acquisition of digital competence by pre-service educators, with a sample of 370 students from different education degrees from the University of Salamanca (Spain). A quantitative methodology was used, employing a non-experimental method and the electronic survey technique to collect information on the dimensions of knowledge, as well as the management of and attitude towards information and communication technologies (ICTs). Data were analyzed inferentially from a comparison of means using nonparametric tests. This analysis was completed with the incorporation of Receiver Operating Characteristic (ROC) curves, which allowed us to graphically verify the differences between the subsamples and thus compare the different groups in relation to the proposed dimensions. The main conclusion was that the three variables studied can be considered as influential, though not determinant, in the acquisition of digital competency

Confidence of a k-Nearest Neighbors Python Algorithm for the 3D Visualization of Sedimentary Porous Media

In a previous paper, the authors implemented a machine learning k-nearest neighbors (KNN) algorithm and Python libraries to create two 3D interactive models of the stratigraphic architecture of the Quaternary onshore Llobregat River Delta (NE Spain) for groundwater exploration purposes. The main limitation of this previous paper was its lack of routines for evaluating the confidence of the 3D models. Building from the previous paper, this paper refines the programming code and introduces an additional algorithm to evaluate the confidence of the KNN predictions. A variant of the Similarity Ratio method was used to quantify the KNN prediction confidence. This variant used weights that were inversely proportional to the distance between each grain-size class and the inferred point to work out a value that played the role of similarity. While the KNN algorithm and Python libraries demonstrated their efficacy for obtaining 3D models of the stratigraphic arrangement of sedimentary porous media, the KNN prediction confidence verified the certainty of the 3D models. In the Llobregat River Delta, the KNN prediction confidence at each prospecting depth was a function of the available data density at that depth. As expected, the KNN prediction confidence decreased according to the decreasing data density at lower depths. The obtained average-weighted confidence was in the 0.44−0.53 range for gravel bodies at prospecting depths in the 12.7−72.4 m b.s.l. range and was in the 0.42−0.55 range for coarse sand bodies at prospecting depths in the 4.6−83.9 m b.s.l. range. In a couple of cases, spurious average-weighted confidences of 0.29 in one gravel body and 0.30 in one coarse sand body were obtained. These figures were interpreted as the result of the quite different weights of neighbors from different grain-size classes at short distances. The KNN algorithm confidence has proven its suitability for identifying these anomalous results in the supposedly well-depurated grain-size database used in this study. The introduced KNN algorithm confidence quantifies the reliability of the 3D interactive models, which is a necessary stage to make decisions in economic and environmental geology. In the Llobregat River Delta, this quantification clearly improves groundwater exploration predictability.Research Project PID2020-114381GB-100 of the Spanish Ministry of Science and Innovation, Research Project 101086497 of the Horizon Europe Framework Programme HORIZON-CL6-2022-GOVERNANCE-01-07, Research Groups and Projects of the Generalitat Valenciana from the University of Alicante (CTMA-IGA), and Research Groups FQM-343 and RNM-188 of the Junta de Andalucía

A Python Application for Visualizing the 3D Stratigraphic Architecture of the Onshore Llobregat River Delta in NE Spain

This paper introduces a Python application for visualizing the 3D stratigraphic architecture of porous sedimentary media. The application uses the parameter granulometry deduced from borehole lithological records to create interactive 3D HTML models of essential stratigraphic elements. On the basis of the high density of boreholes and the subsequent geological knowledge gained during the last six decades, the Quaternary onshore Llobregat River Delta (LRD) in northeastern Spain was selected to show the application. The public granulometry dataset produced by the Water Authority of Catalonia from 433 boreholes in this strategic coastal groundwater body was clustered into the clay–silt, coarse sand, and gravel classes. Three interactive 3D HTML models were created. The first shows the location of the boreholes granulometry. The second includes the main gravel and coarse sand sedimentary bodies (lithosomes) associated with the identified three stratigraphic intervals, called lower (>50 m b.s.l.) in the distal LRD sector, middle (20–50 m b.s.l.) in the central LRD, and upper (<20 m b.s.l.) spread over the entire LRD. The third deals with the basement (Pliocene and older rocks) top surface, which shows an overall steeped shape deepening toward the marine platform and local horsts, probably due to faulting. The modeled stratigraphic elements match well with the sedimentary structures reported in recent scientific publications. This proves the good performance of this incipient Python application for visualizing the 3D stratigraphic architecture, which is a crucial stage for groundwater management and governance.Research Project PID2020-114381GB-100 of the Spanish Ministry of Science and Innovation, Research Groups and Projects of the Generalitat Valenciana from the University of Alicante (CTMA-IGA), and Research Groups FQM-343 and RNM-188 of the Junta de Andalucía

Digital competence of early childhood education teachers: attitude, knowledge and use of ICT

[EN] The main objective of the research described here was to learn how young learners self-evaluate their digital competence. A nonexperimental and descriptive quantitative methodology was employed, an electronic survey being used to collect the data. Among the main results, we can highlight that these learners selfevaluate their attitude towards Information and Communication Technologies (ICT) as favourable, their handling of them as moderate and their knowledge of them as scarce. It became clear that they do not have a level of digital competence suitable for being called ‘digital natives’, nor sufficient ability to use ICT in their academic life or in their professional future

The Digital Competence of Pre-Service Educators: The Influence of Personal Variables

[EN]Currently, 21st century students need competences that enable them to adapt to a new type of individual information and individual knowledge relationship, and, therefore, the education system should contemplate new ways for learners to develop in accordance with this so-called information and knowledge society. One of special importance is so-called digital competency. This article presents the results of a research study to determine the influence that the variables of gender, age, and academic degree have on the acquisition of digital competence by pre-service educators, with a sample of 370 students from different education degrees from the University of Salamanca (Spain). A quantitative methodology was used, employing a non-experimental method and the electronic survey technique to collect information on the dimensions of knowledge, as well as the management of and attitude towards information and communication technologies (ICTs). Data were analyzed inferentially from a comparison of means using nonparametric tests. This analysis was completed with the incorporation of Receiver Operating Characteristic (ROC) curves, which allowed us to graphically verify the differences between the subsamples and thus compare the different groups in relation to the proposed dimensions. The main conclusion was that the three variables studied can be considered as influential, though not determinant, in the acquisition of digital competency

A K-Nearest Neighbors Algorithm in Python for Visualizing the 3D Stratigraphic Architecture of the Llobregat River Delta in NE Spain

The k-nearest neighbors (KNN) algorithm is a non-parametric supervised machine learning classifier; which uses proximity and similarity to make classifications or predictions about the grouping of an individual data point. This ability makes the KNN algorithm ideal for classifying datasets of geological variables and parameters prior to 3D visualization. This paper introduces a machine learning KNN algorithm and Python libraries for visualizing the 3D stratigraphic architecture of sedimentary porous media in the Quaternary onshore Llobregat River Delta (LRD) in northeastern Spain. A first HTML model showed a consecutive 5 m-equispaced set of horizontal sections of the granulometry classes created with the KNN algorithm from 0 to 120 m below sea level in the onshore LRD. A second HTML model showed the 3D mapping of the main Quaternary gravel and coarse sand sedimentary bodies (lithosomes) and the basement (Pliocene and older rocks) top surface created with Python libraries. These results reproduce well the complex sedimentary structure of the LRD reported in recent scientific publications and proves the suitability of the KNN algorithm and Python libraries for visualizing the 3D stratigraphic structure of sedimentary porous media, which is a crucial stage in making decisions in different environmental and economic geology disciplines.Research Project PID2020-114381GB-100 of the Spanish Ministry of Science and Innovation, Research Groups and Projects of the Generalitat Valenciana from the University of Alicante (CTMA-IGA), and Research Groups FQM-343 and RNM-188 of the Junta de Andalucía

Building, coding and programming 3D models via a visual programming environment

[EN] This paper presents the findings of a study conducted in the state-funded Infant, Primary and Secondary School Santísima Trinidad in Salamanca. The main objectives of the research were, to evaluate the use of the visual programming environment, Lego Education WeDo, in natural science and to know the benefits of the use of this tool to teach abstract concepts, solve problems and motivate students. In order to achieve these objectives, we used the case study method since we focused on individuals who represented the phenomenon of our interest, and explored and investigated in depth the phenomenon in its natural context bounded by time and space. In the research were involved a teacher and fifty-two students of 4th grade of primary education. The study found that the project developed was effective to help students to achieve the learning objectives of the unit, and also to begin building, coding and programming 3D models. The research showed the teacher’ fundamental role as a guide and students’ active role as builders, programmers, or presenters. There were evidences of the possibilities offered to acquire the skills of critical thinking, creative thinking, problem solving, reflection, collaboration, communication, and time management. Due to the positive results obtained in this study, it is recommended to incorporate computational thinking in primary education and in core content areas since it is fundamental in the current society

Ultrafiltration of municipal wastewater: study on fouling models and fouling mechanisms

Ultrafiltration (UF) with hollow fiber membranes is a proven membrane technique that can achieve high water quality standards as a tertiary treatment in municipal wastewater treatment plants. However, UF has a major drawback, membrane fouling, which causes losses of productivity and increases operation costs. Thus, the aim of this work is to model membrane fouling in the UF of a secondary treatment effluent. The tests were carried out with a model wastewater solution that consisted of bovine serum albumin and dextran. Three different transmembrane pressures and three different crossflow velocities were tested. Several fouling models available in the literature, and new models proposed, were fitted to permeate flux decline experimental data. The models studied by other authors and considered in this study were: Hermia s models (complete, intermediate, standard pore blocking and gel layer) and Belfort s model. The new models proposed in this work were: modified Belfort s model, quadratic exponential model, logarithmic inversed model, double exponential model and tangent inversed model. The fitting accuracy of the models was determined in terms of the R-squared and standard deviation. The results showed that the model that had the higher fitting accuracy was the logarithmic inversed model. Among the Hermia s models, the model that had the higher fitting accuracy was the intermediate pore blocking model. Therefore, the predominant fouling mechanism was determined and it was the intermediate pore blocking modelThe authors wish to gratefully acknowledge the financial support of the Generalitat Valenciana through the project "Ayudas para la realizacion de proyectos I+D para grupos de investigacion emergentes GV/2013".Soler Cabezas, JL.; Tora Grau, M.; Vincent Vela, MC.; Mendoza Roca, JA.; Martínez Francisco, FJ. (2014). Ultrafiltration of municipal wastewater: study on fouling models and fouling mechanisms. Desalination and Water Treatment. 1-11. doi:10.1080/19443994.2014.969320S111Gadani, V., Irwin, R., & Mandra, V. (1996). Ultrafiltration as a tertiary treatment: Joint research program on membranes. Desalination, 106(1-3), 47-53. doi:10.1016/s0011-9164(96)00091-4Illueca-Muñoz, J., Mendoza-Roca, J. A., Iborra-Clar, A., Bes-Piá, A., Fajardo-Montañana, V., Martínez-Francisco, F. J., & Bernácer-Bonora, I. (2008). Study of different alternatives of tertiary treatments for wastewater reclamation to optimize the water quality for irrigation reuse. Desalination, 222(1-3), 222-229. doi:10.1016/j.desal.2007.01.157Muthukumaran, S., Jegatheesan, J. V., & Baskaran, K. (2013). Comparison of fouling mechanisms in low-pressure membrane (MF/UF) filtration of secondary effluent. Desalination and Water Treatment, 52(4-6), 650-662. doi:10.1080/19443994.2013.826324Delgado, S., Dı́az, F., Vera, L., Dı́az, R., & Elmaleh, S. (2004). Modelling hollow-fibre ultrafiltration of biologically treated wastewater with and without gas sparging. Journal of Membrane Science, 228(1), 55-63. doi:10.1016/j.memsci.2003.09.011Qin, J.-J., Oo, M. H., Lee, H., & Kolkman, R. (2004). Dead-end ultrafiltration for pretreatment of RO in reclamation of municipal wastewater effluent. Journal of Membrane Science, 243(1-2), 107-113. doi:10.1016/j.memsci.2004.06.010Konieczny, K. (1998). Disinfection of surface and ground waters with polymeric ultrafiltration membranes. Desalination, 119(1-3), 251-258. doi:10.1016/s0011-9164(98)00166-0Madaeni, S. S., Fane, A. G., & Grohmann, G. S. (1995). Virus removal from water and wastewater using membranes. Journal of Membrane Science, 102, 65-75. doi:10.1016/0376-7388(94)00252-tArnal Arnal, J. M., Sancho Fernández, M., Martín Verdú, G., & Lora García, J. (2001). Design of a membrane facility for water potabilization and its application to Third World countries. Desalination, 137(1-3), 63-69. doi:10.1016/s0011-9164(01)00205-3Arévalo, J., Garralón, G., Plaza, F., Moreno, B., Pérez, J., & Gómez, M. Á. (2009). Wastewater reuse after treatment by tertiary ultrafiltration and a membrane bioreactor (MBR): a comparative study. Desalination, 243(1-3), 32-41. doi:10.1016/j.desal.2008.04.013Katsoufidou, K., Yiantsios, S. G., & Karabelas, A. J. (2008). An experimental study of UF membrane fouling by humic acid and sodium alginate solutions: the effect of backwashing on flux recovery. Desalination, 220(1-3), 214-227. doi:10.1016/j.desal.2007.02.038Muthukumaran, S., Nguyen, D. A., & Baskaran, K. (2011). Performance evaluation of different ultrafiltration membranes for the reclamation and reuse of secondary effluent. Desalination, 279(1-3), 383-389. doi:10.1016/j.desal.2011.06.040Henderson, R. K., Subhi, N., Antony, A., Khan, S. J., Murphy, K. R., Leslie, G. L., … Le-Clech, P. (2011). Evaluation of effluent organic matter fouling in ultrafiltration treatment using advanced organic characterisation techniques. Journal of Membrane Science, 382(1-2), 50-59. doi:10.1016/j.memsci.2011.07.041Xiao, D., Li, W., Chou, S., Wang, R., & Tang, C. Y. (2012). A modeling investigation on optimizing the design of forward osmosis hollow fiber modules. Journal of Membrane Science, 392-393, 76-87. doi:10.1016/j.memsci.2011.12.006Kaya, Y., Barlas, H., & Arayici, S. (2011). Evaluation of fouling mechanisms in the nanofiltration of solutions with high anionic and nonionic surfactant contents using a resistance-in-series model. Journal of Membrane Science, 367(1-2), 45-54. doi:10.1016/j.memsci.2010.10.037Amin Saad, M. (2004). Early discovery of RO membrane fouling and real-time monitoring of plant performance for optimizing cost of water. Desalination, 165, 183-191. doi:10.1016/j.desal.2004.06.021Yu, C.-H., Fang, L.-C., Lateef, S. K., Wu, C.-H., & Lin, C.-F. (2010). Enzymatic treatment for controlling irreversible membrane fouling in cross-flow humic acid-fed ultrafiltration. Journal of Hazardous Materials, 177(1-3), 1153-1158. doi:10.1016/j.jhazmat.2010.01.022Gao, W., Liang, H., Ma, J., Han, M., Chen, Z., Han, Z., & Li, G. (2011). Membrane fouling control in ultrafiltration technology for drinking water production: A review. Desalination, 272(1-3), 1-8. doi:10.1016/j.desal.2011.01.051Jayalakshmi, A., Rajesh, S., & Mohan, D. (2012). Fouling propensity and separation efficiency of epoxidated polyethersulfone incorporated cellulose acetate ultrafiltration membrane in the retention of proteins. Applied Surface Science, 258(24), 9770-9781. doi:10.1016/j.apsusc.2012.06.028Qu, F., Liang, H., Wang, Z., Wang, H., Yu, H., & Li, G. (2012). Ultrafiltration membrane fouling by extracellular organic matters (EOM) of Microcystis aeruginosa in stationary phase: Influences of interfacial characteristics of foulants and fouling mechanisms. Water Research, 46(5), 1490-1500. doi:10.1016/j.watres.2011.11.051Wang, C., Li, Q., Tang, H., Yan, D., Zhou, W., Xing, J., & Wan, Y. (2012). Membrane fouling mechanism in ultrafiltration of succinic acid fermentation broth. Bioresource Technology, 116, 366-371. doi:10.1016/j.biortech.2012.03.099Zator, M., Ferrando, M., López, F., & Güell, C. (2007). Membrane fouling characterization by confocal microscopy during filtration of BSA/dextran mixtures. Journal of Membrane Science, 301(1-2), 57-66. doi:10.1016/j.memsci.2007.05.038Sheng, G.-P., Yu, H.-Q., & Li, X.-Y. (2010). Extracellular polymeric substances (EPS) of microbial aggregates in biological wastewater treatment systems: A review. Biotechnology Advances, 28(6), 882-894. doi:10.1016/j.biotechadv.2010.08.001Nguyen, S. T., & Roddick, F. A. (2011). Chemical cleaning of ultrafiltration membrane fouled by an activated sludge effluent. Desalination and Water Treatment, 34(1-3), 94-99. doi:10.5004/dwt.2011.2790Xiao, K., Wang, X., Huang, X., Waite, T. D., & Wen, X. (2009). Analysis of polysaccharide, protein and humic acid retention by microfiltration membranes using Thomas’ dynamic adsorption model. Journal of Membrane Science, 342(1-2), 22-34. doi:10.1016/j.memsci.2009.06.016Suh, C., Lee, S., & Cho, J. (2013). Investigation of the effects of membrane fouling control strategies with the integrated membrane bioreactor model. Journal of Membrane Science, 429, 268-281. doi:10.1016/j.memsci.2012.11.042Duclos-Orsello, C., Li, W., & Ho, C.-C. (2006). A three mechanism model to describe fouling of microfiltration membranes. Journal of Membrane Science, 280(1-2), 856-866. doi:10.1016/j.memsci.2006.03.005Davis, R. H. (1992). Modeling of Fouling of Crossflow Microfiltration Membranes. Separation and Purification Methods, 21(2), 75-126. doi:10.1080/03602549208021420Bhattacharjee, S., & Bhattacharya, P. K. (1992). Flux decline behaviour with low molecular weight solutes during ultrafiltration in an unstirred batch cell. Journal of Membrane Science, 72(2), 149-161. doi:10.1016/0376-7388(92)80195-pMallubhotla, H., & Belfort, G. (1996). Semiempirical Modeling of Cross-Flow Microfiltration with Periodic Reverse Filtration. Industrial & Engineering Chemistry Research, 35(9), 2920-2928. doi:10.1021/ie950719tSalahi, A., Abbasi, M., & Mohammadi, T. (2010). Permeate flux decline during UF of oily wastewater: Experimental and modeling. Desalination, 251(1-3), 153-160. doi:10.1016/j.desal.2009.08.006Field, R. W., Wu, D., Howell, J. A., & Gupta, B. B. (1995). Critical flux concept for microfiltration fouling. Journal of Membrane Science, 100(3), 259-272. doi:10.1016/0376-7388(94)00265-zVincent Vela, M. C., Álvarez Blanco, S., Lora García, J., & Bergantiños Rodríguez, E. (2009). Analysis of membrane pore blocking models adapted to crossflow ultrafiltration in the ultrafiltration of PEG. Chemical Engineering Journal, 149(1-3), 232-241. doi:10.1016/j.cej.2008.10.027Hasan, A., Peluso, C. R., Hull, T. S., Fieschko, J., & Chatterjee, S. G. (2013). A surface-renewal model of cross-flow microfiltration. Brazilian Journal of Chemical Engineering, 30(1), 167-186. doi:10.1590/s0104-66322013000100019ANG, W., & ELIMELECH, M. (2007). Protein (BSA) fouling of reverse osmosis membranes: Implications for wastewater reclamation. Journal of Membrane Science, 296(1-2), 83-92. doi:10.1016/j.memsci.2007.03.018Muthukumaran, S., & Baskaran, K. (2013). Comparison of the performance of ceramic microfiltration and ultrafiltration membranes in the reclamation and reuse of secondary wastewater. Desalination and Water Treatment, 52(4-6), 670-677. doi:10.1080/19443994.2013.826333Tasselli, F., Cassano, A., & Drioli, E. (2007). Ultrafiltration of kiwifruit juice using modified poly(ether ether ketone) hollow fibre membranes. Separation and Purification Technology, 57(1), 94-102. doi:10.1016/j.seppur.2007.03.007Chung, T.-S., Qin, J.-J., & Gu, J. (2000). Effect of shear rate within the spinneret on morphology, separation performance and mechanical properties of ultrafiltration polyethersulfone hollow fiber membranes. Chemical Engineering Science, 55(6), 1077-1091. doi:10.1016/s0009-2509(99)00371-1Swaminathan, T., Chaudhuri, M., & Sirkar, K. K. (1979). Anomalous flux behavior in initial time stirred protein ultrafiltration through partially permeable membranes. Journal of Applied Polymer Science, 24(6), 1581-1585. doi:10.1002/app.1979.070240620Ahmad, A. L., & Hairul, N. A. H. (2009). Protein–membrane interactions in forced-flow electrophoresis of protein solutions: Effect of initial pH and initial ionic strength. Separation and Purification Technology, 66(2), 273-278. doi:10.1016/j.seppur.2008.12.027Gu, Z. (2007). Across-sample Incomparability of R2s and Additional Evidence on Value Relevance Changes Over Time. Journal of Business Finance & Accounting, 34(7-8), 1073-1098. doi:10.1111/j.1468-5957.2007.02044.xVincent, T., Parodi, A., & Guibal, E. (2008). Pt recovery using Cyphos IL-101 immobilized in biopolymer capsules. Separation and Purification Technology, 62(2), 470-479. doi:10.1016/j.seppur.2008.02.025Daufin, G., Merin, U., Labbé, J. P., Quémerais, A., & Kerhervé, F. L. (1991). Cleaning of inorganic membranes after whey and milk ultrafiltration. Biotechnology and Bioengineering, 38(1), 82-89. doi:10.1002/bit.260380111Daufin, G., Merin, U., Kerherve, F.-L., Labbe, J.-P., Quemerais, A., & Bousser, C. (1992). Efficiency of cleaning agents for an inorganic membrane after milk ultrafiltration. Journal of Dairy Research, 59(1), 29-38. doi:10.1017/s0022029900030211Morão, A., Nunes, J. C., Sousa, F., Amorim, M. T. P. de, Escobar, I. C., & Queiroz, J. A. (2009). Development of a model for membrane filtration of long and flexible macromolecules: Application to predict dextran and linear DNA rejections in ultrafiltration. Journal of Membrane Science, 336(1-2), 61-70. doi:10.1016/j.memsci.2009.03.007Ouammou, M., Tijani, N., Calvo, J. I., Velasco, C., Martín, A., Martínez, F., … Hernández, A. (2007). Flux decay in protein microfiltration through charged membranes as a function of pH. Colloids and Surfaces A: Physicochemical and Engineering Aspects, 298(3), 267-273. doi:10.1016/j.colsurfa.2006.11.006Mohammadi, T., Kazemimoghadam, M., & Saadabadi, M. (2003). Modeling of membrane fouling and flux decline in reverse osmosis during separation of oil in water emulsions. Desalination, 157(1-3), 369-375. doi:10.1016/s0011-9164(03)00419-3Ng, C. Y., Mohammad, A. W., Ng, L. Y., & Jahim, J. M. (2014). Membrane fouling mechanisms during ultrafiltration of skimmed coconut milk. Journal of Food Engineering, 142, 190-200. doi:10.1016/j.jfoodeng.2014.06.005Mah, S.-K., Chuah, C.-K., Cathie Lee, W. P., & Chai, S.-P. (2012). Ultrafiltration of palm oil–oleic acid–glycerin solutions: Fouling mechanism identification, fouling mechanism analysis and membrane characterizations. Separation and Purification Technology, 98, 419-431. doi:10.1016/j.seppur.2012.07.020Said, M., Ahmad, A., Mohammad, A. W., Nor, M. T. M., & Sheikh Abdullah, S. R. (2015). Blocking mechanism of PES membrane during ultrafiltration of POME. Journal of Industrial and Engineering Chemistry, 21, 182-188. doi:10.1016/j.jiec.2014.02.023Amin, I. N. H. M., Mohammad, A. W., Markom, M., Peng, L. C., & Hilal, N. (2010). Analysis of deposition mechanism during ultrafiltration of glycerin-rich solutions. Desalination, 261(3), 313-320. doi:10.1016/j.desal.2010.04.01

Catálogo de las plantas vasculares de Río Muni (Guinea Ecuatorial): análisis florístico, diversidad, endemicidad y estado de amenaza

Se presenta un catálogo actualizado de las plantas vasculares de la región continental de Guinea Ecuatorial (Río Muni). El catálogo es fruto de la compilación de especímenes de herbario (6,850), registros de especies de la literatura botánica (7,985) y bases de datos en línea (10,109 registros de GBIF y 8,897 de RAINBIO). Se elaboró una base de datos de 23,517 registros georreferenciados realizando la actualización nomenclatural y estandarización de los nombres de localidades de todas estas fuentes. El catálogo comprende 2707 taxones (2598 especies, 81 subespecies y 28 variedades) incluidos en 1,020 géneros y 178 familias. El 90.6% de los taxones se consideran nativos, el 1.17% introducidos y el 5.96% naturalizados. Las 10 familias más diversas son Rubiaceae (294 especies), Fabaceae (290), Orchidaceae (168), Poaceae (105), Euphorbiaceae (87), Apocynaceae (85), Cyperaceae (79), Annonaceae (68), Acanthaceae (65) y Melastomataceae (61), que comprenden el 49.22% de las especies de Río Muni. Solo 11 especies pueden considerarse endémicas de Río Muni; este bajo número refleja la ausencia de barreras naturales en el territorio. El número de taxones amenazados (VU, EN y CR) es de 134 (5.02% del total evaluado) de los cuales 43 se encuentran en riesgo de extinción, al estar dentro de las categorías de En Peligro o En Peligro Crítico. Cinco especies restringidas al Golfo de Guinea se consideran amenazadas: tres En Peligro (Grossera angustifolia, Polyscias aequatoguineensis y Rhipidoglossum montealenense), y dos En Peligro Crítico (Asplenium carvalhoanum y Macropodiella uoroensis), por lo que deberían considerarse prioritarias en los planes de gestión y conservación