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Functionalization of few-layer graphene sheets (GSs) and multilayer carbon nanotubes (MWCNTs) for the generation of hybrid nanostructures is investigated. MWCNTs were oxidized by a mixture of nitric and sulfuric acids, while GSs were functionalized with varying concentrations of branched polyethyleneimine (PEI), investigating the role of the PEI concentration. PEI functionalization of the GSs is assessed by infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and zeta potential measurements. Successful functionalization of the GSs is conspicuously proved through new XPS bands associated with PEI, such as CONH, as well as a sign change in the zeta potential (from −12.7 mV to +11.8 mV). The concentration of 0.266 μl of PEI per ml of distilled water (0.46 mmol/L) proved to be the most effective for functionalizing the GSs, and such a concentration was further used for generating GS-PEI/oxidized MWCNT hybrids as fillers for nanocomposites. The application of such hybrid carbon nanostructures to increase the piezoresistive sensitivity of smart polymeric nanocomposites under bending loading is reported. An increase in the piezoresistive sensitivity (gauge factor) at the tensile side of the flexural coupon is observed for nanocomposites containing hybrid functionalized nanostructures., This material is based upon research partly supported by the U. S. Office of Naval Research Global under award number N62909-19-1- 2119. CS (CVU#240300) acknowledges CONACYT scholarship for his Doctoral studies. Support of the infrastructure acquired through CON- ACYT project No. 268595 (CICY) is greatly appreciated. XPS and Z Po- tential analyzes were carried out at the National Laboratory of Nano and Biomaterials (LANNBIO), CINVESTAV-IPN, directed by Dr. Patricia Quintana and financed by the projects FOMIX-Yucat ́an 2008-108160, CONACYT LAB-2009-01-123913, 292692, 294643, 188345 and 204822. We gratefully thank Montserrat Soria for her help with the Zetasizer, as well as Wilian Cauich (XPS) and Víctor Rej ́on (FE-SEM). Technical assistance of Miguel A. Rivero (CICY) with composite instru- mentation is appreciated, as well as assistance of Raquel Verdejo (ICTP- CSIC) and H ́ector Aguilar with GS synthesis. Authors also wish to acknowledge assistance of Santiago Duarte Aranda and Rossana Faride Vargas Coronado for SEM, as well as Alejandro May for Raman spectroscopy.

The aim of this research was to analyze the relationship between cold executive functions (cEFs), and self-regulated learning management (SRLM) in college students. There is a positive and a statistically significant relationship between cold executive functions (cEFs) and self-regulated learning management (SRLM). This research is a quantitative, cross-sectional, descriptive, and inferential study, with a correlational approach. The sample was non-probabilistic, by convenience sampling, composed of n = 379 college students belonging to pedagogy careers, 64.1% were males, and 35.9% females, aged between 17 and 34 years old (M = 19.82, SD = 2.41). The results show that there is a strong association between cold executive functions and self-regulated learning management since high performance in cEFs would imply a high performance on learning management. On the other hand, it is also observed a predictive value of planning cEF on SRLM, meaning that, an optimal level of planning would imply adequate management of learning processes. The original contribution of this study is to provide evidence to consider supporting plans for college students to improve their skills in the cEFs, due to the negative impact that failure in higher education represents for the student, their families, and university system. Finally, we think it is necessary to continue the research in depth of these variables, and their influence in higher education academic performance. Relación entre las funciones ejecutivas frías y el aprendizaje autorregulado en estudiantes universitarios El propósito de esta investigación fue analizar la relación entre las funciones ejecutivas frías (FEf) y la gestión del aprendizaje autorregulado (GAAR) en universitarios, siguiendo la hipótesis de que existe una relación positiva y significativa entre ambas variables. El estudio fue de tipo cuantitativo de corte transversal, con enfoque correlacional, descriptivo e inferencial. La muestra fue de tipo no probabilística incidental, compuesta por n = 379 estudiantes universitarios de carreras de pedagogía, el 64,1% eran hombres y el 35,9% mujeres, de edades entre 17 y 34 años (M = 19,82, SD = 2,41). Los resultados muestran que existe una asociación fuerte entre funciones ejecutivas frías y gestión del aprendizaje autorregulado, dado que un alto rendimiento en FEf implicaría un alto rendimiento en Gestión del Aprendizaje. También se observa un valor predictor de la FEf de planificación sobre la GAAR, es decir, que un buen nivel de planificación implicaría una adecuada gestión de los procesos de aprendizaje. El aporte original de este estudio es presentar evidencias para que en los planes de apoyo a estudiantes universitarios se consideren estrategias que mejoren sus habilidades en las FEf detectadas, dado el impacto negativo que el fracaso en la educación superior tiene para el estudiante, sus familias y el sistema universitario. Finalmente, creemos que es necesario seguir profundizando en estas variables y en su influencia sobre el desempeño académico en la educación superior.

International audience; Statistical algorithms are usually helping in making decisions in many aspects of our lives. But, how do we know if these algorithms are biased and commit unfair discrimination of a particular group of people, typically a minority? Fairness is generally studied in a probabilistic framework where it is assumed that there exists a protected variable, whose use as an input of the algorithm may imply discrimination. There are different definitions of Fairness in the literature. In this paper we focus on two of them which are called Disparate Impact (DI) and Balanced Error Rate (BER). Both are based on the outcome of the algorithm across the different groups determined by the protected variable. The relationship between these two notions is also studied. The goals of this paper are to detect when a binary classification rule lacks fairness and to try to fight against the potential discrimination attributable to it. This can be done by modifying either the classifiers or the data itself. Our work falls into the second category and modifies the input data using optimal transport theory.