Deactivation of electrically supersaturated Te-doped InGaAs grown by MOCVD

Achieving and sustaining the highest doping level possible in InGaAs is critical for the reduction of contact resistance in future microelectronic applications. Tellurium (Te) is a very promising n-type dopant with high reported n-type doping densities. However, the stability of this dopant during post-growth thermal processing is unknown. Supersaturated Te-doped InGaAs layers were grown by MOCVD at 500 °C. The electrically active concentration of Te doping was 4.4 × 1019 cm−3 as grown. The thermal stability of the Te was investigated by studying the effect of post-growth annealing between 550 and 700 °C on the electrical activation. At all temperatures, the electrical activation decreased from its starting electron concentration of 4.4 × 1019 cm−3 down to 6–7 × 1018 cm−3. The rate of deactivation was measured at each temperature, and the activation energy for the deactivation process was determined to be 2.6 eV. The deactivation could be caused by either Te–Te clustering or a Te-point defect reaction. HAADF-STEM images showed no visible clustering or precipitation after deactivation. Based on previous ab initio calculations that suggest the VIII population increases as the Fermi level moves toward the conduction band, it is proposed that formation of isolated point defect complexes, possibly a Te–VIII complex, is associated with the deactivation process

A perspective review on integrating VR/AR with haptics into STEM education for multi-sensory learning

As a result of several governments closing educational facilities in reaction to the COVID-19 pandemic in 2020, almost 80% of the world’s students were not in school for several weeks. Schools and universities are thus increasing their efforts to leverage educational resources and provide possibilities for remote learning. A variety of educational programs, platforms, and technologies are now accessible to support student learning; while these tools are important for society, they are primarily concerned with the dissemination of theoretical material. There is a lack of support for hands-on laboratory work and practical experience. This is particularly important for all disciplines related to science, technology, engineering, and mathematics (STEM), where labs and pedagogical assets must be continuously enhanced in order to provide effective study programs. In this study, we describe a unique perspective to achieving multi-sensory learning through the integration of virtual and augmented reality (VR/AR) with haptic wearables in STEM education. We address the implications of a novel viewpoint on established pedagogical notions. We want to encourage worldwide efforts to make fully immersive, open, and remote laboratory learning a reality.European Union through the Erasmus+ Program under Grant 2020-1-NO01-KA203-076540, project title Integrating virtual and AUGMENTED reality with WEARable technology into engineering EDUcation (AugmentedWearEdu), https://augmentedwearedu.uia.no/ [34] (accessed on 27 March 2022). This work was also supported by the Top Research Centre Mechatronics (TRCM), University of Agder (UiA), Norwa

The Deep-Water Red Shrimp Fishery in the Spanish Mediterranean Sea

Publicado

Hydrolytic and enzymatic degradation of a poly(å-caprolactone) network

“NOTICE: this is the author’s version of a work that was accepted for publication in Polymer Degradation and Stability. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Polymer Degradation and Stability, [Volume 97, Issue 8, August 2012, Pages 1241–1248] DOI 10.1016/j.polymdegradstab.2012.05.038Long-term hydrolytic and enzymatic degradation profiles of poly(å-caprolactone) (PCL) networks were obtained. The hydrolytic degradation studies were performed in water and phosphate buffer solution (PBS) for 65 weeks. In this case, the degradation rate of PCL networks was faster than previous results in the literature on linear PCL, reaching a weight loss of around 20% in 60 weeks after immersing the samples either in water or in PBS conditions. The enzymatic degradation rate in Pseudomonas Lipase for 14 weeks was also studied, with the conclusion that the degradation profile of PCL networks is lower than for linear PCL, also reaching a 20% weight loss. The weight lost, degree of swelling, and calorimetric and mechanical properties were obtained as a function of degradation time. Furthermore, the morphological changes in the samples were studied carefully through electron microscopy and crystal size through X-ray diffraction. The changes in some properties over the degradation period such as crystallinity, crystal size and Young¿s modulus were smaller in the case of enzymatic studies, highlighting differences in the degradation mechanism in the two studies, hydrolytic and enzymatic.The authors would like to acknowledge the support of the Spanish Ministry of Science and Education through the DPI2010-20399-004-03 project. JM Meseguer-Duenas and A Vidaurre also would like to acknowledge the support of the CIBER-BBN, an initiative funded by the VI National R&D&i Plan 2008-2011, Iniciativa Ingenio 2010, Consolider Program, CIBER Actions and financed by the Instituto de Salud Carlos III with assistance from the European Regional Development Fund. The translation of this paper was funded by the Universidad Politecnica de Valencia, SpainCastilla Cortázar, MIC.; Más Estellés, J.; Meseguer Dueñas, JM.; Escobar Ivirico, JL.; Marí Soucase, B.; Vidaurre, A. (2012). Hydrolytic and enzymatic degradation of a poly(å-caprolactone) network. Polymer Degradation and Stability. 97(8):1241-1248. https://doi.org/10.1016/j.polymdegradstab.2012.05.038S1241124897

The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures

Motivation: Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. Methods: We compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. Results: We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Simple filter methods generally outperform more complex embedded or wrapper methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results. Availability: Code and data are publicly available at http://cbio.ensmp.fr/~ahaury/

Modeling recursive RNA interference.

An important application of the RNA interference (RNAi) pathway is its use as a small RNA-based regulatory system commonly exploited to suppress expression of target genes to test their function in vivo. In several published experiments, RNAi has been used to inactivate components of the RNAi pathway itself, a procedure termed recursive RNAi in this report. The theoretical basis of recursive RNAi is unclear since the procedure could potentially be self-defeating, and in practice the effectiveness of recursive RNAi in published experiments is highly variable. A mathematical model for recursive RNAi was developed and used to investigate the range of conditions under which the procedure should be effective. The model predicts that the effectiveness of recursive RNAi is strongly dependent on the efficacy of RNAi at knocking down target gene expression. This efficacy is known to vary highly between different cell types, and comparison of the model predictions to published experimental data suggests that variation in RNAi efficacy may be the main cause of discrepancies between published recursive RNAi experiments in different organisms. The model suggests potential ways to optimize the effectiveness of recursive RNAi both for screening of RNAi components as well as for improved temporal control of gene expression in switch off-switch on experiments

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research