High-load Mg2Ni nanoparticle-carbon nanofiber composites for hydrogen storage.

Herein, a simple synthesis method for Mg2Ni composites with carbon nanofibers capable of hydrogen storage is presented. Specifically, n-butyl-sec-butyl-magnesium solution in hexane (C8H18Mg, 0.7 M) and bis-cyclopentadienyl nickel(II) (nickelocene or NiCp2) were used as precursors for the Mg2Ni nanoparticles. Subsequently, the nanoparticles were composited with carbon nanofibers (CNF) with high loading of Mg2Ni of 50 wt%, 75 wt%, 90 wt%, and 100 wt%. The physicochemical characterization of the materials indicated that the size of the as-prepared Mg2Ni nanoparticles was less than 5 nm and they were highly agglomerated due to a carbon-based binder. The best hydrogen storage values were determined to be 2.6-2.7 wt%. Among the tested materials, the composite with 75 wt% of Mg2Ni in CNF presented the best hydrogen uptake. The pressure-composition-temperature curves indicated changes in the hydriding equilibrium pressures of the Mg2Ni nanoparticles compared to the material with a similar composition produced using ball-milling and thermodynamic calculations. Thus, the results presented herein indicate the beneficial effect of nanosizing on hydriding reactions.

The impact of city-wide deployment of Wolbachia-carrying mosquitoes on arboviral disease incidence in Medellín and Bello, Colombia: study protocol for an interrupted time-series analysis and a test-negative design study.

Background: Dengue, chikungunya and Zika are viral infections transmitted by Aedes aegypti mosquitoes, and present major public health challenges in tropical regions. Traditional vector control methods have been ineffective at halting disease transmission. The World Mosquito Program has developed a novel approach to arbovirus control using Ae. aegypti stably transfected with the Wolbachia bacterium, which have significantly reduced ability to transmit dengue, Zika and chikungunya in laboratory experiments. Field releases in eight countries have demonstrated Wolbachia establishment in local Ae. aegypti populations. Methods: We describe a pragmatic approach to measuring the epidemiological impact of city-wide Wolbachia deployments in Bello and Medellín, Colombia. First, an interrupted time-series analysis will compare the incidence of dengue, chikungunya and Zika case notifications before and after Wolbachia releases, across the two municipalities. Second, a prospective case-control study using a test-negative design will be conducted in one quadrant of Medellín. Three of the six contiguous release zones in the case-control area were allocated to receive the first Wolbachia deployments in the city and three to be treated last, approximating a parallel two-arm trial for the >12-month period during which Wolbachia exposure remains discordant. Allocation, although non-random, aimed to maximise balance between arms in historical dengue incidence and demographics. Arboviral disease cases and arbovirus-negative controls will be enrolled concurrently from febrile patients presenting to primary care, with case/control status classified retrospectively following laboratory diagnostic testing. Intervention effect is estimated from an aggregate odds ratio comparing Wolbachia-exposure odds among test-positive cases versus test-negative controls. Discussion: The study findings will add to an accumulating body of evidence from global field sites on the efficacy of the Wolbachia method in reducing arboviral disease incidence, and can inform decisions on wider public health implementation of this intervention in the Americas and beyond. Trial registration: ClinicalTrials.gov: NCT03631719. Registered on 15 August 2018.

Geometric Bioinspired Networks for Recognition of 2-D and 3-D Low-Level Structures and Transformations.

This paper presents the design of radial basis function geometric bioinspired networks and their applications. Until now, the design of neural networks has been inspired by the biological models of neural networks but mostly using vector calculus and linear algebra. However, these designs have never shown the role of geometric computing. The question is how biological neural networks handle complex geometric representations involving Lie group operations like rotations. Even though the actual artificial neural networks are biologically inspired, they are just models which cannot reproduce a plausible biological process. Until now researchers have not shown how, using these models, one can incorporate them into the processing of geometric computing. Here, for the first time in the artificial neural networks domain, we address this issue by designing a kind of geometric RBF using the geometric algebra framework. As a result, using our artificial networks, we show how geometric computing can be carried out by the artificial neural networks. Such geometric neural networks have a great potential in robot vision. This is the most important aspect of this contribution to propose artificial geometric neural networks for challenging tasks in perception and action. In our experimental analysis, we show the applicability of our geometric designs, and present interesting experiments using 2-D data of real images and 3-D screw axis data. In general, our models should be used to process different types of inputs, such as visual cues, touch (texture, elasticity, temperature), taste, and sound. One important task of a perception-action system is to fuse a variety of cues coming from the environment and relate them via a sensor-motor manifold with motor modules to carry out diverse reasoned actions.