Plant Copper Amine Oxidases: Key Players in Hormone Signaling Leading to Stress-Induced Phenotypic Plasticity

Polyamines are ubiquitous, low-molecular-weight aliphatic compounds, present in living organisms and essential for cell growth and differentiation. Copper amine oxidases (CuAOs) oxidize polyamines to aminoaldehydes releasing ammonium and hydrogen peroxide, which participates in the complex network of reactive oxygen species acting as signaling molecules involved in responses to biotic and abiotic stresses. CuAOs have been identified and characterized in different plant species, but the most extensive study on a CuAO gene family has been carried out in Arabidopsis thaliana. Growing attention has been devoted in the last years to the investigation of the CuAO expression pattern during development and in response to an array of stress and stress-related hormones, events in which recent studies have highlighted CuAOs to play a key role by modulation of a multilevel phenotypic plasticity expression. In this review, the attention will be focused on the involvement of different AtCuAOs in the IAA/JA/ABA signal transduction pathways which mediate stress-induced phenotypic plasticity events.     

Specific consumption of liquid biofuels in gasoline fuelled engines

Oxygenated fuels increase fuel consumption due to their low enthalpy of combustion; however, their high antiknock index renders them suitable for use in engines with a high compression rate, increasing their thermal yield. This study evaluated the performance of biorenewable oxygenated fuels (ethanol and isoamyl alcohol) and partially renewable fuels (ETBE: ethyl tert-butyl ether, TAEE: tert-amyl ethyl ether and di-TAE: di-tert-amyl ether) with high degree of purity and in mixtures with automotive gasoline, based on tests with Otto cycle engines. Among the oxygenated fuels evaluated here, di-TAE was found to present the best characteristics of performance, both individually and in mixtures with gasoline.     

A qualitative human-centric evaluation of flexibility in middleware implementations

Today middleware is much more powerful, more reliable and faster than it used to be. Nevertheless, for the application developer, the complexity of using middleware platforms has increased accordingly. The volume and variety of application contexts that current middleware technologies have to support require that developers be able to anticipate the widest possible range of execution environments, desired and undesired effects of different programming strategies, handling procedures for runtime errors, and so on. This paper shows how a generic framework designed to evaluate the usability of notations (the Cognitive Dimensions of Notations Framework, or CDN) has been instantiated and used to analyze the cognitive challenges involved in adapting middleware platforms. This human-centric perspective allowed us to achieve novel results compared to existing middleware evaluation research, typically centered around system performance metrics. The focus of our study is on the process of adapting middleware implementations, rather than in the end product of this activity. Our main contributions are twofold. First, we describe a qualitative CDN-based method to analyze the cognitive effort made by programmers while adapting middleware implementations. And second, we show how two platforms designed for flexibility have been compared, suggesting that certain programming language design features might be particularly helpful for developers.     

Direct-Ink-Writing of Electroactive Polymers for Sensing and Energy Storage Applications

Considering the high levels of materials used in the fields of electronics and energy storage systems, it is increasingly necessary to take into consideration environmental impact. Thus, it is important to develop devices based on environmentally friendlier materials and/or processes, such as additive manufacturing techniques. In this work, poly(vinylidene fluoride) (PVDF) and poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP) are prepared by direct-ink-writing (DIW) by varying solvent evaporation temperature and fill density percentage. Different morphologies for both polymers are obtained, including dense films and porous membranes, as well as different electroactive β-phase content, thermal and mechanical properties. The dielectric constant and piezoelectric d₃₃ coefficient for dense films reaches up to 16 at 1 kHz and 4 pC N⁻¹, respectively for PVDF-HFP with a fill density of 80 and a solvent evaporation temperature of 50 °C. Porous structures are developed for battery separator membranes in lithium-ion batteries, with a highest ionic conductivity value of 3.8 mS cm⁻¹ for the PVDF-HFP sample prepared with a fill density of 100 and a solvent evaporation temperature of 25 °C, the sample showing an excellent cycling performance. It is demonstrated that electroactive films and membranes can be prepared by direct-ink writing suitable for sensors/actuators and energy storage systems.     

Non-Pauli observables for CWS codes

It is known that nonadditive quantum codes can have higher code dimensions than stabilizer codes for the same length and minimum distance. The class of codeword stabilized codes (CWS) provides tools to obtain new nonadditive quantum codes by reducing the problem to finding nonlinear classical codes. In this work, we establish some results on the kind of non-Pauli operators that can be used as observables in the decoding scheme of CWS codes and propose a procedure to obtain those observables.     

Refractive indices of layers and optical simulations of Cu(In,Ga)Se2 solar cells

Cu(In,Ga)Se₂-based solar cells have reached efficiencies close to 23%. Further knowledge-driven improvements require accurate determination of the material properties. Here, we present refractive indices for all layers in Cu(In,Ga)Se₂ solar cells with high efficiency. The optical bandgap of Cu(In,Ga)Se₂ does not depend on the Cu content in the explored composition range, while the absorption coefficient value is primarily determined by the Cu content. An expression for the absorption spectrum is proposed, with Ga and Cu compositions as parameters. This set of parameters allows accurate device simulations to understand remaining absorption and carrier collection losses and develop strategies to improve performances.     

Fault detection and isolation in robotic manipulators via neural networks: A comparison among three architectures for residual analysis

In this article we discuss artificial neural networks‐based fault detection and isolation (FDI) applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so‐called residual vector. The residuals, when properly analyzed, provides an indication of the status of the robot (normal or faulty operation). Three ANNs architectures are employed in the residual analysis. The first is a radial basis function network (RBFN) which uses the residuals of position and velocity to perform fault identification. The second is again an RBFN, except that it uses only the velocity residuals. The third is an MLP which also performs fault identification utilizing only the velocity residuals. The MLP is trained with the classical back‐propagation algorithm and the RBFN is trained with a Kohonen self‐organizing map (KSOM). We validate the concepts discussed in a thorough simulation study of a Puma 560 and with experimental results with a 3‐joint planar manipulator. © 2001 John Wiley & Sons, Inc.