Variable‐Wavelength Quick Scanning Nanofocused X‐Ray Microscopy for In Situ Strain and Tilt Mapping

Compression of micropillars is followed in situ by a quick nanofocused X‐ray scanning microscopy technique combined with 3D reciprocal space mapping. Compared to other attempts using X‐ray nanobeams, it avoids any motion or vibration that would lead to a destruction of the sample. The technique consists of scanning both the energy of the incident nanofocused X‐ray beam and the in‐plane translations of the focusing optics along the X‐ray beam. Here, the approach by imaging the strain and lattice orientation of Si micropillars and their pedestals during in situ compression is demonstrated. Varying the energy of the incident beam instead of rocking the sample and mapping the focusing optics instead of moving the sample supplies a vibration‐free measurement of the reciprocal space maps without removal of the mechanical load. The maps of strain and lattice orientation are in good agreement with the ones recorded by ordinary rocking‐curve scans. Variable‐wavelength quick scanning X‐ray microscopy opens the route for in situ strain and tilt mapping toward more diverse and complex materials environments, especially where sample manipulation is difficult.     

Structured prediction with reinforcement learning

We formalize the problem of Structured Prediction as a Reinforcement Learning task. We first define a Structured Prediction Markov Decision Process (SP-MDP), an instantiation of Markov Decision Processes for Structured Prediction and show that learning an optimal policy for this SP-MDP is equivalent to minimizing the empirical loss. This link between the supervised learning formulation of structured prediction and reinforcement learning (RL) allows us to use approximate RL methods for learning the policy. The proposed model makes weak assumptions both on the nature of the Structured Prediction problem and on the supervision process. It does not make any assumption on the decomposition of loss functions, on data encoding, or on the availability of optimal policies for training. It then allows us to cope with a large range of structured prediction problems. Besides, it scales well and can be used for solving both complex and large-scale real-world problems. We describe two series of experiments. The first one provides an analysis of RL on classical sequence prediction benchmarks and compares our approach with state-of-the-art SP algorithms. The second one introduces a tree transformation problem where most previous models fail. This is a complex instance of the general labeled tree mapping problem. We show that RL exploration is effective and leads to successful results on this challenging task. This is a clear confirmation that RL could be used for large size and complex structured prediction problems.     

Integrative Study of the Structural and Dynamical Properties of a KirBac3.1 Mutant: Functional Implication of a Highly Conserved Tryptophan in the Transmembrane Domain

ATP-sensitive potassium (K-ATP) channels are ubiquitously expressed on the plasma membrane of cells in several organs, including the heart, pancreas, and brain, and they govern a wide range of physiological processes. In pancreatic β-cells, K-ATP channels composed of Kir6.2 and SUR1 play a key role in coupling blood glucose and insulin secretion. A tryptophan residue located at the cytosolic end of the transmembrane helix is highly conserved in eukaryote and prokaryote Kir channels. Any mutation on this amino acid causes a gain of function and neonatal diabetes mellitus. In this study, we have investigated the effect of mutation on this highly conserved residue on a KirBac channel (prokaryotic homolog of mammalian Kir6.2). We provide the crystal structure of the mutant KirBac3.1 W46R (equivalent to W68R in Kir6.2) and its conformational flexibility properties using HDX-MS. In addition, the detailed dynamical view of the mutant during the gating was investigated using the in silico method. Finally, functional assays have been performed. A comparison of important structural determinants for the gating mechanism between the wild type KirBac and the mutant W46R suggests interesting structural and dynamical clues and a mechanism of action of the mutation that leads to the gain of function.     

Long-term Uncertainty of Hydropower Revenue Due to Climate Change and Electricity Prices

Hydropower represents the world’s largest renewable energy source. As a flexible technology, it enhances reliability and security of the electricity system. However, climate change and market liberalization may hinder investment due to the evolution of water runoffs and electricity prices. Both alter expected revenue and bring uncertainty. It increases risk and deters investment. Our research assesses how climate change and market fluctuation affect annual revenue. But this paper focuses on the uncertainty, rather than on forecasting. This transdisciplinary topic is investigated by means of a mixed method, i.e. both quantitative and qualitative. The quantitative approach uses established models in natural sciences and economics. The uncertainty is accounted for by applying various scenarios and various datasets coming from different models. Based on those results, uncertainty is discussed through an analysis discerning three dimensions of uncertainty. Uncertainty analysis requires the assessment of a large panel of models and data sets. It is therefore rarely carried out. The originality of the paper also lies on the combination of quantitative established models with a qualitative analysis. The results surprisingly show that the greenhouse gas scenarios may in fact represent a low source of uncertainty, unlike electricity prices. Like forecasting, the main uncertainties are actually case study related and depend on the investigated variables. It is also shown that the nature of uncertainty evolves. Runoff uncertainty goes from variability, i.e. inherent randomness, to epistemic, i.e. limitation of science. The reverse situation occurs with the electricity price. The implications for scientists and policy makers are discussed.     

Entropy-coded lattice vector quantization dedicated to the block mixture densities

Entropy-coded lattice vector quantization (ECLVQ) with codebooks dedicated to independent identically distributed (i.i.d.) generalized Gaussian sources have proven their high coding performances in the wavelet domain. It is well known that wavelet coefficients with high magnitude (corresponding to edges and textures) tend to be clustered in a few amount of vectors. In this paper, we first show that this property has a major influence on the performances of ECLVQ schemes. Since this clustering property cannot be taken into account by the classical i.i.d. assumption, our first proposal is to model the joint distribution of vectors by a multidimensional mixture of generalized Gaussian (MMGG) densities. The main outcome of this MMGG model is to provide a theoretical framework to simply derive from i.i.d. R- D models, the corresponding MMGG R- D models. In a second part, a new codebook better suited to wavelet coding is proposed: the so-called dead zone lattice vector quantizers (DZLVQ). It consists of generalizing the scalar dead zone to vector quantization by thresholding vectors according to their energy. We show that DZLVQ improves the rate-distortion tradeoff. Experimental results are provided for the pyramidal LVQ scheme under the assumption of a multidimensional mixture of Laplacian (MML) densities. Results performed on a set of real life images show the precision of the analytical R- D curves and the efficiency of the DZLVQ scheme.     

Minimizing metastabilities in Cu(In,Ga)Se2/(CBD)Zn(S,O,OH)/i‐ZnO‐based solar cells

Chemical bath deposited (CBD)Zn(S,O,OH) is among the alternatives to (CBD)CdS buffer layers in Cu(In,Ga)Se₂(CIGSe)‐based devices. Nevertheless, the performances reached by devices buffered with (CBD)Zn(S,O,OH) vary strongly from one sample to another and from one laboratory to another, indicating that parameters of minority impact with (CBD)CdS‐buffered devices have major influence when buffered with (CBD)Zn(S,O,OH). Moreover, the literature reports, but not systematically, the requirement of substituting the standard resistive intrinsic ZnO by (Zn,Mg)O and/or soaking the devices in ultraviolet‐containing light in order to reach optimal device operation. The present study investigates the impact of the three following parameters on the optoelectronic behavior of the Cu(In,Ga)Se₂/(CBD)Zn(S,O,OH)/i‐ZnO‐based solar cells: (i) CIGSe surface composition; (ii) (CBD)Zn(S,O,OH) layer thickness; and (iii) i‐ZnO layer resistivity. The first conclusion of this study is that all of these parameters are observed to influence the electrical metastabilities of the devices. The second conclusion is that the light soaking time needed to achieve optimal photovoltaic parameters is decreased by (i) using absorbers with Cu content close to stoichiometry, (ii) increasing the buffer layer thickness, and (iii) increasing the resistivity of i‐ZnO. By optimizing these trends, stable and highly efficient Zn(S,O,OH)‐buffered CIGSe solar cells have been fabricated. Copyright © 2014 John Wiley & Sons, Ltd.     

Assessment of mono and multi-objective optimization to design a hydrogen supply chain

This work considers the potential future use of hydrogen in fuel cell electrical vehicles to face problems such as global warming, air pollution, energy security and competitiveness. The lack of current infrastructure has been identified as one of the main barriers to develop the hydrogen economy. This work is focused on the design of a hydrogen supply chain through mixed integer linear programming used to find the best solutions for a multi-objective optimization problem in which three objectives are involved, i.e., cost, global warming potential and safety risk. This problem is solved by implementing an ?-constraint method. The solution consists of a Pareto front, corresponding to different design strategies in the associated variable space. Multiple choice decision making is then recommended to find the best solution through an M-TOPSIS analysis. The model is applied to the Great Britain case study previously treated in the dedicated literature. Mono and multicriteria optimizations exhibit some differences concerning the degree of centralization of the network and the selection of the production technology type.     

Is Dimethyldecanal a Common Aggregation Pheromone of Tribolium Flour Beetles?

Flour beetles are cosmopolitan and common pests in grain stores and flour mills. Their ability to exploit a wide variety of stored products has contributed to their status as major pests of stored food. Although it was previously reported that the same aggregation pheromone, 4,8-dimethyldecanal (DMD), is shared by three flour beetles species (Tribolium castaneum, T. confusum, and T. freemani), the volatiles released by the other Tribolium species associated with stored products have not yet been examined. In the present study, the volatiles produced by males and females of eight Tribolium species were examined by solid phase microextraction (SPME). SPME samples were analyzed by gas chromatography coupled to mass spectrometry (GC-MS). Experiments were conducted to identify volatiles emitted by the adults of different Tribolium species and to determine whether DMD is a common aggregation pheromone. We observed that DMD is not a common pheromone of the eight species tested, but is common to T. castaneum, T. confusum, T. freemani, and T. madens. Two other volatiles were detected, 1-pentadecene, which is shown here to be a common semiochemical of flour beetles, and 1,6-pentadecadiene, which was detected in five species (T. audax, T. brevicornis, T. destructor, T. freemani, and T. madens).     

Influence of Cu off-stoichiometry on wide band gap CIGSe solar cells

The electric properties of solar cells based on co-evaporated Cu(In,Ga)Se₂ (CIGSe) thin film show a good tolerance regarding the absorber Cu content (y = [Cu]/([In] + [Ga])) for standard Ga concentration, i.e. x = [Ga] / ([In] + [Ga]) ~ 0.3. In the present contribution, we show that this tolerance is lost when the gallium content is increased. Wide bandgap CIGSe samples (x ~ 0.55) with a variation in y from 0.97 to 0.84 have been grown. The efficiency of the cells decreases from 12.6% to 6.5% for y = 0.97 and 0.84 respectively. For the lowest y, the efficiency is harmed because of a low short-circuit current density (J_sc), an increased voltage dependency in the current collection, which affects the fill factor (FF), and a decrease of the open-circuit voltage (V_oc). For y = 0.97 and 0.84 respectively, the decrease of the activation energy (E_a) from 1.36 to 1.24 eV indicates a shift of the area of the dominant recombination from the space charge region towards the interface. There seems to be evidence that reducing the Cu-content in the CIGSe thin film will cause a decrease in the width of the space charge region. Solar cells based on Cu-rich CIGSe (1.03 < y < 1.09) have also been fabricated and characterized. A strong deterioration of their electrical properties is observed despite the KCN etch of the segregated Cu_2 − xSe binary phases at the surface, suggesting the presence of residual Cu_2 − xSe precipitates within the layer.