Solar Wind and Energy Resource Assessment (SWERA): A Usability Case Study

The Solar Wind and Energy Resource Assessment (SWERA) project focused on providing renewable energy planning resources to the public. Examples include wind, solar, and hydro assessments. A major component of the SWERA website is the archive search. This provides for a discovery DSS upon which users can find and access renewable energy data and supporting models. The RREX component of SWERA provides a visualization DSS as an addition to the website archive. RREX provides the discovery through a couple different avenues. RREX maps the renewable energy data that it provides along with a graphing application of the same data. RREX also provides a web service approach to allow for the distribution of the same data sets in multiple forms. The objective of this paper is to evaluate user satisfaction with the system as well as highlight factors affecting user satisfaction and experience. In the paper we provide a discussion of various design decisions used in the construction of the system followed by description of research methodology, and a discussion of key findings. Overall, analysis of results indicates general acceptance of the functionality provided and highlights venues for further improvements of the interface

Synthesis of triazole-linked morpholino oligonucleotides via Cu1 catalysed cycloaddition

Triazole-linked morpholino (TLMO) oligonucleic acids were synthesised using the CuI catalysed (3 + 2) azide–alkyne cycloaddition (CuAAC) reaction. The modified DNA analogues were incorporated into 13-mer sequences via solid phase synthesis. UV melting experiments showed that the TLMO modification gives higher Tm values than the corresponding TLDNA modification

Solving partial integro-differential option pricing problems for a wide class of infinite activity Lévy processes

[EN] In this paper, numerical analysis of finite difference schemes for partial integro-differential models related to European and American option pricing problems under a wide class of Lévy models is studied. Apart from computational and accuracy issues, qualitative properties such as positivity are treated. Consistency of the proposed numerical scheme and stability in the von Neumann sense are included. Gauss Laguerre quadrature formula is used for the discretization of the integral part. Numerical examples illustrating the potential advantages of the presented results are included.This work has been partially supported by the European Union in the FP7-PEOPLE-2012-ITN program under Grant Agreement Number 304617 (FP7 Marie Curie Action, Project Multi-ITN STRIKE-Novel Methods in Computational Finance) and the Ministerio de Economia y Competitividad Spanish grant MTM2013-41765-P.El-Fakharany, M.; Company Rossi, R.; Jódar Sánchez, LA. (2016). Solving partial integro-differential option pricing problems for a wide class of infinite activity Lévy processes. Journal of Computational and Applied Mathematics. 296:739-752. https://doi.org/10.1016/j.cam.2015.10.027S73975229

The development of an EDSS: Lessons learned and implications for DSS research

The Solar and Wind Energy Resource Assessment (SWERA) project is focused on providing renewable energy (RE) planning resources to the public. Examples include wind, solar, and hydro assessments. SWERA DSS consists of three major components. First, SWERA Product Archive provides for a discovery DSS upon which users can find and access renewable energy data and supporting models. Second, the Renewable Resource EXplorer (RREX) component serves as a web-based, GIS analysis tool for viewing RE resource data available through the SWERA Product Archive. Third, the SWERA web service provides computational access to the data available in the SWERA spatial database through a location based query, and is also utilized in the RREX component. We provide a discussion of various design decisions used in the construction of this EDSS, followed by project experiences and implications for EDSS and broader DSS research

Environmental Risk of Groundwater Pollution by Pesticide Leaching through the Soil Profile

Adsorption, degradation, and movement are the key processes conditioning the behavior and fate of pesticides in the soil. Six processes that can move pesticides are leaching, diffusion, volatilization, erosion and run-off, assimilation by microorganisms, and plant uptake. Leaching is the vertical downward displacement of pesticides through the soil profile and the unsaturated zone, and finally to groundwater, which is vulnerable to pollution. Pesticides are frequently leached through the soil by the effect of rain or irrigation water. Pesticide leaching is highest for weakly sorbing and/or persistent compounds, climates with high precipitation and low temperatures, and soils with low organic matter and sandy texture. On the contrary, for pesticides with a low persistence that disappear quickly, the risk of groundwater pollution considerably decreases. Different and varied factors such as physical-chemical properties of the pesticide, a permeability of the soil, texture and organic matter content of the soil, volatilization, crop-root uptake, and method and dose of pesticide application are responsible for the leaching rate of the pesticides. Soils that are high in clays and organic matter will slow the movement of water, attach easily to many pesticides, and generally have a higher diversity and population of soil organisms that can metabolize the pesticides

A hybrid non-dominated sorting genetic algorithm for a multi-objective demand-side management problem in a smart building

One of the most significant challenges facing optimization models for the demand-side management (DSM) is obtaining feasible solutions in a shorter time. In this paper, the DSM is formulated in a smart building as a linear constrained multi-objective optimization model to schedule both electrical and thermal loads over one day. Two objectives are considered, energy cost and discomfort caused by allowing flexibility of loads within an acceptable comfort range. To solve this problem, an integrative matheuristic is proposed by combining a multi-objective evolutionary algorithm as a master level with an exact solver as a slave level. To cope with the non-triviality of feasible solutions representation and NP-hardness of our optimization model, in this approach discrete decision variables are encoded as partial chromosomes and the continuous decision variables are determined optimally by an exact solver. This matheuristic is relevant for dealing with the constraints of our optimization model. To validate the performance of our approach, a number of simulations are performed and compared with the goal programming under various scenarios of cold and hot weather conditions. It turns out that our approach outperforms the goal programming with respect to some comparison metrics including the hypervolume difference, epsilon indicator, number of the Pareto solutions found, and computational time metrics

Organic optoelectronic devices-flexibility versus performance

International audienceIn this paper, we discuss the effect of flexible substrates on the characteristics of two organic optoelectronic devices, namely P3HT:PCBM-based photovoltaic bulk heterojunctions and pentacene-based phototransistors. In addition, we have developed anode materials deposited by ion beam sputtering, a technique which satisfies the low temperature deposition requirements associated with the use of plastic substrates. The anode materials consisted of indium tin oxide (ITO) and ITO/metal/ITO tri-layers. The use of tri-layer anodes in P3HT:PCBM-based solar cells resulted in an increase in the fill factor and the power conversion efficiency reached a value of 2% with an ITO(70 nm)/Ag(14 nm)/ITO(70 nm) anode deposited on a polyphthalate carbonate substrate. In the case of phototransistors, a photosensitivity of 1.6 × 10 under illumination at 365 nm (with a power intensity of 7 mW/cm) was obtained in the off-state of the transistor. We have fine-tuned the anode structure and deposition/annealing conditions towards flexible organic devices and optimal device characteristics

Neural Koopman prior for data assimilation

With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data. While neural networks are often seen as uninterpretable black-box architectures, they can still benefit from physical priors on the data and from mathematical knowledge. In this paper, we use a neural network architecture which leverages the long-known Koopman operator theory to embed dynamical systems in latent spaces where their dynamics can be described linearly, enabling a number of appealing features. We introduce methods that enable to train such a model for long-term continuous reconstruction, even in difficult contexts where the data comes in irregularly-sampled time series. The potential for self-supervised learning is also demonstrated, as we show the promising use of trained dynamical models as priors for variational data assimilation techniques, with applications to e.g. time series interpolation and forecasting

Spatial Graph Signal Interpolation with an Application for Merging BCI Datasets with Various Dimensionalities

BCI Motor Imagery datasets usually are small and have different electrodes setups. When training a Deep Neural Network, one may want to capitalize on all these datasets to increase the amount of data available and hence obtain good generalization results. To this end, we introduce a spatial graph signal interpolation technique, that allows to interpolate efficiently multiple electrodes. We conduct a set of experiments with five BCI Motor Imagery datasets comparing the proposed interpolation with spherical splines interpolation. We believe that this work provides novel ideas on how to leverage graphs to interpolate electrodes and on how to homogenize multiple datasets.Comment: Submitted to the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2023

BaCo2(AsO4)2

Suitable single crystals of the title compound, barium dicobalt(II) bis­[orthoarsenate(V)], were prepared under hydro­thermal conditions. This phase belongs to a series of compounds with general formula AM 2(XO4)2, where A = alkaline earth metal, M = Mg or a divalent first-row transition element, and X = P, As or V. BaCo2(AsO4)2 is isotypic with BaNi2(XO4)2 (X = P, V or As) and is characterized by brucite-like sheets of edge-sharing CoO6 octa­hedra (3 symmetry) parallel to (001), with one-third of the octa­hedral positions being vacant. The sheets are capped above and below by AsO4 tetra­hedra (3 symmetry) and are inter­connected by distorted BaO12 cubocta­hedra ( symmetry)