Abstract
In this paper, we describe Fourier-based wave-front sensors (WFSs) as linear integral operators, characterized by their kernel. In the first part, we derive the dependency of this quantity with respect to the WFS’s optical parameters: pupil geometry, filtering mask, and tip/tilt modulation. In the second part, we focus the study on the special case of convolutional kernels. The assumptions required to be in such a regime are described. We then show that these convolutional kernels allow to drastically simplify the WFS model by summarizing its behavior in a concise and comprehensive quantity called the WFS impulse response. We explain in particular how it allows to compute the sensor’s sensitivity with respect to spatial frequencies. Such an approach therefore provides a fast diagnostic tool to compare and optimize Fourier-based WFSs. In the third part, we develop the impact of the residual phases on the sensor’s impulse response, and show that the convolutional model remains valid. Finally, a section dedicated to the pyramid WFS concludes this work and illustrates how the slope maps are easily handled by the convolutional model.

Abstract
Luminescent metal-organic frameworks (LMOFs) are promising materials for lighting and sensing applications. Herein, exposure of the highly luminescent Zn-2(bpdc)(2)(bpee) MOF (H(2)bpdc = 4,4'-biphenyldicarboxylic acid and bpee = 1,2-bipyridylethene) to subppm amine contents turns on a new absorption band unambiguously ascribed to free bpee molecules concomitant with the gradual appearance of a new photoluminescence band at shorter wavelengths. These findings combined with Fourier-transform infrared spectra, powder X-ray diffraction and thermogravimetric analysis of exposed LMOF powders confirm that bpee ligands are exchanged by amines and released inside the LMOF, triggering absorption and luminescence features which can be exploited for highly sensitive amine recognition. This principle was demonstrated in mixed matrix membranes (MMMs) prepared by a simple solvent-free method consisting of mixing Zn-2(bpdc)(2)(bpee) with dimethylvinyl-terminated dimethylsiloxane and dimethylhydrogen siloxane. This method enabled the production of free-standing, permeable, and highly transparent MMMs which showed enormous potential and sensitivity to the detection of amines in gas phase and aqueous medium.

Abstract
A grand challenge from the wind energy industry is to provide reliable forecasts on mountain winds several hours in advance at microscale (similar to 100 m) resolution. This requires better microscale wind-energy physics included in forecasting tools, for which field observations are imperative. While mesoscale (similar to 1 km) measurements abound, microscale processes are not monitored in practice nor do plentiful measurements exist at this scale. After a decade of preparation, a group of European and U.S. collaborators conducted a field campaign during 1 May-15 June 2017 in Vale Cobrao in central Portugal to delve into microscale processes in complex terrain. This valley is nestled within a parallel double ridge near the town of Perdigao with dominant wind climatology normal to the ridges, offering a nominally simple yet natural setting for fundamental studies. The dense instrument ensemble deployed covered a similar to 4 km x 4 km swath horizontally and similar to 10 km vertically, with measurement resolutions of tens of meters and seconds. Meteorological data were collected continuously, capturing multiscale flow interactions from synoptic to microscales, diurnal variability, thermal circulation, turbine wake and acoustics, waves, and turbulence. Particularly noteworthy are the extensiveness of the instrument array, space-time scales covered, use of leading-edge multiple-lidar technology alongside conventional tower and remote sensors, fruitful cross-Atlantic partnership, and adaptive management of the campaign. Preliminary data analysis uncovered interesting new phenomena. All data are being archived for public use.

Abstract
Day-ahead electricity price forecasting (DAEPF) plays a very important role in the decision-making optimization of electricity market participants, the dispatch control of independent system operators (ISOs) and the strategy formulation of energy trading. Unified modeling that only fits a single mapping relation between the historical data and future data usually produces larger errors because the different fluctuation patterns in electricity price data show different mapping relations. A daily pattern prediction (DPP) based classification modeling approach for DAEPF is proposed to solve this problem. The basic idea is that first recognize the price pattern of the next day from the "rough" day-ahead forecasting results provided by conventional forecasting methods and then perform classification modeling to further improve the forecasting accuracy through building a specific forecasting model for each pattern. The proposed approach consists of four steps. First, K-means is utilized to group all the historical daily electricity price curves into several clusters in order to assign each daily curve a pattern label for the training of the following daily pattern recognition (DPR) model and classification modeling. Second, a DPP model is proposed to recognize the price pattern of the next day from the forecasting results provided by multiple conventional forecasting methods. A weighted voting mechanism (WVM) method is proposed in this step to combine multiple day-ahead pattern predictions to obtain a more accurate DPP result. Third, the classification forecasting model of each different daily pattern can be established according to the clustering results in step 1. Fourth, the credibility of DPP result is checked to eventually determine whether the proposed classification DAEPF modeling approach can be adopted or not. A case study using the real electricity price data from the PJM market indicates that the proposed approach presents a better performance than unified modeling for a certain daily pattern whose DPP results show high reliability and accuracy.

Abstract
Building and maintaining complex systems requires good software engineering practices, including code modularity and reuse. The same applies in the context of coordination of complex component-based systems. This paper investigates how to verify properties of complex coordination patterns built hierarchically, i.e., built from composing blocks that are in turn built from smaller blocks. Most existing approaches to verify properties flatten these hierarchical models before the verification process, losing the hierarchical structure. We propose an approach to verify hierarchical models using containers as actions; more concretely, containers interacting with their neighbours. We present a dynamic modal logic tailored for hierarchical connectors, using Reo and Petri Nets to illustrate our approach. We realise our approach via a prototype implementation available online to verify hierarchical Reo connectors, encoding connectors and formulas into mCRL2 specifications and formulas.

Abstract
The explosion of blockchain projects in last couple of years shows the general interest in the blockchain technology. Looking towards the current state of the art regarding this technology it becomes clear that the main driver of innovation is the private sector. We believe that understanding main applications of the technology, academic contributions, and private solutions can reveal where the interest in the technology exists and where it can be missing. In particular, this work can help identify open source projects that can provide a framework with next-generation features as; lightning network, directed acyclic graph, mobile compatibility, or compute protocols. New applications of the blockchain technology are still being discovered regularly and in this study several blockchain development frameworks were found. However, in the academic world there are few references to operational, testing, and deployment framework related with the technology. With the expected growth of the technology, integration with preexisting solutions, legacy systems replacement, or new implementations, the need for testing, deploying, exploration, and maintenance is expected to intensify for the technology.