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In civil engineering there is a need for lightweight structural elements, specifically in applications such as the rehabilitation of degraded floors in existing buildings and modular construction. Metaheuristic search procedures are suitable to tackle optimization problems concerning material efficiency, but their use in real design applications is still limited. This paper presents the preliminary design in terms of structural, thermal, and acoustic performance of a floor system based on steel web core sandwich panels with polyurethane (PUR) foam core and a new genetic algorithm (GA) procedure developed to optimize its mass. The optimization study also addresses the minimization of cost and environmental impact and includes a wide range of practical engineering requirements. The constraints stemming from the building codes are incorporated in a new adaptive penalty function whose formulation and performance are thoroughly investigated. Examples of how to formulate the optimization problem in terms of Eurocode verifications are provided to foster the use of optimization procedures in current design practice. The results are presented in terms of design variables and constraints search histories as well as optimal feasible and unfeasible solutions. General conclusions and specific recommendations are drawn based on the case study presented for the design of sandwich floor panels and steel web core sandwich panels, respectively., This work was developed within the scope of the research project "Lightslab -Desenvolvimento de solucoes inovadoras de lajes de painel sandwich", supported by FEDER funds through the Operational Program for Operational Program for Competitiveness and Internationalization (POCI) and the Portuguese National Innovation Agency (ANI) -project no. 33865 [POCI-01-0247-FEDER-033865]. This work was partly financed by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020. The first and third authors wish also to acknowledge the grants DFA/BD/8319/2020 and SFRH/BSAB/150266/2019 respectively, provided by Fundacao para a Ciencia e a Tecnologia, IP (FCT), financed by European Social Fund and national funds through the FCT/MCTES.

The fishing industry is recognized as one of the primary sources of at-sea marine litter, largely through its contributions via abandoned, lost or otherwise discarded fishing gear (ALDFG). Individual fleet’s contributions to ALDFG vary significantly across this global industry. While much information is available for some fisheries, the rate of ALDFG remains poorly known for many techniques. In this study, we used data collected by fisheries observers onboard pole-and-line fishing vessels in the Azores (Atlantic Ocean) and the Maldives (Indian Ocean) to provide an accurate and representative estimate of ALDFG for this gear. Our analysis of 993 fishing events demonstrated ALDFG contributions much lower than have been recorded for any other commercial tuna fishing gear. Overall, we found that an angler loses some monofilament line in 1.4% (±0.2) of fishing events. This informs that for every thousand tonnes of tuna harvested using this fishing technique, 0.3 kg of nylon is entering the marine environment. Globally, we estimate that all pole-and-line fisheries together contribute to 96 kg ± 42.6 kg of ALDFG per year. These results further evidence the low environmental impact of this traditional fishing practice, as well as the need for other methods to convert to less damaging gears.

In the scope of an R&D project a new floor system based on sandwich panel has been developed. The lightweight structural system shall be a competitive solution when compared to traditional rehabilitation technique of degraded timber floors in old buildings. The layout of the sandwich prototypes designed involved the use of steel face sheet and i) steel webs and polyurethane (PUR) foam core system, ii) glass fiber-reinforced polymer (GFRP) webs and PUR foam core system and iii) outer steel webs and balsa wood core. The design of the sandwich panels included an optimization procedure. A multi-objective genetic algorithm (GA) was developed for this purpose as it is a search method well suited for the solution of optimization problems. The multi-objective GA aims at the minimization of the three objective functions, i.e. cost, mass and environmental footprint of the sandwich panel. The definition of the main feature of the algorithm includes consideration about encoding procedure, fitness scaling, selection method and handling of constraints. The boundary conditions are imposed so that the retrieved solutions will represent a feasible solution to the problem. These boundary conditions are the analytical formulation of the serviceability, ultimate limit state and thermal transmittance verifications imposed by the building codes to sandwich panels. The present paper deals with the introduction of all the aspects of the optimization problems providing as an example the optimization of the panel with steel face sheets, webs and PUR foam., This work is part of the research project Lightslab - development of innovative slab solutions using sandwich panels (POCI-01-0247-FEDER-033865), co-funded by the European Regional Development Fund (FEDER), through the "Programa Operacional Competitividade e Internacionalizacao" (POCI) and the Portuguese National Innovation Agency (ANI). The first and second authors acknowledge the grants DFA/BD/8319/2020 and SFRH/BSAB/150266/2019, respectively, provided by FCT, financed by European Social Fund and by national funds through the FCT/MCTES.

Refractory linings are commonly employed in Industrial vessels used in high-temperature processes to protect them against extreme working environments. The working lining of an industrial steel ladle frequently employs mortarless refractory masonry to contain the molten steel and limit heat losses. Such masonry lining undergoes high thermomechanical loads, primarily due to thermal expansion arising from thermal and mechanical boundary conditions. Dry joints, described as a physical break in the continuum, reduce some of these effects. Therefore, understanding dry joints is an essential factor for developing numerical constitutive models that employ a meso or macro modelling approach. This work aims to experimentally investigate the normal compressive behaviour of dry joints in refractory masonry. For this purpose, various refractory specimens of alumina-spinel material are tested from ambient temperature to 1200 °C. The results obtained from these experiments are then used to identify critical joint parameters with different mathematical models for the interface. The outcome describes exponential joint closing behaviour with respect to the force applied. It was observed that the pressure required to close a joint reduces with an increase in temperature. The exponential model was then used to numerically simulate the joint closure of mortarless refractory masonry. Finally, numerical and experimental results were compared., This work was supported by the funding scheme of the European Commission, Marie Sklodowska-Curie Actions Innovative Training Networks in the frame of the project ATHOR -Advanced THermo-mechanical multiscale mOdelling of Refractory linings 764987 Grant. The first, third and sixth authors acknowledge the partial funding by FCT/MCTES through national funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020.

This paper studies the application of the generalized method of moments (GMM) to multi-reference alignment (MRA): the problem of estimating a signal from its circularly-translated and noisy copies. We begin by proving that the GMM estimator maintains its asymptotic optimality for statistical models with group symmetry, including MRA. Then, we conduct a comprehensive numerical study and show that the GMM substantially outperforms the classical method of moments, whose application to MRA has been studied thoroughly in the literature. We also formulate the GMM to estimate a three-dimensional molecular structure using cryo-electron microscopy and present numerical results on simulated data.

Marine buoys aid in the battle against Illegal, Unreported and Unregulated (IUU) fishing by detecting fishing vessels in their vicinity. Marine buoys, however, may be disrupted by natural causes and buoy vandalism. In this paper, we formulate marine buoy placement as a clustering problem, and propose dropout k-means and dropout k-median to improve placement robustness to buoy disruption. We simulated the passage of ships in the Gabonese waters near West Africa using historical Automatic Identification System (AIS) data, then compared the ship detection probability of dropout k-means to classic k-means and dropout k-median to classic k-median. With 5 buoys, the buoy arrangement computed by classic k-means, dropout k-means, classic k-median and dropout k-median have ship detection probabilities of 38%, 45%, 48% and 52%., Comment: ICASSP 2020

Plastic pollution is among the most pervasive stressors currently influencing the marine environment and affecting even the most remote areas. To date, there are still fundamental gaps in our understanding of the major pathways and fate of plastic debris in the oceans. Here we show that oceanic insular environments are important transitory repositories of small plastic items floating in the open ocean. Monthly monitoring of seven beaches over a three-year period demonstrate that beaches of the Azores islands with particular characteristics can capture significant quantities of fragments between 2 and 5 mm in length. The beach with the highest plastic loading rates was found to occasionally accumulate densities exceeding 15,000 fragments m−2 on part of the backshore. However, a large portion of these fragments can be rapidly washed back into the marine environment. Detailed characterization of those plastic items revealed the typology and size distribution to be similar throughout the seven beaches and through the 33 months surveyed, suggesting a same and unique source. Our results show that these oceanic islands of the North-East Atlantic are under pressure of high quantities of fragmented plastic debris that probably entered the ocean many years ago.

We develop a framework for estimating unknown partial differential equations (PDEs) from noisy data, using a deep learning approach. Given noisy samples of a solution to an unknown PDE, our method interpolates the samples using a neural network, and extracts the PDE by equating derivatives of the neural network approximation. Our method applies to PDEs which are linear combinations of user-defined dictionary functions, and generalizes previous methods that only consider parabolic PDEs. We introduce a regularization scheme that prevents the function approximation from overfitting the data and forces it to be a solution of the underlying PDE. We validate the model on simulated data generated by the known PDEs and added Gaussian noise, and we study our method under different levels of noise. We also compare the error of our method with a Cramer-Rao lower bound for an ordinary differential equation (ODE). Our results indicate that our method outperforms other methods in estimating PDEs, especially in the low signal-to-noise (SNR) regime.

The presence of microplastic in marine fishes has been well documented but few studies have directly examined differences between fishes occupying contrasting environmental compartments. In the present study, we investigated the gut contents of 390 fishes belonging to three pelagic (blue jack mackerel, chub mackerel, skipjack tuna) and two deep-sea species (blackbelly rosefish, blackspot seabream) from the Azores archipelago, North-East Atlantic for microplastic contamination. Our results revealed that pelagic species had significantly more microplastic than the deep-water species. In all of the species studied, fragments were the most common plastic shape recovered and we found a significant difference in the type of polymer between the pelagic and deep-water species. In deep-sea fish we found almost exclusively polypropylene, whereas in the pelagic fish, polyethylene was the most abundant polymer type. Overall, the proportion of fish containing plastic items varied across our study species from 3.7% to 16.7% of individuals sampled, and the average abundance of plastic items ranged from 0.04 to 0.22 per individual (the maximum was 4 items recovered in one stomach). Despite the proximity of the Azores archipelago to the North Atlantic subtropical gyre, a region of elevated plastic abundance, the proportion of individuals containing plastic (9.49%) were comparable with data reported elsewhere.

We obtain a refinement of the degrees of freedom estimate of Landau and Pollak. More precisely, we estimate, in terms of $\epsilon$, the increase in the degrees of freedom resulting upon allowing the functions to contain a certain prescribed amount of energy $\epsilon $ outside a region delimited by a set $T$ in time and a set $\Omega $ in frequency. In this situation, the lower asymptotic Nyquist density $\vert T\vert \vert \Omega \vert /2\pi$ is increased to $(1+\epsilon)\vert T\vert \vert \Omega \vert /2\pi$. At the technical level, we prove a pseudospectra version of the classical spectral dimension result of Landau and Pollak, in the multivariate setting of Landau. Analogous results are obtained for Gabor localization operators in a compact region of the time-frequency plane., Comment: 13 pages, no figures

We analyze the problem of estimating a signal from multiple measurements on a $\mbox{group action channel}$ that linearly transforms a signal by a random group action followed by a fixed projection and additive Gaussian noise. This channel is motivated by applications such as multi-reference alignment and cryo-electron microscopy. We focus on the large noise regime prevalent in these applications. We give a lower bound on the mean square error (MSE) of any asymptotically unbiased estimator of the signal's orbit in terms of the signal's moment tensors, which implies that the MSE is bounded away from 0 when $N/\sigma^{2d}$ is bounded from above, where $N$ is the number of observations, $\sigma$ is the noise standard deviation, and $d$ is the so-called $\mbox{moment order cutoff}$. In contrast, the maximum likelihood estimator is shown to be consistent if $N /\sigma^{2d}$ diverges., Comment: 5 pages, conference

In the multireference alignment model, a signal is observed by the action of a random circular translation and the addition of Gaussian noise. The goal is to recover the signal’s orbit by accessing multiple independent observations. Of particular interest is the sample complexity, i.e., the number of observations/samples needed in terms of the signal-to-noise ratio (SNR) (the signal energy divided by the noise variance) in order to drive the mean-square error to zero. Previous work showed that if the translations are drawn from the uniform distribution, then, in the low SNR regime, the sample complexity of the problem scales as $\omega (1/ \mathrm {SNR}^{3})$ . In this paper, using a generalization of the Chapman–Robbins bound for orbits and expansions of the $\chi ^{2}$ divergence at low SNR, we show that in the same regime the sample complexity for any aperiodic translation distribution scales as $\omega (1/ \mathrm {SNR}^{2})$ . This rate is achieved by a simple spectral algorithm. We propose two additional algorithms based on non-convex optimization and expectation–maximization. We also draw a connection between the multireference alignment problem and the spiked covariance model.

The Boolean multireference alignment problem consists in recovering a Boolean signal from multiple shifted and noisy observations. In this paper we obtain an expression for the error exponent of the maximum A posteriori decoder. This expression is used to characterize the number of measurements needed for signal recovery in the low SNR regime, in terms of higher order autocorrelations of the signal. The characterization is explicit for various signal dimensions, such as prime and even dimensions., 5 pages, submitted to ISIT

We investigate an inverse problem in time-frequency localization: the approximation of the symbol of a time-frequency localization operator from partial spectral information by the method of accumulated spectrograms (the sum of the spectrograms corresponding to large eigenvalues). We derive a sharp bound for the rate of convergence of the accumulated spectrogram, improving on recent results., Comment: 14 pages, 2 figures

Weyl-Heisenberg ensembles are a class of determinantal point processes associated with the Schr\"odinger representation of the Heisenberg group. Hyperuniformity characterizes a state of matter for which (scaled) density fluctuations diminish towards zero at the largest length scales. We will prove that Weyl-Heisenberg ensembles are hyperuniform. Weyl-Heisenberg ensembles include as a special case a multi-layer extension of the Ginibre ensemble modeling the distribution of electrons in higher Landau levels, which has recently been object of study in the realm of the Ginibre-type ensembles associated with polyanalytic functions. In addition, the family of Weyl-Heisenberg ensembles includes new structurally anisotropic processes, where point-statistics depend on the different spatial directions, and thus provide a first means to study directional hyperuniformity., Comment: 17 pages, 2 figures. (Typos corrected.)