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Abstract During a visual search through the Kepler main-field light curves, we have discovered a cataclysmic variable (CV) that experienced only a single 4-d long outburst over four years, rising to three times the quiescent flux. During the four years of non-outburst data the Kepler photometry of KIC 5608384 exhibits ellipsoidal light variations (‘ELVs’) with a ∼12 per cent amplitude and period of 8.7 h. Follow-up ground-based spectral observations have yielded a high-quality radial velocity curve and the associated mass function. Additionally, H α emission lines were present in the spectra even though these were taken while the source was presumably in quiescence. These emission lines are at least partially eclipsed by the companion K star. We utilize the available constraints of the mass function, the ELV amplitude, Roche lobe filling condition, and inferred radius of the K star to derive the system masses and orbital inclination angle: $M_{\rm wd} \simeq 0.46 \pm 0.02 \, \mathrm{M}_\odot$, $M_{\rm K} \simeq 0.41 \pm 0.03 \, \mathrm{M}_\odot$, and i ≳ 70°. The value of Mwd is the lowest reported for any accreting WD in a CV. We have also run binary evolution models using mesa to infer the most likely parameters of the pre-cataclysmic binary. Using the mass-transfer rates from the model evolution tracks we conclude that although the rates are close to the critical value for accretion disc stability, we expect KIC 5608384 to exhibit dwarf nova outbursts. We also conclude that the accreting white dwarf most likely descended from a hot subdwarf and, most notably, that this binary is one of the first bona fide examples of a progenitor of AM CVn binaries to have evolved through the CV channel.

It is well known that beef produces volatile molecules. In this work, the detection of volatiles released by post-mortem bovine fast-twitch muscles (Musculus longissimus dorsi and Musculus cutaneus trunci) was done using GC/MS–SPME (gas chromatography/mass spectrum–solid-phase microextraction). The releases of volatile molecules were modeled against three factors (rigor-mortis, animal age and oxidative capacity) using a chemometrics approach (experimental design and partial least squares regression). The GC/MS–SPME technique produced more than 30 reproducible chromatographic peaks, but only 13 were associated significantly with two factors (rigor-mortis and animal age). The volatile profile was composed mainly of alcohols, aldehydes and alkanes. The factor “animal age” was the main variable related to the release of volatile molecules. The results strongly suggest that the release of volatile molecules change according to post-mortem metabolism and the animal age.

Abstract The combination of maintenance planning and key performance indicators are relevant to create a more holistic scenario of the mining activities. On the one hand, reliability and maintainability are system characteristics suitable for planning maintenance strategies. On the other hand, key performance indicators are suitable to analyze cost and resource consumption information about mining equipment. Nevertheless in practice, both approaches are modeled separately and frequently by different team-works of a mining company. With this in mind, a computer-aided application was conceived to drive with better efficacy the operational and maintenance strategy in a complex process where the equipment is in continuous movement such as the transportation process in an open-mine pit.

In this work, a constraint programming (CP) formulation of the multi-mode resource-constrained project scheduling problem (MMRCPSP) is proposed for solving the flexible job shop scheduling problem (FJSSP) under the makespan minimization criterion. The resulting CP model allows us to tackle the classical instances of the FJSSP (such as where the operations of a given job follow a linear order). It can also handle FJSSP instances where the precedence relationships between operations are defined by an arbitrary directed acyclic graph (sequencing flexibility). The performance of our approach was tested using 271 classical FJSSP instances and 50 FJSSP instances with sequencing flexibility. We establish the validity of our approach by achieving an average relative percentage deviation of 3.04% and 0.18% when compared to the best-known lower and upper bounds, respectively. Additionally, we were able to contribute to the literature with ten new lower bounds and two new upper bounds. Our CP approach is relatively simple yet competitive and can be quickly applied and adapted by new practitioners in the area.

In this work, we propose a local search-based strategy to determine high-quality allocation of vaccines under restricted budgets and time periods. For this, disease spread is modeled as a SEAIR pandemic model. Subgroups are used to understand and evaluate movement restrictions and their effect on interactions between geographical divisions. A tabu search heuristic method is used to determine the number of vaccines and the groups to allocate them in each time period, minimizing the maximum number of infected people at the same time and the total infected population. Available data for COVID-19 daily cases was used to adjust the parameters of the SEAIR models in four study cases: Austria, Belgium, Denmark, and Chile. From these, we can analyze how different vaccination schemes are more beneficial for the population as a whole based on different reproduction numbers, interaction levels, and the availability of resources in each study case. Moreover, from these experiments, a strong relationship between the defined objectives is noticed.

In this paper we analyze a locking-free numerical scheme for the LQR control of a Timoshenko beam. We consider a non-conforming finite element discretization of the system dynamics and a control law constant in the spatial dimension. To solve the LQR problem we seek a feedback control which depends on the solution of an algebraic Riccati equation. An optimal error estimate for the feedback operator is proved in the framework of the approximation theory for control of infinite dimensional systems. This estimate is valid with constants that do not depend on the thickness of the beam, which leads to the conclusion that the method is locking-free. In order to assess the performance of the method, numerical tests are reported and discussed.

Context. Currently, one of the standard procedures used to determine stellar and wind parameters of massive stars involves to comparing the observed spectral lines with a grid of synthetic lines. These synthetic lines are calculated using non-local thermodynamic equilibrium radiative transfer codes. In this standard procedure, after estimating the stellar-projected rotational speed (v sin i), all synthetic models need to be convolved using this value in order to perform the comparison with the observed line and estimate the stellar parameters. Aims: In this work, we propose a methodology to deconvolve the observed line profile to one from a non-rotating star. Thus, to perform a comparison, we will not need to convolve all the synthetic profiles, saving significant time and resources. Methods: The proposed deconvolution method is based on transforming this inverse problem into an optimization of a direct problem. We propose using a Gaussian sum approximation (GSA) to obtain the line profile without the broadening effect due to stellar rotation. After selecting the most adequate model to derive the fundamental GSA parameters, we convolved it with the known v sin i in order to obtain the profile considering the v sin i. Finally, we compared this approximated line profile directly with the observed spectrum. Results: The performance of the proposed method is analyzed using synthetic and observed lines. The results show that the proposed deconvolution method yields accurate non-rotating profiles. Conclusions: The proposed approach utilizing GSA is an accurate method to deconvolve spectral lines.

For functions f whose Taylor coefficients at the origin form a Hausdorff moment sequence we study the behaviour of w(y) := |f(γ + iy)| for y > 0 (γ ≤ 1 fixed)

Using the gauge-invariant but path-dependent variables formalism, we examine the effect of the space–time dimensionality on a physical observable in the unparticle scenario. We explicitly show that long-range forces between particles mediated by unparticles are still present whenever we go over into lower dimensions.

A low-mass planet around a white dwarf Numerous exoplanets have been detected around Sun-like stars. These stars end their lives as white dwarfs, which should inherit any surviving planetary systems. Manser et al. found periodic shifts in emission lines from a disc of gas orbiting around a white dwarf (see the Perspective by Fossati). They used numerical simulations to show that the most likely explanation for the spectral shifts is a low-mass planet orbiting within the disc. The planet must be unusually small and dense to avoid being ripped apart by tidal forces. The authors speculate that it may be the leftover core of a planet whose outer layers have been removed. Science , this issue p. 66 ; see also p. 25

In two and three dimensional Lipschitz, but not necessarily convex, polytopal domains, we devise and analyze a reliable and efficient a posteriori error estimator for a semilinear optimal control problem; control constraints are also considered. We consider a fully discrete scheme that discretizes the state and adjoint equations with piecewise linear functions and the control variable with piecewise constant functions. The devised error estimator can be decomposed as the sum of three contributions which are associated to the discretization of the state and adjoint equations and the control variable. We extend our results to a scheme that approximates the control variable with piecewise linear functions and also to a scheme that approximates the solution to a nondifferentiable optimal control problem. We illustrate the theory with two and three-dimensional numerical examples.

We consider an unconstrained linear–quadratic optimal control problem governed by a singularly perturbed convection–reaction–diffusion equation. We discretize the optimality system by using standard piecewise bilinear finite elements on the graded meshes introduced by Durán and Lombardi in (Duŕan and Lombardi 2005, 2006). We prove convergence of this scheme. In addition, when the state equation is a singularly perturbed reaction–diffusion equation, we derive quasi-optimal a priori error estimates for the approximation error of the optimal variables on anisotropic meshes. We present several numerical experiments when the state equation is both a reaction–diffusion and a convection–reaction–diffusion equation. These numerical experiments reveal a competitive performance of the proposed solution technique.

This search, a type not previously performed at ATLAS, uses a comparison of the production cross sections for e+μ− and e−μ+ pairs to constrain physics processes beyond the Standard Model. It uses 139 fb−1 of proton–proton collision data recorded at √s = 13 TeV at the LHC. Targeting sources of new physics which prefer final states containing e+μ− to e−μ+, the search contains two broad signal regions which are used to provide model-independent constraints on the ratio of cross sections at the 2% level. The search also has two special selections targeting supersymmetric models and leptoquark signatures. Observations using one of these selections are able to exclude, at 95% confidence level, singly produced smuons with masses up to 640 GeV in a model in which the only other light sparticle is a neutralino when the Rparity-violating coupling λ 231 is close to unity. Observations using the other selection exclude scalar leptoquarks with masses below 1880 GeV when geu 1R = g μc 1R = 1, at 95% confidence level. The limit on the coupling reduces to geu 1R = g μc 1R = 0.46 for a mass of 1420 GeV.

Information disorders on social media can have a significant impact on citizens’ participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.

The scheduling of conferences is a challenging task that aims at creating successful conference programs that fulfill an often wide variety of requirements. In this work, we focus on the problem of generating conference programs that organize talks into tracks: subevents within the conference that are group-related talks. The main contributions of this work can be organized into three scopes: literature review, problem formulation and benchmarking, and heuristic approach. We provide a literature review of conference scheduling approaches that organizes these approaches within a timetabling problem taxonomy. We also describe the main characteristics of the conference scheduling approaches in the literature and propose a classification scheme for such works. To study the scheduling of conferences that include tracks, we introduce the definition of the track-based conference scheduling problem, a new problem that incorporates tracks in the conference program. We provide a binary integer linear programming model formulation for this problem. Our formulation considers the availability of presenters, chairs, and organizers, the avoidance of parallel tracks, and best paper sessions, among other classical constraints of conference scheduling problems. Additionally, based on our formulation, we propose a simple instance-generation procedure that we apply to generate a set of artificial instances. We complete our work by proposing a heuristic method based on the simulated annealing metaheuristic for solving the track-based conference scheduling problem. We compare the results obtained by our heuristic approach and the Gurobi solver regarding execution time and solution quality. The results show that the proposed heuristic method is a practical approach for tackling the problem as it obtains solutions in a fraction of the time required by Gurobi, while Gurobi is also unable to obtain an optimal solution in the defined time for a subset of the instances. Finally, from a general perspective, this work provides a new conference scheduling problem formulation that can be extended in the future to include other features common in conference programs. Moreover, thanks to the instance generation procedure, this formulation can be used as a benchmark for designing and comparing new solving approaches. © 2022 by the authors

Filtering and smoothing algorithms are key tools to develop decision-making strategies and parameter identification techniques in different areas of research, such as economics, financial data analysis, communications, and control systems. These algorithms are used to obtain an estimation of the system state based on the sequentially available noisy measurements of the system output. In a real-world system, the noisy measurements can suffer a significant loss of information due to (among others): (i) a reduced resolution of cost-effective sensors typically used in practice or (ii) a digitalization process for storing or transmitting the measurements through a communication channel using a minimum amount of resources. Thus, obtaining suitable state estimates in this context is essential. In this paper, Gaussian sum filtering and smoothing algorithms are developed in order to deal with noisy measurements that are also subject to quantization. In this approach, the probability mass function of the quantized output given the state is characterized by an integral equation. This integral was approximated by using a Gauss–Legendre quadrature; hence, a model with a Gaussian mixture structure was obtained. This model was used to develop filtering and smoothing algorithms. The benefits of this proposal, in terms of accuracy of the estimation and computational cost, are illustrated via numerical simulations.

A virtual environment (VE) of an industrial splitter has been developed to test a dynamic real-time optimizer prior to on-site deployment. The paper describes the architecture of the VE, which represents the process using the EcosimPro© modeling and simulation software, and a real-time manager to maintain a controlled pace of simulation progress. The virtual process has been integrated with the same commercial predictive controller (Aspen© DMC) as installed in the plant (even with the same configuration) and a dynamic real-time optimizer has been developed in Matlab© and implemented on top of it, allowing the VE to test the optimization layer before deployment in the real factory. The data generated by the plant is stored using InfluxDB©, a time series data platform, for further analysis. The results show that the VE allows revamp studies to be conducted with less cost and more confidence.

In two dimensions, we show existence of solutions to the stationary Navier Stokes equations on weighted spaces (H^1)0(ω,Ω)×L^2(ω,Ω), where the weight belongs to the Muckenhoupt class A2. We show how this theory can be applied to obtain a priori error estimates for approximations of the solution to the Navier Stokes problem with singular sources.

In a previous work, we introduced a discretization scheme for a control-constrained optimal control problem involving the fractional Laplacian. For such a problem, we derived a near optimal a priori error estimate, for the approximation of the optimal control variable, that demands the convexity of the domain and some compatibility conditions on the data. To relax such restrictions, in this article, we introduce and analyse an efficient and, under certain assumptions, reliable a posteriori error estimator. We realize the fractional Laplacian as the Dirichlet-to-Neumann map for a nonuniformly elliptic problem posed on a semi infinite cylinder in one more spatial dimension. This extra dimension further motivates the design of an a posteriori error indicator. The latter is defined as the sum of three contributions that come from the discretization of the state and adjoint equations and the control variable. The indicator for the state and adjoint equations relies on an anisotropic error estimator in Muckenhoupt-weighted Sobolev spaces. We present an analysis that is valid in any dimension. On the basis of the devised a posteriori error estimator, we design a simple adaptive strategy that exhibits optimal experimental rates of convergence.

We propose an a posteriori error estimator for an optimal control problem with sparsity promoting cost: the control variable lies in the space of regular Borel measures. We consider a solution technique that relies on the discretization of the control variable as a linear combination of Dirac measures. The proposed a posteriori error estimator can be decomposed into the sum of two contributions: an error estimator in the maximum norm for the discretization of the adjoint equation and an estimator in the -norm that accounts for the approximation of the state equation. We prove that the designed error estimator is locally efficient and we explore its reliability properties. The analysis is valid for two and three-dimensional domains. We illustrate the theory with numerical examples.