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A quasi-one-dimensional critical-throat acoustic boundary condition for thermally choked dual-mode ramjet nozzles
No full textThermally choked nozzles, where choking is induced by heat addition rather than a geometrical throat, are a promising solution for dual-mode ramjet transitions to hypersonic speed. Despite their relevance, thermal throat boundary conditions for quasi-one-dimensional acoustic modelling are not derived in the state-of-the-art. This study introduces a generalized critical-throat acoustic boundary condition applicable to both geometrically and thermally choked flow configurations. A dedicated one-dimensional linear acoustic solver is formulated to incorporate this condition and is validated against two-dimensional Euler simulations. Particular attention is paid to the impact of entropy and acoustic waves at the critical throat. The results show that the new boundary condition improves the prediction of acoustic reflection, entropy noise production, and transmission coefficients, especially under thermally choked conditions where the commonly used quasi-steady assumption fails. For both the thermal-throat and geometric-throat configurations, the deviation in the acoustic transmission coefficient between the linear acoustic model using the proposed boundary condition and the simulations remains below 2%, while the deviation in the entropy-noise transmission coefficient remains below 5%, demonstrating the robustness of the proposed boundary condition
An extension of sellke construction and uncertainty quantification for non-Markovian epidemic models
No full textSeveral major epidemic events over the past two decades have highlighted the importance of developing and studying non-Markovian compartmental models. [T. Sellke, J. Appl. Probab. 20 (1983) 390−394] introduced an ingenious construction for the SIR epidemic process to study the final size of epidemics. In this paper, we extend this construction to the SEI1I2RS model. This model is chosen for its compactness, while including parallel infectious stages (I1 and I2) and cycles (aka loops) due to reinfection. Our methodology easily generalizes to a general class of stochastic compartmental models in closed populations, including SIR-like models (a series of compartments in one row), SEIAR-like models (parallel compartments), but also models with cycles. Our construction inherits from Sellke construction its ability to handle both Markovian and non-Markovian frameworks. Also, it naturally leads to a representation of the epidemic process under the form of a deterministic function of uncertain parameters (such as epidemic parameters) and variables modeling internal noise. Based on this representation, we propose a global sensitivity analysis of the SEI1I2RS model. With our methodology we are able to quantify epistemic uncertainty due to the lack of knowledge on epidemic parameters and statistical uncertainty induced by stochasticity of the model. Finally we provide numerical experiments in both Markovian and non-Markovian frameworks
Zooming into the water snow line: High-resolution water observations of the HL Tau disk
No full textContext. Water is one of the central molecules for the formation and habitability of planets. In particular, the region where water freezes out, the water snow line, could be a favorable location for planets to form in protoplanetary disks.
Aims. We aimed to spatially resolve the water emission in the HL Tau disk using high-resolution ALMA observations of the H2O 183 GHz line (Eu = 205 K). We compared the spatially resolved H2O emission with that of H13CO+, a chemical tracer of the water snow line, to observationally test their anticorrelation. In addition, we aimed to quantify the fraction of the water reservoir hidden by optically thick dust at ALMA wavelengths versus far- and mid-IR wavelengths.
Methods. We used high-resolution ALMA observations to spatially resolve the H2O 31,3–22,0 line at 183 GHz, H13CO+ J = 2–1, and the SO 44–33 transition in the HL Tau disk. A rotational diagram analysis was used to characterize the water reservoir seen with ALMA and compare it to the reservoir visible at mid- and far-IR wavelengths.
Results. We find that the H2O 183 GHz line has a compact central component and a diffuse component that is seen out to ∼75 au. A radially resolved rotational diagram shows that the excitation temperature of the water is ∼350 K, independent of radius. The steep drop in the water brightness temperature outside the central beam of the observations where the emission is optically thick is consistent with the water snow line being located inside the central beam (≲6 au) at the height probed by the observations. Comparing the ALMA lines to those seen at shorter wavelengths shows that only 0.02–2% of the water reservoir is visible at mid- and far-IR wavelengths due to optically thick dust hiding the emission, whereas 35–70% is visible with ALMA. An anticorrelation between the H2O and H13 CO+ emission is found, but it is likely caused by optically thick dust hiding the H13CO+ emission in the disk center. Finally, we see SO emission tracing the disk and, for the first time in SO, a molecular outflow and the infalling streamer out to ∼2′′. The velocity structure hints at a possible connection between the SO and the H2O emission.
Conclusions. Spatially resolved observations of H2O lines at (sub)millimeter wavelengths provide valuable constraints on the location of the water snow line while probing the bulk of the gas-phase reservoirs
Two-stage stochastic pricing and coordinating problem in a stochastic sustainable supply chain considering deterioration and carbon cap-and-trade regulation
No full textDue to the rapid growth of industrial activities worldwide and the importance of sustain- able supply chain management in controlling carbon emissions, this study examines the pricing and coordination issues of a two-stage sustainable supply chain for deteriorating products, including one manufacturer and one retailer. Assuming two productions and selling periods, the manufacturer has to adopt the governmental carbon cap-and-trade regulation due to using some equipment during the production period. Centralized and decentralized scenarios are solved precisely and heuristically con- sidering a stochastic demand. According to the analytical results, to design a cooperation rule between the members, two-part tariff (TPT) and two-part tariff-option (TPTO) contracts are proposed to coor- dinate chain decisions. Due to existing constraints defined during problem analysis, three new heuristic algorithms are developed to analyze the decentralized case and two contract problems. In addition to coordinating the problem and representing win–win outcomes using two contracts, numerical results show that TPT is more competent and gives win–win outcomes for all experiments. Also, it is more profitable for the manufacturer as the game leader. The experimental results prove that this problem is highly sensitive regarding parameter changes
Regional power grid carbon emission hierarchical optimization model based on power flow calculation and BP neural network
No full textAfter multiple energy sources are integrated into the grid, the power flow distribution of the regional power system undergoes changes, which affects its operational status and carbon emissions. To address this, a combined approach of power flow calculation and a BP neural network is proposed to reduce the carbon emissions of the regional power grid and mitigate the impact of power flow variations. A hierarchical optimization model for the grid’s carbon emissions is developed. The upper- level model predicts the carbon emissions of the regional power grid using the BP neural network, while the lower-level model incorporates the carbon emission prediction results and power flow characteristics to formulate an optimization objective function. This function aims to minimize the average carbon emissions, reduce the disparity in regional average carbon emissions, and lower the power flow cost. Subject to predefined constraints, the improved mayfly algorithm is employed to solve the objective function and obtain the optimal solution set. A logistic membership function is introduced to evaluate the satisfaction level of the objective function, enabling the selection of the most favorable compromise solution from the set. The test results show that the model has good carbon emission prediction performance, with correlation coefficients all above 0.927. It can provide a non-dominated solution set for each objective function, reducing carbon emissions in the regional power grid. The average prediction error of carbon emissions in the regional power grid is 0.08 tons; the maximum average carbon emission difference across regions is only 212.2 tons, indicating better stratified optimization effects
Retailer private brand introduction decisions in the presence of manufacturer information disclosure
No full textIn the current dual-channel structure whereby manufacturers sell both via retailers and their own direct online channel, retailers may introduce private brands (PBs) to compete with manufacturers’ products. With this in mind, manufacturers, better informed about product quality, carefully assess the potential impact of disclosing information on product quality through wholesale pricing and retailers’ incentives to introduce PBs. Using a two-stage game model, we show that manufacturers may strategically disclose low product quality to shift demand toward their direct channel, particularly when market demand is weak or PBs are introduced. However, PB introduction is not always profitable, as it depends on demand conditions and cost considerations. Surprisingly, we find that manufacturers’ disclosure strategies can deter retailers from launching PBs, even when product quality is fully disclosed. These findings reveal the complexities of dual-channel competition and offer insights into supply chain strategies
Jet reorientation revealed by intermittent jet activity in radio galaxy 0954+556
No full textContext. Intermittent jet activity of active galactic nuclei (AGNs) is a common phenomenon, whereas significant jet reorientation during episodic jet activity in relatively young radio galaxies is rarely reported. The quasar 0954+556 at z = 0.903 is an intriguing source exhibiting an unusual radio jet structure with significantly different jet directions at kiloparsec (kpc) and parsec (pc) scales. At kpc scales, images from the Very Large Array (VLA) exhibit a bright core, a linear jet extending ∼24 kpc to the northwest, and a discrete jet component ∼16 kpc to the northeast. At pc scales, images from the Very Long Baseline Array (VLBA) show a two-component structure with a projected separation of ∼360 pc in the north–south direction.
Aims. The peculiar structure of 0954+556 might result from jet reorientation. Here, our aim was to investigate the possible mechanism via multiscale and multifrequency deep radio images.
Methods. We performed VLA and VLBA observations of 0954+556. Together with some existing data in the NRAO data archive, we made multiple VLA images at 1.4–22 GHz and VLBA images at 1.7–43 GHz for various image analyses of the jet structure.
Results. We identified the location of the radio core at pc scales, detected the faint counter-jets at both pc and kpc scales for the first time, and revealed a diffuse emission region connecting pc- and kpc-scale forward jets. Our spectral index distribution and spectral aging analysis indicate that 0954+556 might undergo at least two episodes of jet activity during the current AGN phase. Moreover, pc-scale polarization maps display a well-resolved spine-sheath polarization structure.
Conclusions. It seems that the jet direction of 0954+556 changed significantly during intermittent jet activity. This may explain the different jet orientations and spectral ages observed from kpc to pc scales. The research provides a strong case that AGN jet direction might change rapidly on timescales of one million years
The miniJPAS survey quasar selection
No full textAims. Quasar catalogues from narrow-band photometric data are used in a variety of applications, including targeting for spectroscopic follow-up, measurements of supermassive black hole masses, or baryon acoustic oscillations. Here, we present the final quasar catalogue, including redshift estimates, from the miniJPAS Data Release constructed using several flavours of machine-learning algorithms.
Methods. In this work, we use a machine learning algorithm to classify quasars, optimally combining the output of eight individual algorithms. We assess the relative importance of the different classifiers. We include results from three different redshift estimators to also provide improved photometric redshifts. We compare our final catalogue against both simulated data and real spectroscopic data. Our main comparison metric is the f1 score, which balances the catalogue purity and completeness.
Results. We evaluate the performance of the combined algorithm using synthetic data. In this scenario, the combined algorithm out-performs the rest of the codes, reaching f1 = 0.88 and f1 = 0.79 for high- and low-z quasars (with z ≥ 2.1 and z < 2.1, respectively) down to magnitude r = 23.6. We further evaluate its performance against real spectroscopic data, finding different performances (some of the codes show a better performance, some a worse one, and the combined algorithm does not outperform the rest). We conclude that our simulated data are not realistic enough and that a new version of the mocks would improve the performance. Our redshift estimates on mocks suggest a typical uncertainty of σNMAD = 0.11, which, according to our results with real data, could be significantly smaller (as low as σNMAD = 0.02). We note that the data sample is still not large enough for a full statistical consideration
The ExoGRAVITY survey: A
No full textContext. Direct observations of exoplanet and brown dwarf companions with near-infrared interferometry, first enabled by the dualfield mode of VLTI/GRAVITY, provide unique measurements of the objects’ orbital motions and atmospheric compositions.
Aims. Here we compile a homogeneous library of all exoplanet and brown dwarf K-band spectra observed by GRAVITY thus far. This ExoGRAVITY Spectral Library is made publicly available online.
Methods. We re-reduced all the available GRAVITY dual-field high-contrast data in a uniform and highly automated way and, where companions were detected, extracted their ~2.0-2.4 μm K-band contrast spectra. We then derived stellar model atmospheres for all the employed flux references (either the host star or the swap calibrator), which we used to convert the companion contrast into companion flux spectra. Solely from the resulting GRAVITY K-band flux spectra, we extracted spectral types, spectral indices, and bulk physical properties for all the companions. Finally, and with the help of age constraints from the literature, we also derived isochronal masses for most of the companions using evolutionary models.
Results. The resulting library contains R ~ 500 GRAVITY K-band spectra of 39 substellar companions from late M to late T spectral types, including the entire L-T transition. Throughout this transition, a shift from CO-dominated late M- and L-type dwarfs to CH4-dominated T-type dwarfs can be observed in the K-band. The GRAVITY spectra alone constrain the objects’ bolometric luminosity to typically within ±0.15 dex. The derived isochronal masses agree with dynamical masses from the literature where available, except for HD 4113 c for which we confirm its previously reported potential underluminosity.
Conclusions. Medium-resolution spectroscopy of substellar companions with GRAVITY provides insight into the carbon chemistry and the cloudiness of these objects’ atmospheres. It also constrains these objects’ bolometric luminosities, which can yield measurements of their formation entropy if combined with dynamical masses, for instance from Gaia and GRAVITY astrometry
Bayesian multiband imaging of SN1987A in the Large Magellanic Cloud with SRG/eROSITA
No full textThe eROSITA Early Data Release (EDR) and eROSITA All-Sky Survey (eRASS1) data have already revealed a remarkable number of undiscovered X-ray sources. Using Bayesian inference and generative modeling techniques for X-ray imaging, we aim to increase the sensitivity and scientific value of these observations by denoising, deconvolving, and decomposing the X-ray sky. Leveraging information field theory, we can exploit the spatial and spectral correlation structures of the different physical components of the sky with non-parametric priors to enhance the image reconstruction. By incorporating instrumental effects into the forward model, we developed a comprehensive Bayesian imaging algorithm for eROSITA pointing observations. Finally, we applied the developed algorithm to EDR data of the Large Magellanic Cloud (LMC) SN1987A, fusing datasets from observations made by five different telescope modules. The final result is a denoised, deconvolved, and decomposed view of the LMC, which enables the analysis of its fine-scale structures, the identification of point sources in this region, and enhanced calibration for future work