Abstract
Context. The formation and evolution of planetary systems impact the evolution of the primordial accretion disk in its dust and gas content. HD 141569 is a peculiar object in this context as it is the only known pre-main sequence star characterized by a hybrid disk. Observations with 8 m class telescopes probed the outer-disk structure showing a complex system of multiple rings and outer spirals. Furthermore, interferometric observations attempted to characterize its inner 5 au region, but derived limited constraints. Aims. The goal of this work was to explore with new high-resolution interferometric observations the geometry, properties, and dynamics of the dust and gas in the internal regions of HD 141569. Methods. We observed HD 141569 on milliarcsecond scales with GRAVITY/VLTI in the near-infrared (IR) at low (R similar to 20) and high (R similar to 4000) spectral resolution. We interpreted the interferometric visibilities and spectral energy distribution with geometrical models and through radiative transfer techniques using the code MCMax to constrain the dust emission. We analyzed the high spectral resolution quantities (visibilities and differential phases) to investigate the properties of the Brackett-gamma (Br gamma) line emitting region. Results. Thanks to the combination of three different epochs, GRAVITY resolves the inner dusty disk in the K band with squared visibilities down to V-2 similar to 0.8. A differential phase signal is also detected in the region of the Br gamma line along most of the six baselines. Data modeling shows that an IR excess of about 6% is spatially resolved and that the origin of this emission is confined in a ring of material located at a radius of similar to 1 au from the star with a width less than or similar to 0.3 au. The MCMax modeling suggests that this emission could originate from a small amount (1.4 x 10(-8) M-circle plus) of quantum-heated particles, while large silicate grain models cannot reproduce at the same time the observational constraints on the properties of near-IR and mid-IR fluxes. The high spectral resolution differential phases in the Br gamma line clearly show an S-shape that can be best reproduced with a gaseous disk in Keplerian rotation, confined within 0.09 au (or 12.9 R-star). This is also hinted at by the double-peaked Br gamma emission line shape, known from previous observations and confirmed by GRAVITY. The modeling of the continuum and gas emission shows that the inclination and position angle of these two components are consistent with a system showing relatively coplanar rings on all scales. Conclusions. With a new and unique observational dataset on HD 141569, we show that the complex disk of this source is composed of a multitude of rings on all scales. This aspect makes HD 141569 a potentially unique source to investigate planet formation and disk evolution in intermediate-mass pre-main sequence stars.

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Abstract
Photoplethysmography (PPG) is a noninvasive way to monitor various aspects of the circulatory system, and is becoming more and more widespread in biomedical processing. Recently, deep learning methods for analyzing PPG have also become prevalent, achieving state of the art results on heart rate estimation, atrial fibrillation detection, and motion artifact identification. Consequently, a need for interpretable deep learning has arisen within the field of biomedical signal processing. In this paper, we pioneer novel explanatory metrics which leverage domain-expert knowledge to validate a deep learning model. We visualize model attention over a whole testset using saliency methods and compare it to human expert annotations. Congruence, our first metric, measures the proportion of model attention within expert-annotated regions. Our second metric, Annotation Classification, measures how much of the expert annotations our deep learning model pays attention to. Finally, we apply our metrics to compare between a signal based model and an image based model for PPG signal quality classification. Both models are deep convolutional networks based on the ResNet architectures. We show that our signal-based one dimensional model acts in a more explainable manner than our image based model; on average 50.78% of the one dimensional model's attention are within expert annotations, whereas 36.03% of the two dimensional model's attention are within expert annotations. Similarly, when thresholding the one dimensional model attention, one can more accurately predict if each pixel of the PPG is annotated as artifactual by an expert. Through this testcase, we demonstrate how our metrics can provide a quantitative and dataset-wide analysis of how explainable the model is.

Abstract
This paper proposes a data-driven chance-constrained optimal gas-power flow (OGPF) calculation method without any prior assumption on the distribution of uncertainties of wind power generation. The Gaussian mixture model is employed to fit the uncertainty distribution, where the Bayesian nonparametric Dirichlet process is adopted to tune the component number. To facilitate the online application of the proposed methods, an online-offline double-track distribution construction approach is established, where the frequency of training the relatively time-consuming Dirichlet process Gaussian mixture model can be reduced. On account of the quadratic gas consumption expression of gas-fired generators as well as the linear decision rule based uncertainty mitigation mechanism, the chance constraints would become quadratic ones with quadratic terms of uncertainties, which makes the proposed model more intractable. An equivalent linear separable counterpart is then provided for the quadratic chance constraints, after which the intractable chance constraints could be converted into traditional linear ones. The convex-concave procedure is used to crack the nonconvex Weymouth equation in the gas network and the auxiliary quadratic equalities. Simulation results on two test systems validate the effectiveness of the proposed methods.

Abstract
Technological advances in the last two decades have aroused great interest in the class of dynamic vehicle routing problems (DVRPs), which is reflected in the significant growth of the number of articles published in this period. Our work presents a comprehensive review of the DVRP literature of the last seven years (2015-2021) focusing mainly on applications and solution methods. Consequently, we provide a taxonomy of the problem and a taxonomy of the related solution methods. The papers considered for this review are discussed, analyzed in detail and classified according to the proposed taxonomies. The results of the analysis reveal that 65% of the articles deal with dynamic and stochastic problems (DS) and 35% with dynamic and deterministic problems (DD). With respect to applications, 40% of articles correspond to the transportation of goods, 17.5% to services, 17.5% to the transport of people and 25% to generic applications. Among the solution methods, heuristics and metaheuristics stand out. We discussed the application opportunities associated with DVRPs in recent business models and new concepts of logistical operations. An important part of these new applications that we found in our review is in the segment of business-to-consumer crowd-sourced services, such as peer-to-peer ride-sharing and online food ordering services. In our review many of the applications fall into the stochastic and dynamic category. This means that for many of these applications, companies usually possess historical data about the dynamic and uncertainty sources of their routing problems. Finally, we present the main solution streams associated with DVRPs.