Abstract
Pushed by a number of advances, electromagnetic observatories have now reached the horizon scale of supermassive black holes. The existence and properties of horizons in our universe is one of the outstanding fundamental issues that can now be addressed. Here we investigate the ability to discriminate between black holes and compact, horizonless objects, focusing on the lensing of hot spots around compact objects. We work in particular with boson and Proca stars as central objects, and show that the absence of a horizon gives rise to a characteristic feature-photons that plow through the central object and produce an extra image. This feature should be universal for central objects made of matter weakly coupled to the standard model.

Abstract
In the conventional robust optimization (RO) context, the uncertainty is regarded as residing in a predetermined and fixed uncertainty set. In many applications, however, uncertainties are affected by decisions, making the current RO framework inapplicable. This paper investigates a class of two-stage RO problems that involve decision-dependent uncertainties. We introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem. A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision coupling. The computational tractability, robust feasibility and optimality, and convergence performance of the proposed algorithm are guaranteed with theoretical proof. Four motivating application examples that feature the decision-dependent uncertainties are provided. Finally, the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

Abstract
New paths to increase the sensing performance of plasmonic sensors have been reported in recent years. There are several methodologies to achieve such purpose, namely by optimizing the nanostructure, nanomaterial and even the sensing platform. Recently the use nanoparticles over plasmonic thin films have been reported and shown sensitivity enhancement, when compared to a bare thin film. Nevertheless, a nanomaterial combination between NP and thin film has not been studied. In this work it was studied such plasmonic materials in order to optimize not only refractometric sensitivity but also decrease the resultant plasmonic band width. It was found that for Au, Ag and Cu thin films, the deposition of plasmonic nanoparticles resulted in an overall refractometric sensitivity and figure of merit (FOM) increase. The larger FOM increase was obtained for the Ag thin film, from 42 to 162 when coupled to Si nanoparticles. The greater sensitivity increase was achieved for a Cu thin film coupled to a Si nanoparticle, with an increase from 1745 to 3230 nm/RIU. © Published under licence by IOP Publishing Ltd.

Abstract
This research aims to identify the information sources used by backpackers before and during the visit to an urban destination and its influence on the movements performed. It uses a questionnaire survey combined with a GPS app to track the movements of backpackers to identify backpackers' spatiotemporal behavior. A bivariate analysis using non-parametric tests was used to examine the relationships between variables. This study concludes that the consulting online information before the visit has a great deal of influence on the spatiotemporal behavior of the backpackers, namely on the linearity and intensity of the visit. If online information sources are very important due to the intense use of information by backpackers, word of mouth from other travelers should not be disregarded as an important information source. This research can contribute to the definition of more informed and differentiating tourism marketing strategy considering the information sources used and its influence on the movements performed during the visit.

Abstract
Stellar orbits at the Galactic Center provide a very clean probe of the gravitational potential of the supermassive black hole. They can be studied with unique precision, beyond the confusion limit of a single telescope, with the near-infrared interferometer GRAVITY. Imaging is essential to search the field for faint, unknown stars on short orbits which potentially could constrain the black hole spin. Furthermore, it provides the starting point for astrometric fitting to derive highly accurate stellar positions. Here, we present G(R), a new imaging tool specifically designed for Galactic Center observations with GRAVITY. The algorithm is based on a Bayesian interpretation of the imaging problem, formulated in the framework of information field theory and building upon existing works in radio-interferometric imaging. Its application to GRAVITY observations from 2021 yields the deepest images to date of the Galactic Center on scales of a few milliarcseconds. The images reveal the complicated source structure within the central 100mas around Sgr A*, where we detected the stars S29 and S55 and confirm S62 on its trajectory, slowly approaching Sgr A*. Furthermore, we were able to detect S38, S42, S60, and S63 in a series of exposures for which we offset the fiber from Sgr A*. We provide an update on the orbits of all aforementioned stars. In addition to these known sources, the images also reveal a faint star moving to the west at a high angular velocity. We cannot find any coincidence with any known source and, thus, we refer to the new star as S300. From the flux ratio with S29, we estimate its K-band magnitude as m(K)(S300)similar or equal to 19.0 - 19.3. Images obtained with CLEAN confirm the detection. To assess the sensitivity of our images, we note that fiber damping reduces the apparent magnitude of S300 and the effect increases throughout the year as the star moves away from the field center. Furthermore, we performed a series of source injection tests. Under favorable circumstances, sources well below a magnitude of 20 can be recovered, while 19.7 is considered the more universal limit for a good data set.

Abstract
The proliferation of demand response programs in the smart grid provides the system operator unique opportunities to reduce the load peak and alleviate network congestions. This paper considers the economic dispatch problem with elastic demands which flexibly respond to the locational marginal prices (LMPs). However, LMP is the dual variable of optimal power flow (OPF) problem and thus is unknown before the OPF problem is solved. Without LMP, the elastic demand is unclear, and the OPF problem cannot be set up. Given this interactive nature, it is difficult to acquire the dispatch strategy as well as the LMP according to the traditional OPF method. This paper thoroughly addresses this problem. Specifically, the limitation of the traditional LMP scheme in the described situation is analyzed. An equilibrium solution may not exist because the demand function and the discontinuous LMP may not have an intersection. To overcome this difficulty, LMP at the discontinuity point is redefined, so that the dispatch problem always has an equilibrium solution. A mixed-integer linear programming model for the economic dispatch problem with LMP-dependent load is proposed, and the equilibrium solution simultaneously offers the dispatch strategy and LMPs. Case studies demonstrate the difficulties of traditional approaches and the effectiveness of the proposed method.