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

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.

Abstract
Despite significant advances in web archive infrastructures, the problem of exploring the historical heritage preserved by web archives is yet to be solved. Timeline generation emerges in this context as one possible solution for automatically producing summaries of news over time. Thanks to this, users can gain a better sense of reported news events, entities, stories or topics over time, such as getting a summary of the most important news about a politician, an organisation or a locality. Web archives play an important role here by providing access to a historical set of preserved information. This particular characteristic of web archives makes them an irreplaceable infrastructure and a valuable source of knowledge that contributes to the process of timeline generation. Accordingly, the authors of this chapter developed "Tell me Stories" (), a news summarisation system, built on top of the infrastructure of Arquivo.pt-the Portuguese web-archive-to automatically generate a timeline summary of a given topic. In this chapter, we begin by providing a brief overview of the most relevant research conducted on the automatic generation of timelines for past-web events. Next, we describe the architecture and some use cases for "Tell me Stories". Our system demonstrates how web archives can be used as infrastructures to develop innovative services. We conclude this chapter by enumerating open challenges in this field and possible future directions in the general area of temporal summarisation in web archives. © Springer Nature Switzerland AG 2021. All rights reserved.

Abstract
Accurate monthly electricity consumption forecasting is indispensable for electricity retailers to mitigate trading risks in the electricity market. Clustering-based forecasting method are commonly used to generate accurate monthly electricity consumption forecasting results. This paper focuses on the problem that the existing clustering-based monthly electricity consumption forecasting methods perform clustering and forecasting independently, causing that the joint optimization of two steps cannot be achieved. The reason for this situation is that the target of current clustering algorithms, maximizing individual similarity in a group, is not consistent with the final target of improving the forecasting accuracy. To solve the above problem, the greedy clustering-based monthly electricity consumption forecasting model (GCMECF) is proposed in this paper. Its clustering step takes improving the overall predictability as the optimization target, which is closely related to the forecasting target. In this way, with matching targets, the joint optimization of clustering and forecasting can be achieved. Meanwhile, the selection of the optimal number of clusters is decided based on the forecasting performance under multiple clustering scenarios. The case study verifies the effectiveness and superiority of the proposed method via a realworld dataset.

Abstract
Geophysical techniques can be successfully applied towards the detection of buried explosive devices, the ground-penetrating radar (GPR) being an example of one such method. This technology works through emission and reception of electromagnetic radio waves being thus able to detect the presence of a subsurface object fundamentally due to reflections from contrasting electromagnetic properties between the object and the surrounding medium (e.g., soil). Many factors can influence the success of a GPR survey (e.g., target type, soil type, environmental conditions, GPR antenna frequency, data processing techniques), being essential to know and understand their likely effects before performing GPR studies, mainly in real cases. In this paper, through the analysis of case studies related to the use of GPR technology towards the detection of buried explosive devices, we intend to arrange and layout the main prior knowledge that a forensic geophysical expert must have when dealing with this type of fieldwork.