Abstract
Chest radiography is one of the most ubiquitous medical imaging exams used for the diagnosis and follow-up of a wide array of pathologies. However, chest radiography analysis is time consuming and often challenging, even for experts. This has led to the development of numerous automatic solutions for multipathology detection in chest radiography, particularly after the advent of deep learning. However, the black-box nature of deep learning solutions together with the inherent class imbalance of medical imaging problems often leads to weak generalization capabilities, with models learning features based on spurious correlations such as the aspect and position of laterality, patient position, equipment and hospital markers. In this study, an automatic method based on a YOLOv3 framework was thus developed for the detection of markers and written labels in chest radiography images. It is shown that this model successfully detects a large proportion of markers in chest radiography, even in datasets different from the training source, with a low rate of false positives per image. As such, this method could be used for performing automatic obscuration of markers in large datasets, so that more generic and meaningful features can be learned, thus improving classification performance and robustness.

Abstract
Travel behavior adaptations resulting from international temporary relocation is understudied, despite their increasing relevance. The scarce published literature on the subject overlooks the local contexts and ignores aspects related to the adaptation processes and motivations. This study aims to partially fill this gap by addressing the travel behavior adaptation of international students and researchers, focusing on public transport (PT) frequency of use and satisfaction. To investigate this, a Bayesian Structural Equation Model was estimated using data collected from a tailor-made online survey answered by temporary international exchange students and researchers. The model confirms that (i) travel behavior habit in the city of origin influences the residential location choice in the host city; (ii) the higher the frequency of PT use in the city of origin, the higher the PT use in the host city; (iii) the residential location in the host city affects individuals' frequency of PT use and satisfaction; (iv) perceiving technology as helpful to move around leads to perceiving the PT system as easier to use at the beginning of the stay; (v) perceiving the PT as easier to use, leads to a higher frequency of its use and a higher level of satisfaction with the PT system.

Abstract
In recent years, extensive sequencing and annotation of bacterial genomes has revealed an unexpectedly large number of secondary metabolite biosynthetic gene clusters whose products are yet to be discovered. For example, cyanobacterial genomes contain a variety of gene clusters that likely incorporate fatty acid derived moieties, but for most cases we lack the knowledge and tools to effectively predict or detect the encoded natural products. Here, we exploit the apparent absence of a functional beta-oxidation pathway in cyanobacteria to achieve efficient stable-isotope-labeling of their fatty acid derived lipidome. We show that supplementation of cyanobacterial cultures with deuterated fatty acids can be used to easily detect natural product signatures in individual strains. The utility of this strategy is demonstrated in two cultured cyanobacteria by uncovering analogues of the multidrug-resistance reverting hapalosin, and novel, cytotoxic, lactylate-nocuolin A hybrids-the nocuolactylates.

Abstract
We propose supporting hybrid conference experiences using the Augmented Reality Edge Network Architecture (ARENA). ARENA is a platform based on web technologies that simplifies the creation of collaborative mixed reality for standard Web Browsers (Chrome, Firefox) in VR, Headset AR/VR Browsers (Magic Leap, Hololens, Oculus Quest 2), and mobile AR (WebXR Viewer for iOS, Chrome with experimental flags for Android, and our own custom WebXR fork for iOS). We use a 3D scan of the conference venue as the backdrop environment for remote users and a model to stage various AR interactions for in-person users. Remote participants can use VR in a browser or a VR headset to navigate the scene. In-person participants can use AR headsets or mobile AR through WebXR browsers to see and hear remote users. ARENA can scale up to hundreds of users in the same scene and provides audio and video with spatial sound that can more closely capture real-world interactions.

Abstract
Optical fiber gratings have long shown their sensing capabilities. One of the main challenges, however, is the interrogation method applied, since typical systems tend to use broadband light sources with optical spectrum analyzers, laser scanning units or CCD (Charged Coupled Device) spectrometers. The following paper presents the development of an interrogation system, which explores the temperature response of a multimode laser diode, in order to interrogate long period fiber gratings. By performing a spectral sweep along one of its rejection bands, a discrete attenuation spectrum is created. Through a curve fitting technique, the original spectrum is restored. The built unit, while presenting a substantially reduced cost compared with typical interrogation systems, is capable of interrogating along a 10 nm window with measurement errors reaching minimum values as low as 0.4 nm, regarding the grating central wavelength, and 0.4 dB for its attenuation. Given its low cost and reduced dimensions, the developed system shows potential for slow-changing field applications.

Abstract
This paper deals with the bi-level two-stage operation scheduling of hydrogen-based microgrids within a distribution system where the wind and solar generation and load demands are considered as uncertain variables. The distribution system is considered as a leader in the upper level and microgrids as followers in the lower level. Unlike previous approaches, the upper-level is within the day-ahead market and considered a deterministic problem, and the lower-level is considered a stochastic problem and consists of two stages. The first stage determines the purchasing power from the distribution system, while the second stage adjusts the outputs and power dispatch for any realizations of scenarios. This model is transformed from a bi-level to a linear single-level model by applying the Karush- Kuhn-Tucker (KKT) optimally conditions, strong duality, and Fortuny-Amat methods. Several comparisons have been carried out regarding the single clearing price for all microgrids or separate prices for each microgrid. Furthermore, power exchange and dispatch in the distribution system are investigated under the mentioned frameworks.