Abstract: The centrality of heavy-ion collisions is directly related to the created medium in these interactions. A procedure to determine the centrality of collisions with the LHCb detector is implemented for lead-lead collisions root s(NN) = 5 TeV and lead-neon fixed-target collisions at root s(NN) = 69 GeV. The energy deposits in the electromagnetic calorimeter are used to determine and define the centrality classes. The correspondence between the number of participants and the centrality for the lead-lead collisions is in good agreement with the correspondence found in other experiments, and the centrality measurements for the lead-neon collisions presented here are performed for the first time in fixed-target collisions at the LHC.

Abstract: Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.

Abstract: In the high luminosity era of the Large Hadron Collider, the instantaneous luminosity is expected to reach unprecedented values, resulting in about 200 proton-proton interactions in a typical bunch crossing. To cope with the resultant increase in occupancy, bandwidth and radiation damage, the ATLAS Inner Detector will be replaced by an all-silicon system, the Inner Tracker (ITk). The ITk consists of a silicon pixel and a strip detector and exploits the concept of modularity. Prototyping and testing of various strip detector components has been carried out. This paper presents the developments and results obtained with reduced-size structures equivalent to those foreseen to be used in the forward region of the silicon strip detector. Referred to as petalets, these structures are built around a composite sandwich with embedded cooling pipes and electrical tapes for routing the signals and power. Detector modules built using electronic flex boards and silicon strip sensors are glued on both the front and back side surfaces of the carbon structure. Details are given on the assembly, testing and evaluation of several petalets. Measurement results of both mechanical and electrical quantities are shown. Moreover, an outlook is given for improved prototyping plans for large structures.