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For the High-Luminosity (HL-LHC) phase, the upgrade of the Compact Muon Solenoid (CMS) experiment at CERN will include a novel MIP Timing Detector (MTD). The central part of MTD, the barrel timing layer (BTL), is designed to provide a measurement of the time of arrival of charged particles with a precision of 30 ps at the beginning of HL-LHC, progressively degrading to 60 ps while operating in an extremely harsh radiation environment for over a decade. In this paper we present a comparative analysis of the time resolution of BTL module prototypes made of LYSO:Ce crystal bars read out by silicon photo-multipliers (SiPMs). The timing performance measured in beam test campaigns is presented for prototypes with different construction and operation parameters, such as different SiPM cell sizes (15, 20, 25 and 30 $\rm \mu m$), SiPM manufacturers and crystal bar thicknesses. The evolution of time resolution as a function of the irradiation level has been studied using non-irradiated SiPMs as well as SiPMs exposed up to $2\times 10^{14}~n_{eq}/cm^2$ fluence. The key parameters defining the module time resolution such as SiPM characteristics (gain, photon detection efficiency, radiation induced dark count rate) and crystal properties (light output and dimensions) are discussed. These results have informed the final choice of the MTD barrel sensor configuration and offer a unique starting point for the design of future large-area scintillator-based timing detectors in either low or high radiation environments.

Efficient allocation of resources to activities is pivotal in executing business processes but remains challenging. While resource allocation methodologies are well-established in domains like manufacturing, their application within business process management remains limited. Existing methods often do not scale well to large processes with numerous activities or optimize across multiple cases. This paper aims to address this gap by proposing two learning-based methods for resource allocation in business processes. The first method leverages Deep Reinforcement Learning (DRL) to learn near-optimal policies by taking action in the business process. The second method is a score-based value function approximation approach, which learns the weights of a set of curated features to prioritize resource assignments. To evaluate the proposed approaches, we first designed six distinct business processes with archetypal process flows and characteristics. These business processes were then connected to form three realistically sized business processes. We benchmarked our methods against traditional heuristics and existing resource allocation methods. The results show that our methods learn adaptive resource allocation policies that outperform or are competitive with the benchmarks in five out of six individual business processes. The DRL approach outperforms all benchmarks in all three composite business processes and finds a policy that is, on average, 13.1% better than the best-performing benchmark.

Cerium-doped Lutetium-Yttrium Oxyorthosilicate (LYSO:Ce)is one of the most widely used Cerium-doped Lutetium based scintillation crystals. Initially developed for medical detectors it rapidly became attractive for High Energy Particle Physics (HEP) applications, especially in the frame of high luminosity particle colliders. In this paper, a comprehensive and systematic study of LYSO:Ce ($[Lu_{(1-x)}Y_x]_2SiO_5$:$Ce$) crystals is presented. It involves for the first time a large number of crystal samples (180) of the same size from a dozen of producers.The study consists of a comparative characterization of LYSO:Ce crystal products available on the market by mechanical, optical and scintillation measurements and aims specifically, to investigate key parameters of timing applications for HEP., Comment: 38 pages, 22 figures