Modern statistical process monitoring (SPM) applications focus on profile monitoring, i.e., the monitoring of process quality characteristics that can be modeled as profiles, also known as functional data. Despite the large interest in the profile monitoring literature, there is still a lack of software to facilitate its practical application. This article introduces the funcharts R package that implements recent developments on the SPM of multivariate functional quality characteristics, possibly adjusted by the influence of additional variables, referred to as covariates. The package also implements the real-time version of all control charting procedures to monitor profiles partially observed up to an intermediate domain point. The package is illustrated both through its built-in data generator and a real-case study on the SPM of Ro-Pax ship CO2 emissions during navigation, which is based on the ShipNavigation data provided in the Supplementary Material.

The high design luminosity of the SuperKEKB electron-positron collider will result in challenging levels of beam-induced backgro\ unds in the interaction region. Understanding and mitigating these backgrounds is critical to the success of the Belle~II experi\ ment. We report on the first background measurements performed after roll-in of the Belle II detector, a period known as SuperKE\ KB Phase 2, utilizing both the BEAST II system of dedicated background detectors and the Belle II detector itself. We also repor\ t on first revisions to the background simulation made in response to our findings. Backgrounds measured include contributions f\ rom synchrotron radiation, beam-gas, Touschek, and injection backgrounds. At the end of Phase 2, single-beam backgrounds origina\ ting from the 4 GeV positron Low Energy Ring (LER) agree reasonably well with simulation, while backgrounds from the 7 GeV elect\ ron High Energy Ring (HER) are approximately one order of magnitude higher than simulation. We extrapolate these backgrounds for\ ward and conclude it is safe to install the Belle II vertex detector.

Critical Infrastructures (CIs), such as smart power grids, transport systems, and financial infrastructures, are more and more vulnerable to cyber threats, due to the adoption of commodity computing facilities. Despite the use of several monitoring tools, recent attacks have proven that current defensive mechanisms for CIs are not effective enough against most advanced threats. In this paper we explore the idea of a framework leveraging multiple data sources to improve protection capabilities of CIs. Challenges and opportunities are discussed along three main research directions: i) use of distinct and heterogeneous data sources, ii) monitoring with adaptive granularity, and iii) attack modeling and runtime combination of multiple data analysis techniques.