Nonlinear Model Predictive Control for the Superfluid Helium Cryogenic Circuit of the Large Hadron Collider

Abstract-Superfluid helium is used in the cryogenic circuit that cools down and stabilizes temperature of more than 1600 high performance, main superconducting magnets of the Large Hadron Collider (LHC) -the new particle accelerator at European Organization for Nuclear Research (CERN). This paper presents a simulation study of the application of Nonlinear Model Predictive Control (NMPC) to the Superfluid Helium Cryogenic Circuit. First, the new first principles, distributed parameter model of the circuit to be used in online optimization is reviewed. Then stabilization of the superconducting magnets temperature using NMPC based on the model and Continuation/Generalized Minimum Residual (C/GMRES) algorithm is described. Finally the small computational cost of C/GMRES solution/approximation method and resulting real-time feasibility are highlighted

Tracking the antibody immunome in sporadic colorectal cancer by using antigen self-assembled protein arrays

© 2021 by the authors.Sporadic Colorectal Cancer (sCRC) is the third leading cause of cancer death in the Western world, and the sCRC patients presenting with synchronic metastasis have the poorest prognosis. Genetic alterations accumulated in sCRC tumor cells translate into mutated proteins and/or abnormal protein expression levels, which contribute to the development of sCRC. Then, the tumor-associated proteins (TAAs) might induce the production of auto-antibodies (aAb) via humoral immune response. Here, Nucleic Acid Programmable Protein Arrays (NAPPArray) are employed to identify aAb in plasma samples from a set of 50 sCRC patients compared to seven healthy donors. Our goal was to establish a systematic workflow based on NAPPArray to define differential aAb profiles between healthy individuals and sCRC patients as well as between non-metastatic (n = 38) and metastatic (n = 12) sCRC, in order to gain insight into the role of the humoral immune system in controlling the development and progression of sCRC. Our results showed aAb profile based on 141 TAA including TAAs associated with biological cellular processes altered in genesis and progress of sCRC (e.g., FSCN1, VTI2 and RPS28) that discriminated healthy donors vs. sCRC patients. In addition, the potential capacity of discrimination (between non-metastatic vs. metastatic sCRC) of 7 TAAs (USP5, ML4, MARCKSL1, CKMT1B, HMOX2, VTI2, TP53) have been analyzed individually in an independent cohort of sCRC patients, where two of them (VTI2 and TP53) were validated (AUC ~75%). In turn, these findings provided novel insights into the immunome of sCRC, in combination with transcriptomics profiles and protein antigenicity characterizations, wich might lead to the identification of novel sCRC biomarkers that might be of clinical utility for early diagnosis of the tumor. These results explore the immunomic analysis as potent source for biomarkers with diagnostic and prognostic value in CRC. Additional prospective studies in larger series of patients are required to confirm the clinical utility of these novel sCRC immunomic biomarkers.We gratefully acknowledge financial support from the Spanish Health Institute Carlos III (ISCIII) for the grants: FIS PI14/01538, FIS PI17/01930 and CB16/12/00400. We also acknowledge Fondos FEDER (EU) “Una manera de hacer Europa” and Junta Castilla-León (COVID19 grant COV20EDU/00187). Fundación Solórzano FS/38-2017. The Proteomics Unit belongs to ProteoRed, PRB3-ISCIII, supported by grant PT17/0019/0023, of the PE I + D + I 2017-2020, funded by ISCIII and FEDER. CNPq-National Council for Scientific and Technological Development (Brazil) (306258/2019-6) and FAPERJ-Foundation for Research Support of Rio de Janeiro State for the financial support (E-26/201.670/2017 and 210.379/2018). M. González-González is supported by MINECOPTA2019-017870-I.A. Landeira-Viñuela is supported by VIII Centenario-USAL PhD Program. P.J.-V. is supported by JCYL PhD Program and scholarship JCYL-EDU/601/2020. P.D. and E.B. are supported by a JCYL-EDU/346/2013 Ph.D. scholarship

Effectiveness of an intervention for improving drug prescription in primary care patients with multimorbidity and polypharmacy:Study protocol of a cluster randomized clinical trial (Multi-PAP project)

This study was funded by the Fondo de Investigaciones Sanitarias ISCIII (Grant Numbers PI15/00276, PI15/00572, PI15/00996), REDISSEC (Project Numbers RD12/0001/0012, RD16/0001/0005), and the European Regional Development Fund ("A way to build Europe").Background: Multimorbidity is associated with negative effects both on people's health and on healthcare systems. A key problem linked to multimorbidity is polypharmacy, which in turn is associated with increased risk of partly preventable adverse effects, including mortality. The Ariadne principles describe a model of care based on a thorough assessment of diseases, treatments (and potential interactions), clinical status, context and preferences of patients with multimorbidity, with the aim of prioritizing and sharing realistic treatment goals that guide an individualized management. The aim of this study is to evaluate the effectiveness of a complex intervention that implements the Ariadne principles in a population of young-old patients with multimorbidity and polypharmacy. The intervention seeks to improve the appropriateness of prescribing in primary care (PC), as measured by the medication appropriateness index (MAI) score at 6 and 12months, as compared with usual care. Methods/Design: Design:pragmatic cluster randomized clinical trial. Unit of randomization: family physician (FP). Unit of analysis: patient. Scope: PC health centres in three autonomous communities: Aragon, Madrid, and Andalusia (Spain). Population: patients aged 65-74years with multimorbidity (≥3 chronic diseases) and polypharmacy (≥5 drugs prescribed in ≥3months). Sample size: n=400 (200 per study arm). Intervention: complex intervention based on the implementation of the Ariadne principles with two components: (1) FP training and (2) FP-patient interview. Outcomes: MAI score, health services use, quality of life (Euroqol 5D-5L), pharmacotherapy and adherence to treatment (Morisky-Green, Haynes-Sackett), and clinical and socio-demographic variables. Statistical analysis: primary outcome is the difference in MAI score between T0 and T1 and corresponding 95% confidence interval. Adjustment for confounding factors will be performed by multilevel analysis. All analyses will be carried out in accordance with the intention-to-treat principle. Discussion: It is essential to provide evidence concerning interventions on PC patients with polypharmacy and multimorbidity, conducted in the context of routine clinical practice, and involving young-old patients with significant potential for preventing negative health outcomes. Trial registration: Clinicaltrials.gov, NCT02866799Publisher PDFPeer reviewe

MONARC: Supervising the Archiving Infrastructure of CERN Control Systems

The CERN industrial control systems, using WinCC OA as SCADA (Supervisory Control and Data Acquisition), share a common history data archiving system relying on an Oracle infrastructure. It consists of 2 clusters of two nodes for a total of more than 250 schemas. Due to the large number of schemas and of the shared nature of the infrastructure, three basic needs arose: (1) monitor, i.e. get the inventory of all DB nodes and schemas along with their configurations such as the type of partitioning and their retention period; (2) control, i.e. parameterise each schema individually; and (3) supervise, i.e. have an overview of the health of the infrastructure and be notified of misbehaving schemas or database node. In this publication, we are presenting a way to monitor, control and supervise the data archiving system based on a classical SCADA system. The paper is organized in three parts: the first part presents the main functionalities of the application, while the second part digs into its architecture and implementation. The third part presents a set of use cases demonstrating the benefit of using the application

Testing Solutions for Siemens PLCs Programs Based on PLCSIM Advanced

Testing Programmable Logic Controllers (PLCs) is challenging, partially due to the lack of tools for testing. Isolating a part of the PLC program, feeding it with test inputs and checking the test outputs often require manual work and physical hardware. The Siemens PLCSIM Advanced tool can simulate PLCs and provide a rich application programming interface (API). This paper presents a new CERN made tool based on PLCSIM Advanced and the TIA Portal Openness API. The tool takes a test case described in an intuitive, tabular format, which is then executed with the full PLC program or a selected part of it, effectively allowing unit testing. The inputs can be fed and the outputs can be captured via the PLCSIM API. This way the tests can be executed and evaluated automatically, without manual work or physical hardware. Therefore, it is possible to provide an automated and scalable continuous testing solution for PLC programs to reveal errors as early as possible

Continuous Integration for PLC-based Control Systems

Continuous integration is widespread in software development, but a number of factors have thus far limited its use in PLC (Programmable Logic Controller) application development. A key requirement of continuous integration is that build and test stages must be automated. Automation of the build stage can be difficult for PLC developers, as building is typically performed with proprietary engineering tools. This has been solved by developing command line utilities which use the APIs of these tools. Another issue is that the program must be deployed to a real target (PLC) in order to test, something that is typically easier to do in other types of software development, where virtual environments may easily be used. This is solved by expanding the command line utilities to allow fully automated deployment of the PLC program. Finally, testing the PLC program presents its own challenges, as it is typically undesirable to alter the program in order to implement the tests natively in the PLC. This is avoided by using an industry standard protocol (OPC UA) to access PLC variables for testing purposes, allowing tests to be performed on an unaltered program

Continuous Integration for PLC-based Control System Development

Continuous Integration and Continuous Deployment (CI/CD) is a software engineering methodology which emphasises frequent, small changes committed to a version control system, which are verified by a suite of automatic tests, and which may be deployed to different environments. While CI/CD is well established in software engineering, it is not yet widely used in the development of industrial controls systems. However, the advantages of using CI/CD for such systems are clear. In this paper we describe a complete CI/CD pipeline able to automatically build Siemens PLC projects from sources, download the program to a PLC, and run a sequence of tests which interact with the PLC via both a Simulation Unit Profibus simulator and an OPC UA interface provided by Simatic NET. To achieve this, a gRPC service wrapping the Simatic API was used to provide an interface to the PLC project from the pipeline. In addition, a Python wrapper was created for the Simulation Unit API, as well as for the OPC UA interface, which allowed the test suite to be implemented in Python. A particle accelerator interlock system based on Siemens S7-300 PLCs has been taken as a use case to demonstrate the concept

What is special about PLC software model checking?

Model checking is a formal verification technique to check given properties of models, designs or programs with mathematical precision. Due to its high knowledge and resource demand, the use of model checking is restricted mainly to core parts of highly critical systems. However, we and many other authors have argued that automated model checking of PLC programs is feasible and beneficial in practice. In this paper we aim to explain why model checking is applicable to PLC programs even though its use for software in general is too difficult. We present an overview of the particularities of PLC programs which influence the feasibility and complexity of their model checking. Furthermore, we list the main challenges in this domain and the solutions proposed in previous works

Online analysis for anticipated failure diagnostics of the CERN cryogenic systems

The cryogenic system is one of the most critical component of the CERN Large Hadron Collider (LHC) and its associated experiments ATLAS and CMS. In the past years, the cryogenic team has improved the maintenance plan and the operation procedures and achieved a very high reliability. However, as the recovery time after failure remains the major issue for the cryogenic availability new developments must take place. A new online diagnostic tool is developed to identify and anticipate failures of cryogenics field equipment, based on the acquired knowledge on dynamic simulation for the cryogenic equipment and on previous data analytic studies. After having identified the most critical components, we will develop their associated models together with the signature of their failure modes. The proposed tools will detect deviation between the actual systems and their model or identify preliminary failure signatures. This information will allow the operation team to take early mitigating actions before the failure occurrence. This contribution will present the overall architecture of the proposed tool, the methods used to identify critical components, the characteristic failure model to recognize together with the implementation plan and the achieved results.The cryogenic system is one of the most critical component of the CERN Large Hadron Collider (LHC) and its associated experiments ATLAS and CMS. In the past years, the cryogenic team has improved the maintenance plans, the operation procedures and achieved a very high reliability. However, as the recovery time after failure remains the major issue for the cryogenic availability new developments must take place. A new online diagnostic tool is developed to identify and anticipate failures of cryogenics field equipment, based on the acquired knowledge on dynamic simulation for the cryogenic equipment and on previous data analytic studies. After having identified the most critical components, we will develop their associated models together with the signature of their failure modes. The proposed tools will detect deviation between the actual systems and their model or identify preliminary failure signatures. This information will allow the operation team to take early mitigating actions before the failure occurrence