bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prevalence of burnout among healthcare professionals: a survey at fort portal regional referral hospital
Authors: Batanda Ian 
Published: May 2024

AbstractThe work environment in most hospitals is characterised by activities that are strenuous both physically and mentally. These can result in physical and mental exhaustion, which can lead to burnout if not adequately addressed. Burnout among healthcare professionals can negatively affect their clinical decision-making, quality of communication with patients and colleagues as well as their ability to cope with work-related pressure, and ultimately affect the quality of care and patient outcomes. The inclusion of burnout in the 11th revision of the International Classification of Diseases (ICD-11) as an occupational phenomenon indicates that it is an issue of concern in the workplace for which people may need professional attention. This descriptive cross-sectional survey aimed to determine the point prevalence of burnout among healthcare professionals at Fort Portal Regional Referral Hospital and the factors contributing to burnout. The study also evaluated the linear relationship between the age of workers, their work duration at the hospital, and their burnout score, in addition to the possible impact on patient care. Participants were selected from the hospital WhatsApp group and invitations to participate were sent to their individual accounts. Burnout was assessed using the Copenhagen Burnout Inventory. Generally, burnout scores ranged from 16% to 86%, with an overall mean burnout score of 57.4%. The notable factors contributing to burnout included imbalances in duty allocation, physically strenuous work, and resource constraints. Burnout of varying levels was found to be prevalent across all carders in the hospital, although the results indicate that most healthcare professionals experience moderate burnout. Most of the factors contributing to burnout are within the scope of hospital leadership to address. The possible impact on staff performance and patients’ clinical outcomes is speculative, and additional studies are required.
Distributed auto disturbances rejection resilient control of permanent magnetic maglev trains based on the optimized deep deterministic policy gradient algorithm
Authors: Guo Zhen‐yu , Li Zhong‐qi 
Published: May 2024

AbstractDue to time‐varying external disturbances and uncertain system models, distributed cooperative controllers with poor adaptability are unable to meet the cooperative control requirements of multiple permanent magnetic maglev trains in virtual coupling mode. In this work, a new effective distributed auto disturbance rejection resilient controller based on the optimized deep deterministic policy gradient algorithm (DDPG) is proposed. The DDPG algorithm is used to improve the adaptability of the controller against the time‐varying disturbances. An adaptive particle swarm optimization method (APSO) is also proposed to optimize the hyperparameters of DDPG in the search space. The simulation results show that, compared to the particle swarm optimization (PSO)‐actor‐critic (AC), PSO‐policy gradient (PG), and PSO‐DDPG algorithms, the proposed APSO‐DDPG algorithm performs better during training and verification. The proposed method achieves adaptive online adjustment of the controller parameters effectively and greatly improves the stability of cooperative control.
Integrating FTS in the Fenix HPC Infrastructure
Authors: Long Shiting , Pleiter Dirk , Patrascoiu Mihai , Padrin Cristiano , Carpene Michele , More Sergi , Carpio Miguel 
Published: May 2024

As compute requirements in experimental high-energy physics are expected to significantly increase, there is a need for leveraging high-performance computing (HPC) resources. However, HPC systems are currently organised and operated in a way that this is not easily possible. Here we will focus on a specific e-infrastructure that incorporates HPC resources, namely Fenix, which is based on a consortium of 6 leading European supercomputing centres. Fenix was initiated through the Human Brain Project (HBP) but also provides resources to other research communities in Europe. The Fenix sites are integrated into a common AAI and provide a so-called Archival Data Repository that can be accessed through a Swift API.
In this paper, we report on our efforts to realise a data transfer service that allow to exchange data with the Fenix e-infrastructure. This has been enabled by implementing support of Swift in FTS3 and related software components. We will, in particular, discuss how FTS3 has been integrated into the Fenix AAI, which largely follows the architectural principles of the European Open Science Cloud (EOSC). Furthermore, we show how end-users can use this service through a WebFTS service that has been integrated into the science gateway of the HBP, which is also known as the HBP Collaboratory. Finally, we discuss how transfer commands can be automatically distributed over several FTS3 instances to optimise transfer between different Fenix sites.
Overview of the distributed image processing infrastructure to produce the Legacy Survey of Space and Time
Authors: Hernandez Fabio , Beckett George , Clark Peter , Doidge Matt , Jenness Tim , Karavakis Edward , Le Boulc’h Quentin , Love Peter , Mainetti Gabriele , Noble Timothy , White Brandon , Yang Wei 
Published: May 2024

The Vera C. Rubin Observatory is preparing to execute the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently the final phase of construction is under way in the Chilean Andes, with the Observatory’s ten-year science mission scheduled to begin in 2025. Rubin’s 8.4-meter telescope will nightly scan the southern hemisphere collecting imagery in the wavelength range 320–1050 nm covering the entire observable sky every 4 nights using a 3.2 gigapixel camera, the largest imaging device ever built for astronomy. Automated detection and classification of celestial objects will be performed by sophisticated algorithms on high-resolution images to progressively produce an astronomical catalog eventually composed of 20 billion galaxies and 17 billion stars and their associated physical properties.In this article we present an overview of the system currently being constructed to perform data distribution as well as the annual campaigns which reprocess the entire image dataset collected since the beginning of the survey. These processing campaigns will utilize computing and storage resources provided by three Rubin data facilities (one in the US and two in Europe). Each year a Data Release will be produced and disseminated to science collaborations for use in studies comprising four main science pillars: probing dark matter and dark energy, taking inventory of solar system objects, exploring the transient optical sky and mapping the Milky Way.Also presented is the method by which we leverage some of the common tools and best practices used for management of large-scale distributed data processing projects in the high energy physics and astronomy communities. We also demonstrate how these tools and practices are utilized within the Rubin project in order to overcome the specific challenges faced by the Observatory.
ATLAS Liquid Argon Calorimeter Commissioning for LHC Run-3
Authors: Furukawa Marin 
Published: May 2024

The Liquid Argon calorimeter of the ATLAS detector provides inputs to the ﬁrst level of the ATLAS trigger. After the successful period of data-taking during the second run of the LHC (Run-2) the ATLAS detector entered into a long period of shutdown. In 2022 the LHC has started a new data taking period Run-3. During the Run-3 period (2022 ­ 2025), it expects to have higher luminosity than Run-2 with pile-up of up to 80 interactions per bunch crossing.
To cope with these harsh conditions, a new trigger readout system has been installed during the long shutdown. The new system should improve the triggering performances for electromagnetic objects signiﬁcantly. This will be achieved by increasing the granularity of the readout system available at triggerlevel by up to a factor of ten. This new trigger system is currently undergoing commissioning in order to become the main LAr trigger readout in 2023.
This contribution will present the current status of the Liquide Argon Calorimeter system and the commissioning to be completed towards the full operation of the new trigger readout system for Run-3 of the LHC.
Commissioning of the ALICE readout software for LHC Run 3
Authors: Chapeland Sylvain 
Published: May 2024

ALICE (A Large Ion Collider Experiment) is a heavy-ion detector studying the physics of strongly interacting matter and the quarkgluon plasma at the CERN LHC (Large Hadron Collider). During the second long shut-down of the LHC, the ALICE detector was upgraded to cope with an interaction rate of 50 kHz in Pb-Pb collisions, producing in the online computing system (O2) a sustained input throughput of 3.5 TB/s.In the past years, the O2/FLP project built the new data-acquisition system capable of handling this load. It consists of 200 readout nodes, collecting the data transferred from over 8000 detector links to PCs memory by dedicated PCI boards. The readout software manages the hardware and software memory buffers used for DMA and inter-process communication. It initiates the data flow, performs on-the-fly consistency checks, formats the data, reports performance, and finally pushes the data to the local processing pipeline. The output is then sent by the data distribution software over 100Gb/s links to a dedicated event processing farm.The readout software modular design allowed to address the manifold needs faced during the prototyping, installation and commissioning phases, which proved essential from the lab tests to physics production, like file replay and recording, or online multi-threaded LZ4 compression.We will describe the hardware and software implementation of the O2readout system, and review the challenges met during the commissioning and first months of operation with LHC collisions in 2022.
Multicore workflow characterisation methodology for payloads running in the ALICE Grid
Authors: Bertran Ferrer Marta , Grigoras Costin , Badia Rosa M. 
Published: May 2024

For LHC Run3 the ALICE experiment software stack has been completely refactored, incorporating support for multicore job execution. Whereas in both LHC Run 1 and 2 the Grid jobs were single-process and made use of a single CPU core, the new multicore jobs spawn multiple processes and threads within the payload. Some of these multicore jobs deploy a high amount of shortlived processes, in the order of more than a dozen per second. The overhead of starting so many processes impacts the overall CPU utilization of the payloads, in particular its System component. Furthermore, the short-lived processes were not correctly accounted for by the monitoring system of the experiment. This paper presents the developed new methodology for supervising the payload execution.
We also present a black box analysis of the new multicore experiment software framework tracing the used resources and system function calls issued by MonteCarlo simulation jobs. Multiple sources of overhead in the lifecycle of processes and threads have thus been identified. This paper describes how the source of each was traced and what solutions were implemented to address them. These improvements have impacted the resource consumption and the overall turnaround time of these payloads with a notable 35% reduction in execution time for a reference production job. We also introduce how this methodology will be used to further improve the efficiency of our experiment software and what other optimization venues are currently under research.
Analysis Grand Challenge benchmarking tests on selected sites
Authors: Koch David , Kuhr Thomas , Duckeck Günter , Hartmann Nikolai 
Published: May 2024

A fast turn-around time and ease of use are important factors for systems supporting the analysis of large HEP data samples. We study and compare multiple technical approaches.
This article will be about setting up and benchmarking the Analysis Grand Challenge (AGC) [1] using CMS Open Data. The AGC is an effort to provide a realistic physics analysis with the intent of showcasing the functionality, scalability and feature-completeness of the Scikit-HEP Python ecosystem.
We will present the results of setting up the necessary software environment for the AGC and benchmarking the analysis’ run time on various computing clusters: the institute SLURM cluster at LMU Munich, a SLURM cluster at LRZ (WLCG Tier-2 site) and the analysis facility Vispa [2], operated by RWTH Aachen.
Each site provides slightly different software environments and modes of operation which poses interesting challenges on the flexibility of a setup like that intended for the AGC.
Comparing these benchmarks to each other also provides insights about different storage and caching systems. At LRZ and LMU we have regular Grid storage (HDD) as well as an SSD-based XCache server and on Vispa a sophisticated per-node caching system is used.
AUDITOR: Accounting for opportunistic resources
Authors: Boehler Michael , Gamel Anton J. , Kroboth Stefan , Rottler Benjamin , Sammel Dirk , Schumacher Markus 
Published: May 2024

The increasing computational demand in High Energy Physics (HEP) as well as increasing concerns about energy efficiency in highperformance/high-throughput computing are driving forces in the search for more efficient ways to utilise available resources. Since avoiding idle resources is key in achieving high efficiency, an appropriate measure is sharing of idle resources of underutilised sites with fully occupied sites. The software COBalD/TARDIS can automatically, transparently, and dynamically (dis)integrate such resources in an opportunistic manner. Sharing resources however also requires accounting.
In this work we introduce AUDITOR (AccoUnting DatahandlIng Toolbox for Opportunistic Resources), a flexible and extensible accounting system that is able to cover a wide range of use cases and infrastructures. AUDITOR gathers accounting data via so-called collectors which are designed to monitor batch systems, COBalD/TARDIS, cloud schedulers, or other sources of information. The data is stored in a database and provided to so-called plugins, which act based on accounting records. An action could for instance be creating a bill of utilised resources, computing the CO2 footprint, adjusting parameters of a service, or forwarding accounting information to other accounting systems. Depending on the use case, a suitable collector and plugin can be chosen from a growing ecosystem of collectors and plugins. Libraries for interacting with AUDITOR are provided to facilitate the development of collectors and plugins by the community.
The ALICE Grid Workflow for LHC Run 3
Authors: Storetvedt Maxim , Betev Latchezar , Helstrup Håvard , Hetland Kristin Fanebust , Kileng Bjarte 
Published: May 2024

In preparation for LHC Run 3 and 4 the ALICE Collaboration has moved to a new Grid middleware, JAliEn, and workflow management system. The migration was dictated by the substantially higher requirements on the Grid infrastructure in terms of payload complexity, increased number of jobs and managed data volume, all of which required a complete rewrite of the middleware using modern software languages and technologies. Through containerisation, self-contained binaries, managed by the JAliEn middleware, we provide a uniform execution environment across sites and various architectures, including accelerators. The model and implementation have proven their scalability and can be easily deployed across sites with minimal intervention.This contribution outlines the architecture of the new Grid workflow as deployed in production and the workflow process. Specifically shown is how core components are moved and bootstrapped through CVMFS, enabling the middleware to run anywhere fully independent of the host system. Furthermore, we examine how new middleware releases, containers and their runtimes are centrally maintained and easily deployed across the Grid, also by the means of a common build system.
Physics analysis for the HL-LHC: Concepts and pipelines in practice with the Analysis Grand Challenge
Authors: Held Alexander , Kauffman Elliott , Shadura Oksana , Wightman Andrew 
Published: May 2024

Realistic environments for prototyping, studying and improving analysis workflows are a crucial element on the way towards user-friendly physics analysis at HL-LHC scale. The IRIS-HEP Analysis Grand Challenge (AGC) provides such an environment. It defines a scalable and modular analysis task that captures relevant workflow aspects, ranging from large-scale data processing and handling of systematic uncertainties to statistical inference and analysis preservation. By being based on publicly available Open Data, the AGC provides a point of contact for the broader community. Multiple different implementations of the analysis task that make use of various pipelines and software stacks already exist.
This contribution presents an updated AGC analysis task. It features a machine learning component and expanded analysis complexity, including the handling of an extended and more realistic set of systematic uncertainties. These changes both align the AGC further with analysis needs at the HL-LHC and allow for probing an increased set of functionality.
Another focus is the showcase of a reference AGC implementation, which is heavily based on the HEP Python ecosystem and uses modern analysis facilities. The integration of various data delivery strategies is described, resulting in multiple analysis pipelines that are compared to each other.
Machine Learning for Columnar High Energy Physics Analysis
Authors: Kauffman Elliott , Held Alexander , Shadura Oksana 
Published: May 2024

Machine learning (ML) has become an integral component of high energy physics data analyses and is likely to continue to grow in prevalence. Physicists are incorporating ML into many aspects of analysis, from using boosted decision trees to classify particle jets to using unsupervised learning to search for physics beyond the Standard Model. Since ML methods have become so widespread in analysis and these analyses need to be scaled up for HL-LHC data, neatly integrating ML training and inference into scalable analysis workflows will improve the user experience of analysis in the HL-LHC era.We present the integration of ML training and inference into the IRISHEP Analysis Grand Challenge pipeline to provide an example of how this integration can look like in a realistic analysis environment. We also utilize Open Data to ensure the project’s reach to the broader community. Different approaches for performing ML inference at analysis facilities are investigated and compared, including performing inference through external servers. Since ML techniques are applied for many different types of tasks in physics analyses, we showcase options for ML integration that can be applied to various inference needs.
Outlines in hardware and software for new generations of exascale interconnects
Authors: Ammendola Roberto , Biagioni Andrea , Chiarini Carlotta , Cretaro Paolo , Frezza Ottorino , Lo Cicero Francesca , Lonardo Alessandro , Martinelli Michele , Pastorelli Elena , Paolucci Pier Stanislao , Pontisso Luca , Rossi Cristian , Simula Francesco , Vicini Piero 
Published: May 2024

RED-SEA (https://redsea-project.eu/) is a European project funded in the framework of the H2020-JTI-EuroHPC-2019-1 call that started in April 2021. The goal of the project is to evaluate the architectural design of the main elements of the interconnection networks for the next generation of HPC systems supporting hundreds of thousands of computing nodes enabling the Exascale for HPC, HPDA and AI applications while providing preliminary prototypes.
The main technological feature is the BXI network, originally designed and produced by ATOS (France). The plan is to integrate in the next release of the network – BXI3 – the architectural solutions and novel IPs developed within the framework of the RED-SEA project.
The consortium is composed of 11 well-established research teams across Europe, with extensive experience in interconnects, including network design, deployment and evaluation.
Within RED-SEA, INFN is adopting a hardware/software co-design approach to design APEnetX, a scalable interconnect prototyped on latest generation Xilinx FPGAs, adding innovative components for the improvement of the performance and resiliency of the interconnect. APEnetX is an FPGA-based, PCIe Gen3/4 network interface card equipped with RDMA capabilities being the endpoint of a direct multidimensional toroidal network and suitable for integration in the BXI environment. APEnetX design will be benchmarked on project testbeds using real scientific applications like NEST, a spiking neural network simulator.
Optimization of Wire Arc Additive Manufacturing Based Cold Metal Transfer arc length and dynamic correction parameters using geometrical and Digital Image Corelation analysis
Authors: Rabhi Mounir , Ben Khalifa Ated , Ftoutou Ezzeddine , Chatti Sami 
Published: May 2024

The control of the heat input and the geometrical characteristics of the joint during and after a layer deposition remains the concern of the manufacturers in Wire Arc Additive Manufacturing, despite the existence of all new technologies such as the Cold Metal Transfer process. The understanding of the effect of arc parameters having a remarkable role in the optimization of the layer deposition heat input is essential. In the following work, we will focus on the influence of the combination of the parameters of Cold Metal Transfer arc length and arc dynamics correction on the geometrical and mechanical characteristics of the final weld bead.
Experimental and statistical investigations have been carried out on F57 filler metal for the assembly of S235 thin metal sheets by the butt joining method using the universal Cold Metal Transfer mode. The main parameters: (i) Arc length correction and (ii) Dynamic Arc correction have been varied to investigate the effect of the combination of these parameters using surface response from experimental plan methods under Minitab software. Furthermore, mechanical investigation using tensile tests coupled with Digital Image Correlation analysis has been realized.
The obtained results show that the variation of arc length correction affects considerably the geometrical and mechanical characteristics of the one-layer deposition. However, the effect of the variation of the dynamic arc correction doesn’t affect the width of the welded bead but more the penetration. In the same way, the optimization of the arc’s length enabled us to control the wall’s width and appearance. In addition, optimizing the arc’s dynamics enabled us to control the right blend between layers. These results contribute to the optimization of the Wire Arc Additive Manufacturing-Cold Metal Transfer process parameters for future applications.