bluuebunny/crossref_metadata_2025 huggingface dataset:
Data handling of CYGNO experiment using INFN-Cloud solution
Authors: Amaro F.D. , Antonacci M. , Antonietti R. , Baracchini E. , Benussi L. , Bianco S. , Borra F. , Calanca A. , Capoccia C. , Caponero M. , Cardoso D.S. , Cavoto G. , Ciangottini D. , Costa I.A. , D’Imperio G. , Dané E. , Dho G. , Di Giambattista F. , Di Marco E. , Duma C. , Iacoangeli F. , Lima Júnior H.P. , Kemp E. , Lopes G.S.P. , Maccarrone G. , Mano R.D.P. , Marcelo Gregorio R.R. , Marques D.J.G. , Mazzitelli G. , McLean A.G. , Meloni P. , Messina A. , Monteiro C.M.B. , Nobrega R.A. , Pains I.F. , Paoletti E. , Passamonti L. , Pellegrino C. , Petrucci F. , Piacentini S. , Piccolo D. , Pierluigi D. , Pinci D. , Prajapati A. , Renga F. , Roque R.J.d.C. , Rosatelli F. , Russo A. , dos Santos J.M.F. , Saviano G. , Spiga D. , Spooner N.J.C. , Stalio S. , Tesauro R. , Tomassini S. , Torelli S.
Published: May 2024

The INFN Cloud project was launched at the beginning of 2020, aiming to build a distributed Cloud infrastructure and provide advanced services for the INFN scientific communities. A Platform as a Service (PaaS) was created inside INFN Cloud that allows the experiments to develop and access resources as a Software as a Service (SaaS), and CYGNO is the betatester of this system. The aim of the CYGNO experiment is to realize a large gaseous Time Projection Chamber based on the optical readout of the photons produced in the avalanche multiplication of ionization electrons in a GEM stack. To this extent, CYGNO exploits the progress in commercial scientific Active Pixel Sensors based on Scientific CMOS for Dark Matter search and Solar Neutrino studies. CYGNO, like many other astroparticle experiments, requires a computing model to acquire, store, simulate and analyze data typically far from High Energy Physics (HEP) experiments. Indeed, astroparticle experiments are typically characterized by being less demanding of computing resources with respect to HEP ones but have to deal with unique and unrepeatable data, sometimes collected in extreme conditions, with extensive use of templates and montecarlo, and are often re-calibrated and reconstructed many times for a given data set. Moreover, the varieties and the scale of computing models and requirements are extremely large. In this scenario, the Cloud infrastructure with standardized and optimized services offered to the scientific community could be a useful solution able to match the requirements of many small/medium size experiments. In this work, we will present the CYGNO computing model based on the INFN cloud infrastructure where the experiment software, easily extendible to similar experiments to similar applications on other similar experiments, provides tools as a service to store, archive, analyze, and simulate data.
Experience deploying an analysis facility for the Rubin Observatory’s Legacy Survey of Space and Time (LSST) data
Authors: Mainetti Gabriele , Hernandez Fabio , Jammes Fabrice , Le Boulc’h Quentin
Published: May 2024

The Vera C. Rubin Observatory is preparing for the execution of the most ambitious astronomical survey ever attempted, the Legacy Survey of Space and Time (LSST). Currently in its final phase of construction in the Andes mountains in Chile and due to start operations in 2025 for 10 years, its 8.4-meter telescope will nightly scan the southern sky and collect images of the entire visible 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 paper, we briefly present the infrastructure deployed at the French Rubin data facility (operated by IN2P3 computing center, CC-IN2P3) to deploy the Rubin Science Platform, a set of web-based services to provide effective and convenient access to LSST data for scientific analysis. We describe the main services of the platform, the components that provide those services and our deployment model. We also present the Kubernetes-based infrastructure we are experimenting with for hosting the LSST astronomical catalog, a petabyte-scale relational database developed for the specific needs of the project.
Federated Heterogeneous Compute and Storage Infrastructure for the PUNCH4NFDI Consortium
Authors: Drabent Alexander , Freyermuth Oliver , Giffels Manuel , Hoeft Matthias , Künsemöller Jörn , Roland Benoit , Schwarz Dominik , Wissing Christoph
Published: May 2024

PUNCH4NFDI, funded by the Germany Research Foundation initially for five years, is a diverse consortium of particle, astro-, astroparticle, hadron and nuclear physics embedded in the National Research Data Infrastructure initiative. In order to provide seamless and federated access to the huge variety of compute and storage systems provided by the participating communities covering their very diverse needs, the Compute4PUNCH and Storage4PUNCH concepts have been developed. Both concepts comprise state-of-the-art technologies such as a token-based AAI for standardized access to compute and storage resources. The community supplied heterogeneous HPC, HTC and Cloud compute resources are dynamically and transparently integrated into one federated HTCondorbased overlay batch system using the COBalD/TARDIS resource meta-scheduler. Traditional login nodes and a JupyterHub provide entry points into the entire landscape of available compute resources, while container technologies and the CERN Virtual Machine File System (CVMFS) ensure a scalable provisioning of community-specific software environments. In Storage4PUNCH, community supplied storage systems mainly based on dCache or XRootD technology are being federated in a common infrastructure employing methods that are well established in the wider HEP community. Furthermore existing technologies for caching as well as metadata handling are being evaluated with the aim for a deeper integration. The combined Compute4PUNCH and Storage4PUNCH environment will allow a large variety of researchers to carry out resource-demanding analysis tasks. In this contribution we will present the Compute4PUNCH and Storage4PUNCH concepts, the current status of the developments as well as first experiences with scientific applications being executed on the available prototypes.
NOTED: An intelligent network controller to improve the throughput of large data transfers in File Transfer Services by handling dynamic circuits
Authors: Misa Moreira Carmen , Martelli Edoardo , Cass Tony
Published: May 2024

The NOTED (Network Optimised Transfer of Experimental Data) project has successfully demonstrated the ability to dynamically provision network links to increase the effective bandwidth available for FTS-driven transfers between endpoints, such as WLCG sites, by inspecting on-going data transfers and so identifying those that are bandwidth-limited for a long period of time. Recently, the architecture of NOTED has been improved and the software has been packaged for easy distribution.

These improved capabilities and features of NOTED have been tested and demonstrated at various international conferences. For example, during demonstrations at Supercomputing 2022, independent instances of NOTED at CHCERN (Switzerland) and DE-KIT (Germany) monitored large data transfers generated by the ATLAS experiment between these sites and CA-TRIUMF (Canada). We report here on this and other events, highlighting how NOTED can predict link congestion or a notable increase in the network utilisation over an extended period of time and, where appropriate, automatically reconfigure network topology to introduce an additional or an alternative and betterperforming path by using dynamic circuit provisioning systems such as SENSE and AutoGOLE.
P4flow: A software-defined networking approach with programmable switches for accounting and forwarding IPv6 packets with user-defined flow label tags
Authors: Misa Moreira Carmen , Martelli Edoardo , Cass Tony
Published: May 2024

A comprehensive analysis of the HEP (High Energy Physics) experiment traffic across LHCONE (Large Hadron Collider Open Network Environment) and other networks, is essential for immediate network optimisation (for example by the NOTED project) and highly desirable for long-term network planning. Such an analysis requires two steps: tagging of network packets to indicate the type and owner of the traffic being carried and then processing of the tagged packets. The RNTWG (Research Network Technical Working Group) has defined a specification to identify the experiment and the application that originates a given network flow, named scitags (Scientific Network Tags) that is applied to the flow label field of the IPv6 header. We report here on the second step: processing of packets tagged according to this specification.

We developed P4flow as a software-defined networking approach by using P4 (Programming Protocol-Independent Packet Processors), a language for programming the data plane of network devices to accounting and process IPv6 packets with a scitags-based stamp in the flow label field, to understand the network utilisation and the applications used by the WLCG (Worldwide Large Hadron Collider Computing Grid) sites. With P4flow, and exploiting the control plane capabilities provided by RARE/FreeRtr (an Open Source Network Operating System developed by the GÉANT community), we cannot only generate statistics concerning the traffic per experiment and application but can also, using an Intel Tofino P4-programmable ASIC Ethernet Switch, decide how to forward traffic matching defined flow labels. This latter capability is particularly interesting as we prepare for a future where LHC experiments will be sharing network links with other major science collaborations such as SKA and ITER.
The Global Network Advancement Group A Next Generation System for the LHC Program and Data Intensive Sciences
Authors: Newman Harvey
Published: May 2024

This paper presents the rapid progress, vision and outlook across multiple state of the art development lines within the Global Network Advancement Group (GNA-G) and its Data Intensive Sciences and SENSE/AutoGOLE working groups, which are designed to meet the present and future needs and address the challenges of the Large Hadron Collider and other science programs with global reach. Since it was founded in the Fall of 2019 and the working groups were formed in 2020, in partnership with ESnet, Internet2, CENIC, GEANT, ANA, RNP, StarLight, NRP, N-DISE, AmLight, and many other leading research and education networks and network R&D projects, as well as Caltech, UCSD/SDSC, Fermilab, CERN, LBL, and many other leading universities and laboratories, the GNA-G working groups have deployed two virtual circuit and programmable testbeds spanning six continents which supports continuous developments aimed at the next generation of programmable networks interworking with the science programs’ computing and data management systems. The talk covers examples of recent progress in developing and deploying new methods and approaches in multidomain virtual circuits, flow steering, path selection, load balancing and congestion avoidance, segment routing and machine learning based traffic prediction and optimization.
BABAR’s Experience with the Preservation of Data and Analysis Capabilities
Authors: Ebert Marcus , Roney Michael , Sobie Randall
Published: May 2024

The BABAR experiment collected electron-positron collisions at the SLAC National Accelerator Laboratory (SLAC) from 1999-2008. Although data taking has stopped 15 years ago, the collaboration is still actively doing data analyses, publishing results, and giving presentations at international conferences. Special considerations were needed to do analyses using a computing environment that was developed decades ago. A framework is required that preserves the data, data access, and the capability of doing analyses using a well defined and preserved environment. Also, BABAR’s support by SLAC ended at the beginning of 2021. Fortunately, the High Energy Physics Research Computing group at the University of Victoria (UVic), Canada, offered to provide the new home for the main BABAR computing infrastructure, the Grid Computing Centre Karlsruhe offered to host all data for access by analyses running at UVic, and CERN and the IN2P3 Computing Centre offered to store a backup of all data. This paper presents what was done at BABAR to preserve the data and analysis capabilities and what was needed to move the whole computing infrastructure, including collaboration tools and data files, away from SLAC. It will be shown how BABAR preserved the ability to continue to do data analyses and also have a working collaboration tools infrastructure. This paper will describe on BABAR’s experience with such a big change in its infrastructure and what was learned from it, which may be useful to other experiments which are interested in long term analysis support and data preservation in general.
ScienceBox 2.0: Evolving the demonstrator package for CERN storage and analysis services
Authors: Bocchi Enrico , Desai Jimil , Alfageme Sainz Samuel
Published: May 2024

With containers being the de-facto standard to package, distribute, and run applications, Helm charts are on the rise for application deployment in managed clusters (e.g., Kubernetes, OpenShift), providing developers and operators with a rich ecosystem of tools to utilize, as well as the means to configure applications and roll changes out in a programmatic way.

This paper describes the reboot of the ScienceBox project: The containerized software bundle providing the ability to deploy CERN storage and analysis services on any cloud or local infrastructure. While the service offering provided through ScienceBox remains unchanged, we evolved the original implementation to make use of Helm charts across the entire stack and incorporated a major architectural update to CERNBox, replacing the previous PHP backend with a distributed microservices architecture. ScienceBox maintains the flexibility to be deployed in a few clicks for demonstration purposes, or scaled out on managed clusters for the provisioning of a multi-user service. It also removes external dependencies by deploying additional services (e.g., IDP, DBs) and facilitates the configuration of the service stack by providing automated configuration scripts, making the package fully self-contained and easy to use.
Sharing ATLAS Science: Communicating to the Public
Authors: Le Boulicaut Ennis Elise
Published: May 2024

Communicating the science and achievements of the ATLAS Experiment is a core objective of the ATLAS Collaboration. This contribution will explore the range of communication strategies adopted by ATLAS. In particular, an overview of ATLAS’ digital communication platforms will be given, including the ATLAS website, social media, Virtual Visits, and Open Data.
Influencer Loss: End-to-end Geometric Representation Learning for Track Reconstruction
Authors: Murnane Daniel
Published: May 2024

Significant progress has been made in applying graph neural networks (GNNs) and other geometric ML ideas to the track reconstruction problem. State-of-the-art results are obtained using approaches such as the Exatrkx pipeline, which currently applies separate edge construction, classification and segmentation stages. One can also treat the problem as an object condensation task, and cluster hits into tracks in a single stage, such as in the GravNet architecture. However, condensation with such an architecture may still require non-differentiable operations, and arbitrary post-processing. In this work, I extend the ideas of geometric attention to the task of fully geometric (and therefore fully differentiable) end-to-end track reconstruction in a single step. To realize this goal, I introduce a novel condensation loss function called Influencer Loss, which allows an embedded representation of tracks to be learned in tandem with the most representative hit(s) in each track. This loss has global optima that formally match the task of track reconstruction, namely smooth condensation of tracks to a single point, and I demonstrate this empirically on the TrackML dataset. The model not only significantly outperforms the physics performance of the baseline model, it is up to an order of magnitude faster in inference.
Simulation of Hadronic Interactions with Deep Generative Models
Authors: Pham Tuan Minh , Ju Xiangyang
Published: May 2024

Accurate simulation of detector responses to hadrons is paramount for all physics programs at the Large Hadron Collider (LHC). Central to this simulation is the modeling of hadronic interactions. Unfortunately, the absence of first-principle theoretical guidance has made this a formidable challenge. The state-of-the-art simulation tool, Geant4, currently relies on phenomenology-inspired parametric models. Each model is designed to simulate hadronic interactions within specific energy ranges and for particular types of hadrons. Despite dedicated tuning efforts, these models sometimes fail to describe the data in certain physics processes accurately. Furthermore, finetuning these models with new measurements is laborious. Our research endeavors to leverage generative models to simulate hadronic interactions. While our ultimate goal is to train a generative model using experimental data, we have taken a crucial step by training conditional normalizing flow models with Geant4 simulation data. Our work marks a significant stride toward developing a fully differentiable and data-driven model for hadronic interactions in High Energy and Nuclear Physics.
Symbolic Regression on FPGAs for Fast Machine Learning Inference
Authors: Tsoi Ho Fung , Pol Adrian Alan , Loncar Vladimir , Govorkova Ekaterina , Cranmer Miles , Dasu Sridhara , Elmer Peter , Harris Philip , Ojalvo Isobel , Pierini Maurizio
Published: May 2024

The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic regression (SR). It searches the equation space to discover algebraic relations approximating a dataset. We use PySR (a software to uncover these expressions based on an evolutionary algorithm) and extend the functionality of hls4ml (a package for machine learning inference in FPGAs) to support PySR-generated expressions for resource-constrained production environments. Deep learning models often optimize the top metric by pinning the network size because the vast hyperparameter space prevents an extensive search for neural architecture. Conversely, SR selects a set of models on the Pareto front, which allows for optimizing the performance-resource trade-off directly. By embedding symbolic forms, our implementation can dramatically reduce the computational resources needed to perform critical tasks. We validate our method on a physics benchmark: the multiclass classification of jets produced in simulated proton-proton collisions at the CERN Large Hadron Collider. We show that our approach can approximate a 3-layer neural network using an inference model that achieves up to a 13-fold decrease in execution time, down to 5 ns, while still preserving more than 90% approximation accuracy.
Using a Neural Network to Approximate the Negative Log Likelihood Function
Authors: Liu Shenghua , Jamieson Nathan , Lannon Kevin , Mohrman Kelci , Negash Sirak , Wan Yuyi , Yates Brent
Published: May 2024

An increasingly frequent challenge faced in HEP data analysis is to characterize the agreement between a prediction that depends on a dozen or more model parameters—such as predictions coming from an effective field theory (EFT) framework—and the observed data. Traditionally, such characterizations take the form of a negative log likelihood (NLL) function, which can only be evaluated numerically. The lack of a closed-form description of the NLL function makes it difficult to convey results of the statistical analysis. Typical results are limited to extracting “best fit” values of the model parameters and 1D intervals or 2D contours extracted from scanning the higher dimensional parameter space. It is desirable to explore these high-dimensional model parameter spaces in more sophisticated ways. One option for overcoming this challenge is to use a neural network to approximate the NLL function. This approach has the advantage of being continuous and differentiable by construction, which are essential properties for an NLL function and may also provide useful handles in exploring the NLL as a function of the model parameters. In this talk, we describe the advantages and limitations of this approach in the context of applying it to a CMS data analysis using the framework of EFT.
ICSC: The Italian National Research Centre on HPC, Big Data and Quantum computing
Authors: Grandi Claudio , Bettoni Diego , Boccali Tommaso , Carlino Gianpaolo , Cesini Daniele , dell’Agnello Luca , Donvito Giacinto , Salomoni Davide , Zoccoli Antonio
Published: May 2024

ICSC (“Italian Center for SuperComputing”) is one of the five Italian National Centres created within the framework of the NextGenerationEU funding by the European Commission. The aim of ICSC, designed and approved through 2022 and eventually started in September 2022, is to create the national digital infrastructure for research and innovation, leveraging existing HPC, HTC and Big Data infrastructures and evolving towards a cloud data-lake model. It will be available to the scientific and industrial communities through flexible and uniform cloud web interfaces and will be relying on a high-level support team; as such, it will form a globally attractive ecosystem based on strategic public-private partnerships to fully exploit top level digital infrastructure for scientific and technical computing and promote the development of new computing technologies. The ICSC IT infrastructure is built upon existing scientific digital infrastructures as provided by the major national players: GARR, the Italian NREN, provides the network infrastructure, whose capacity will be upgraded to multiples of Tbps; CINECA hosts Leonardo, one of the world largest HPC systems, with a power of over 250 Pflops, to be further increased and complemented with a quantum computer; INFN contributes with its distributed Big Data cloud infrastructure, built in the last decades to respond to the needs of the HEP community. On top of the IT infrastructure, several thematic activities will be funded and will focus on the development of tools and applications in several research domains. Of particular relevance to this audience are the activities on "Fundamental Research and Space Economy" and "Astrophysics and Cosmos Observations", strictly aligned with the INFN and HEP core activities. Finally, two technological research activities will foster research on "Future HPC and Big Data" and "Quantum Computing".
Application of performance portability solutions for GPUs and many-core CPUs to track reconstruction kernels
Authors: Kwok Ka Hei Martin , Kortelainen Matti , Cerati Giuseppe , Strelchenko Alexei , Gutsche Oliver , Reinsvold Hall Allison , Lantz Steve , Reid Michael , Riley Daniel , Berkman Sophie , Lee Seyong , Ather Hammad , Norris Boyana , Wang Cong
Published: May 2024

Next generation High-Energy Physics (HEP) experiments are presented with significant computational challenges, both in terms of data volume and processing power. Using compute accelerators, such as GPUs, is one of the promising ways to provide the necessary computational power to meet the challenge. The current programming models for compute accelerators often involve using architecture-specific programming languages promoted by the hardware vendors and hence limit the set of platforms that the code can run on. Developing software with platform restrictions is especially unfeasible for HEP communities as it takes significant effort to convert typical HEP algorithms into ones that are efficient for compute accelerators. Multiple performance portability solutions have recently emerged and provide an alternative path for using compute accelerators, which allow the code to be executed on hardware from different vendors. We apply several portability solutions, such as Kokkos, SYCL, C++17 std::execution::par, Alpaka, and OpenMP/OpenACC, on two mini-apps extracted from the mkFit project: p2z and p2r. These apps include basic kernels for a Kalman filter track fit, such as propagation and update of track parameters, for detectors at a fixed z or fixed r position, respectively. The two mini-apps explore different memory layout formats.

We report on the development experience with different portability solutions, as well as their performance on GPUs and many-core CPUs, measured as the throughput of the kernels from different GPU and CPU vendors such as NVIDIA, AMD and Intel.
Enabling INFN–T1 to support heterogeneous computing architectures
Authors: Dal Pra Stefano , Spiga Daniele , Boccali Tommaso , Chierici Andrea , Morganti Lucia , Sapunenko Vladimir , Cesini Daniele , Rinaldi Lorenzo , Gregori Daniele , Cicala Marco
Published: May 2024

The INFN–CNAF Tier-1 located in Bologna (Italy) is a center of the WLCG e-Infrastructure providing computing power to the four major LHC collaborations and also supports the computing needs of about fifty more groups - also from non HEP research domains. The CNAF Tier1 center has been historically very active putting effort in the integration of computing resources, proposing and prototyping solutions both for extension through Cloud resources, public and private, and with remotely owned sites, as well as developing an integrated HTC+HPC system with the PRACE CINECA supercomputer center located 8Km far from the CNAF Tier-1 located in Bologna. In order to meet the requirements for the new Tecnopolo center, where the CNAF Tier-1 will be hosted, the resource integration activities keep progressing. In particular, this contribution will detail the challenges that have recently been addressed, providing opportunistic access to non standard CPU architectures, such as PowerPC and hardware accelerators (GPUs). We explain the approach adopted to both transparently provision x86_64, ppc64le and NVIDIA V100 GPUs from the Marconi 100 HPC cluster managed by CINECA and to access data from the Tier1 storage system at CNAF. The solution adopted is general enough to enable seamless integration of other computing architectures at the same time from different providers, such as ARM CPUs from the TEXTAROSSA project, and we report about the integration of these within the computing model of the CMS experiment. Finally we will discuss the results of the early experience.
Evaluation of ARM CPUs for IceCube available through Google Kubernetes Engine
Authors: Sfiligoi Igor , Schultz David , Riedel Benedikt , Würthwein Frank
Published: May 2024

The IceCube experiment has substantial simulation needs and is in continuous search for the most cost-effective ways to satisfy them. The most CPU-intensive part relies on CORSIKA, a cosmic ray air shower simulation. Historically, IceCube relied exclusively on x86-based CPUs, like Intel Xeon and AMD EPYC, but recently server-class ARM-based CPUs are also becoming available, both on-prem and in the cloud. In this paper we present our experience in running a sample CORSIKA simulation on both ARM and x86 CPUs available through Google Kubernetes Engine (GKE). We used the production binaries for the x86 instances, but had to build the binaries for ARM instances from source code, which turned out to be mostly painless. Our benchmarks show that ARM-based CPUs in GKE were not only the most cost-effective but were also the fastest in absolute terms in all the tested configurations. While the advantage is not drastic, about 20% in cost-effectiveness and less than 10% in absolute terms, it is still large enough to warrant an investment in ARM support for IceCube.
Running GPU-enabled CMSSW workflows through the production system
Authors: Koraka Charis Kleio
Published: May 2024

The CMS experiment at CERN accelerates several stages of its online reconstruction by making use of GPU resources at its High Level Trigger (HLT) farm for LHC Run 3. Additionally, during the past years, computing resources available to the experiment for performing offline reconstruction, such as Tier-1 and Tier-2 sites, have also started to integrate accelerators into their systems. In order to make efficient use of these heterogeneous platforms, it is essential to adapt both the CMS production system and the CMSSW reconstruction code to make use of GPUs. The CMSSW offline reconstruction can now partially run on GPUs, inheriting from the work done at the HLT. Parts of the production systems infrastructure have also been adapted to successfully map, schedule and run the available GPU-enabled workflows on different sites across the computing grid. This talk will describe the process of commissioning GPU-enabled CMSSW workflows through the production system and will present first results from the deployment of GPU-enabled offline reconstruction workflows.
Vectorization of CMSSW offline software
Authors: Gartung Patrick
Published: May 2024

The CMS experiment has been utilizing vectorization, or SIMD, in parts of its data processing applications for over a decade. On x86 platforms the vectorization level is still SSE3. In the past attempts to use wider vector instruction sets such as AVX or AVX-512 have, in practice, not resulted in improvements in the overall event processing throughput, because the CPUs scale down their frequency when processing AVX instructions. In addition, a notable part of the global pool of CMS resources has been old systems either not supporting AVX, or where the CPU frequency downscaling impacts all cores of the CPU. CMS has nevertheless continued to vectorize more of its application code, and in this work we review profiling methods we have found effective to find out pieces of code that would benefit from vectorization, and techniques to transform those codes such that the GCC compiler is able to auto-vectorize those codes. The build system used for CMSSW, Scram, has also been enhanced to be able to build code for multiple CPU microarchitectures such that the shared libraries of desired microarchitecture level can be loaded based on the CPU of the system. This multi-microarchitecture setup is invisible to the workflow management system, which makes its deployment straightforward. We describe in detail how this multimicroarchitecture build is set up, and measure the impact of using wider vector units than SSE3 on the event processing throughput of CMS applications such as simulation and reconstruction on recent x86 CPUs
Numerical investigation of the melt pool geometry evolution during selective laser melting of 316L SS
Authors: Ben Slama Mouna , Chatti Sami , Hassine Nada , Kolsi Lioua
Published: May 2024

In this study, the melt pool size, precisely its width and depth, are numerically investigated for a wide range of values for both laser power and beam speed. A thermal model, developed on Ansys Additive Science, simulates the SLM of a single bead. A parametric study is achieved aiming at understanding the melt pool evolution and the defects appearing while varying these two parameters. The discussed porosity defects, namely the LOF and keyhole, are determined using the calculated melt pool dimensions and through mathematical correlations from the literature. Moreover, these numerical results are validated with experimental results for the reliability of the study. This investigation reveals a proportional relationship between the melt pool size and the laser power and an inversely proportional relationship with the scan speed. The optimal combination of these two parameters has to be well studied to avoid LOF and keyhole, which is afforded by this paper.

At lower laser power levels, such as 100 W, it is advisable to choose a slower scan speed ranging from 400 to 500 mm/s. As the laser power increases, so does the optimal scan speed. For instance, with 150 W, the ideal speed falls between 600 to 900 mm/s. Similarly, for 200 W, the recommended scan speed range extends from 900 to 1200 mm/s, and for 250 W, the optimal speed range lies between 1100 and 1400 mm/s.
Numerical prediction of hatch spacing effect on porosity appearance in parts manufactured by laser powder bed fusion
Authors: Nada Hassine , Sami Chatti , Lioua Kolsi
Published: May 2024

Laser powder bed fusion, as a brand-new technology, has significant advantages over the traditional manufacturing process, including design flexibility and short production times. The resulting workpiece characteristics are dramatically impacted by the process parameters variation. In the current study, a 3D thermal model was developed by means of the Ansys Additive software to predict the relationship between the hatch spacing, the porosity occurrence, and the thermal history of the Ti-6Al-4V powder layers using a series of numerical simulations. The numerical results reveal that the hatch distance has a significant impact both on porosity appearance and on thermal history. When the hatch spacing increases, the porosity will also increase which negatively affects the part’s properties. The predicted findings are compared with experimental investigations from literature. Reasonable accordance of the suggested finite element model with the experimental investigations was obtained.
Reliability based design optimization of a two-stage wind turbine gearbox
Authors: Karmi Bilel , Saouab Abdelghani , Guerine Ahmed , Bouaziz Slim , EL Hami Abdelkhalak , Haddar Mohamed , Dammak Khalil
Published: May 2024

This paper describes a multi-objective reliability-based design optimization (MORBDO) of a two-stage wind turbine gearbox. The optimization process incorporates the gear's reliability of accounting for the uncertainty of its internal geometric parameters. It also ensures that constraints relating to the gear's reliability index and efficiency are respected. The objective functions are to minimize both the total volume and the center distance. A specific reliability target is established, and to address the multi-objective reliability-based design optimization (MORBDO), the hybrid method (HM) in conjunction with the constrained non-dominated sorting genetic algorithm (C-NSGA-II) is employed. The outcomes demonstrate that applying C-NSGA-II to solve the multi-objective reliability-based design optimization problem yields dependable Pareto solutions that are well-distributed in relation to the desired reliability level. The optimization using C-NSGA-II with a population size of 300 particles and 1000 generations produced the most favorable outcomes. This research significantly contributes to the multi-objective design optimization of wind turbine gear while simultaneously considering their reliability.
Fabrication and characterization of CNT-reinforced Ni-Co composite coatings: microstructural evolutions and corrosion behavior
Authors: Sharifi Hassan , Erfani Mobarakeh Seyed Ali
Published: May 2024

In this study, Ni-Co coatings were synthesized on 304L stainless steel substrates using the spin coating method and the effects of adding multi-walled carbon nanotubes (MWCNTs) on the microstructural, mechanical and corrosion properties of the produced coatings have been investigated. The morphology of the synthesized coatings containing high amounts of nickel was observed to be spherical. With increasing the cobalt content, a change from spherical to needle-fibrous morphology occurred in the microstructure of the coatings. Carbon nanotubes caused the accumulation of dislocations and increased the hardness of the coatings by restricting the movement of dislocations and the slipping of grain boundaries. Moreover, by adding multi-walled nanotubes to the coating structure, the preferred nucleation sites for nickel and cobalt were increased and the resulting coating became finer in the microstructure. Therefore, the maximum hardness and corrosion resistance were observed in the CNT-reinforced coating of the lowest cobalt content.
Erwartungen und Motive des arbeitsmedizinischen Nachwuchses: eine
quantitative Befragung in fünf deutschen Akademien
Authors: Kaboth Pauline , Makowka Marie , Kämpf Daniel , Efimov Ilona , Droessler Stephanie , Schubert Melanie , Letzel Stephan , Harth Volker , Seidler Andreas
Published: May 2024

Zielsetzung Die vorliegende Studie soll die Charakteristika angehender
Arbeits- und Betriebsmediziner:innen abbilden sowie deren Erwartungen und Motive
für den Beginn einer arbeitsmedizinischen Weiterbildung untersuchen. Damit wird
eine Grundlage dafür geschaffen, die Attraktivität der präventiv ausgerichteten
betriebsärztlichen Tätigkeit noch weiter zu erhöhen und einem Mangel an Arbeits-
und Betriebsmediziner:innen entgegenzuwirken.

Methodik An fünf Standorten in Deutschland wurden angehende Arbeits- und
Betriebsmediziner:innen zu ihren Erwartungen an die Arbeitsmedizin, zu ihren
Motiven, eine Weiterbildung in der Arbeitsmedizin zu beginnen, und zu ihrer
Einschätzung dieser Weiterbildung hinsichtlich der Qualität befragt. Die
Befragung fand im Zeitraum von 2018 bis 2021 statt. Die erhobenen Daten wurden
deskriptiv ausgewertet. Anhand einer Clusteranalyse wurden die Motive
identifiziert, die Ärzt:innen zu einer arbeitsmedizinischen Weiterbildung
bewegen.

Ergebnisse Von insgesamt 233 befragten Personen war der Großteil weiblich
(68,5%) und im Mittel 43,1 Jahre alt (SD=7,9 Jahre). Die Response an der
Sächsischen Landesärztekammer (SLÄK) lag bei 50% und die an den restlichen vier
Akademien zwischen 18% und 23%. Die Clusteranalyse ergab vier verschiedene
Motive: „Karriere & Interesse“, „Work & Life Balance“,
„Selbstständigkeit“ und „Veränderungswunsch“. Zwei Drittel sprachen sich für
eine stärkere Einbindung des Faches Arbeitsmedizin in das Medizinstudium
aus.

Schlussfolgerung Die Ergebnisse zeigen, dass unterschiedliche Motive
Ärzt:innen dazu bewegen, eine arbeitsmedizinische Weiterbildung anzustreben. Auf
diese Motive sollte v. a. bei der Akquise von arbeitsmedizinischem Nachwuchs
eingegangen werden.
Indikation und Kontraindikation videobasierter Psychotherapie: Eine
qualitative Delphi-Befragung zur Erfassung der Reflexions- und Abwägungsprozesse
von Psychotherapeut*innen
Authors: Thurmann Isabelle , Leukhardt Alena , Eichenberg Christiane
Published: May 2024

Ziel der Studie Die Corona-Pandemie zwang viele Psychotherapeut*innen
dazu, einen schnellen Übergang in das Videosetting zu ermöglichen. Auch nach der
akuten Phase der Pandemie sind videobasierte Psychotherapiesitzungen (VP) zu
einem gewissen Anteil in der Praxis bestimmbar, weshalb Indikations- (IN) und
Kontraindikationsempfehlungen (KIN) zur Sicherstellung der Behandlungsqualität
unerlässlich sind. Empirische Untersuchungen fehlen hierzu allerdings nahezu
vollständig. Im Rahmen dieser Arbeit sollen Entscheidungs- und Abwägungsprozesse
von Psychotherapeut*innen im Hinblick auf VP erfasst und jene Faktoren
identifiziert werden, die einen Einfluss auf die Indikationsstellung haben
können.

Methodik Zweistufige, qualitativ angelegte Delphi-Befragung mit, als
Expert*innen für die untersuchte Thematik eingestuften psychodynamisch
orientierten Psychotherapeut*innen (PD; n=9) und Verhaltenstherapeut*innen (VT;
n=7), die den Arbeitsfeldern Forschung und Praxis zugeordnet werden können.

Ergebnisse Es zeigte sich, dass die Qualität der therapeutischen Allianz
für die Teilnehmer*innen in der Wahl des Settings handlungsleitend ist. Eine
Indikation für das Videosetting ergibt sich u. a. aus der Durchführbarkeit
therapeutischer Interventionen und dem jeweiligen Behandlungsziel. Die
Therapeut*innen wogen die Kosten und Nutzen des Videosettings für verschiedene
Störungsbilder ab. Das beiderseitig veränderte Gefühl der Kontrolle und die
Möglichkeit der Versorgungsverbesserung spielen dabei eine zentrale Rolle.

Diskussion Auf Grundlage der Ergebnisse wurde ein Entscheidungsbaum für
Psychotherapeut*innen erstellt, welcher die identifizierten Indikationskriterien
aufgreift. Schlüsselwörter: videobasierte Psychotherapie;
Indikationsempfehlungen; Delphi-Befragung; Entscheidungsbaum
Zwischen Vernunft und Wahnsinn
Authors: Pöhner Julien , Stanze Henrikje
Published: May 2024

Auf Intensivstationen werden nicht selten Entscheidungen über Leben und Tod getroffen. Wenn der Patient sich dazu nicht selbst äußern kann und keine Patientenverfügung bestenfalls im Rahmen von Advance Care Planning vorliegt, auf die sich das behandelnde Team berufen kann, sind die Angehörigen gefragt. Wie kann es gelingen, dass es dabei nicht zu Konflikten kommt? Das klärt der folgende Beitrag anhand eines Fallberichts.
Leistungserfassung auf der Intensivstation – quo vadis?
Authors: Eck Ingo , Tack Anna-Katharina
Published: May 2024

Ob, wann und welches Leistungserfassungssystem für die Intensivpflegestationen in Deutschland eingeführt wird, ist immer noch ungeklärt. Der folgende Beitrag zeigt den aktuellen Stand am Universitätsklinikum Heidelberg.
Die Kunst eines gelungenen Praktikums – Praktische Ausbildung
Authors: Nicolai Ann-Kathrin
Published: May 2024

Lehrreiche Erfahrungen sind ein wichtiger Bestandteil der praktischen Ausbildung. Schlüsselkomponenten sind unter anderem gute Kontakte zu den Praxisanleitenden, Offenheit aller Beteiligten und eine adäquate Unterstützung durch die Schule.
Eltern als Erfolgsfaktor – Systemische Fragen in der Angehörigenarbeit
Authors: Wunderling Melanie
Published: May 2024

Der Therapieerfolg hängt in der Pädatrie nicht allein von den Kindern und Jugendlichen in Behandlung ab, sondern auch maßgeblich von der Mitarbeit der Eltern und Bezugspersonen. Melanie Wunderling gibt Tipps, wie Elternarbeit effizient und klientenzentriert gelingt.
Schlüsselrolle einnehmen – Gesundheitskompetenz fördern
Authors: Schwendemann Hanna , Thieme Holm
Published: May 2024

Therapeut*innen können und sollten eine Schlüsselrolle bei der Förderung von Gesundheitskompetenz einnehmen. Doch wie können sie konkret im Kontext ihrer Arbeit die Gesundheitskompetenz ihrer Patient*innen untersuchen und einschätzen? Und wie können sie durch ihre Arbeit dazu beitragen, die Gesundheitskompetenz der Menschen positiv zu beeinflussen? Hanna Schwendemann und Holm Thieme geben Antworten.
Interprofessionelle Handlungsfelder in der Intensivmedizin – Empfehlungen der DIVI
Authors: Waydhas Christian , Deininger Matthias , Dubb Rolf , Hoffmann Florian , van den Hooven Thomas , Janssens Uwe , Kaltwasser Arnold , Markewitz Andreas , Pelz Sabrina ,   und das Präsidium der Deutschen Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin
Published: May 2024

Auf keiner anderen Station arbeiten Ärzte und Pflegekräfte so eng zusammen wie auf der Intensivstation. Die Pflege hat dabei schon immer intensivmedizinische Tätigkeiten durchgeführt – allerdings ohne feste Regelung. Darum hat eine paritätisch zusammengesetzte Arbeitsgruppe interprofessionelle Handlungsfelder definiert, die das Präsidium der DIVI Ende 2023 einstimmig beschlossen hat. Sie sollen eine verlässliche Grundlage für die interprofessionelle Zusammenarbeit bilden.
Grundlagen ethischer Entscheidungen im OP – eine Annäherung
Authors: Michalsen Andrej , Elbe Christian
Published: May 2024

Bei schwer verletzten oder erheblich vorerkrankten Patienten im OP muss das Behandlungsteam gelegentlich schwerwiegende und folgenreiche Entscheidungen im Hinblick auf Therapie, Therapieumfang und Therapieziel treffen. Welche ethischen Grundsätze und Prinzipien dabei zu beachten sind, erfahren Sie in diesem Beitrag.
Cariprazine Orodispersible Tablet: A New Formulation for
Cariprazine
Authors: Meszár Viktória , Magyar Gabriella , Pásztor Gabriella Mészárosné , Szatmári Balázs , Péter Krisztina , Marton Lívia , Dombi Zsófia B. , Barabássy Ágota
Published: May 2024

IntroductionCariprazine is an atypical dopamine receptor partial agonist
antipsychotic available in the form of capsules. Although capsules are one of
the most desirable routes of administration, there are certain situations
(e. g., in an acute psychiatric setting, or when swallowing difficulties, or
liquid shortages are present) when they cannot be administered. Therefore,
alternative solutions like orodispersible tablets are needed. This study aimed
to investigate the bioequivalence of a newly developed orodispersible tablet to
the commercially available hard gelatine capsule of cariprazine 1.5 mg.

MethodsThis was a phase I, open-label, randomized, single-dose
bioequivalence study. It had a 2-period, 2-sequence, cross-over design, where
each subject received one test and one reference product in a randomized
sequence, separated by a wash-out period of 55 days. Blood sampling was
performed over 72 h after dosing. Cariprazine concentrations were analyzed by a
validated HPLC-MS/MS method. Standard bioequivalence statistics was applied to
PK parameters calculated by non-compartmental analysis. Safety measures were
analyzed descriptively.

ResultPharmacokinetic data of 43 healthy volunteers and safety data of 54
subjects was analyzed. Cariprazine AUC0–72h and Cmax
geometric mean ratios were 117.76% and 100.88%, respectively. The 90% confidence
intervals were within the pre-defined bioequivalence acceptance limits of 80.00%
– 125.00%. Safety data was in line with the Summary of Product Characteristics
of Cariprazine.

Discussion The result of this clinical trial proved the bioequivalence of
the new orodispersible tablet formulation when compared to hard gelatine
capsules, enabling an alternative option for treatment of those suffering from
schizophrenia.
Pentoxifylline as a Novel Add-on Therapy for Major Depressive
Disorder in Adult Patients: A Randomized, Double-Blind, Placebo-Controlled
Trial
Authors: Merza Mohammad Talar A. , Merza Mohammad Tavgah A. , Salman Dyar M. , Jaafar Halmat M.
Published: May 2024

Background Evidence indicates an association between immune dysregulation
and major depressive disorder (MDD). Pentoxifylline (PTX), a phosphodiesterase
inhibitor, has been shown to reduce pro-inflammatory activities. The aim of this
study was to evaluate changes in depressive symptoms and pro-inflammatory
markers after administration of PTX as an adjunctive agent to citalopram in
patients with MDD.

Methods One hundred patients were randomly assigned to either citalopram
(20 mg/day) plus placebo (twice daily) (n=50) or citalopram (20 mg/day) plus PTX
(400 mg) (twice daily) (n=50). The Hamilton Depression Rating Scale-17
(HAM-D-17) scores at baseline, weeks 2, 4, 6, 8, 10, and 12 and serum levels of
interleukin1-β (IL-1-β), tumor necrosis factor-α, C-reactive protein, IL-6,
serotonin, IL-10, and brain-derived neurotrophic factor (BDNF) at baseline and
week 12 were evaluated.

Results HAM-D-17 score in the PTX group significantly reduced in
comparison to the control group after weeks 4, 6, 8,10, and 12 ((LSMD): − 2.193,
p=0.021; − 2.597, p=0.036; − 2.916, p=0.019; − 4.336, p=0.005; and − 4.087,
p=0.008, respectively). Patients who received PTX had a better response (83%)
and remission rate (79%) compared to the placebo group (49% and 40%, p=0.006 and
p=0.01, respectively). Moreover, the reduction in serum concentrations of
pro-inflammatory factors and increase in serotonin and BDNF in the PTX group was
significantly greater than in the placebo group (p<0.001).

Conclusion These findings support the safety and efficacy of PTX as an
adjunctive antidepressant agent with anti-inflammatory effects in patients with
MDD.