bluuebunny/crossref_metadata_2025 huggingface dataset:
CAI formation in the early Solar System
Authors: Woitke P. , Drążkowska J. , Lammer H. , Kadam K. , Marigo P.
Published: May 2024

Calcium-aluminium-rich inclusions (CAIs) are the oldest dated solid materials in the Solar System, and are found as light-coloured crystalline ingredients in carbonaceous chondrite meteorites. Their formation time is commonly associated with age zero of the Solar System. Nevertheless, the physical and chemical processes that once led to the formation of these submillimetre- to centimetre-sized mineral particles in the early solar nebula are still a matter of debate. In this paper, we propose a pathway to form such inclusions during the earliest phases of disc evolution. We combine 1D viscous disc evolutionary models with 2D radiative transfer, equilibrium condensation, and new dust opacity calculations. We show that the viscous heating associated with the high accretion rates in the earliest evolutionary phases causes the midplane inside of about 0.5 au to heat up to limiting temperatures of about 1500–1700 K, but no further. These high temperatures force all refractory material components of the inherited interstellar dust grains to sublimate – except for a few Al-Ca-Ti oxides, such as Al2O3, Ca2Al2SiO7, and CaTiO3. This is a recurring and very stable result in all our simulations, because these minerals form a natural thermostat. Once the Mg-Fe silicates are gone, the dust becomes more transparent and the heat is more efficiently transported to the disc surface, which prevents further warming. This thermostat mechanism keeps these minerals above their annealing temperature for hundreds of thousands of years, allowing them to form large pure crystalline particles. These particles are dragged out by the viscously spreading disc, and once they reach a distance of about 0.5 au, the silicates recondense on the surface of the Ca-Al-rich particles, adding an amorphous silicate matrix. We estimate that this mechanism of CAI production works during the first 50 000 yr of disc evolution. These particles then continue to move outward and populate the entire disc up to radii of about 50 au, before the accretion rate eventually subsides, the disc cools, and the particles start to drift inwards.
Quantification of the Wave Energy Potential of the Atlantic Coast of Honduras
Authors: Romero Mejia J. , Villatoro Flores H.
Published: May 2024

The growing demand for electricity in Honduras became a critical problem in 2022, evidencing an increase of 3.05%. This situation generated difficulties in the energy supply, and an increase of 10% to 15% in the cost of electricity was projected for the next quarter of 2023. Despite the efforts in renewable energies, the wave energy potential generated by the Atlantic waves is limited due to the lack of research in Honduras. To obtain a wave energy potential, meteorological data was collected on the swell, including wave height and wave interval, taking into account other factors such as density and sea depth. For this process, A variety of available resources were used, such as web scraping programming made in Python, and the data were obtained using the online software Windfinder. These were stored in Microsoft Excel, and mathematical models such as wave energy power and wave power formula were used, which considered parameters such as wave height and wave intervals. In addition, Airy’s theory equation was applied, taking into account the depth; these models allowed calculating the wave energy potential and comparing the results. Preliminary results indicate that the wave energy generation potential varies according to the model used, with the first model, wave energy power, varying 51%, the second, wave power formula, 46%, and Airy’s theory equation. This research was done to quantify the wave energy potential in the country of Honduras, specific to the Atlantic coast on the coasts of the departments of Cortés, Atlántida, Colón, Gracias a Dios, and Islas de la Bahía over a period of 10 weeks and contribute to future research.
Slovenia in 2055 on Renewables
Authors: Senegačnik Andrej
Published: May 2024

Slovenia’s desired green transition by 2055 is discussed. Europe should in 2055 achieve climate neutrality with near zero greenhouse gas emissions. The starting point of the research is the energy balance of Slovenia for 2019. The possibilities and potentials of Slovenia for the use of RES resources are presented. Photovoltaic modules with system seasonal energy storage in the form of potential water energy are selected as the main, technologically mature, RES technology. In the analysis, an increase in food self-sufficiency to 80% and complete energy self-sufficiency without fossil fuels and nuclear energy are predicted. The obtained results show the technical impossibility of such politically imposed ideas.
Application of molecular simulation in mobile phase development of HPLC
Authors: Wang Yanmeng , Yao Qian , Zhang Shijian , Li Lixuan , Tian Liran , Du Tianqiang , Zhu Peipei , Yang Zhiwen , Li Jingyuan
Published: May 2024

The selection of mobile phase is significant for the separation result of HPLC, for it may have influence on the quantitative analysis. During HPLC analysis, engineers usually use (water-acetonitrile) mobile phase system to separate aldehydes and ketones, however, the existence of acetone will affect the separation effect of other substances. The paper researches one new (water-acetonitrile-tetrahydrofuran) mobile phase system to separate aldehydes and ketones. By analyzing the standard curves and test results of unknown samples, the new mobile phase can meet the analyzing needs, also it can eliminate the influence of acetone in the analysis process. What’s more, the paper uses molecular simulation[1–3] (MS) to calculate the interactions between the mobile phase and the aldehydes and ketones, mean square displacement value (MSD) and radial distribution function value (RDF), and the separation phenomenon can be well explained. In this paper, MS technology is first proposed for the development of new mobile phase, which can predict new unknown mobile phase, improve the efficiency of method development, also it can benefit the accurate qualitative analysis and prediction of peak out behavior of unknown substances.
Numerical simulation of flow field coupling with electric field for leakage current particulate matter sensor
Authors: Chu Guoliang , Lv Huimin , Shi Shengyao , Li Tianxiang , Tang Dong
Published: May 2024

With the diesel particulate filter more and more widely applied on engine, the particulate matter (PM) sensor is used to detect malfunctions of diesel particulate filter (DPF) in on board diagnostics (OBD). This paper focused on the new leakage current particulate matter sensor, which has more practical significance. The electric field and flow field were simulated by the COMSOL Multihysics, and the influence rules of sensor electrode parameters on the electric field and flow field were analyzed. The dynamic characteristics of particulate matters with different charges was studied. The simulation results showed that the average electric field strength was higher with higher electrode voltage and larger electrode spacing, which made the motion trend of charged particulate matter to electrodes more obvious. The decrease of electrode spacing or the increase of electrode length made the exhaust flow more stable, and the motion trend of charged particulate matters to electrodes is more obvious with the increase of electrode length. It was concluded that the flow of the exhaust and particulate matter was in good condition when the electrode spacing was 12.5mm, the electrode length is 12.5mm and the electrode voltage ranged between 1000V and 1500V.
Machine vision recognition system for aerospace machined parts based on edge detection
Authors: Chen Feng , Shi Xiang , Zhang Haoyu , Cai Aoyang , Song Kechen
Published: May 2024

Aerospace T-shaped machined parts are varied and have small structural differences. Manual identification has the problems of low efficiency and low accuracy. In order to realize efficient and accurate classification of aerospace machining parts, we built an image acquisition platform. To improve the edge detail extraction capability, we improved the edge detection algorithm based on deep learning. Furthermore, we employed the VisionTrain software to train recognition classification models for both large classes and subclasses. We then established a cross-granularity image classification process using VisionMaster software. Experimental results show that the improved edge detection algorithm in this paper is better than the existing common algorithm. The system achieves the goal of quickly and accurately recognizing all 60 machined parts.
Design of intelligent desktop fan based on MQTT
Authors: Xie Zaijin , Chen Yuxiang , Deng Minjin
Published: May 2024

At present, although the desktop fan has frequency conversion speed regulation, automatic head swing, remote control, etc., it is unable to learn the usage habits, lack of environmental perception, and lack of comprehensive analysis of data feedback, etc. This scheme proposes to design a desktop fan based on MQTT protocol and artificial intelligence principle, users learn from past operations, and predict user needs according to the existing sensor data. Intelligent analysis of the operating environment and status of the fan, timely shutdown or power adjustment, optimize the energy efficiency of the fan, through the deep application of advanced artificial intelligence technology, significantly improve the user experience.
Research on welding defect examination technique based on phased array ultrasonic for high voltage transformer oil tank welds
Authors: Shi Lei , Qi Xiao , Li Xinhui , Hao Chenggang , Chai Fangsen , Han Dongxu , Yang Le , Li Guang
Published: May 2024

Oil tank of high voltage transformer plates. During welding and operation, defects such as incomplete fusion, incomplete penetration , air holes and cracks may be produced which influence the integrity and density of the welds and the whole tank. In order to test the integrity of the shell weld of high-pressure transformer box and ensure no leakage of transformer oil, the phased array testing technology is used to detect the defects of the butt weld of the transformer oil tank. The characteristics of weld defect detection under the condition of incomplete penetration were studied, and the sound beam accessibility, process selection principle and probe selection principle were theoretically analyzed. The test tests were carried out on the artificial samples of the butt weld of the transformer oil tank plate. The results show that the phased array can effectively detect the defects in the weld of transformer tank shell under the condition of incomplete welding, and the imaging results are intuitive and easy to judge, which can effectively evaluate the integrity of transformer tank weld. During the process selection, the probe-wedge combination with appropriate front distance and detection Angle should be selected according to the wall thickness, weld penetration depth and crown width of the transformer oil tank. During defects judgment, the interference of structure echo wave and incomplete penetration area should be considered. The assistant defect positioning analysis based on incomplete penetration is also suggested.
An instant loss analysis model for complex magnetic core materials
Authors: Chen Kuofeng
Published: May 2024

This paper tries to deduce the time domain model of magnetic power loss without integral term from the basic magnetic induction, eddy loss, and supposed hysteresis loss equations. Based on the results of the50~5000Hz frequency domain and magnetic flux density of 0.1~1.8T, the overall average error rate is lower than 6%. It applies to both periodic and non-periodic magnetic field conditions. Based on the new instant loss equation in the changing magnetic field, the power loss of magnetic materials includes the induced eddy current loss. It is related to the instantaneous variation of the reciprocal of the magnetic permeability. It is found that the hysteresis loss is dependent on the saturation of magnetic field intensity.
How does the central bank stabilize the stock market under macro uncertainty: Evidence from China stock market
Authors: Gao Ge , Jiang Xiaoqing , He Qizhi
Published: May 2024

This paper establishes mathematical models and PVAR models to verify the micro transmission path and the validity of central bank communication and actual interest rate intervention on China stock market. Through heterogeneity analysis, we further test whether the level of macroeconomic uncertainty affects their effect. We confirmthat both central bank communication and actual interest rate intervention have a significant effect. However, central bank communication is ineffective in the high macroeconomic uncertainty sample while the interest rate intervention can have a greater effect. Overall, the results suggest that central bank communication is a more effective monetary policy tool on stock market in low macroeconomic uncertainty period, while interest rate intervention is a better choice in high macroeconomic uncertainty period.
Proper definitions and comparable test methods for new heaters to tackle the challenge of a sustainable decarbonization of existing residential buildings
Authors: Zambrelli Cristian , D’Acunti Valentina , Tomasi Roberta , Bronzoni Cristiana
Published: May 2024

In the last few years there has been a growing offer and development of technologies for hydronic central heating space heaters and combination heaters. In our opinion as a manufacturer, not all new and forthcoming products seem to be covered by current standards and Ecodesign Regulation proposals. For example, some “hybrid heaters” consisting of gas boilers integrated with small-sized heat pumps seem not to fit previous definitions. Considering that future development of appliances will rely on the integration of many energy carriers, different technologies and devices in order to enhance the efficiency and environmental performance, we think that all possible and alternative design solutions should be investigated and evaluated. These appliances will contribute to widen the portfolio of possible solutions for building renovations and will provide an adding opportunity to reduce greenhouse gas emissions, with acceptable costs. Taking into account the current product standards, we propose additional definitions and new test methods that consider any possible heat recovery and integration aiming to place on the market new products under the same rules. The proposal aims to avoid loopholes and to accompany the product with the proper information on realistic significant environmental and energy performance in order to be comparable with already regulated products.
The impact of the new EPBD “Green Houses” in the Italian building context: Technical-economic analysis on improving the energy class of existing buildings
Authors: D’Agostino Diana , Minelli Federico , Minichiello Francesco , Russo Nicola
Published: May 2024

The new European directive “Green Houses” aims at the decarbonization of the existing building stock. In this work, three building types widespread in Italy are analysed, virtually positioned in Palermo, Naples, and Milan. Considering that almost 60% of Italian residential buildings fall within the G and F energy classes, three energy retrofit measures are proposed (thermal insulation of vertical walls and roofs; replacement of the methane gas boiler for building heating and domestic hot water with a high temperature air-to-water heat pump; installation of a photovoltaic system), in order to reach at least D energy class based on Italian energy performance certification. A simulation-based approach is used. A declining tendency is noted in the normalized prices of the interventions when moving from detached house to terraced house and from terraced house to apartment in a tower building. Shifting from an independent to a centralized air conditioning system results in the same savings. Considering equal building features, hotter and milder climate zones reach higher energy classes; this also results in more difficult energy efficiency improvement in colder climates. Potential inequalities result from the implementation of the new European directive. Economic incentives to support the energy efficiency transition should be tailored to more specific conditions.
Towards a new conditions data infrastructure in ATLAS
Authors: Alexandrov Evgeny , Canali Luca , Costanzo Davide , Formica Andrea , Gallas Elizabeth J. , Mineev Mikhail , Ozturk Nurcan , Roe Shaun , Tsulaia Vakho , Vogel Marcelo
Published: May 2024

The ATLAS experiment is preparing a major change in the conditions data infrastructure in view of LHC Run 4. In this paper we describe the ongoing changes in the database architecture which have been implemented for Run 3, and describe the motivations and the on-going developments for the deployment of a new system (called CREST for Conditions Representational State Transfer, as a reference to REST architectures). The main goal is to set up a parallel infrastructure for full scale testing before the end of Run 3.
Examining the Impact of Data Layout on Tape on Data Recall Performance for ATLAS
Authors: Misawa Shigeki
Published: May 2024

Increases in data volumes are forcing high-energy and nuclear physics experiments to store more frequently accessed data on tape. Extracting the maximum performance from tape drives is critical to make this viable from a data availability and system cost standpoint. The nature of data ingest and retrieval in an experimental physics environment make achieving high access performance difficult given the inherent limitations of magnetic tape. Tailoring the layout of data on tape is one key to improving read performance. This paper highlights the work in progress to characterize ATLAS data ingested in the tape system, understand how data layout, i.e. file co-location on tape and file distribution over tapes, affect read performance and how optimal data layout might be achieved in a production environment.
POSIX access to remote storage via OpenID Connect
Authors: Fornari Federico , Alkhansa Ahmad , Costantini Alessandro , Pellegrino Carmelo , Salomoni Davide
Published: May 2024

INFN-CNAF is one of the Worldwide Large Hadron Collider Computing Grid (WLCG) Tier-1 data centers, providing support in terms of computing, networking, storage resources and services also to a wide variety of scientific collaborations, ranging from physics to bioinformatics and industrial engineering [1]. Recently, several collaborations working with our data center have developed computing and data management workflows that require access to S3 storage services and/or the integration with POSIX capabilities. Nevertheless, the access to the data must be regulated by federated authentication and authorization mechanisms, such as OpenID Connect (OIDC), which is largely adopted by communities like WLCG [2] and within the European Open Science Cloud (EOSC) [3]. In the present work, the possibility to regulate POSIX access by integrating JSON Web Token (JWT) [4] authentication, provided by INDIGO-IAM as Identity Provider [5], with solutions based on S3 (for object storage) and WebDAV (for hierarchical storage) protocols has been evaluated an related results have been reported. In such respect, a comparison between the performance yielded by S3 and WebDAV protocols has been carried out within the same distributed environment with the aim to better identify the solution most suitable for the different use cases.
Understanding Data Access Patterns for dCache System
Authors: Bellavita Julian , Sim Caitlin , Wu Kesheng , Sim Alex , Yoo Shinjae , Ito Hiro , Garonne Vincent , Lancon Eric
Published: May 2024

The storage management system dCache acts as a disk cache for high-energy physics (HEP) data from the US ATLAS community. Since its disk capacity is considerably smaller than the total volume of ATLAS data, a heuristic is needed to determine what data should be kept on disks. An effective heuristic would be to keep the data files that are expected to be heavily accessed in the near future. Through a careful study of access statistics, we find a few most popular datasets are accessed much more frequently than others, even though these popular datasets change over time. If we could predict the near-term popularity of datasets, we could pin the most popular ones in the disk cache to prevent their accidental removal and guarantee their availability. To predict a dataset popularity, we present several methods for forecasting the number of times a dataset will be accessed in the next day. Test results show that these methods could predict the next-day access counts of popular datasets reliably. This observation is confirmed with dCache logs from two separate time ranges.
AI Driven Experiment Calibration and Control
Authors: Britton Thomas , Bedwell Cullan , Chawhan Abhijeet , Crowe Julie , Jarvis Naomi , Jeske Torri , Kalra Nikhil , Lawrence David , McSpadden Diana
Published: May 2024

One critical step on the path from data taking to physics analysis is calibration. For many experiments this step is both time consuming and computationally expensive. The AI Experimental Calibration and Control project seeks to address these issues, starting first with the GlueX Central Drift Chamber (CDC). We demonstrate the ability of a Gaussian Process to estimate the gain correction factor (GCF) of the GlueX CDC accurately, and also the uncertainty of this estimate. Using the estimated GCF, the developed system infers a new high voltage (HV) setting that stabilizes the GCF in the face of changing environmental conditions. This happens in near real time during data taking and produces data which are already approximately gain-calibrated, eliminating the cost of performing those calibrations which vary ±15% with fixed HV. We also demonstrate an implementation of an uncertainty aware system which exploits a key feature of a Gaussian process.
Status of DUNE Offline Computing
Authors: Kirby Michael
Published: May 2024

We summarize the status of Deep Underground Neutrino Experiment (DUNE) Offline Software and Computing program. We describe plans for the computing infrastructure needed to acquire, catalog, reconstruct, simulate and analyze the data from the DUNE experiment and its prototypes in pursuit of the experiment’s physics goals of precision measurements of neutrino oscillation parameters, detection of astrophysical neutrinos, measurement of neutrino interaction properties and searches for physics beyond the Standard Model. In contrast to traditional HEP computational problems, DUNE’s Liquid Argon Time Projection Chamber data consist of simple but very large (many GB) data objects which share many characteristics with astrophysical images. We have successfully reconstructed and simulated data from 4% prototype detector runs at CERN. The data volume from the full DUNE detector, when it starts commissioning late in this decade will present memory management challenges in conventional processing but significant opportunities to use advances in machine learning and pattern recognition as a frontier user of High Performance Computing facilities capable of massively parallel processing. Our goal is to develop infrastructure resources that are flexible and accessible enough to support creative software solutions as HEP computing evolves.
Computing Challenges for the Einstein Telescope project
Authors: Bagnasco Stefano , Bozzi Antonella , Fragos Tassos , Gonzalvez Alba , Hahn Steffen , Hemming Gary , Lavezzi Lia , Laycock Paul , Merino Gonzalo , Pardi Silvio , Schramm Steven , Stahl Achim , Tanasijczuk Andres , Tonello Nadia , Vallero Sara , Veitch John , Verdier Patrice
Published: May 2024

The discovery of gravitational waves, first observed in September 2015 following the merger of a binary black hole system, has already revolutionised our understanding of the Universe. This was further enhanced in August 2017, when the coalescence of a binary neutron star system was observed both with gravitational waves and a variety of electromagnetic counterparts; this joint observation marked the beginning of gravitational multimessenger astronomy. The Einstein Telescope, a proposed next-generation ground-based gravitational-wave observatory, will dramatically increase the sensitivity to sources: the number of observations of gravitational waves is expected to increase from roughly 100 per year to roughly 100’000 per year, and signals may be visible for hours at a time, given the low frequency cutoff of the planned instrument. This increase in the number of observed events, and the duration with which they are observed, is hugely beneficial to the scientific goals of the community but poses a number of significant computing challenges. Moreover, the currently used computing algorithms do not scale to this new environment, both in terms of the amount of resources required and the speed with which each signal must be characterised. This contribution will discuss the Einstein Telescope's computing challenges, and the activities that are underway to prepare for them. Available computing resources and technologies will greatly evolve in the years ahead, and those working to develop the Einstein Telescope data analysis algorithms will need to take this into account. It will also be important to factor into the initial development of the experiment's computing model the availability of huge parallel HPC systems and ubiquitous Cloud computing; the design of the model will also, for the first time, include the environmental impact as one of the optimisation metrics.
Improved Pilot Logging in DIRAC
Authors: Fayer Simon , Martyniak Janusz , Stagni Federico
Published: May 2024

DIRAC is a widely used framework for distributed computing. It works by building a layer between the users and the resources offering a common interface to a number of heterogeneous providers. DIRAC, like many other workload management systems, uses pilot jobs to check and configure the worker-node environment before fetching a user payload. The log messages generated by these pilot jobs are crucial for diagnosing problems in the infrastructure. In these proceedings we present a configurable, resource independent remote pilot logging system.
Job splitting on the ALICE grid, introducing the new job optimizer for the ALICE grid middleware
Authors: Reme-Ness Haakon André , Grigoras¸ Costin , Helstrup Håvard , Kileng Bjarte , Storetvedt Maksim , Betev Latchezar
Published: May 2024

This contribution introduces the job optimizer service for the nextgeneration ALICE Grid middleware, JAliEn (Java Alice Environment). It is a continuous service running on central machines and is essentially responsible for splitting jobs into subjobs, to then be distributed and executed on the ALICE grid. There are several ways of creating subjobs based on various strategies relevant to the aim of any particular grid job. Therefore a user has to explicitly declare that a job is to be split, and also define the strategy to be used. The new job optimizer service aims to retain the old ALICE grid middleware functionalities from the user’s point of view while increasing the performance and throughput. One aspect of increasing performance is looking at how the job optimizer interacts with the job queue database. A different way of describing subjobs in the database is presented, to minimize resource usage. There is also a focus on limiting communications with the database, as this is already a congested area. Furthermore, a new solution to splitting based on the locality of job input data will be presented, aiming to split into subjobs more efficiently, therefore making better use of resources on the grid to further increase throughput. Added options for the user regarding splitting by locality, such as setting a minimum limit for a subjob size, will also be explored.
Scalable ATLAS pMSSM computational workflows using containerised REANA reusable analysis platform
Authors: Donadoni Marco , Feickert Matthew , Heinrich Lukas , Liu Yang , Mečionis Audrius , Moisieienkov Vladyslav , Šimko Tibor , Stark Giordon , Vidal García Marco
Published: May 2024

In this paper we describe the development of a streamlined framework for large-scale ATLAS pMSSM reinterpretations of LHC Run-2 analyses using containerised computational workflows. The project is looking to assess the global coverage of BSM physics and requires running O(5k) computational workflows representing pMSSM model points. Following ATLAS Analysis Preservation policies, many analyses have been preserved as containerised Yadage workflows, and after validation were added to a curated selection for the pMSSM study. To run the workflows at scale, we utilised the REANA reusable analysis platform. We describe how the REANA platform was enhanced to ensure the best concurrent throughput by internal service scheduling changes. We discuss the scalability of the approach on Kubernetes clusters from 500 to 5000 cores. Finally, we demonstrate a possibility of using additional ad-hoc public cloud infrastructure resources by running the same workflows on the Google Cloud Platform.
Using Kerberos Tokens in Distributed Computing System at IHEP
Authors: Jiang Xiaowei , Guo Chaoqi , Hu Qingbao , Du Ran , Shi Jingyan , Sun Gongxing
Published: May 2024

The token-based certification method is spreading in the distributed computing system of high energy physics. More and more software and middleware are supporting tokens as one of the certification methods. As an example, WLCG has upgraded all the services to support WLCG tokens [1]. In IHEP (Institute of High Energy Physics in China), the Kerberos [2] token has been used as the main certification method in the local cluster. Naturally, it is selected as the certification method in the distributed computing system. In this case, a set of toolkits were developed or introduced to use Kerberos tokens in the distributed computing system, including token producer, token repository, token transfer and token client engine. The token producer is responsible for creating a token and publishing the token file to the token repository. The token repository stores all the latest token files and a refresh service periodically renews the lifetime of those tokens stored in the token repository. The token transfer brings the token file to the worker node. The token client engine initializes the token environment and renews the token’s lifetime on the worker node. With these toolkits, the jobs can run in any worker node in any site and use the Kerberos token to access other services, such as EOS [3] and the XRootd [4] proxy service. In IHEP, the Kerberos toolkit has been deployed in the distributed computing system. Currently, three experiments (LHAASO [5], BES [6] and HERD [7]) are using Kerberos tokens to remotely access the data in EOS or Lustre [8].
Integrating the Rivet analysis tool into EPOS 4
Authors: Jahan Johannes , Werner Klaus , Vintache Damien
Published: May 2024

EPOS 4 is the last version of the high-energy collision event generator EPOS, released publicly in 2022. It was delivered with improvements on several aspects, whether about the theoretical bases on which it relies, how they are handled technically, or regarding user’s interface and data compatibility.

This last point is especially important, as part of a commitment to provide the widest possible use. In this regard, a new output data format have been implemented, based on the HepMC standard libraries. This feature enables in particular the analysis of EPOS simulations with Rivet , an analysis and validation toolkit for Monte Carlo event generators, with recent major upgrades on concerning heavy-ion analysis methods. In order to take advantage of this, the use of Rivet has been implemented directly in the EPOS analysis machinery, ensuring an easy and fast solution for comparison with experimental data, beneficial for both developers and users. We will hence present here the details of this implementation and the results obtained thanks to it.
Awkward Just-In-Time (JIT) Compilation: A Developer’s Experience
Authors: Osborne Ianna , Pivarski Jim , Ifrim Ioana , Hollands Angus , Schreiner Henry
Published: May 2024

Awkward Array is a library for performing NumPy-like computations on nested, variable-sized data, enabling array-oriented programming on arbitrary data structures in Python. However, imperative (procedural) solutions can sometimes be easier to write or faster to run. Performant imperative programming requires compilation; JIT-compilation makes it convenient to compile in an interactive Python environment. Various functions in Awkward Arrays JIT-compile a user’s code into executable machine code. They use several different techniques, but reuse parts of each others’ implementations. We discuss the techniques used to achieve the Awkward Arrays acceleration with JITcompilation, focusing on RDataFrame, cppyy, and Numba, particularly Numba on GPUs: conversions of Awkward Arrays to and from RDataFrame; standalone cppyy; passing Awkward Arrays to and from Python functions compiled by Numba; passing Awkward Arrays to Python functions compiled for GPUs by Numba; and header-only libraries for populating Awkward Arrays from C++ without any Python dependencies
Facilitating the preservation of LHCb Analyses with APD
Authors: Burr Chris , Couturier Ben , O’Neil Ryunosuke
Published: May 2024

High Energy Physics experiments at the Large Hadron Collider generate petabytes of data per year that go through multiple transformations before final analysis and paper publication. Recording the provenance of these data is therefore crucial to maintain the quality of the final results. While tools are in place within LHCb to keep this information for the common experiment-wide transforms, analysts have had to implement their own solutions for the steps dealing with ntuples. This gap between centralised and interactive processing can become problematic. In order to facilitate the task, ntuples extracted by LHCb analysts via so-called “Analysis Productions” are tracked in the experiment bookkeeping database and can be enriched with extra information about their meaning and intended use. This information can then be used to access ntuples more easily: a set of Python tools allow querying of ntuple file locations with associated metadata, and integrate their processing within analysis workflows. The tools are designed with the intention of ensuring analysis code continues to be functional into the future and are robust against evolutions in how data is accessed. This paper presents the integration of these new tools into the LHCb codebase and demonstrates how they will be used in LHCb data processing and analysis.
ML_INFN project: Status report and future perspectives
Authors: Anderlini Lucio , Boccali Tommaso , Dal Pra Stefano , Duma Doina Cristina , Giommi Luca , Spiga Daniele , Vino Gioacchino
Published: May 2024

The ML_INFN initiative (“Machine Learning at INFN”) is an effort to foster Machine Learning (ML) activities at the Italian National Institute for Nuclear Physics (INFN). In recent years, artificial intelligence inspired activities have flourished bottom-up in many efforts in Physics, both at the experimental and theoretical level. Many researchers have procured desktop-level devices, with consumer-oriented GPUs, and have trained themselves in a variety of ways, from webinars, books, and tutorials. ML_INFN aims to help and systematize such effort, in multiple ways: by offering state-of-the-art hardware for ML, leveraging on the INFN Cloud provisioning solutions and thus sharing more efficiently GPUs and leveling the access to such resources to all INFN researchers, and by organizing and curating Knowledge Bases with productiongrade examples from successful activities already in production. Moreover, training events have been organized for beginners, based on existing INFN ML research and focused on flattening the learning curve. In this contribution, we will update the status of the project reporting in particular on the development of tools to take advantage of High-Performance Computing resources provisioned by CNAF and ReCaS computing centers for interactive support to activities and on the organization of the first in-person advanced-level training event, with a GPU-equipped cloud-based environment provided to each participant.
Energy Reconstruction with Semi-Supervised Autoencoders for Dual-Phase Time Projection Chambers
Authors: Li Ivy , Higuera Aarón , Liang Shixiao , Qin Juehang , Tunnell Christopher
Published: May 2024

This paper presents a proof-of-concept semi-supervised autoencoder for the energy reconstruction of scattering particle interactions inside dualphase time projection chambers (TPCs), such as XENONnT. This autoencoder model is trained on simulated XENONnT data and is able to simultaneously reconstruct photosensor array hit patterns and infer the number of electrons in the gas gap, which is proportional to the energy of ionization signals in the TPC. Development plans for this autoencoder model are discussed, including future work in developing a faster simulation technique for dual-phase TPCs.
Measurements With A Quantum Vision Transformer: A Naive Approach
Authors: Pasquali Dominic , Grossi Michele , Vallecorsa Sofia
Published: May 2024

In mainstream machine learning, transformers are gaining widespread usage. As Vision Transformers rise in popularity in computer vision, they now aim to tackle a wide variety of machine learning applications. In particular, transformers for High Energy Physics (HEP) experiments continue to be investigated for tasks including jet tagging, particle reconstruction, and pile-up mitigation.

An improved Quantum Vision Transformer (QViT) with a quantum-enhanced self-attention mechanism is introduced and discussed. A shallow circuit is proposed for each component of self-attention to leverage current Noisy Intermediate Scale Quantum (NISQ) devices. Variations of the hybrid architecture/model are explored and analyzed.

The results demonstrate a successful proof of concept for the QViT, and establish a competitive performance benchmark for the proposed design and implementation. The findings also provide strong motivation to experiment with different architectures, hyperparameters, and datasets, setting the stage for implementation in HEP environments where transformers are increasingly used in state of the art machine learning solutions.
Zc(3900) observation at BESIII with QSVM method
Authors: Ding Biao , Meng Zhaoxia , Zou Jiaheng , Li Teng , Lin Tao
Published: May 2024

In recent years, quantum computing shows significant potentials in many areas. In this proceeding, we revisit the observation of the Zc(3900) resonance with quantum machine learning techniques, specifically quantum support vector machine (QSVM). Meanwhile, the outcomes are compared with classical support vector machine (SVM) method. With the IBM Qiskit toolkit, the QSVM method achieves a competitive signal and background classification accuracy compared to classical methods. This study emphasizes the potential of quantum machine learning in high-energy physics research, and it reveals the feasibility of applying quantum computing in future physics data analysis.
Massive Scale Data Analytics at LCLS-II
Authors: Thayer Jana , Chen Zhantao , Claus Richard , Damiani Daniel , Ford Christopher , Dubrovin Mikhail , Elmir Victor , Kroeger Wilko , Li Xiang , Marchesini Stefano , Mariani Valerio , Melcchiori Riccardo , Nelson Silke , Peck Ariana , Perazzo Amedeo , Poitevin Frederic , O’Grady Christopher Paul , Otero Julieth , Quijano Omar , Shankar Murali , Uervirojnangkoorn Monarin , Veraldi Riccardo , Weaver Matthew , Weninger Clemens , Yamajala Seshu , Wang Cong , Yoon Chun Hong
Published: May 2024

The increasing volumes of data produced at light sources such as the Linac Coherent Light Source (LCLS) enable the direct observation of materials and molecular assemblies at the length and timescales of molecular and atomic motion. This exponential increase in the scale and speed of data production is prohibitive to traditional analysis workflows that rely on scientists tuning parameters during live experiments to adapt data collection and analysis. User facilities will increasingly rely on the automated delivery of actionable information in real time for rapid experiment adaptation which presents a considerable challenge for data acquisition, data processing, data management, and workflow orchestration. In addition, the desire from researchers to accelerate science requires rapid analysis, dynamic integration of experiment and theory, the ability to visualize results in near real-time, and the introduction of ML and AI techniques. We present the LCLS-II Data System architecture which is designed to address these challenges via an adaptable data reduction pipeline (DRP) to reduce data volume on-thefly, online monitoring analysis software for real-time data visualization and experiment feedback, and the ability to scale to computing needs by utilizing local and remote compute resources, such as the ASCR Leadership Class Facilities, to enable quasi-real-time data analysis in minutes. We discuss the overall challenges facing LCLS, our ongoing work to develop a system responsive to these challenges, and our vision for future developments.
Early entrant’s strategy for cooperating with a competing fourth-party platform
Authors: Xin Wen , Lin Jun
Published: May 2024

By integrating several third-party platforms that enter the market late (later entrants), a fourth-party platform can effectively compete with the third-party platform that enters the market early (early entrant). Our research develops an analytical framework to address the platform coopetition problem that the early entrant faces, i.e., integrating its service into the fourth-party platform or not, when there is a fourth-party platform in the market. This study provides optimal conditions and platform decisions for platform coopetition and then explores the impact of the implementation of the coopetition strategy on platform prices and consumer demands. Our analytical results show that the coopetition strategy is effective only when the sum of the strengths of direct and indirect network effects is low and the quality difference between the early and later entrants is higher than a certain threshold. Moreover, the early entrant always partially integrates its service into the fourth-party platform when the coopetition strategy is adopted. Such adoption always lowers the prices of the early and later entrants. The implementation of the coopetition strategy always increases the total consumer demand of the early entrant, but it also results in a reduction in the number of consumers in the standalone application of the early entrant.
Is a routine histopathological examination justified for all tumours
resected from the upper extremity?
Authors: Zyluk Andrzej
Published: May 2024

Most lesions of the upper extremity are common and benign, and the need for a
routine pathology evaluation of these specimens has often been questioned. This
study aimed to evaluate the concordance of the initial clinical and final
histological diagnoses of tumours which, based on clinical presentation and
intraoperative findings, are most likely benign or malignant, and to answer the
question whether or not a routine histopathological examination is justified for
all tumours in the upper extremity.

Material and Methods We analysed the results of histopathological
examinations of benign tumours resected in 346 patients and malignant tumours
resected in 6 patients.

Results Our analysis showed a 100% concordance between the initial
(clinical) diagnoses of the tumours as benign or malignant and their final
histopathological diagnoses. Only in 12 cases (3.5%) of initially benign tumours
did the clinical presentation and/or intraoperative findings raise doubts.

Conclusions The results of this study show that a routine histological
evaluation of all tumours resected from the upper extremity is not justified and
may be confined to selected cases in which clinical presentation and/or
intraoperative findings raise doubts.
Takotsubo's Cardiomyopathy in a Young Female with Severe Traumatic Brain Injury: A Case Report
Authors: Kumari Gayatri , Singh Anoop K. , Jaishree Sharma V. , Tiwari Ashutosh
Published: May 2024

Takotsubo's cardiomyopathy (TC) typically presents with acute cardiac dysfunction due to regional wall motion abnormality, but unlike other cardiac pathologies, it recovers within a short period. Here, we report the case of a 23-year-old woman who presented to us following severe traumatic brain injury (TBI). Her Glasgow coma scale (GCS) deteriorated rapidly in the preoperative period and she developed TC following surgery. Despite an uneventful surgery, she needed cardiovascular support by vasopressors and inotropes in the postoperative period. She was diagnosed with TC on serial transthoracic echocardiography, with complete cardiac function recovery within 9 days. The diagnosis of TC was supported by electrocardiography (not correlating coronary artery disease), elevated troponin I and N-terminal prohormone of brain natriuretic peptide (NT-proBNP), and the presence of a physical sessor like TBI. As an unrecognized TC due to a low GCS score after severe TBI may negatively impact outcomes, we aim to emphasize that vigilant perioperative management may give good outcomes even in less commonly encountered serious TC.
A Case of Spontaneous Intracranial Hematoma with Critically Low Platelet Count: A Neurointensivist's Nightmare
Authors: Samanta Sukhen , Prasanna Malligere
Published: May 2024

Complications of immune thrombocytopenia (ITP) are more frequent at a platelet count lower than 10,000/μL of blood. Intracranial hematoma (ICH) is one of the most severe complications of ITP. Subdural hematoma (SDH) and subarachnoid hemorrhage (SAH) usually occur as extensions of ICH and is generally spontaneous in nature. We report a case of ICH along with SDH and SAH who presented with critically low platelet count managed using apheresis platelet transfusion, desmopressin, intravenous immunoglobulin, and methylprednisolone. The patient was discharged successfully from hospital without any neurological deficit. The importance of early referral and immediate aggressive management and monitoring at an advanced center is suggested.
Diagnostic Accuracy of Frozen Section Analysis and Factors Associated with Discordance of Final Histopathology in Mucinous Ovarian Tumor
Authors: Gupta Rubi , Patel Bijal , Dave Pariseema , Arora Ruchi , Parekh Chetna , Dama Swetha
Published: May 2024

Aim and Objective This article determines the diagnostic accuracy of frozen section (FS) in case of mucinous ovarian tumor (MOT) and tailoring of intraoperative treatment decision.

Material and Methods This was an observational study in which retrospective data was analyzed prospectively from January 2008 to December 2015 at the Regional Cancer Center. This study included 158 patients with MOT who underwent FS analysis during staging laparotomy at our institute. FS's sensitivity, specificity, and diagnostic accuracy were calculated and compared with final paraffin histopathological examination (HPE). Univariate and multivariate analyses were done to decide factors associated with changes in final HPE.

Results FS analysis and final HPE report were concordant in 77.8% (123/158) of the cases and discordant in 22.1% (35/158). Out of 35 discordant cases, 29 (18.3%) were upgraded and 06 (5.6%) were downgraded. Out of 33 borderline cases, 16 (48.4%) were upgraded to malignant MOT and 6 (18.1%) were downgraded to benign MOT on final HPE. Out of 40 malignant MOT, 40 (100%) showed malignancy on final HPE. In the case of benign MOT on FS, 13 out of 85 (15.2%) upgraded to borderline or malignant. Sensitivity and specificity of FS were 76.60 and 55.17%, respectively, for benign MOT, 55.17 and 86.96% for borderline MOT, and 86.96 and 100% for malignant MOT.

Conclusion In MOT, due to its large size and heterogeneous nature, it displays spectrum of appearance ranging from benign cyst adenoma, and borderline tumor to adenocarcinoma. Our study showed significant discordance between FS and final histopathological diagnosis in borderline MOT.