bluuebunny/crossref_metadata_2025_split huggingface dataset:
A broadband zigzag-shaped energy harvester for both wind energy and vibration energy: modeling and experimental verification
Authors: Pan Jianan , Zhang Xuhui , Qin Weiyang , Xu Hengtao , Tian Hao , Zhu Fulin , Guo Yan 
Published: February 2023

Abstract
In this work, a novel zigzag-shaped energy harvester is proposed to harvest wind energy and vibration energy simultaneously in a practical environment. The harvester is constituted of an inclined beam and a horizontal beam with a bluff body fixed at the free end. The inclined beam is covered by a piezoelectric patch. The vibration induced by the wind flow and base excitation produces electric energy through the piezoelectric material and realizes energy harvesting. In particular, the softening characteristic created by the magnetic interaction can extend the working bandwidth. Dynamical coupling equations are derived, and the corresponding simulations are carried out. The results show that the cubic bluff body can help increase the wind-induced energy harvesting. The responses obtained under base excitation combined with wind flow demonstrate that the hybrid excitation can provide a significant enhancement to the non-resonance region. Related validation experiments are carried out. The experimental results are in good agreement with the numerical results. Compared with the conventional base excitation or wind flow excitation, the output power obtained under hybrid excitation increases by 106% and 206%, respectively.
A novel compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer
Authors: Wang Guangbin , Zhao Shubiao , Chen Jinhua , Zhong Zhixian 
Published: February 2023

Abstract
Metric transfer learning overcomes the problem of no domain distribution alienation in traditional sample transfer, and also improves the classification accuracy. However, some problems exist; for example, the target domain must have labelled samples without considering the manifold connection between data samples, and the algorithm is not robust under sparse samples. A compound fault diagnosis method for rolling bearings based on graph label manifold metric transfer is proposed. First, the pseudo-label of the target domain is acquired through a weighted graph constructed on the source domain with known fault labels. In order to ensure the accuracy of metrics in manifold space, we adopt the method of graph label propagation (LP) to gradually spread the label information. Then, the sample data are mapped to the low-dimensional manifold space by locality preserving projections (LPP), which gives higher weight to the source domain samples close to the target domain to solve the domain offset problem. At the same time, combined with the labelled samples of the target domain in LP, the manifold distance measure is learned to minimize the intra-class distance and maximize the class spacing, eliminate the overlap phenomenon in the domain and realize the distribution alienation between classes in the domain. After each iteration, unsupervised manifold metric transfer learning is realized by increasing the range of LP and gradually reducing the error of graph LP. Experiments show that the new unsupervised transfer learning method has better fault identification ability than the semi-supervised transfer learning method. In the case of sparse training samples, it still maintains a high diagnostic accuracy, and the highest fault recognition accuracy can reach more than 97% with good robustness.
A novel method for measuring radial and axial errors in a cylindrical coordinate system using a combined double ball bar
Authors: Sun Tao , Wang Wen , Wang Chuanyong , Chen Zhanfeng , Sang Zhiqian , Lu Keqing , Xu Kaifei , Wu Haimei , Ju Bing-Feng 
Published: February 2023

Abstract
Multiaxis machine tools are widely used in manufacturing. The measurement of motion error is important to improve the machining accuracy of machine tools. A double ball bar (DBB), as a high-precision single-dimensional position reading device, is widely used to measure the motion error of the circular path of machine tools. However, the motion errors contain the cylindrical radial and axial errors with respect to the circular path plane. Therefore, the measurement results of the DBB are incomplete because they do not contain axial errors. To measure the motion error of the circular path accurately, completely and concisely, a novel measurement method based on the combined DBB (C-DBB) is proposed in this paper, which can directly measure the radius and axial motion error of the circular path in one measurement. First, the measuring principle of the C-DBB method is introduced, and the measurement model of the radius and axial motion error of the circular path is established. Then, a measuring device using the C-DBB method was designed based on the lever principle, and the error parameters of the designed device were analyzed. According to the error analysis results, the measurement results of the C-DBB method were compensated and corrected. Finally, the C-DBB method is used to measure the circular path on the vertical CNC milling machine, and the results are compared with the measurement results using the DBB method. It is shown that the proposed C-DBB method can obtain high-precision multidimensional motion error in a single measurement, and the designed measuring device based on the C-DBB method has high repeatability and good circular path measuring ability. The proposed method provides an effective method for machine tool multidimensional motion error measurement and compensation.
Influence of filament number and size on the basic properties of in situ made MgB2 wires
Authors: Kováč P , Berek D , Melišek T , Kováč J , Hušek I , Búran M , Lee D G , Choi J H 
Published: February 2023

Abstract
Monel-sheathed MgB2 wires with 6, 18 and 54 filaments of diameter 0.83 mm manufactured by Sam Dong Co., Ltd, have been subjected to twisting, drawing and rolling and measurements at low temperatures. R(T), I–V characteristics, critical currents and stress/strain tolerances of these wires were analyzed. Low reduction of critical current (⩽10%) by twisting between 50 mm and 12.5 mm was observed, especially for the wire with 54 filaments, which also showed the best strain tolerance to tension, ∼0.43% at 4.2 K. The engineering current density of the 18-filament wire was decreased by 56% by drawing from 0.83 mm to 0.4 mm, but improved by ∼24% by rolling from 0.83 mm to 0.64 mm. A small uninsulated coil was made from wire flattened from 0.83 mm to 0.64 mm by the wind and react process, resulting in a very high winding packing factor of 0.91 and also a high winding current density of 104 A cm−2 at B ⩽ 5.6 T. The presented properties are interesting for possible applications of these MgB2 wires in DC windings.
A practical guide to electromagnetically induced transparency in atomic vapor
Authors: Finkelstein Ran , Bali Samir , Firstenberg Ofer , Novikova Irina 
Published: February 2023

Abstract
This tutorial introduces the theoretical and experimental basics of electromagnetically induced transparency (EIT) in thermal alkali vapors. We first give a brief phenomenological description of EIT in simple three-level systems of stationary atoms and derive analytical expressions for optical absorption and dispersion under EIT conditions. Then we focus on how the thermal motion of atoms affects various parameters of the EIT system. Specifically, we analyze the Doppler broadening of optical transitions, ballistic versus diffusive atomic motion in a limited-volume interaction region, and collisional depopulation and decoherence. Finally, we discuss the common trade-offs important for optimizing an EIT experiment and give a brief ‘walk-through’ of a typical EIT experimental setup. We conclude with a brief overview of current and potential EIT applications.
A study on the mechanical and antimicrobial properties of biomimetic shark skin fabrics with different denticle size via 3D printing technology
Authors: Wen Jiayi , Prawel David , Li Yan Vivian 
Published: February 2023

Abstract
Previous studies have shown that biomimetic shark skin fabrics can reduce water drag and increase swimming speed. It was also known that small denticles on shark skin surfaces promoted high water drag reduction. The smallest size of 3D printed denticles on a biomimetic shark skin fabric previously reported was about 2mm, which was still larger than the denticles on shark skin found in nature (100 – 500 µm). In this study, small denticles ranging from 0.65mm to 1.30mm were fabricated to decorate on a smooth film using a Form3 3D printer, resulting in biomimetic shark skin fabrics. The effect of denticle size on mechanical properties (stiffness, Young’s modulus, tensile strength, and breaking elongation) and antimicrobial properties of the biomimetic shark skin fabrics were evaluated to assess the applications in functional clothing. The results suggested that when the size of the denticles was decreased, the stiffness of the fabrics was increased. The tensile strength and the breaking elongation of the fabric with 1.04mm denticles were larger than those of fabric materials used in commercial swimwear. In antimicrobial testing, the shark skin fabrics with 0.65mm and 1.04mm denticles were found to be less susceptible to bacterial attachment, suggesting great potential for functional clothing applications.
A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection
Authors: Hermans Tim , Smets Laura , Lemmens Katrien , Dereymaeker Anneleen , Jansen Katrien , Naulaers Gunnar , Zappasodi Filippo , Van Huffel Sabine , Comani Silvia , De Vos Maarten 
Published: February 2023

Abstract

Objective. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of expert artefact annotations challenges the development of deep learning models for artefact detection. This paper proposes a semi-supervised deep learning approach for artefact detection in neonatal EEG that requires few labelled data by training a multi-task convolutional neural network (CNN). Approach. An unsupervised and a supervised objective were jointly optimised by combining an autoencoder and an artefact classifier in one multi-output model that processes multi-channel EEG inputs. The proposed semi-supervised multi-task training strategy was compared to a classical supervised strategy and other existing state-of-the-art models. The models were trained and tested separately on two different datasets, which contained partially annotated multi-channel neonatal EEG. Models were evaluated using the F1-statistic and the relevance of the method was investigated in the context of a functional brain age (FBA) prediction model. Main results. The proposed multi-task and multi-channel CNN methods outperformed state-of-the-art methods, reaching F1 scores of 86.2% and 95.7% on two separate datasets. The proposed semi-supervised multi-task training strategy was shown to be superior to a classical supervised training strategy when the amount of labels in the dataset was artificially reduced. Finally, we found that the error of a brain age prediction model correlated with the amount of automatically detected artefacts in the EEG segment. Significance. Our results show that the proposed semi-supervised multi-task training strategy can train CNNs successfully even when the amount of labels in the dataset is limited. Therefore, this method is a promising semi-supervised technique for developing deep learning models with scarcely labelled data. Moreover, a correlation between the error of FBA estimates and the amount of detected artefacts in the corresponding EEG segments indicates the relevance of artefact detection for robust automated EEG analysis.
High Performance Analysis, Today and Tomorrow
Authors: Bendavid Josh 
Published: February 2023

Abstract
The unprecedented volume of data and Monte Carlo simulations at the HL-LHC will pose increasing challenges for data analysis both in terms of computing resource requirements as well as ”time to insight”. Discussed are the evolution and current state of analysis data formats, software, infrastructure and workflows at the LHC, and the directions being taken towards fast, efficient, and effective physics analysis at the HL-LHC.
Accelerating the Inference of the Exa.TrkX Pipeline
Authors: Lazar Alina , Ju Xiangyang , Murnane Daniel , Calafiura Paolo , Farrell Steven , Xu Yaoyuan , Spiropulu Maria , Vlimant Jean-Roch , Cerati Giuseppe , Gray Lindsey , Klijnsma Thomas , Kowalkowski Jim , Atkinson Markus , Neubauer Mark , DeZoort Gage , Thais Savannah , Hsu Shih-Chieh , Aurisano Adam , Hewes Jeremy , Ballow Alexandra , Acharya Nirajan , Wang Chun-yi , Liu Emma , Lucas Alberto 
Published: February 2023

Abstract
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.
Adding multi-core support to the ALICE Grid Middleware
Authors: Weisz Sergiu , Ferrer Marta Bertran 
Published: February 2023

Abstract
The major upgrade of the ALICE experiment for the LHC Run3 poses unique challenges and opportunities for new software development. In particular, the entirely new data taking and processing software of ALICE relies on process parallelism and large amounts of shared objects in memory. Thus from a single-core single thread workload in the past, the new workloads are exclusively multithreaded. This requires a profound change in the ALICE Grid midleware job handling, from scheduling to execution, and thus the entire middleware has been rewritten during the past 3 years to support the new multithreaded reality.
This paper presents the ALICE middlewre development for multi-core job management and the tools used to achieve an efficient and secure environment. In particular, it covers job isolation and scheduling and how they can be implemented in different site configurations, such as sites shared with other experiments or High Performance Computing resources.
Data transfer for STAR grid jobs
Authors: Chakaberia Irakli , Lauret Jerome , Poat Michael , Porter Jefferson 
Published: February 2023

Abstract
The Solenoidal Tracker at RHIC (STAR) is a multipurpose experiment at the Relativistic Heavy Ion Collider (RHIC) with the primary goal to study the formation and properties of the quark-gluon plasma. STAR is an international collaboration of member institutions and laboratories from around the world. Yearly data-taking period produces PBytes of raw data collected by the experiment. STAR primarily uses its dedicated facility at BNL to process this data, but has routinely leveraged distributed systems, both high throughput (HTC) and high performance (HPC) computing clusters, to significantly augment the processing capacity available to the experiment.
The ability to automate the efficient transfer of large data sets on reliable, scalable, and secure infrastructure is critical for any large-scale distributed processing campaign. For more than a decade, STAR computing has relied upon GridFTP with its x509-based authentication to build such data transfer systems and integrate them into its larger production workflow. The end of support by the community for both GridFTP and the x509 standard requires STAR to investigate other approaches to meet its distributed processing needs.
In this study we investigate two multi-purpose data distribution systems, Globus.org and XRootD, as alternatives to GridFTP. We compare both their performance and the ease by which each service is integrated into the type of secure and automated data transfer systems STAR has previously built using GridFTP. The presented approach and study may be applicable to other distributed data processing use cases beyond STAR.
Design of a request/response buffering application for I/O intensive workloads
Authors: Grötschla F , Lehmann Miotto G , Sipos R 
Published: February 2023

Abstract
The performance of I/O intensive applications is largely determined by the organization of data and the associated insertion/extraction techniques. In this paper we present the design and implementation of an application that is targeted at managing data received (upto ~ 150 Gb/s payload throughput) into host DRAM, buffering data for several seconds, matched with the DRAM size, before being dropped. All data are validated, processed and indexed. The features extracted from the processing are streamed out to subscribers over the network; in addition, while data resides in the buffer, about 0.1 ‰ of them are served to remote clients upon request. Last but not least, the application must be able to locally persist data at full input speed when instructed to do so. The characteristics of the incoming data stream (fixed or variable rate, fixed or variable payload size) heavily influences the choice of implementation of the buffer management system. The application design promotes the separation of interfaces (concepts) and application oriented specializations (models) that makes it possible to generalize most of the workflows and only requires minimal effort to integrate new data sources. After the description of the application design, we will present the hardware platform used for validation and benchmarking of the software, and the performance results obtained.
Experience in dynamic tape drive allocation to manage scientific data
Authors: Cavalli A. , Cesini D. , Falabella A. , Fattibene E. , Fornari F. , Morganti L. , Prosperini A. , Sapunenko V. 
Published: February 2023

Abstract
The main computing and storage facility of INFN (Italian Institute for Nuclear Physics) running at CNAF hosts and manages tens of Petabytes of data produced by the LHC (Large Hadron Collider) experiments at CERN and other scientific collaborations in which INFN is involved. Most of these data are stored on tape resources of different technologies. All the tape drives can be used for administrative tasks (as repack, audit, space reclamation), as well to write and read data of all the experiments. Moreover, the usage of tape resources by scientific communities will become considerably more intense in the next years and the amount of data on tape will double by 2025. For these reasons, the issue of the concurrent access to tape drives is significant. We designed a software solution to optimize the efficiency of the shared usage of tape drives in our environment and put it in production in January 2020. In this paper we present the experience with such dynamic tape resources allocation in production. Comparing it with the previous static allocation method, we observed an improvement in reading throughput up to 85%. Moreover, we describe the new features added to our solution to optimize the efficiency of the shared usage of tape drives of different technologies.
Parallel processing in data analysis of the JUNO experiment
Authors: Yang Yixiang 
Published: February 2023

Abstract
The JUNO experiment is being built mainly to determine the neutrino mass hierarchy by detecting neutrinos generated in the Yangjiang and Taishan nuclear plants in southern China. The detector will record 5.6 TB raw data every day for offline analysis, but each day it can only collect about 60 neutrino events scattered among huge background events. Selection of extremely sparse neutrino events brings a big challenge to offline data analysis. A typical neutrino physics event normally spans across a number of consecutive readout events, flagged by a fast positron signal followed by a slow neutron signal within a varying-size time window. To facilitate this analysis, a two-step data processing scheme has been proposed. In the first step (called data preparation), the event index data is produced and skimmed, which only contains information of minimum physics quantities of events as well as their addresses in the original reconstructed data file. In the second step (called time correlation analysis), event index data is further selected with stricter criteria. And then, for each selected event, the time correlation analysis is performed by reading all associated events within a pre-defined time window from the original data file according to the selected event’s address and timestamp.
This contribution will start to introduce the design of the above data processing scheme and then focus on the multi-threaded implementation of time correlation analysis based on the Intel Threading Building Block (TBB) in the SNiPER framework. Afterwards, this contribution will describe the implementation of distributed analysis using MPI in which the time correlation analysis task is divided into sub-tasks running on multiple computing nodes. At last, this contribution will present the detailed performance measurements made on a multiple-node test bed. By using both skimming and indexing techniques, the total amount of data finally used for neutrino signal time correlation analysis is significantly reduced, and the processing time could be reduced by two orders of magnitude.
Acceleration with GPUs and other RooFit news
Authors: Michalainas Emmanouil , Rembser Jonas , Hageboeck Stephan , Moneta Lorenzo 
Published: February 2023

Abstract
RooFit is a toolkit for statistical modeling and fitting, and together with RooStats it is used for measurements and statistical tests by most experiments in particle physics, particularly the LHC experiments. As the LHC program progresses, physics analysis becomes more computationally demanding. Therefore, RooFit development in recent years is focused on modernizing RooFit, improving its ease of use, and on performance optimization. This paper presents the new RooFit vectorized computation mode, which supports calculations on the GPU. Additionally, we discuss new features in the upcoming ROOT 6.26 release, highlighting the new pythonizations in particular.