bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machinery fault diagnosis using multi-scale feature focused network and adaptive cost-sensitive loss towards imbalanced dataset
Authors: Yang Jinsong , Min Zhishan , Han Songyu , Li Wei , Shao Haidong 
Published: August 2023

Abstract
The current deep learning based machinery fault diagnosis models still face challenges in effectively capturing rich multi-scale feature information and dynamically compensating training loss when dealing with imbalanced dataset. This paper presents a novel approach for machinery fault diagnosis using multi-scale feature focused network and adaptive cost-sensitive loss. Firstly, a multi-scale feature focused network is constructed with improved multi-scale CNN and point-wise attention mechanism module, in which the former can synthetically fuse the features at different scales to expand the coverage of the equivalent receptive field, and the latter can further refine fine-grained features and filter out irrelevant feature interference. Then, an adaptive cost-sensitive loss function is designed to adjust the cost matrix in the training process, dynamically assigning more loss weights for small samples that are difficult to distinguish. The experimental results of planetary gearbox fault diagnosis demonstrate that the proposed approach exhibits superior diagnostic performance compared to other existing methods.
A radiomics-incorporated deep ensemble learning model for multi-parametric MRI-based glioma segmentation
Authors: Chen Yang , Yang Zhenyu , Zhao Jingtong , Adamson Justus , Sheng Yang , Yin Fang-Fang , Wang Chunhao 
Published: August 2023

Abstract

Objective. To develop a deep ensemble learning (DEL) model with radiomics spatial encoding execution for improved glioma segmentation accuracy using multi-parametric magnetic resonance imaging (mp-MRI). Approach. This model was developed using 369 glioma patients with a four-modality mp-MRI protocol: T1, contrast-enhanced T1 (T1-Ce), T2, and FLAIR. In each modality volume, a 3D sliding kernel was implemented across the brain to capture image heterogeneity: 56 radiomic features were extracted within the kernel, resulting in a fourth-order tensor. Each radiomic feature can then be encoded as a 3D image volume, namely a radiomic feature map (RFM). For each patient, all RFMs extracted from all four modalities were processed using principal component analysis for dimension reduction, and the first four principal components (PCs) were selected. Next, a DEL model comprised of four U-Net sub-models was trained for the segmentation of a region-of-interest: each sub-model utilizes the mp-MRI and one of the four PCs as a five-channel input for 2D execution. Last, four softmax probability results given by the DEL model were superimposed and binarized using Otsu’s method as the segmentation results. Three DEL models were trained to segment the enhancing tumor (ET), tumor core (TC), and whole tumor (WT), respectively. The segmentation results given by the proposed ensemble were compared to the mp-MRI-only U-Net results. Main Results. All three radiomics-incorporated DEL models were successfully implemented: compared to the mp-MRI-only U-net results, the dice coefficients of ET (0.777 → 0.817), TC (0.742 → 0.757), and WT (0.823 → 0.854) demonstrated improvement. The accuracy, sensitivity, and specificity results demonstrated similar patterns. Significance. The adopted radiomics spatial encoding execution enriches the image heterogeneity information that leads to the successful demonstration of the proposed DEL model, which offers a new tool for mp-MRI-based medical image segmentation.
CTG-Net: an efficient cascaded framework driven by terminal guidance mechanism for dilated pancreatic duct segmentation
Authors: Zou Liwen , Cai Zhenghua , Qiu Yudong , Gui Luying , Mao Liang , Yang Xiaoping 
Published: August 2023

Abstract
Pancreatic duct dilation indicates a high risk of various pancreatic diseases. Segmentation for dilated pancreatic duct (DPD) on computed tomography (CT) image shows the potential to assist the early diagnosis, surgical planning and prognosis. Because of the DPD’s tiny size, slender tubular structure and the surrounding distractions, most current researches on DPD segmentation achieve low accuracy and always have segmentation errors on the terminal DPD regions. To address these problems, we propose a cascaded terminal guidance network to efficiently improve the DPD segmentation performance. Firstly, a basic cascaded segmentation architecture is established to get the pancreas and coarse DPD segmentation, a DPD graph structure is build on the coarse DPD segmentation to locate the terminal DPD regions. Then, a terminal anatomy attention module is introduced for jointly learning the local intensity from the CT images, feature cues from the coarse DPD segmentation and global anatomy information from the designed pancreas anatomy-aware maps. Finally, a terminal distraction attention module which explicitly learns the distribution of the terminal distraction regions is proposed to reduce the false positive and false negative predictions. We also propose a new metric called tDice to measure the terminal segmentation accuracy for targets with tubular structures and two other metrics for segmentation error evaluation. We collect our dilated pancreatic duct segmentation dataset with 150 CT scans from patients with five types of pancreatic tumors. Experimental results on our dataset show that our proposed approach boosts DPD segmentation accuracy by nearly 20% compared with the existing results, and achieves more than 9% improvement for the terminal segmentation accuracy compared with the state-of-the-art methods.
Observation of impurity accumulation and its compatibility with high plasma performance in W7-X
Authors: Zhang D , Buttenschön B , Jablonski S , Kubkowska M , Ford O , Alcusón J A , Beidler C D , Burhenn R , Beurskens M N A , Langenberg A , Pablant N , Reimold F , Rahbarnia K , Smith H M , Wegner Th , Wurden G , Bozhenkov S A , Feng Y , Brunner K J , Fuchert G , Gao Y , Geiger J , Giannone L , Höfel U , Hirsch M , Huang Z , Knauer J , Kremeyer T , Krychowiak M , Kwak S , Laqua H P , Laube R , Neuner U , Pasch E , Pavone A , von Stechow A , Svensson J , Thomsen H 
Published: August 2023

Abstract
At the W7-X stellarator, the bolometer system has measured an intensive radiation zone in the inner plasma region (at a normalized radius ρ ∼ 0.3–0.4) in the hydrogen plasma generated by electron cyclotron resonance heating; it differs from the normal plasma radiation distribution with an edge-localized emission zone. Spectroscopic diagnostics have recorded high-Z elements such as iron. This phenomenon happens in the plasma phases after gas supply turn-off, which results in all impurity relevant diagnostic signals increasing for several seconds. Despite the enhancement of the core radiation, the plasma energy confinement is improved. A transport analysis shows that this impurity radiation behavior is associated with a low diffusion coefficient (D∼ 0.02 m2 s−1) and a reversal of the convection around the radial position of the emission peak, which, under normal conditions, separates the zones of outward convection in the central (|V| ∼ 0.1 m s−1) and inward convection in the outer region (|V| ∼ 0.3 m s−1). An impurity accumulation around this radial position has been identified. The transport coefficients obtained are comparable with the theoretical predictions of collisional impurity transport. In the plasma phases studied, both impurity and energy confinement are enhanced. The mechanism responsible for the improvement is believed to be a reduction of micro-instabilities associated with the observed steepening of the density profile, initiated by a low edge plasma density (<1.0 × 1019 m−3) after switching off the gas fueling. The normalized temperature and density gradients fulfil the condition for the suppression of ITG turbulence.
Experimental study on vibration suppression for robotic milling using an MRE absorber
Authors: Zhao Wei , Tian Wei , Liu Peng , Li Bo 
Published: August 2023

Abstract
During the robotic milling process, vibration is one of the main factors that affect the machining accuracy and surface quality due to the low stiffness of the robot structure. Intelligent material, magneto-rheological elastomer (MRE), owns the features of adjustable stiffness, reversible and fast response, has been gradually used for vibration suppression. Therefore, to suppress the forced vibration for robotic milling, this paper used the principle of vibration absorption and combined it with MRE material to design a snubber. A series of excitation and milling experiments were carried out to evaluate its vibration attenuation characteristics. In the excitation experiment, sine signals with frequencies of 40 Hz, 42.02 Hz and 60 Hz with the same amplitude of 5 N were applied to the MRE absorber. In the milling experiment, the dominant frequencies under speeds of 800 RPM, 840 RPM and 1200 RPM were analyzed first. Then, the vibration mitigation characteristics of the MRE absorber during robotic milling of aluminum alloy were explored by adjusting the current. Experimental results showed that under the action of MRE absorber, the acceleration responses are suppressed effectively. In detail, after tuning the current, the amplitude and the root mean square of vibration acceleration in three directions of sinusoidal excitation could be suppressed by more than 19.23% and 7.35% correspondingly. The power spectral density value of the peak frequency could be reduced by more than 69.13%. In milling experiments, the MRE absorber also achieved certain vibration absorption effects. Furthermore, the surface roughness of the workpiece was also fallen by more than 16.51% which provides support for further engineering application in the field of robotic milling. Undoubtedly, the absorber needs further optimization to achieve better vibration suppression ability and a larger working bandwidth.
Nonlinear characteristics of Ti, Nb, and NbN superconducting resonators for parametric amplifiers
Authors: Zhao Songyuan , Withington S , Thomas C N 
Published: August 2023

Abstract
Superconducting resonators and parametric amplifiers are important components in scientific systems such as kinetic inductance detector arrays, frequency-domain multiplexers for other superconducting bolometers, spin-ensemble based memories, and circuit quantum electrodynamics demonstrators. In this paper, we report microwave measurements of superconducting Ti, Nb, and NbN resonators and their use as parametric amplifiers. These half-wave resonators were fabricated under near identical sputtering and lithographic conditions to ensure a like-for-like comparison of material properties. We report a wide range of properties and behaviours in terms of transition temperatures, resistivities, rate-limiting nonlinear response times, nonlinear dissipation, signs of the nonlinear inductances and their dependences on temperature and resonance harmonic. We have successfully operated Nb and NbN resonators as high gain parametric amplifiers, achieving greater than 20 dB of power amplification. We have shown that for a half-wave resonator, amplification can be realised not only in the fundamental resonance but also in the higher harmonic resonances. Further, for materials with high transition temperatures, e.g. Nb and NbN, amplification can be achieved at

∼

4

K, i.e. a temperature maintained by a pulse tube cooler. Finally, in materials systems that have very fast response times, e.g. NbN, we have found that a cross-harmonic type of amplification can be achieved by placing pump tone in a different resonant mode as the signal and the idler. This wide range of observations will have important implications on the design and application of superconducting parametric amplifiers.
Spectral properties of two coupled Fibonacci chains
Authors: Moustaj Anouar , Röntgen Malte , Morfonios Christian V , Schmelcher Peter , Morais Smith Cristiane 
Published: August 2023

Abstract
The Fibonacci chain, i.e. a tight-binding model where couplings and/or on-site potentials can take only two different values distributed according to the Fibonacci word, is a classical example of a one-dimensional quasicrystal. With its many intriguing properties, such as a fractal eigenvalue spectrum, the Fibonacci chain offers a rich platform to investigate many of the effects that occur in three-dimensional quasicrystals. In this work, we study the eigenvalues and eigenstates of two identical Fibonacci chains coupled to each other in different ways. We find that this setup allows for a rich variety of effects. Depending on the coupling scheme used, the resulting system (i) possesses an eigenvalue spectrum featuring a richer hierarchical structure compared to the spectrum of a single Fibonacci chain, (ii) shows a coexistence of Bloch and critical eigenstates, or (iii) possesses a large number of degenerate eigenstates, each of which is perfectly localized on only four sites of the system. If additionally, the system is infinitely extended, the macroscopic number of perfectly localized eigenstates induces a perfectly flat quasi band. Especially the second case is interesting from an application perspective, since eigenstates that are of Bloch or of critical character feature largely different transport properties. At the same time, the proposed setup allows for an experimental realization, e.g. with evanescently coupled waveguides, electric circuits, or by patterning an anti-lattice with adatoms on a metallic substrate.
Commutation simulator for open quantum dynamics
Authors: Joo Jaewoo , Spiller Timothy P 
Published: August 2023

Abstract
Recent progress in quantum simulation and algorithms has demonstrated a rapid expansion in capabilities. The search continues for new techniques and applications to exploit quantum advantage. Here we propose an innovative method to investigate directly the properties of a time-dependent density operator

ρ
ˆ

(
t
)

. Using quantum commutation simulators, we can directly compute the expectation value of the commutation relation and thus of the rate of change of

ρ
ˆ

(
t
)

. The approach can be utilized as a quantum eigen-vector solver for the von Neumann equation and a decoherence investigator for the Lindblad equation, by using just the statistics of single-qubit measurements. A simple but important example is demonstrated in the single-qubit case and we discuss extension of the method for practical quantum simulation with many qubits, towards investigation of more realistic quantum systems.
Cooling microwave fields into general multimode Gaussian states
Authors: Yazdi Nahid , José García-Ripoll Juan , Porras Diego , Navarrete-Benlloch Carlos 
Published: August 2023

Abstract
We show that a collection of lossy multichromatic modulated qubits can be used to dissipatively engineer arbitrary Gaussian states of a set of bosonic modes. Our ideas are especially suited to superconducting-circuit architectures, where all the required ingredients are experimentally available. The generation of such multimode Gaussian states is necessary for many applications, most notably measurement-based quantum computation. We build upon some of our previous proposals, where we showed how to generate single-mode and two-mode squeezed states through cooling and lasing. Special care must be taken when extending these proposals to many bosonic modes, and we discuss here how to overcome all the limitations and hurdles that naturally appear. For the sake of illustration, we work out two examples of Gaussian-state families consisting of Greenberger–Horne–Zeilinger and cluster states, which allow us to show that it is possible to use a set of N lossy qubits to cool down a bosonic chain of N modes to any desired Gaussian state.
Comparative study of surface disorder in gold and silver nanoparticles obtained by molecular dynamics in cooling processes
Authors: Agudelo-Giraldo J D , Cantillo-Galindo N , López-Salguero J S , Reyes-Pineda H 
Published: August 2023

Abstract
This work presents structural analyses of gold and silver nanoparticles with a surface-to-volume aspect ratio of about 50%. The simulations were performed by molecular dynamics using the Sutton–Chen potential. The atoms were randomly placed in a box with periodic boundary conditions to realize a cooling process under the consideration of a canonical ensemble. The results of the potential energy as a function of temperature are presented, showing the transitions to the solidification phase. At low temperatures, the radial distribution for all nanoparticles follows patterns similar to those reported for metals with FCC structure. Coordination number and interatomic distance were established as parameters to analyze surface particularities. In general, a decrease in the inter-atomic distance was observed with respect to the bulk. In gold nanoparticles, the formation of surface planes is favored and the interatomic distances increase with the coordination number under a well-defined tendency. Interior atoms have an interatomic distances closer to the expected value. In silver nanoparticles, atoms with a coordination number equal 9, located in surface, are at distances closer to the expected value than the internal atoms, at the scale considered. However, the trend with the increments of particle size shows go to a similar gold tendency. Silver led to a more disordered surface with respect to Gold. Therefore, the properties that depend on the surface ordering will exhibit more abrupt transitions with particle size in Silver at the treated scale.
Exceptional strength-ductility synergy of Ni and Co–Mg–La ferrite nanoparticles reinforced Sn-1Ag-0.5Cu matrix composite
Authors: Eid N A M , El-Daly A A , Hammad A E , Ibrahiem A A 
Published: August 2023

Abstract
The use of innovative Co–Mg–La ferrite nanoparticles (ferrite) facilitates the production of superior Pb-free solder alloy materials that suffer from the strength-ductility trade-off (SDT). The current study introduces an original Pb-free Sn-1Ag-0.5Cu (SAC105) electrical connection alloy. It is fortified with cutting-edge ferrite and nickel providing a novel approach for improving SDT by avoiding early necking throughout different temperatures. The heterogeneous structures in Sn-1Ag-0.5Cu (SAC105) alloy are assembled by controlling a fraction of reinforcing ferrite and Ni, processed under cold-drawn and partial recrystallization. The optimal ferrite concentration in SAC105 composites is about 0.3 wt%, which possesses a significant strain hardening potential and the highest strength-ductility synergy. The excessive degree of heterogeneity of SAC105-0.3ferrite induces numerous strengthening mechanisms and dislocation accumulation, resulting in attaining high ultimate tensile strength (UTS) of 40.7 MPa with significant ductility of ∼47.7%, with ∼34.3% and 39% increasing when compared to SAC105. Once SAC105 solder was doped with 0.3 wt% ferrite or 0.05Ni with 0.3 wt% ferrite, the activation energy transferred from pipe diffusion to lattice diffusion. The solitary Ni addition, on the other hand, results in the SDT of plain SAC105.
Multi-frequency test of dark matter annihilation into long-lived particles in Sirius
Authors: Chen Yu-Xuan , Zu Lei , Xia Zi-Qing , Sming Tsai Yue-Lin , Fan Yi-Zhong 
Published: August 2023

Abstract
New long-lived particles produced at the colliders may escape from conventional particle detectors.
Using satellites or ground telescopes, we can detect the photons
generated from the annihilation of the star-captured dark matter into a pair of long-lived particles.
When the propagation length of these long-lived particles surpasses the interplanetary distance
between the Sun and Jupiter, it becomes unfeasible to detect such dark matter signals originating from the Sun or Jupiter on Earth.
Our analysis of the dark matter-induced photons produced by prompt radiation, inverse Compton scattering, and synchrotron radiation mechanisms
reveals that a decay length of about 10-3 pc for long-lived particles is required for maximum detectability.
We investigate the parameters that allow the long-lived particle's lifetime to be consistent with Big Bang nucleosynthesis
while also allowing it to escape the confines of our solar system.
The Sirius system is proposed as a promising target for the indirect detection of such long-lived particles.
Utilizing the prompt, inverse Compton scattering, and synchrotron radiation,
upper limits on the dark matter-proton spin-independent and spin-dependent cross section are estimated with
the Fermi-LAT null-signal observation and the capabilities of the upcoming Square Kilometre Array radio telescope.
One-component fermion plasma on a sphere at finite temperature: the anisotropy of the path conformations
Authors: Fantoni Riccardo 
Published: August 2023

Abstract
In our previous work (Fantoni 2018 Int. J. Mod. Phys. C 29 1850064) we studied, through a computer experiment, a one-component fermion plasma on a sphere at finite, non-zero temperature. We extracted thermodynamic properties, such as the kinetic and internal energy per particle, and structural properties, such as the radial distribution function, and produced some snapshots of the paths to study their shapes. Here, we revisit this study, giving more theoretical details explaining the path shape anisotropic conformation due to the inhomogeneity in the polar angle of the variance of the random walk diffusion from the kinetic action.
Registration Method of Single-Line Point Clouds and Images Based on Depth Learning
Authors: Peng Saiqian 
Published: August 2023

Abstract
In high-definition map production, the point cloud and image data collected by the collection vehicle play a supportive role in high-definition map production. Point cloud data can provide accurate 3D spatial positioning, while image data can provide rich semantic information. The generation of high-definition maps depends on the fusion of multi-sensor data, and sensor registration is a requirement for multi-sensor fusion. The relative position between the LiDAR and the camera is largely resolved by the registration of single-line point clouds with images. Conventional manual registration techniques require a lot of labor and specialized gear for calibration, and they must be recalibrated if the distances between the sensors vary. This work suggests an online, unsupervised, and automatic deep learning-based registration approach for single-line point clouds and images. The technique was evaluated on an internal dataset, and it decreased the average displacement error to 33.22 cm and the average rotation error to 0.54° when the highest displacement error was 1.5 m and the maximum rotation error was 20°.
Quantum and Condor-Based Brainstorming Optimization Algorithm for NOx Prediction
Authors: Qin Hongwu , Fu Yu , Wang Lizheng , Yang Songhao , Liu Zhenqi , Sui Muxuan 
Published: August 2023

Abstract
This paper proposed a quantum bald eagle brainstorm (QBBSO) based on quantum initialization and combined with the bald eagle optimization algorithm in light of the original Brain Storm Optimization (BSO) algorithm’s strong local search ability, which would result in local optimization, a poor optimization effect, and difficult development. To accomplish the randomness of the number and increase the randomness of the population, we first modified the initialization method of the original brainstorming population, introduced the idea of quantum code, and then translated the binary numbers 0 and 1 to the decimal number. To achieve the best outcome, the original step size formula was employed for global selection, local search, and final selection using the vulture search method. The original BSO was then optimized to attract more global individuals. The algorithm versions were compared using the common benchmark function test. The findings demonstrated that QBBSO had a greater capacity for global search and a faster convergence speed. This research also applied the QBBSO algorithm to the long-term and short-term memory network (LSTM) to forecast the NOx concentration in the boiler, further demonstrating the algorithm’s superiority in real-world settings.