bluuebunny/crossref_metadata_2025_split huggingface dataset:
Self‐supervised vessel trajectory segmentation via learning spatio‐temporal semantics
Authors: Zhang Rui , Ren Haitao , Yu Zhipei , Xiao Zhu , Liu Kezhong , Jiang Hongbo 
Published: October 2024

AbstractThe study of vessel trajectories (VTs) holds significant benefits for marine route management and resource development. VT segmentation serves as a foundation for extracting vessel motion primitives and enables analysis of vessel manoeuvring habits and behavioural intentions. However, existing methods relying on predefined behaviour patterns face high labelling costs, which hinder accurate pattern recognition. This paper proposes a self‐supervised vessel trajectory segmentation method (SS‐VTS), which segments VTs based on their inherent spatio‐temporal semantics. SS‐VTS adaptively divides VTs into cells of optimal size. Then, it extracts split points on different semantic levels from the multi‐dimensional feature sequence of the VTs using self‐supervised learning. Finally, spatio‐temporal distance fusion module is performed on split points to determine change points and obtain VT segments with multiple semantics. Experiments on a real automatic identification system datasets show that SS‐VTS achieves state‐of‐the‐art segmentation results compared to seven baseline methods.
Dynamic indoor mapping for AVP: Crowdsourcing mapping without prior maps
Authors: Jiang ZhiHong , Jiang Haobin , Ma ShiDian 
Published: October 2024

AbstractHigh‐definition maps are essential for autonomous vehicle navigation, but indoor parking lots remain poorly mapped due to high costs. To address this, a crowdsourcing model gathers data from consumer‐grade sensors in mass‐produced vehicles to create semantic maps. Indoor parking lots lack GNSS signals, and most of them do not have high‐definition maps or navigation maps as references, making it difficult to ensure the accuracy of the final mapping results. Additionally, the semantic information of indoor parking lots is relatively limited, and the geometric features are overly similar, which significantly impacts the accuracy of point cloud registration. Therefore, this article proposes a crowdsourcing‐based approach, where vehicles generate local semantic maps at the client end and upload them to the cloud. Leveraging the scene characteristics of indoor parking lots, the cloud optimizes and fits a large amount of crowdsourced data to obtain a high‐precision base map without prior information. Enhanced ICP point cloud registration merges subsequent maps with the base. Additionally, parking space occupancy information is provided. This map can furnish the necessary information for Autonomous Valet Parking (AVP) tasks. Evaluation on the BEVIS dataset shows a root mean square error of 0.482446 m for vehicle localization on the cloud‐based map.
Retraction Note: Ecofriendly Approaches in nanomaterial synthesis for sustainable healthcare applications
Authors: Verma Rajan , Kumar Gaurav , Upadhyay Viyat Varun , Ratur Anuj , Rao A.L.N. , Kumar Anil , Boob Nandini Shirish 
Published: October 2024

This paper has been formally retracted from publication by the Publisher due to the fact that the scientific relevance of this work is questionable.
Request approved by the Publisher on October 9, 2024.
Factor Replacement Treatment for Hemophilia A: Achievements and Perspectives
Authors: Mancuso Maria Elisa 
Published: October 2024

AbstractThe mainstay of treatment for persons with hemophilia A (PwHA) with severe bleeding phenotype is prophylaxis. The pharmacokinetic (PK) profile of native factor VIII (FVIII) imposes the need for rather frequent intravenous injections to ensure effective prophylaxis, but this represents a relevant treatment burden and is associated with suboptimal adherence to treatment. In this light, the advent of extended half-life (EHL) FVIII molecules has improved prophylaxis feasibility and outcomes by favoring treatment individualization and tailoring protection according to specific clinical and nonclinical needs. Different technologies have been used to enhance FVIII PK properties including Fc-fusion and conjugation with polyethylene glycol. Data from clinical development programs for such molecules, together with growing real-world experience, have shown numerous benefits related to the use of EHL FVIII in PwHA. Recently a new class of ultra-long-acting EHL FVIII has been developed to further improve protection against bleeding episodes and achieve the ambitious goal of providing PwHA with hemostatic protection in the nonhemophilia range over longer time periods, hence ensuring very low bleeding rates and improving joint health and quality of life. In this review, the achievements and perspectives of replacement therapies for PwHA are summarized and discussed.
Transfer learning for molecular property predictions from small datasets
Authors: Kirschbaum Thorren , Bande Annika 
Published: October 2024

Machine learning has emerged as a new tool in chemistry to bypass expensive experiments or quantum-chemical calculations, for example, in high-throughput screening applications. However, many machine learning studies rely on small datasets, making it difficult to efficiently implement powerful deep learning architectures such as message passing neural networks. In this study, we benchmark common machine learning models for the prediction of molecular properties on two small datasets, for which the best results are obtained with the message passing neural network PaiNN as well as SOAP molecular descriptors concatenated to a set of simple molecular descriptors tailored to gradient boosting with regression trees. To further improve the predictive capabilities of PaiNN, we present a transfer learning strategy that uses large datasets to pre-train the respective models and allows us to obtain more accurate models after fine-tuning on the original datasets. The pre-training labels are obtained from computationally cheap ab initio or semi-empirical models, and both datasets are normalized to mean zero and standard deviation one to align the labels’ distributions. This study covers two small chemistry datasets, the Harvard Organic Photovoltaics dataset (HOPV, HOMO–LUMO-gaps), for which excellent results are obtained, and the FreeSolv dataset (solvation energies), where this method is less successful, probably due to a complex underlying learning task and the dissimilar methods used to obtain pre-training and fine-tuning labels. Finally, we find that for the HOPV dataset, the final training results do not improve monotonically with the size of the pre-training dataset, but pre-training with fewer data points can lead to more biased pre-trained models and higher accuracy after fine-tuning.
Spectral and spatial resolution of an extreme ultraviolet broadband imaging spectrometer based on dispersion-matched zone plates
Authors: Babenko Ievgeniia , Mostafa Yahia , Bouza Zoi , Versolato Oscar O. , Bayraktar Muharrem 
Published: October 2024

We present a combined 1D imaging and broadband spectroscopy tool for analyzing laser-produced plasma sources of extreme ultraviolet light using a tapered zone plate that is dispersion-matched to a transmission grating. Specifically, we follow up on prior work [Mostafa et al. Opt. Lett. 48, 4316 (2023)] to obtain the actual spectral and spatial resolution of the imaging spectrometer and compare it to the design values. The imaging spectrometer is shown to have a spectral resolution of 1.2 nm at 13.5 nm, close to its design value, by assessing spectra obtained from carbon laser-produced plasma in a 5–180 nm wavelength band. The spatial resolution was obtained by placing slits near the object plane and back-illuminating the slit with a tin laser-produced plasma and found to be 17(5) µm, somewhat larger than the design specifications but still well within design limits for use for diagnosing plasma.
The Virgo Newtonian calibration system for the O4 observing run
Authors: Aubin F , Dangelser E , Estevez D , Masserot A , Mours B , Pradier T , Syx A , Van Hove P 
Published: October 2024

Abstract
After initial tests performed during previous observing runs, a Newtonian calibrator (NCal) system was developed and installed on the Virgo gravitational wave detector for the O4 observing run. This system, which is continuously operated, provides the absolute calibration of Virgo for this run. Its

1

-

σ

uncertainty of 0.17% on the amplitudes of the injected signals is better than that obtained with other calibration techniques like the photon calibrator (PCal). This paper presents this NCal system and details the different sources of uncertainties.
Progress and challenges in the synthesis of two-dimensional van der Waals ferroic materials and heterostructures
Authors: Wang Jia , Kang Junzhe , Chyczewski Stasiu , Lin Ye , Lee Hanwool , Zhu Wenjuan , Hong Xia 
Published: October 2024

Abstract
Two-dimensional (2D) ferroelectric and magnetic van der Waals materials are emerging platforms for the discovery of novel cooperative quantum phenomena and development of energy-efficient logic and memory applications as well as neuromorphic and topological computing. This review presents a comprehensive survey of the rapidly growing 2D ferroic family from the synthesis perspective, including brief introductions to the top-down and bottom-up approaches for fabricating 2D ferroic flakes, thin films, and heterostructures as well as the important characterization techniques for assessing the sample properties. We also discuss the key challenges and future directions in the field, including scalable growth, property control, sample stability, and integration with other functional materials.
Thermodynamic analysis of the inverse electrocaloric effect in ferroelectric thin films
Authors: Arli Cihan , Rana Atilgan Ali , Burc Misirlioglu I 
Published: October 2024

Abstract
Inverse electrocaloric (i-EC) effect occurs in ferroelectrics when the applied electric field aligns anti-parallel to polarization. In this study, the dependence of the i-EC effect on temperature and misfit strain is formulated and applied to clamped BaTiO3 thin films using the Landau–Ginzburg–Devonshire formalism. It is found that an interplay exists between the pyroelectric coefficient and the maximum possible inverse electric field. We demonstrate that the temperature change is strongly dependent on the inverse field amplitude and is maximal at lower temperatures away from the ferroelectric-paraelectric transition for a given misfit strain. Such an outcome is opposite to the direct electrocaloric effect, where it is desirable to remain near the transition temperature for the maximum electrocaloric temperature change. The fact that the i-EC effect can be maximum at lower temperatures could allow for the potential tailoring of this effect in strained films at moderate temperatures for device applications.
FEM-driven machine learning approach for characterizing stress magnitude, peak temperature and weld zone deformation in ultrasonic welding of metallic multilayers: application to battery cells
Authors: Al-Matarneh Feras Mohammed 
Published: October 2024

Abstract
This study investigates the innovative application of machine learning (ML) models to predict critical parameters—stress magnitude (SM), peak temperature (PT), and plastic strain (PS)—in ultrasonic welding of metallic multilayers. Extensive numerical simulations were employed to develop and evaluate three ML models: Radial Basis Function (RBF), Random Forest (RF), and Kernel Ridge Regression (KRR). According to the results, the KRR model demonstrated superior performance, achieving the lowest RMSE and highest R
2 values of 0.068 (R
2 = 0.941) for SM, 0.075 (R
2 = 0.929) for PT, and 0.071 (R
2 = 0.946) for PS, with fewer data samples required. KRR also exhibited low squared bias and variance values, ranging from

1
×

10

−
4

−
3.2
×

10

−
4

for bias and

2.2
×

10

−
4

−
3.6
×

10

−
4

for variance, indicating its precision in predicting the output targets. Moreover, the systematic categorization of input features, including material properties, geometrical factors, and welding parameters, highlighted their significant influence on predictive accuracy, particularly the crucial role of welding parameters at higher output values. Finally, a case study on ultrasonic welding of copper multilayers underscores the model’s effectiveness in unraveling complex relationships, providing a robust tool for optimizing and advancing ultrasonic welding processes.
Interpretable diagnosis of breast lesions in ultrasound imaging using deep multi-stage reasoning
Authors: Cui Kaixuan , Liu Weiyong , Wang Dongyue 
Published: October 2024

Abstract

Objective. Ultrasound is the primary screening test for breast cancer. However, providing an interpretable auxiliary diagnosis of breast lesions is a challenging task. This study aims to develop an interpretable auxiliary diagnostic method to enhance usability in human-machine collaborative diagnosis. Approach. To address this issue, this study proposes the deep multi-stage reasoning method (DMSRM), which provides individual and overall breast imaging-reporting and data system (BI-RADS) assessment categories for breast lesions. In the first stage of the DMSRM, the individual BI-RADS assessment network (IBRANet) is designed to capture lesion features from breast ultrasound images. IBRANet performs individual BI-RADS assessments of breast lesions using ultrasound images, focusing on specific features such as margin, contour, echogenicity, calcification, and vascularity. In the second stage, evidence reasoning (ER) is employed to achieve uncertain information fusion and reach an overall BI-RADS assessment of the breast lesions. Main results. To evaluate the performance of DMSRM at each stage, two test sets are utilized: the first for individual BI-RADS assessment, containing 4322 ultrasound images; the second for overall BI-RADS assessment, containing 175 sets of ultrasound image pairs. In the individual BI-RADS assessment of margin, contour, echogenicity, calcification, and vascularity, IBRANet achieves accuracies of 0.9491, 0.9466, 0.9293, 0.9234, and 0.9625, respectively. In the overall BI-RADS assessment of lesions, the ER achieves an accuracy of 0.8502. Compared to independent diagnosis, the human-machine collaborative diagnosis results of three radiologists show increases in positive predictive value by 0.0158, 0.0427, and 0.0401, in sensitivity by 0.0400, 0.0600 and 0.0434, and in area under the curve by 0.0344, 0.0468, and 0.0255. Significance. This study proposes a DMSRM that enhances the transparency of the diagnostic reasoning process. Results indicate that DMSRM exhibits robust BI-RADS assessment capabilities and provides an interpretable reasoning process that better suits clinical needs.
Power handling in a highly-radiative negative triangularity pilot plant
Authors: Miller M A , Arnold D , Wigram M , Nelson A O , Witham J , Rutherford G , Choudhury H , Cummings C , Paz-Soldan C , Whyte D G 
Published: October 2024

Abstract
This work explores detailed power handling solutions for a class of high-field, highly-radiative negative triangularity (NT) reactors based around the MANTA concept (Rutherford et al 2024 Plasma Phys. Control. Fusion). The divertor design is kept as simple as possible, opting for a standard divertor with standard leg length. FreeGS is used to create an equilibrium for the boundary region, prioritizing a short outer leg length of only ∼50 cm (∼40% of the minor radius). The UEDGE code package is used for the boundary plasma solution to track plasma temperatures and fluxes to the divertor targets. It is found that for

P

SOL

= 25 MW and

n

sep

=
0.96
×

10

20

m−3, conditions consistent with initial core transport modeling, little additional power mitigation is necessary. For a fixed impurity fraction of just 0.13% Ne in the plasma, the peak heat flux density at the more heavily loaded outer targets falls to 7.8 MW m−2, while the electron temperature

T

e

remains just under 5 eV. Scans around the parameter space reveal that even at densities lower than in the primary operating scenario,

P

SOL

can be increased up to 50 MW, so long as a slightly higher fraction of extrinsic radiator is used. With less than 1% neon (Ne) impurity content, the divertor still experiences less than 10 MW m−2 at the outer target. Design of the plasma-facing components includes a close-fitting vacuum vessel with a tungsten inner surface as well as FLiBe-carrying cooling channels fashioned into the VV wall directly behind the divertor targets. For the seeded heat flux profile, Ansys Fluent heat transfer simulations estimate that the outer target temperature remains at just below 1550 ∘C. Initial scoping of advanced divertor designs shows that for an X-divertor, detachment of the outer target becomes much simpler, and plasma fluxes to the targets drop considerably with only 0.01% Ne content.
Adaptive control of an amplified piezoelectric-driven micropositioning stage based on notch filter structure inspiration and neural network online tuning
Authors: Wang Wenwen , Lai Leijie , Zhu Limin 
Published: October 2024

Abstract
Piezoelectric-driven flexure micro/nanopositioning stages often exhibit a low-damping resonance mode, which can easily excite mechanical resonance during high-speed movement, and significantly impact the control system’s stability, control bandwidth, and trajectory tracking accuracy. To mitigate the reliance on the precise modeling of stage dynamics inherent in current resonant controllers, an adaptive control method based on a back propagation (BP) neural network was designed to suppress resonance in real-time. First, a piezoelectric-driven flexure micro/nanopositioning stage system was constructed. Next, a feedback controller similar to a notch filter was designed, with bilinear transformation applied based on the system’s inherent parameters to determine the initial values. Finally, the designed adaptive control method was tested through trajectory tracking experiments using a triangular wave signal. The experimental results showed that, when tracking the triangular wave signal, the maximum tracking error was reduced by 74.62% compared to proportional-integral (PI) control alone and by 69.91% compared to proportional integral control combined with a traditional notch filter. The tracking results demonstrate a significant improvement in the stage’s stability and tracking accuracy.
Breit interaction overtaking Coulomb force at low energies: an unexpectedly efficient mechanism for ionization in slow collisions
Authors: Jacob A , Müller C , Voitkiv A B 
Published: October 2024

AbstractIt is generally assumed that ionization in slow collisions of light atomic particles, whose constituents (electrons and nuclei) move with velocities orders of magnitude smaller than the speed of light, is driven solely by the Coulomb force. Here we show, however, that the Breit interaction—a relativistic correction to the Coulomb interaction between electrons—can become the main actor when the colliding system couples resonantly to the quantum radiation field. Our results demonstrate that this ionization mechanism can be very efficient in various not too dense physical environments, including stellar plasmas and atomic beams propagating in gases.
Revealing symmetries in quantum computing for many-body systems
Authors: Leeuwen Robert van 
Published: October 2024

Abstract
We develop a method to deduce the symmetry properties of many-body Hamiltonians when they are prepared in Jordan–Wigner form in which they can act on multi-qubit states. Symmetries, such as point-group symmetries in molecules, are apparent in the standard second quantized form of the Hamiltonian. They are, however, masked when the Hamiltonian is translated into a Pauli matrix representation required for its operation on qubits. To reveal these symmetries we prove a general theorem that provides a straightforward method to calculate the transformation of Pauli tensor strings under symmetry operations. They are a subgroup of the Clifford group transformations and induce a corresponding group representation inside the symplectic matrices. We finally give a simplified derivation of an affine qubit encoding scheme which allows for the removal of qubits due to Boolean symmetries and thus reduces effort in quantum computations for many-body systems.
The poisson property of extreme events in optics
Authors: Zheltikov A M 
Published: October 2024

Abstract
Statistics of extreme events in optics, defined as above-threshold counts of an optical signal, is shown to converge, in the large-sample-size limit, to a generalized Poisson distribution whose mean is found via the exponent of the respective extreme-value distribution. Specifically, extreme-event readouts from polynomial and exponential optical nonlinearities are shown to converge in their statistics to Poisson distributions whose means are, respectively, exponential and slower-than-exponential functions of the extreme-event-counter threshold. Extreme-event counts of a phase readout, on the other hand, converge to a Poisson process whose mean is a light-tailed function of the threshold. The Poisson-limit property of extreme events in optics suggests a powerful resource for a unified treatment of a vast variety of extreme-event phenomena, ranging from optical rogue waves to laser-induced damage.
Thin film development on a double layer of fluids over a stretching sheet
Authors: Satheesh Aswathy , Panda Satyananda 
Published: October 2024

Abstract
This research investigates two layers of immiscible fluids over a stretching sheet, where the primary layer is a Newtonian fluid and the secondary layer is a non-Newtonian second-grade fluid. The governing equations describing the flow are the two-dimensional mass and momentum equations supported by the interface and boundary conditions. The small aspect ratio of the fluid domain allows the application of long-wave theory and thereby two partial differential equations for the dynamics of thin liquid film for each layer are obtained. The coupled partial differential equations thus obtained are solved numerically by the finite volume approach where the spatial derivatives are approximated using the upwind difference scheme and the time derivatives are by forward difference. The study focuses on analyzing the impact of fluid processing parameters like Reynolds number, viscosity ratio, non-Newtonian parameter, and surface tension on the flow dynamics. The study reveals that the non-Newtonian parameter delays the thinning effect of the fluids across the fluid layers and may have possible applications in coating technologies, biomedical devices, and advanced manufacturing.
A new chemical networked system: spatial-temporal evolution and control
Authors: Li Hua , Xiao Min , Wang Zhengxin , Xu Fengyu , Wang Zhen , Zheng Weixing , Rutkowski Leszek 
Published: October 2024

Abstract
This paper constructs a scale-free chemical network based on the Gierer-Meinhardt (GM) system and investigates its Turing instability. We establish a fractional-order single-node GM system with delay and design a fractional-order proportional derivative (PD) control strategy for the issue of bifurcation control. Using delay as bifurcation parameter, the existence of Hopf bifurcation is proven, and control over bifurcation threshold points is achieved through a fractional-order PD control strategy. For the scale-free chemical network based on the GM system, we obtain the condition of how the Turing instability occurs. We derive how the number of edges for the new nodes changes the stability of the network-organized system and investigate the relationship between degrees of nodes and eigenvalues of the network matrix. We give the instability condition caused by diffusion in the network-organized system. Finally, the numerical simulations verify analytical results.