bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fine-structure changing collisions in 87Rb upon D2 excitation in the hyperfine Paschen-Back regime
Authors: Higgins Clare R , Pizzey Danielle , Hughes Ifan G 
Published: October 2024

Abstract
We investigate fine structure changing collisions in 87Rb vapour upon D2 excitation in a thermal vapour at 75 ∘C; the atoms are placed in a 0.6 T axial magnetic field in order to gain access to the hyperfine Pashen-Back regime. Following optical excitation on the D2 line, the exothermic transfer 5P

3
/
2

→

5P

1
/
2

occurs as a consequence of buffer-gas collisions; the 87Rb subsequently emits a photon on the D1 transition. We employ single-photon counting apparatus to monitor the D1 fluorescence, with an etalon filter to provide high spectral resolution. By studying the D1 fluorescence when the D2 excitation laser is scanned, we see that during the collisional transfer process the

m

J

′

quantum number of the atom changes, but the nuclear spin projection quantum number,

m

I

′

, is conserved. A simple kinematic model incorporating a coefficient of restitution in the collision accounted for the change in velocity distribution of atoms undergoing collisions, and the resulting fluorescence lineshape. The experiment is conducted with a nominally ‘buffer-gas free’ vapour cell; our results show that fine structure changing collisions are important with such media, and point out possible implications for quantum-optics experiments in thermal vapours producing entangled photon pairs with the double ladder configuration, and solar physics magneto-optical filters.
Miniaturized and high-efficiency end-reflective coaxial cerenkov oscillator
Authors: Fan Zhiqiang , Zhang Zeyi , Cao Yibing , Xu He , Sun Jun 
Published: October 2024

Abstract
High-power microwaves have extensive applications, but their practical implementation necessitates miniaturized and efficient devices. This paper introduces a novel miniaturized and highly efficient end-reflective coaxial Cerenkov device for high-power microwave generation. The design incorporates a coaxial collector structure that achieves total reflection of forward waves, significantly enhancing the end-feedback effect without increasing the structural length. Additionally, an integrated coaxial diffractive structure works synergistically with the strong end-feedback structure. This configuration not only enables highly efficient microwave output but also establishes a strong modulation field in the diode region, thereby facilitating effective electron beam modulation without extending the device length. Simulations demonstrate an output microwave power of 2.82 GW at 9.1 GHz, with a power conversion efficiency of 54%. Notably, the axial length of the beam-wave interaction region is only 2.6 λ, substantially shorter than that of conventional high-efficiency Cerenkov oscillators. The unique combination of the strong end-feedback coaxial structure and the coaxial diffractive output contributes to achieving both high efficiency and miniaturization in the generator.
GPF-Plus: a three-stage MLS point cloud ground segmentation method based on ground plane fitting
Authors: Xiao Dexu , Guo Liang , Liu Ruijun , Liu Jun 
Published: October 2024

Abstract
Point cloud ground segmentation is a key preprocessing task in mobile laser scanning (MLS)-based measurement and sensing. However, ground segmentation currently faces major challenges such as diverse ground morphology, sparse point cloud data, and interference from reflection noise. Meanwhile, since the existing principal component analysis-based ground plane fitting methods lack the judgment of iterative convergence and automatic correction of non-ground plane fitting results, this not only leads to unnecessary computational overhead, but also affects the accuracy of ground segmentation. To address these issues, this paper proposes a three-stage MLS point cloud ground segmentation method based on ground plane fitting, called GPF-Plus. This method adopts a three-stage strategy based on ground plane fitting to achieve ground segmentation, which is able to effectively deal with the challenges of various terrains. Firstly, the initial ground segmentation of the original point cloud is performed to quickly produce a coarse segmentation result. Secondly, the false negative points extraction is performed to improve the recall. Finally, the false positive points extraction is performed to improve the precision. At the same time, the infinite polar grid model is used to divide the point cloud, which reduces the number of grids and effectively alleviates the problem caused by point cloud sparsity. The reflection noise removal mechanism is introduced to enhance the robustness to reflection noise. In addition, the improved ground plane fitting improves the accuracy and speed of ground plane fitting. In this paper, experimental validation is carried out using the SemanticKITTI dataset, the SimKITTI32 dataset, and the collected point clouds of the mine environment. Compared with the state-of-the-art methods, GPF-Plus has excellent accuracy, real-time performance and robustness, and has high application potential in the field of measurement and sensing.
An affine formulation of eigenstrain-based homogenization method and its application to polycrystal plasticity
Authors: Nasirov Aslan , Oskay Caglar 
Published: October 2024

Abstract
Reduced order models (ROMs) are typically incorporated into concurrent multiscale approaches to allow for efficient nonlinear multiscale simulations and to alleviate high cost of direct nonlinear computational homogenization schemes. ROMs based on the ideas of transformation field analysis are among the most popular in the literature since they only require linear elastic simulations for model construction and typically have low number of degrees of freedom. However, these models have been shown to deliver overly stiff response in simulating wide range of materials. The present study focuses on mitigating this problem in the context of eigenstrain-based homogenization method (EHM) using instantaneous moduli information for polycrystal elastoviscoplasticity. For this purpose, a new EHM model is developed with the intention of using affine moduli for recomputation of the instantaneous localization tensors. The accuracy of the method is compared to the original EHM and direct crystal plasticity finite element simulations for several synthetic polycrystal microstructures, loading conditions and varying phase contrast. We show that the affine model delivers consistently softer response compared to the original EHM model. In particular, the affine model delivers notably more accurate response in the presence of high phase contrast. The affine EHM is able to capture local load redistribution through recomputation of the localization tensors.
Comparing growth of titania and carbonaceous dusty nanoparticles in weakly magnetised capacitively coupled plasmas
Authors: Ramkorun Bhavesh , Chandrasekhar Gautam , Rangari Vijaya , Thakur Saikat C , Comes Ryan B , Thomas Jr Edward 
Published: October 2024

Abstract
This study compares the growth cycles and spatial distribution of dust cloud for titania and carbonaceous dusty nanoparticles in capacitively coupled radiofrequency plasmas, with and without the presence of a weak magnetic field of approximately 500 Gauss. Findings on cycle time, growth rate, and spatial distribution of dust cloud are discussed. The growth of nanoparticles in these plasmas is cyclic, with particles reaching their maximum size and subsequently moving out of the plasma, followed by the generation of a new particle growth cycle. The presence of the magnetic field speeds up the growth cycle in both plasma. The magnetic field also makes the spatial distribution of the two dust cloud different from each other. Langmuir probe measurement of the background plasma parameters such as electron temperature and floating potential reveal radial variations in floating potential but not electron temperature. Furthermore, the magnetic field changes the radial variation of floating potential. These measurements, however, are not sufficient to explain why the two dust clouds appear differently. It is possible that the differences occur due to a gradient in the radial distribution of the magnetic field.
Modeling optimal control strategies for HIV and gonorrhea co-infection: incorporating screening along with treatment
Authors: Arunkumar M , Murugesan K 
Published: October 2024

Abstract
Human immunodeficiency virus (HIV) and gonorrhea are significant infectious diseases that pose considerable public health challenges worldwide. In this study, a control-induced model is developed to explore the dynamics of HIV and gonorrhea co-infection in the presence of treatment, incorporating screening strategies as control variables. We establish qualitative behavior, such as nonnegativity and boundedness of the solutions, and compute the basic reproduction number by utilizing the next generation matrix method. Subsequently, the stability of the disease-free equilibrium is analyzed, and then sensitivity analysis is undertaken to pinpoint the most critical parameters. Furthermore, an optimal control problem is formulated to diminish the total count of infected individuals and associated costs. The existence of an optimal control is shown, and Pontryagin’s principle is employed to derive the necessary conditions for an optimality system. Additionally, numerical simulations reveal that without control measures, the model predicts a continual rise in the number of infections. However, the graphical results demonstrate that the simultaneous implementation of screening for both HIV and gonorrhea as control measures significantly reduces single infections as well as co-infections. These insights are vital for researchers and policymakers to develop effective intervention strategies for eradicating co-infections of HIV and gonorrhea.
DFT molecular modeling investigation and optical properties characterization of CR-39 films
Authors: Abdallah Asmaa S , Rashad A M , El-Lateef A M Abd , Zaki M F , Elkalashy Sh I 
Published: October 2024

Abstract
In this study, the structural and optical characteristics of CR-39 films were investigated both theoretically and experimentally. A number of parameters for the CR-39 structure were theoretically computed by the density functional theory (DFT) method at B3LYP/6–311 G (p, d) level of theory. FTIR and UV/Vis spectroscopy were used to determine the structure compositions and the optical parameters of the CR39 film, respectively. The computed and experimental findings show good concordance. It was found that the CR-39 compound’s total energy, dipole moment, and energy difference between the LUMO and HOMO were, respectively, −994.575 a.u., 2.772 Debye, and 7.045 eV. The MEP showed a progressive change in color, with blue signifying a low electron density and red denoting a high electron density linked to nucleophilic reactivity and electrophilic reactivity. Further, the positive charges on all hydrogen atoms, which range from 0.16506 to 0.22032 a.u., imply that they are acceptors. The positive H34 is strongly produced when electrons from the negatively charged C17 are taken up. Because they are donor atoms, part of the carbon atoms in the structure is negative, while the other atoms are positive. The highest electronegative atoms H23 and H24 were substituted, resulting in the extremely negative carbon atom C7 (−0.32436). According to the experimentally determined absorption coefficient and energy gap values, CR-39 films may find application in optoelectronic devices.
Summary of presentations reported in EXperiment confinement (EX-C) session at the 29th IAEA fusion energy conference
Authors: Xu M. , Meng C. , Hao G.Z. 
Published: October 2024

Abstract
This is a summary paper of the research activities presented in the magnetic fusion EXperiment Confinement (EX-C) session at the 29th International Atomic Energy Agency (IAEA) Fusion Energy Conference (FEC 2023), London, United Kingdom, 16–21 October 2023. This session covers a wide range of topics related to confinement and transport of magnetic fusion plasmas. The key aspects addressed in the presentations include: (i) scenario development, (ii) turbulent transport and L-H transition physics, (iii) isotope effects on confinement, (iv) impurity transport and impact of impurities on confinement, (v) influence of fueling and energetic particles on confinement, and (vi) improvements in experimental capabilities. Highlighted results presented in those manuscripts are summarized in this paper.
Effects of plasma nonuniformity on zero frequency zonal structure generation by drift Alfvén wave instabilities in toroidal plasmas
Authors: Qiu Zhiyong , Wei Guangyu , Chen Liu , Ma Ruirui 
Published: October 2024

Abstract
The effects of plasma nonuniformity on zero frequency zonal structure (ZFZS) excitation by drift Alfvén wave (DAW) instabilities in toroidal plasmas are investigated using nonlinear gyrokinetic theory. The governing equations describing nonlinear interactions among ZFZSs and DAWs are derived, with the contribution of DAW self-beating and radial modulation accounted for on the same footing, and the physics picture contributing to both channels clarified. The obtained equations are then used to derive the nonlinear dispersion relation, which is then applied to investigate ZFZS generation in several scenarios. In particular, it is found that the condition for zonal flow excitation by the kinetic ballooning mode (KBM) could be sensitive to plasma parameters and a more detailed investigation is needed to understand KBM nonlinear saturation, crucial for bulk plasma transport in future reactors.
A ferry route in the Skagerrak optimised via VISIR-2
Authors: Mannarini Gianandrea , Leonardo Salinas Mario 
Published: October 2024

Abstract
Urgent measures are needed for the shipping industry to contribute proportionately to climate change mitigation. To date, the potential of weather routing has not been reproducibly assessed, especially for short sea shipping. We employ the open-source VISIR-2 model on a ferry passage in the Skagerrak (the strait among Norway, Sweden, and Denmark), considering both sea currents and waves. By computing 5,840 least-CO2 optimal routes for 2023, we find promising results, particularly for voyages from Denmark. The savings follow a bi-exponential distribution, with over 10% CO2 emissions savings achievable on about ten days per year. Specifically, the Norwegian coastal current can be harnessed to enhance speed over ground and reduce overall emissions. The vessel performance curve needs to be tailored to specific ships in service, and the optimisation algorithm should be developed to enable just-in-time arrivals.
Manoeuvring in shallow and confined water with model predictive track controllers
Authors: Eloot Katrien , Mansuy Marc 
Published: October 2024

Abstract
Autonomous and computer-assisted navigation, for example through portable pilot units and data-driven decision supportive systems, is in full development. Remote-controlled vessels (manned but without a captain steering continuously on board) are used in Belgium (by Seafar in inland shipping) but also in other countries. A model predictive controller has been used to investigate environmental and operational parameters for the passage of the river Seine in Paris by different ship types. These fast-time simulations have been compared with simulations in real time executed by skippers on a ship manoeuvring simulator or measured real life tracks. A good predictability of the real situation requires accurate manoeuvring models. But what if this accuracy cannot be guaranteed? An illustration is made by comparing the tracks if, in the prescience prediction phase for the controller, wind or bank effects are neglected or doubled. The development of model predictive deep reinforcement learning control algorithms for navigation will be one of the challenges of the Data-driven Smart Shipping project that started May 2024.
Risk Analysis on Autonomous Vessels based on Systems Theory – Application of NET-HARMS method
Authors: Koimtzoglou Alexandros , Ventikos Nikolaos P. , Routsis Dimitrios , Louzis Konstantinos 
Published: October 2024

Abstract
The shift towards Maritime Autonomous Surface Ships is a significant development in the maritime logistics industry, with the potential to enhance efficiency, safety, and environmental sustainability. However, the integration of autonomous systems also presents new challenges and risks, particularly in the absence of empirical data for traditional risk assessment methodologies. This research tackles this problem by utilizing the Net-worked Hazard Analysis and Risk Management System (Net-HARMS) method, a systems thinking method that hasn’t been previously employed in examining MASS. The method analyses the risks associated with the EC-funded, H2020, MOSES Project, which included a concept for automating the manoeuvring and docking processes with autonomous tugboats. The Net-HARMS method offers a comprehensive and holistic approach to risk assessment, overcoming the limitations of conventional probabilistic models. By constructing a Hierarchical Task Analysis and a task network, the research maps the system’s operational framework and explores task interdependencies. The use of a risk mode taxonomy allows for the identification of task-specific and emergent risks, which are then assessed by utilising the risk matrix of the Risk-Based Assessment Tool developed by DNV, to assess the final risk as a function of the effectiveness of each risk mitigation layer and the severity of the identified task consequences. The findings provide valuable insights into critical tasks requiring enhanced risk control measures and contribute to the development of safety constraints necessary for the successful implementation of autonomous shipping technologies. By applying Net-HARMS method to the realm of autonomous ships, this research not only fills a significant gap in maritime risk analysis but also sets a precedent for future studies in this rapidly evolving field.
Data-driven construction of maritime traffic networks for AI-based route prediction
Authors: Hoffmann Volker , Webert Jan-Hendrik , Murray Brian , Graf Robert 
Published: October 2024

Abstract
Predicting the routes of maritime traffic can improve economic efficiency, decrease ecological impact, and improve safety at sea. Over scales that are small (few hundred meters) and large (dozens to hundreds of kilometers), vessel trajectories have successfully been predicted by deep learning and (static) network-based approaches, respectively.
We present an approach for medium to large scales (few kilometers) where (a) a maritime traffic network is automatically constructed from AIS messages, and (b) vessel trajectories are predicted as most likely paths through the network. Using three regions (Stavanger, Tromsø, and Oslo), we show that the network can capture up to ∼ 90 per cent of all maritime traffic (excluding pleasure craft) with a median absolute error of ∼ 80 meters.
Vessel paths are sequences of waypoints and legs (nodes and edges) and are map-matched onto the network from vessel trajectories. Once mapped, we predict future paths for two subproblems – (i) known destination, and (ii) unknown destination. We use four algorithms (Dijkstra, Markov, MOGen, GRETEL).
For known destinations, we find that Dijkstra performs best. In Stavanger (Tromsø, Oslo), Dijkstra predicts 64 (42, 68) per cent of path segments correctly and keeps the median path error below 15 (33 and 55) meters. For unknown destinations, performance depends on the forecast horizon (the number of legs k to predict). For k ≤ 5, Markov is best and predicts 62 (48, 72) per cent of legs correctly. For k > 5, GRETEL performs best and predicts 54 (47, 63) per cent of legs correctly.
For some types of vessels, models improve by considering vessel type. For passenger vessels, models specific to them predict ∼ 10 per cent better paths with half the distance error. For tankers, paths (and distance errors) are 6 (20) per cent worse. For auxiliary vessels, path quality is unchanged, but distance error improves ∼ 36 per cent.
A Remaining Useful Life Prediction of Turbofan Engines Based on Multi-Scale Temporal Convolutional Networks with Dual Squeeze-Excitation Attention Mechanism
Authors: Wang Hairui , Li Dongjun , Li Dongwen , Li Ya , Zhu Guifu 
Published: October 2024

Abstract
To better handle temporal data and delve into learning the features of the data, a turbofan engine residual life prediction method is proposed, which integrates a dual-squeeze-excitation attention mechanism with a multi-scale temporal convolutional network. Firstly, utilizing a sliding window, the extracted multi-dimensional sensor features undergo overlapping sampling to enhance the model’s perception of temporal data. Secondly, a hybrid network prediction model based on DSE-MTCN is constructed, employing multi-scale convolutional kernels to expand the receptive field of convolution, assigning different weights to features, and adaptively allocating weights to hidden layer units. Lastly, the DSE-MTCN prediction model is globally optimized using the RAdam algorithm. The results demonstrate that this method effectively enhances the accuracy and generalization ability of the prediction model.
Research on Graph Multi-Attention Neural Network for Power System Transient Stability Assessment
Authors: Wang Qingwen , Yu Tenglong , Chu Hongliang , Xiong Xiaozhou , Huang Kang , Qiu Rixuan , Hu Bing , Yan Haoran , Wang Fan , Bai Yang , Wang Song , Qi Jun , Liu Siyan , Shi Danni 
Published: October 2024

Abstract
Diagnosing the stability of power grids based on artificial intelligence technology is a research trend. The existing artificial intelligence stability analysis methods rely on a large number of fault case data, while the graph neural network method ignores the correlation characteristics of nodes themselves and the correlation of long-distance nodes. In order to solve the problems, the graph multi-attention neural network (GMANN) was proposed. The self-attention, which characterizes the correlation between different state quantities of a single node, is proposed, firstly. Then, long-distance association attention, which represents the correlation of distant nodes in the power grid, was proposed. The long-distance correlation attention and the adjacent correlation attention in graph attention network are fused to form a global attention that reflects the global importance of the power grid. The channel attention that characterizes the importance of different pooling methods of graph convolutional network is extracted and used to obtain the importance of different convolution operations. Finally, based on the multi-source attention fusion strategy, global attention and channel attention are embedded in the graph attention neural network to form a multi-level evaluation model to achieve power grid stability evaluation efficiently guided by multiple attentions. The proposed GMANN is verified based on simulated fault cases obtained from the 10-machine 39-node system in New England. The results show that the accuracy of the GMANN can reach 97.34%, which is better than other methods. And the missed judgment rate of important indicators is significantly better than other methods.