bluuebunny/crossref_metadata_2025_split huggingface dataset:
Vacuum DC breakdown characteristics between grid electrodes with ion sputtering
Authors: Liu Hao-Yan , Liu Wen-Xuan , Sun Guang-Yu , Liu Yue-Lin , Zhang Li-Wei , Zhou Sheng , Qi Chang-Chun , Sun An-Bang , Zhang Guan-Jun 
Published: January 2025

Abstract
In ion thrusters, grid electrodes are biased at high voltage to extract and accelerate ions. However, they are susceptible to vacuum breakdown which considerably undermines the ion thruster performance. To optimize the grid electrode design and improve the ion thruster longevity, the impact of ion sputtering on the vacuum direct-current breakdown characteristics of grid electrodes is systematically examined. The adopted seven-aperture grid electrodes are made of three materials including molybdenum, graphite, and stainless steel, which are exposed to Ar+ and Xe+ sputtering at energies of 400 eV and 1000 eV for durations ranging from 0.5 to 8 h. It is found that the initial breakdown voltage, which means the first breakdown voltage after sputtering, generally decreased regardless of the electrode material as the sputtering time increased. However, the breakdown voltage rapidly increases with more repeated discharges. Each kind of electrode material demonstrates unique responses to ion sputtering and breakdown. In comparison with metal electrodes, the graphite surface is relatively easy to appear defects but exhibits the least insulation degradation. The metal surface remains relatively smooth, but breakdown voltage is significantly affected, with electrode materials such as stainless steel failing to recover to their initial insulation levels. Xe+ sputtering leads to greater breakdown voltage fluctuations during repeated discharges due to higher sputtering yield. Dark current is measured, and the field enhancement factor

β

FE

is calculated to reveal correlations between local electric field and the breakdown voltage. Furthermore, micro particles are identified as major contributors to the first few times breakdown. Based on above observations, two kinds of distinct discharge mechanisms, including the cascade discharge induced by micro particles and the discharge induced by field emission, are proposed to interpret experimental data.
Diffusion, mechanical and thermal properties of sT hydrogen hydrate by machine learning potential
Authors: Song Zixuan , Li Yuan , Shi Qiao , Qu Yongxiao , Hao Yongchao , Ma Rui , Zhang Zhisen , Wu Jianyang 
Published: January 2025

Abstract
Newly-synthesized structure T (sT) hydrate show promising practical applications in hydrogen storage and transport, yet the properties remain poorly understood. Here, we develop a machine learning potential (MLP) of sT hydrogen hydrate derived from quantum-mechanical molecular dynamics simulations. Using this MLP forcefield, the structural, hydrogen diffusion, mechanical and thermal properties of sT hydrogen hydrate are extensively explored. It is revealed that the translational and rotational mobilities of hydrogen molecule in sT hydrate are limited due to unique shape and finite dimensional cavities, and tiny windows of neighboring cavities. sT hydrogen hydrate exhibits unique uniaxial tension stress-strain response, with first nonlinear increase to GPa-level but followed by deep drop in the stretching stress, indicating brittle failure, similar to that by Density Functional Theory and empirical forcefields. Moreover, temperature-dependent thermal conductivity in sT hydrogen hydrate is mainly contributed by hydrogen-bonded network formed by host water molecules, while hydrogen guest molecules play an insignificant role in the thermal transport.
Turning tool wear state recognition based on EMDResNeStTime and sequence-global-encoding
Authors: Zhang Hucheng , Hu Fangmin , Xie Tao 
Published: January 2025

Abstract
Machining is the primary method used for producing parts and equipment. Tool wear is inevitable during this process and directly impacts the quality of the finished parts. Accurately recognising the tool wear status helps to reduce the risk of processing failure and improve overall processing efficiency. By establishing a mapping relationship between tool wear information and its associated status characteristics, accurate recognition of tool wear can be achieved. This paper proposes a novel intelligent recognition approach for tool wear state in machining process based on vibration signals. The EMDResNeStTime module uses empirical mode decomposition (EMD) and two-dimensional convolution to extract the main trend features of vibration signals, and the sequence-global-encoding module uses the self-attention mechanism to extract the global features of vibration signals, are designed. A new multi-feature parallel-time feature extraction backbone is constructed using these two modules. The use of this backbone effectively improves the ability of the network model to extract key features of complex trends in vibration signals. Experimental results show that the proposed method achieves 71.65%, 74.89%, 71.36% and 72.78% in accuracy, precision, recall and F1 score, respectively. Compared with other network models, it achieves higher recognition accuracy and more stable performance. The ablation experiment further confirms the classification effectiveness of the method used in this network model. The model can accurately recognise the tool status during the cutting process based on monitoring data, which will facilitate the implementation of a more effective tool change strategy in intelligent manufacturing and improving processing efficiency.
Boundary sample extraction support vector data description: a novel anomaly detection method for large-scale data
Authors: Wang Xiaofei , Chen Yongzhan , Xu Fenglei , Gao Yanli , Wang Yuanxin , Qiao Yuchuan , Dai Haomin , Wang Qiang 
Published: January 2025

Abstract
Support vector data description (SVDD) has been effectively used in many anomaly detection problems. Equipped with kernel functions, its training complexity grows exponentially with the increase in training data, which makes it less practical for large-scale datasets. In this paper, we propose a boundary sample extraction SVDD (BSE-SVDD) anomaly detection method based on data reduction, aiming for use with large-scale data. Firstly, the BSE mechanism is established on the basis of demonstrating that the spatial position of the sample is related to its corresponding Lagrange multiplier. Then, the BSE mechanism is used to search for the local optimal solution of the Lagrange multiplier, and all the training samples are sorted. Finally, the top p of ranked samples are extracted as boundary samples for training, while most of the training samples that may be non-support vectors are removed. Compared with SVDD studies based on data reduction, the experimental results on large-scale datasets show that BSE-SVDD can obtain comparable classification accuracy with greatly improved training speed.
TCN-MBMAResNet: a novel fault diagnosis method for small marine rolling bearings based on time convolutional neural network in tandem with multi-branch residual network
Authors: Li Yuanjiang , Yang Zhenyu , Zhang Shuo , Mao Runze , Ye Linchang , Liu Yun 
Published: January 2025

Abstract
Centrifugal pumps are key components of marine systems, and the health of their internal rolling bearings is critical for the normal operation of ships. In small ship replenishment systems, the rolling bearings of centrifugal pumps often have short failure times, making it difficult to gather balanced failure data. This paper addresses the challenges in extracting fault features from centrifugal pump bearings in ship water replenishment systems and the low accuracy of fault identification caused by unbalanced data samples. A fault diagnosis method is proposed that combines a time convolutional neural network (TCN) with multi-branch mixed attention residual networks (MBMAResNet). This paper introduces TCN to capture dependencies in long time series and optimize the feature distribution of raw data. The captured multi-channel 1-D feature sequences are converted into 2D feature maps to provide spatially correlated features for the designed MBMAResNet. Multiple single path fusion residual blocks with different scales are designed in the MBMAResNet to further extract effective fault features from the unbalanced samples, and a feature fusion structure is utilized for further feature enhancement. Additionally, improved convolutional block attention modules are integrated into the network to increase the significance of valid channels and spatial locations. Experiments on an unbalanced public dataset and a private centrifugal pump faulty bearing dataset achieved diagnostic accuracies of 99.71% and 98.50%, respectively, demonstrating that the proposed method offers higher accuracy and generalizability compared to other methods.
A distributed training framework for abnormal sound detection in industrial devices: aggregation strategies and performance analysis
Authors: Yang Rui , Wang Kunpeng , Li Xinrong 
Published: January 2025

Abstract
In today’s digital and networked industrial landscape, the detection of abnormal sounds in device has emerged as a vital aspect for guaranteeing the normal operation of industrial machinery. Nevertheless, traditional centralized training approaches demand substantial amounts of audio data, imposing considerable burdens on data storage and transmission, and concurrently presenting obstacles to data privacy and security. Federated learning, as a distributed machine learning paradigm, enables model training with local data from each client without sharing the original data, thereby effectively safeguarding data privacy. Hence, in this study, we propose a distributed training framework based on federated training, which enables multiple clients to collaboratively train an abnormal sound detection model, thereby mitigating the risk of data privacy exposure. In the distributed training framework, each client possesses data from different types of device or various machines within the same device type, posing significant challenges for distributed training. To overcome this, we devised two client device distribution scenarios and proposed aggregation strategies based on client sample size, model performance, and domain shift among clients. Additionally, we introduced a sample-performance-shift (SPS) aggregation strategy to ensure robust model performance across diverse device scenarios in industrial settings. The proposed methods were evaluated on the DCASE 2020 Challenge Task 2 dataset. Experimental results demonstrate that the SPS aggregation strategy enhances the accuracy and reliability of abnormal sound detection for industrial device within the distributed training framework while simultaneously reducing the risk of data privacy leakage.
Ameliorating macrophage pyroptosis via ANXA1/NLRP3/Caspase-1/GSDMD pathway: Ac2-26/OGP-loaded intelligent hydrogel enhances bone healing in diabetic periodontitis
Authors: Li Ruoyu , Li Wenfeng , Teng Yungshan , Li Runze , Kong Siyi , Chen Xin , Luo Haotian , Chen Danying , Guo Yuqing , Qing Yangqiao , Leong Hio Cheng , Guo Bingyan , Chen Meihan , Pan Zixin , Zheng Shushuo , Deng Yihong , Cao Yang , Zhou Chen , Zou Xuenong , Wang Weicai 
Published: January 2025

Abstract
Craniofacial bone defect healing in periodontitis patients with diabetes background has long been difficult due to increased blood glucose levels which cause overproduction of reactive oxygen species (ROS) and a low pH environment. These conditions negatively affect the function of macrophages, worsen inflammation and oxidative stress, and ultimately, hinder osteoblasts’ bone repair potential. In this study, we for the first time found that annexin A1 (ANXA1) expression in macrophages was reduced in a diabetic periodontitis (DP) environment, with the activation of the NLRP3/Caspase-1/GSDMD signaling pathway, and, eventually, increased macrophage pyroptosis. Next, we have developed a new GPPG intelligent hydrogel system which was ROS and pH responsive, and loaded with Ac2-26, an ANXA1 bioactive peptide, and osteogenic peptide OGP as well. We found that Ac2-26/OGP/GPPG can effectively reduce ROS, mitigates macrophage pyroptosis via the ANXA1/NLRP3/Caspase-1/GSDMD pathway and enhanced osteogenic differentiation. The effect of Ac2-26/OGP/GPPG in regulation of pyroptosis and bone defect repair was also further validated by animal experiments on periodontitis-induced tooth loss model in diabetic rats. To conclude, our study unveils the effect of ANXA1 on macrophage pyroptosis in periodontitis patients with diabetes, based on which we introduced a promising innovative hydrogel system for improvement of bone defects repair in DP patients via targeting macrophage pyroptosis and enhancing osteogenic potential.
Investigation on zinc oxide as an antireflection coating on silicon-based solar cells using ray tracing
Authors: Aziz Nur Amelia Shazana , Yahaya Bermakai Madhiyah , Mohd Yusoff Mohd Zaki 
Published: January 2025

Abstract
Since antireflective coating (ARC) has the ability to lessen optical loss experienced by solar cells, it is beginning to gain traction in the solar cell market. Without the use of ARC, the solar cell’s surface reflects about 30% of light and leads to a decrease in efficiency. The Zinc Oxide (ZnO) material has been chosen in this study as the ARC to study the enhancement of the solar cells’ performance using simulation approach. The ZnO layer is used as an ARC on top of the silicon based solar cell with certain thicknesses ranging from 60 to 90 nm. The optimum thickness of ZnO that results in the maximum current density is chosen for further modification on surface texturing. Four types of textured surface on the front surface of silicon based solar cell are proposed. It is shown that 80 nm thickness of ZnO ARC revealed the best performance in terms of current density generated which is reach 34.92 mA cm−2. Positive increment of current density is shown on further modification of surface texturing. Random inverted pyramids appear to be the best front surface with another 17.5% increment in current density.
Investigations on the high-temperature wear behaviour of LM25 aluminium alloy/silicon carbide foam interpenetrating phase composites synthesized using gas pressure infiltration
Authors: Senthilkumar M , T Satish V , Bright Renjin J , K Somasundara Vinoth , G Vasundhara M 
Published: January 2025

Abstract
Ceramic foam-based interpenetrating phase composites (IPCs) are progressively garnering interest towards the construction of brake components. To comprehend the use of IPCs for real-time braking applications, it is necessary to study their wear behaviour at elevated temperatures. The present study is solemnly envisioned to examine the high-temperature wear and friction behaviour of the novel LM25 aluminium alloy/Silicon Carbide foam (SiCf)- IPCs developed using the gas pressure infiltration process. SiCf of pore densities 10, 20 and 30 pores per inch (PPI) were used for synthesizing the IPCs. A pin-on-disc wear testing apparatus in which the pin specimen prepared from the IPCs connected to a heating element was used to perform the wear study. The experimental design for conducting the wear study was developed using response surface methodology (RSM) with four continuous factors namely pore density (PPI), applied load (N), sliding speed (m/s) and temperature (οC). Each factor was assigned with three different levels which resulted in 30 experimental runs for which wear rate and coefficient of friction were estimated. The study testified that the IPCs prepared using 30 PPI SiCf possessed a low wear rate and higher coefficient of friction compared to those prepared using 10 and 20 PPI SiCf. The worn surface morphology of the IPCs revealed the predominant wear mechanisms at low temperatures as abrasive and that at higher temperatures as adhesive mode.
Controllable anisotropic wettability behaviours of droplets by varying profile stripe structures
Authors: Wu Boqi , Dong Litong , Liu Mengnan , Hu Jing 
Published: January 2025

Abstract
Anisotropic wettability of bionic structures is often achieved through anisotropic patterns, so the parameters are key to managing surface wettability behaviours. In this work, we fabricate three kinds of one-dimensional (1D) stripe structures with distinct profile shapes to study the method of controlling the anisotropic wettability behaviours of water droplets. By controlling their profiles and depths, the anisotropic wettability of stripes can be effectively managed and mitigated. Even with the same stripe periods, we can achieve two opposing wetting effects, namely isotropic hydrophobicity and anisotropic hydrophilicity. The experiments show that the π-shaped stripes possess isotropic superhydrophobicity, and their adhesion force to water droplets in the direction perpendicular to the groove is approximately ten times that in the parallel direction. Meanwhile, the willow-leaf-shaped stripes show anisotropic hydrophilic properties and good ductility to water droplets. Taking advantage of inductively coupled plasma-reactive ion etching (ICP-RIE) with a low undercut, this method provides a novel perspective on the design of interfaces for droplet manipulation, pick-and-place applications, and the localised control of reactions.
A reinforcement learning approach to the design of quantum chains for optimal energy and state transfer
Authors: Sgroi S , Zicari G , Imparato A , Paternostro M 
Published: January 2025

Abstract
We propose a bottom–up approach, based on reinforcement learning, to the design of a chain achieving efficient excitation-transfer performances. We assume distance-dependent interactions among particles arranged in a chain under tight-binding conditions. Starting from two particles and a localised excitation, we gradually increase the number of constitutents of the system so as to improve the transfer probability. We formulate the problem of finding the optimal locations and numbers of particles as a Markov decision process: we use proximal policy optimization to find the optimal chain-building policies and the optimal chain configurations under different scenarios. We consider both the case in which the target is a sink connected to the end of the chain and the case in which the target is the right-most particle in the chain. We address the problem of disorder in the chain induced by particle positioning errors. We apply our methodology to a simplified model of a relevant physical platform, consisting of trapped ions. We are able to achieve extremely high excitation transfer in all cases, with different chain configurations and properties depending on the specific conditions.
Culpability, Consequences, and Sentencing
Authors: Ashworth Andrew 
Published: January 2025

Abstract
How should the relative strength of culpability and consequences be assessed in sentencing? Setting out three scenarios that currently engage the courts of England and Wales, this chapter explores the intent principle and the belief principle that underlie the emphasis on culpability, taking particular account of moral luck and the focus on trying and exertions. The chapter argues in favour of a fully subjective principle of criminal liability and of sentencing but leaves open the possibility that there may be a role for public opinion. This would require further research on public opinion and an active role for a sentencing commission.
The Evolution of Proportionality in Sentencing
Authors: Manikis Marie 
Published: January 2025

Abstract
The principle of proportionality has developed considerably over the years with various complementary and challenging additions to its original framing, in great part due to the substantial reflections and perspectives of Professor Roberts. Indeed, Roberts has infused this principle with relevant empirical and theoretical considerations, which have contributed to its enduring relevance, legitimacy, and influence throughout the years in theory, policy, and practice. This chapter discusses some of these developments by highlighting Roberts’ contribution towards a more dynamic form of censure; a greater individualization of harm, notably, by including victims, and an expanded understanding of moral blameworthiness. It then builds on Roberts’ commitment towards a more accurate ascription of censure and culpability within just deserts, by relying on empirical studies and experiential evidence in order to revisit the just deserts position on social deprivation and proportionality. It argues that individual background, social context, and empirical knowledge offer relevant context, which can enable a more accurate ascription of responsibility and censure within a desert-based understanding of proportionality.
Sentencing Ethically
Authors: Watson Gabrielle 
Published: January 2025

Abstract
The work of Julian V Roberts conveys a deep ethical sensibility that marks him out as one of the most humane sentencing scholars of our time. This chapter brings into focus the subtler ethical dimension of Roberts’ scholarship and evaluates its significance. It does so with reference to three distinctive strands of his work: first, on the sentencing of women, second, on guilty pleas, and third, on ‘second look’ sentence reviews for long-term prisoners. There, we find an implicit commitment to moral principles and values including fairness, respect, and hope, together with a distinctive orientation towards them that is worthy of sustained attention. In Roberts’ worldview, fairness, respect, and hope are not merely analytic ideals but institutional commitments: grounded, pragmatic, and attainable, with immediate implications for sentencing practice and reform.
No doubt our honorand has left an indelible mark on sentencing scholarship and practice on the global stage. It is less often observed, however, that Roberts has become one of the great mediators of our discipline, appealing to pragmatists and normative theorists alike and foregrounding both styles of scholarship in the collaborative projects he has led. Only at first glance are Roberts’ writings on criminal justice principally and inescapably pragmatic. On closer inspection, Roberts gives important contextual cues—especially in his more recent works—that he is also an ethicist of the highest calibre, with profound insights to offer on the moral rights and wrongs of the sentencing court.
In Search of Proportionate Sentencing
Authors: Doob Anthony N , Sprott Jane B 
Published: January 2025

Abstract
This chapter examines a sentencing concept that Julian V Roberts has addressed in a large number of publications in the past forty years or so: proportionality. It first examines the efforts that Roberts and others have made to try to find mechanisms to give weight to an offender’s criminal record in a manner that does not undermine proportionality. Second, the chapter examines the Canadian ‘conditional sentence of imprisonment’ (a version of the suspended sentence) suggesting that its inadequate legislative framing in Canada reflected the fact that its real purpose was only to reduce the use of imprisonment. In both of these areas, any attempt to come up with clever ways of maintaining the pretence that sentences are proportional may be futile. For example, since criminal records vary on a large number of dimensions, some of which will affect the manner in which a sentence is served, it may be impossible, at sentencing, to go beyond a general statement related to the importance of proportionality and an indication that sentences can be affected by criminal records. Similarly, a broad statement saying that all sentences—custodial, suspended, and community—should be roughly proportionate to the severity of the offence may be all that can be meaningfully stated.