bluuebunny/crossref_metadata_2025_split huggingface dataset:
The translator’s imperial experience and the dual role of translation
Authors: Liu Rui 
Published: November 2023

Abstract
The present research, which focuses on the rarely examined dynamic reception process, aims to extend and enrich the current discussion of the role of translation in colonial and postcolonial history. Based on a case study of George Jamieson’s English translation of the Qing Code, this study examines how this translation operates in Hong Kong courts, paying special attention to judges and expert witnesses, who interact closely with Jamieson’s work. The cooperative and competing relationship between experts and Jamieson’s translation sheds light on the centrality of the translator’s imperial experience, highlighting both the colonizing and decolonizing roles of the translation. Moreover, the findings illustrate that Jamieson’s imperial experience facilitates the court’s acceptance, on the one hand, while making his translation obsolete and subject to challenges from experts, on the other.
Dynamic mode decomposition for data-driven analysis and reduced-order modeling of E × B plasmas: I. Extraction of spatiotemporally coherent patterns
Authors: Faraji F , Reza M , Knoll A , Kutz J N 
Published: November 2023

Abstract
The advent of data-driven/machine-learning based methods and the increase in data available from high-fidelity simulations and experiments has opened new pathways toward realizing reduced-order models for plasma systems that can aid in explaining the complex, multi-dimensional phenomena and enable forecasting and prediction of the systems’ behavior. In this two-part article, we evaluate the utility and the generalizability of the dynamic mode decomposition (DMD) algorithm for data-driven analysis and reduced-order modeling of plasma dynamics in cross-field E × B configurations. The DMD algorithm is an interpretable data-driven method that finds a best-fit linear model describing the time evolution of spatiotemporally coherent structures (patterns) in data. We have applied the DMD to extensive high-fidelity datasets generated using a particle-in-cell (PIC) code based on the cost-efficient reduced-order PIC scheme. In this part, we first provide an overview of the concept of DMD and its underpinning proper orthogonal and singular value decomposition methods. Two of the main DMD variants are next introduced. We then present and discuss the results of the DMD application in terms of the identification and extraction of the dominant spatiotemporal modes from high-fidelity data over a range of simulation conditions. We demonstrate that the DMD variant based on variable projection optimization (OPT-DMD) outperforms the basic DMD method in identification of the modes underlying the data, leading to notably more reliable reconstruction of the ground-truth. Furthermore, we show in multiple test cases that the discrete frequency spectrum of OPT-DMD-extracted modes is consistent with the temporal spectrum from the fast Fourier transform of the data. This observation implies that the OPT-DMD augments the conventional spectral analyses by being able to uniquely reveal the spatial structure of the dominant modes in the frequency spectra, thus, yielding more accessible, comprehensive information on the spatiotemporal characteristics of the plasma phenomena.
Dynamic mode decomposition for data-driven analysis and reduced-order modeling of E × B plasmas: II. Dynamics forecasting
Authors: Faraji F , Reza M , Knoll A , Kutz J N 
Published: November 2023

Abstract
In part I of the article, we demonstrated that a variant of the dynamic mode decomposition (DMD) algorithm based on variable projection optimization, called optimized DMD (OPT-DMD), enables a robust identification of the dominant spatiotemporally coherent modes underlying the data across various test cases representing different physical parameters in an E × B simulation configuration. We emphasized that the OPT-DMD significantly improves the analysis of complex plasma processes, revealing information that cannot be derived using conventionally employed analyses such as the fast Fourier transform. As the OPT-DMD can be constrained to produce stable reduced-order models (ROMs) by construction, in this paper, we extend the application of the OPT-DMD and investigate the capabilities of the linear ROM from this algorithm toward forecasting in time of the plasma dynamics in configurations representative of the radial-azimuthal and axial-azimuthal cross-sections of a Hall thruster and over a range of simulation parameters in each test case. The predictive capacity of the OPT-DMD ROM is assessed primarily in terms of short-term dynamics forecast or, in other words, for large ratios of training-to-test data. However, the utility of the ROM for long-term dynamics forecasting is also presented for an example case in the radial-azimuthal configuration. The model’s predictive performance is heterogeneous across various test cases. Nonetheless, a remarkable predictiveness is observed in the test cases that do not exhibit highly transient behaviors. Moreover, in all investigated cases, the error between the ground-truth and the reconstructed data from the OPT-DMD ROM remains bounded over time within both the training and the test window. As a result, despite its limitation in terms of generalized applicability to all plasma conditions, the OPT-DMD is proven as a reliable method to develop low computational cost and highly predictive data-driven ROMs in systems with a quasi-periodic global evolution of the plasma state.
Research on substation intrusion event identification method based on MTF and CNN
Authors: Shao Xiangxin , Jiang Yongxiang , Jiang Hong , Li Jiacheng 
Published: November 2023

Abstract
This research provides a detection approach based on Markov transition field (MTF) and convolutional neural network (CNN) for substation perimeter intrusion event recognition. Because of the complexity and variety of external signals, which makes sensor detection more challenging, determining and analyzing the intrusion behavior of vibration signals induced by intrusion has become critical to improving the identification rate of intrusion-like occurrences. The obtained one-dimensional signals are mapped into two-dimensional pictures using MTF to reflect better the properties of intrusion-like signals, which can yield deeper signal details than the usual feature extraction approach. The CNN-vgg19 model’s excellent image feature identification capabilities are utilized to detect and categorize the acquired 2D feature pictures. The experimental results show that the average recognition rate of the six intrusion events is 96.7%, and the average recognition rate of the noise events is 99%, which can effectively identify the noise events and reduce the false-positive rate. The study’s findings are valuable for substation and peripheral security intrusion products.
Deep residual shrinkage networks with adaptively convex global parametric rectifier linear units for fault diagnosis
Authors: Zhang Zhijin , Zhang Chunlei , Zhang Xin , Gao Sen , Li He 
Published: November 2023

Abstract
In response to the challenge posed by traditional deep learning methods, which apply uniform nonlinear transformations to all vibration signals and thus struggle to address fault diagnosis under variable working conditions, a novel activation function called the convex global parametric rectifier linear unit (CGPReLU) is developed based on our prior research. Initially, an analysis of the numerical patterns governing the adaptive derivation process of GPReLU’s two slope parameters revealed the surprising observation that these convex parameter combinations invariably appear in pairs. This discovery serves as the primary motivation for the development of CGPReLU. Leveraging this convex regularity, we subsequently redesigned a lightweight convex sub-network for the adaptive derivation of the CGPReLU’s slope. Simultaneously, a deep residual shrinkage network with CGPReLU is constructed for fault diagnosis. Furthermore, we introduce an innovative evaluation metric designed to measure the collective influence of diagnostic accuracy and computational complexity after the process of model lightweight. Finally, it is experimentally demonstrated that the developed method can maintain a better diagnostic performance while greatly improving the diagnostic efficiency under variable operating conditions compared to our previous work.
Validation of large area capacitive sensors for impact damage assessment
Authors: Vereen Alexander , Downey Austin R J , Sockalingam Subramani , Laflamme Simon 
Published: November 2023

Abstract
Impacts in fiber-reinforced polymer matrix composites can severely inhibit their functionality and prematurely lead to the composite’s failure. This research focuses on determining the efficacy of a novel capacitive sensor, termed as the soft elastomeric capacitor (SEC), to monitor the magnitude of out-of-plane deformations in composites. This work forwards the development of a sensing skin that can be used as an in situ monitoring tool for composites. The capacitive sensor can be made to arbitrary sizes and geometries. The sensor is composed of an elastomer composite that measures strains experienced by the material it is bonded to. The structure of the sensor, fabricated to function as a parallel plate capacitor, responds to impacts by transducing strains into a measurable change in capacitance. In this work, the SECs are deployed on randomly oriented fiberglass-reinforced plates with a polyester resin matrix. The material is impacted at various energy levels until the monitored composite material reaches its yielding point. The behavior of the sensor in impact detection applications below the proof resilience shows little to no change in the capacitance of the sensor. As the impacts surpass this yielding point, the sensor responds linearly with induced change in the area. The sensor performed within the expectations of the proposed model and demonstrated the efficacy of the proposed large-area sensor as a damage quantification tool in the structural health monitoring of composites.
Method for extracting the free vibration response of transmission tower
Authors: Zhao Long , Wen Guanru , Liu Zhicheng , Du Yan , Li Jin , Huang Xinbo 
Published: November 2023

Abstract
The analysis of the free vibration in transmission towers is aimed at better evaluating their health status. Due to the non-stationarity of the height of non-uniform wind-induced vibration, there is a sustained forced vibration response in the vibration signal of transmission towers. This will lead to a decrease in the stability of vibration characteristics, which brings great difficulty to transmission tower status identification. In order to solve this problem, this paper proposes a method for extracting the free vibration response of transmission towers. By using Void–Kalman filtering (VKF) and generalized demodulation transform (GDT) for modal frequency tracking and pseudo-peak elimination, the signal is maximally cleaned while retaining all modal information. In addition, this paper combines variational theory and time-frequency domain features to decompose all effective modal orders in the vibration response. This not only solves the errors that exist when GDT and VKF directly process the original signal, but also makes the extraction of frequency parameters of each modal orders more convenient. The effectiveness of this approach is demonstrated by extracting the free vibration response of a 110 kV cat-head type tower. And by comparing it with existing mainstream signal processing methods using the example of bolt loosening faults. The results show that the signal processed by this approach can more stably and accurately capture the fault characteristics, providing a new idea for the extraction of free vibration response in the field of transmission tower health monitoring.
The chemical state of iron species influence on the performance of Fe–N–C bifunctional electrocatalyst for Zn–air batteries
Authors: Dong Wenjing , Liu Wei , Feng Yuan , Huang Naibao 
Published: November 2023

Abstract
Fe–N–C materials have emerged as promising alternatives to precious metals for oxygen reduction reaction/oxygen evolution reaction (ORR/OER). In this study, a strategy is presented to investigate the influence of different chemical states of iron species in Fe–N–C materials on their electrocatalytic performance. Three Fe–N–C catalysts, containing either zero-valent Fe or Fe3O4 nanoparticles, are synthesized using acid pickling, high-speed centrifugation and ultrasound-assisted hydrothermal methods, respectively. The findings manifest that the chemical state of iron significantly affects the electrocatalytic activity of Fe–NX active sites, namely zero-valent Fe enhancing Fe–NX activity while Fe3O4 weakening its activity. Notably, the Fe@FeNC catalyst containing only zero-valent iron, demonstrates the only 0.621 V potential difference between the ORR half-wave potential and the OER potential at 10 mA cm−2. Furthermore, the rechargeable Zn–air battery assembled with Fe@FeNC as the air cathode exhibits a remarkable peak power density of 179.0 mW cm−2, excellent cycling stability over 210 h (with a cycle frequency of one every 10 min), and the minimal voltage gap of 0.710 V. These results reveal the significance of different chemical states of metal-based nanoparticles in Fe–NX activity of Fe–N–C catalysts and offer insights into the rational design of electrocatalysts with exceptional activity and versatile applications.
Self-supervised enhanced thyroid nodule detection in ultrasound examination video sequences with multi-perspective evaluation
Authors: Liu Ningtao , Fenster Aaron , Tessier David , Chun Jin , Gou Shuiping , Chong Jaron 
Published: November 2023

Abstract

Objective. Ultrasound is the most commonly used examination for the detection and identification of thyroid nodules. Since manual detection is time-consuming and subjective, attempts to introduce machine learning into this process are ongoing. However, the performance of these methods is limited by the low signal-to-noise ratio and tissue contrast of ultrasound images. To address these challenges, we extend thyroid nodule detection from image-based to video-based using the temporal context information in ultrasound videos. Approach. We propose a video-based deep learning model with adjacent frame perception (AFP) for accurate and real-time thyroid nodule detection. Compared to image-based methods, AFP can aggregate semantically similar contextual features in the video. Furthermore, considering the cost of medical image annotation for video-based models, a patch scale self-supervised model (PASS) is proposed. PASS is trained on unlabeled datasets to improve the performance of the AFP model without additional labelling costs. Main results. The PASS model is trained by 92 videos containing 23 773 frames, of which 60 annotated videos containing 16 694 frames were used to train and evaluate the AFP model. The evaluation is performed from the video, frame, nodule, and localization perspectives. In the evaluation of the localization perspective, we used the average precision metric with the intersection-over-union threshold set to 50% (AP@50), which is the area under the smoothed Precision-Recall curve. Our proposed AFP improved AP@50 from 0.256 to 0.390, while the PASS-enhanced AFP further improved the AP@50 to 0.425. AFP and PASS also improve the performance in the valuations of other perspectives based on the localization results. Significance. Our video-based model can mitigate the effects of low signal-to-noise ratio and tissue contrast in ultrasound images and enable the accurate detection of thyroid nodules in real-time. The evaluation from multiple perspectives of the ablation experiments demonstrates the effectiveness of our proposed AFP and PASS models.
Exploring the synthesis, structure, and optoelectronic properties of lead-free halide perovskite Cs3Bi2I9 in single crystal and polycrystalline forms
Authors: P Sujith , Parne Saidi Reddy , T Abhinav 
Published: November 2023

Abstract
In recent years, caesium bismuth iodide (Cs3Bi2I9), a lead (Pb)-free halide perovskite, has drawn more attention as a potential material than traditional semiconductor materials due to its lack of Pb toxicity and its outstanding stability against atmospheric air and moisture. Herein, the inverse temperature crystallization method is adopted to grow high-quality hexagonal-phase Cs3Bi2I9 perovskite single crystals. Furthermore, a Cs3Bi2I9 perovskite thin film is fabricated by a solution process using the two-step spin coating technique. A collective analysis of the structural properties, surface morphology, thermal stability, phase transition, and optoelectronic properties of these single crystal and polycrystalline thin films provides a comprehensive understanding and design strategy to develop environmentally stable, Pb-free, and high-performance photovoltaic and optoelectronic devices based on Cs3Bi2I9 perovskite. The findings of this study contribute to the advancement of perovskite-based technologies and pave the way for their successful integration into the renewable energy and optoelectronics industries.
Fast quantum state transfer and entanglement preparation in strongly coupled bosonic systems
Authors: Xu Yilun , Zhu Daoquan , Sun Feng-Xiao , He Qiongyi , Zhang Wei 
Published: November 2023

Abstract
Continuous U(1) gauge symmetry, which guarantees the conservation of total excitations in linear bosonic systems, will be broken when it comes to the strong-coupling regime where the rotation wave approximation (RWA) fails. Here we develop analytic solutions for multi-mode bosonic systems with XX-type couplings beyond RWA, and propose a novel scheme to implement high-fidelity quantum state transfer (QST) and entanglement preparation (EP) with high speed. The scheme can be realized with designated coupling strength and pulse duration with which the excitation number keeps unchanged regardless of the breakdown of the global U(1) symmetry. In QST tasks, we consider several typical quantum states and demonstrate that this method is robust against thermal noise and imperfections of experimental sequence. In EP tasks, the scheme is successfully implemented for the preparation of Bell states and W-type states, within a shortest preparation time.
Graphene quantum dots: preparations, properties, functionalizations and applications
Authors: Tian Pin , Tang Libin , Teng Kar-Seng , Lau Shu-Ping 
Published: November 2023

Abstract
Zero-dimensional graphene quantum dots (GQDs) exhibit many different properties, such as strong fluorescence, nonzero bandgap and solubility in solvents, compared to two-dimensional graphene. GQDs are biocompatible and have low toxicity; hence, they are widely used in the biomedical field. The edge effect of GQDs is of particular interest because edge modification can regulate the performance of nanomaterials. In this review, various preparation methods for GQDs, which can be divided into three main categories, namely top-down, bottom-up and chemical methods, are discussed. The unique optical, electrical, thermal and magnetic properties of GQDs are reviewed. The functionalization of GQDs by doping with heteroatoms and forming composites with other materials is studied, and the characteristics of these GQDs are also discussed. The applications of these GQDs in the fields of optics, electricity, optoelectronics, biomedicine, energy, agriculture and other emerging interdisciplinary fields are reviewed to highlight the enormous potential of nanomaterials. This review reports on the recent advancement in GQD research and suggests future directions for the development of GQDs.
The gastro-intestinal tract and liver
Authors: Peters A M , Myers M J 
Published: November 2023

Abstract
The salivary gland is one of the organs, along with gastric mucosa, thyroid gland and choroid plexus, that concentrates pertechnetate. The radionuclide is excreted in saliva at a rate that can be measured from dynamic imaging of the salivary glands. Obstruction of a salivary gland, as for instance by a stone in the salivary duct, produces a rising curve (sialogram), similar to the rising renogram of urinary obstruction. The similarity with renography can be extended by examining the effect on the sialogram of the administration of a sialogogue by mouth, such as lemon juice, 10 min after injection of pertechnetate.
Haematology and the spleen
Authors: Peters A M , Myers M J 
Published: November 2023

Abstract
Most of the subject matter relating to radionuclides and haematology covers blood cell kinetics. The most widely used techniques for the quantification of blood cell kinetics involve tagging the cells of interest with a radioactive marker for the quantification of cell traffic, either from blood sampling alone or, more usefully, in combination with external scintigraphic monitoring. In general, blood cells can be labelled either as a cohort (cohort labelling) or following isolation from peripheral blood, in which case they are sampled at random (random labelling). By cohort labelling is meant the simultaneous labelling, usually in vivo, of a cohort of cells all of the same age and at the same stage of development.
The Internet ward round
Authors: Murphy Jeannette , Ingram, David , Howard William 
Published: November 2023

Abstract
The end of the twentieth century will doubtless be remembered as the age of the Internet. In a few short years, the Net has gone from being a vehicle for scientific communication, to a playground for the many. Phrases such as ‘information superhighway’, ‘surfing the Net’, and ‘navigating through cyberspace’ have become part of everyday vocabulary. Those who are not connected feel that they are not part of the modern world. Faced with this rapidly expanding information warehouse, educationalists are trying to decide whether the Internet is in fact a cornucopia of new learning resources.
The preregistration year in the Uk
Authors: Dent Thomas H S , Gillard Jonathan H 
Published: November 2023

Abstract
The months before and after graduation are among the most demanding and the most exciting in the career of a doctor. The preparation for final examinations in a medical school which follows a traditional pattern of assessment requires mental stamina and concentration and culminates in a longer and more demanding set of examinations than the students are likely to have experienced hitherto. After this, the medical student becomes a doctor: the longed-for prize at the end of undergraduate training and an alarming prospect for which few feel properly prepared.
Practical Pain Management
Authors: Coniam S W , Diamond A W 
Published: November 2023

Abstract
Pain is something that everyone feels but has always been difficult to understand and investigate due to its subjective nature. Practical Pain Management is written for all those involved in the care of people suffering from pain. The authors explain why pain is so often disregarded and poorly managed by clinicians. With an emphasis on the practical management of both chronic and acute pain, the book explores the mechanisms involved in pain perception, the use of analgesic drugs, local anaesthetics, nerve blocks and stimulation techniques. The rehabilitation and care of patients whose pain cannot be adequately relieved is also considered and some way in which specialist pain services can help with chronic pain syndromes are described. This comprehensive guide will help all whose caring for patients in pain to have a better understanding of pain and to work more effectively towards its management.