bluuebunny/crossref_metadata_2025_split huggingface dataset:
Traumatic Ulnar Carpal Translocation, Distal Radioulnar Joint Subluxation with Occult Perilunate Injury
Authors: Visser Timothy , Borusiewicz Mikayla , Payatakes Alexander 
Published: April 2024

Abstract
Background Perilunate injuries pose a significant treatment challenge, particularly when initially missed or underestimated. These injuries have a multitude of variations as propagation of the injury may be through ligaments and/or bone, with more recent appreciation given to nondislocated perilunate injuries.
Case Description We present a new variant of perilunate injuries, not dislocated (PLIND). This occult ligamentous perilunate injury was associated with ulnar carpal translocation, radiocarpal subluxation, and distal radioulnar subluxation. The extent of injury was fully appreciated intraoperatively, with only subtle clues present on preoperative exam and imaging, emphasizing the high index of suspicion required to identify these injuries. Clinical outcome at 1-year follow-up was very satisfactory.
Literature Review PLIND consist a distinct subset within the spectrum of perilunate injuries in which there is no observed disruption of the capitolunate relationship. Based on few cases reported in the literature (attributable to their rare occurrence and likely additional lack of awareness surrounding the injury pattern), current recommendations for surgical management include arthroscopic or open approach to appropriately address osseous and ligamentous injuries.
Clinical Relevance Recognition of PLIND injuries is challenging given the lack of frank dislocation of the capitate head on the lunate, as observed in classic perilunate injuries. Heightened clinical suspicion in patients with consistent mechanism and presentation, followed by timely surgical management, may limit sequelae of persistent instability and arthritis. Our case contributed to relevant literature by presenting a previously undescribed PLIND variant combined with radiocarpal instability, ulnar carpal translocation, and distal radioulnar subluxation.
Constructing cultural integration through rhetoric in Modi’s speech in Lumbini, Nepal
Authors: Ghimire Surendra Prasad 
Published: April 2024

AbstractThis article reports how the rhetorical devices and logical argument utilized in the speech of Narendra Modi, the prime minister of India, delivered in Lumbini, addressing the 2566th birth ceremony of Lord Buddha, assisted him to establish his purpose by persuading the audience. I adopted the concept of rhetoric developed by Aristotle and the notion of argument advanced by Toulmin as a theoretical perspective to analyze his speech. The findings reported that Modi used rhetorical devices and layers of argument to establish religious and cultural relationships between Nepal and India; however, he focused on religions, particularly Hinduism and Buddhism, and excluded the religious and cultural sentiments of people belonging to other religious and cultural backgrounds living in both countries. The finding further revealed that Modi’s seemingly religious rhetoric was directed towards the political mission of promoting relations between Nepal and India by expanding cultural nationalism and establishing the supremacy of Hinduism and Buddhism. This study has greater significance because it adds new insights into the existing literature on rhetoric and communication and provides a clear direction for further analyzing the rhetorical strategy in Modi’s speeches.
Asymmetric optical properties and bandgap shift of pre-strained flexible ZnO films
Authors: Liu Jiamin , Zhou Zhikang , Gu Honggang , Zhu Jinlong , Jiang Hao , Liu Shiyuan 
Published: April 2024

Strain engineering has been extensively explored to modulate the various intrinsic properties of flexible inorganic semiconductor films. However, experimental characterization of tensile and compressive strain-induced modulation of optoelectronic properties and their differences has not been easily implemented in flexible inorganic semiconductor films. Herein, the strain-dependent structural, optical, and optoelectronic properties of flexible ZnO films under pre-tensile and pre-compressive strains are systemically investigated by a Mueller matrix ellipsometry-based quantitative characterization method combined with x-ray diffraction and first-principle calculation. With extended prestress-driven deposition processing under bi-direction bending modes, pre-tensile and pre-compressive strains with symmetric magnitudes can be achieved in flexible ZnO films, which allows precise observation of the strain-driven asymmetric modulation of optoelectronic properties. When the applied prestrain varies approximately equally from 0% (baseline) to −0.99% (compression) and 1.07% (tensility), respectively, the relative changes for the c-axis lattice constant are 0.0133 and 0.0104 Å, respectively. Meanwhile, the dependence factors of the bandgap energy on the pre-compression and pre-tensile strains were determined as −0.0099 and −0.0156 eV/%, respectively, and the complex refractive index also presents an asymmetric varying trend. With the help of the strain–stress analysis and the first-principle calculation, the intriguing asymmetric strain-optical modulation effect could be attributed to the biaxial strain mechanism and the difference in the deformation potential between the two prestrain modes. These systematic investigation consequences are thus promising as a basis for the booming applications of the flexible inorganic semiconductor ensemble.
A three-dimensional coupled structure triboelectric nanogenerator for vertical and horizontal mechanical energy harvesting and fitness gait monitoring
Authors: Luo Cheng , Li Hai 
Published: April 2024

Recently, the self-powered monitoring device used for wearable sensors has attracted attention from various industries. It is worth noting that previous self-powered sensors were mostly focused on unidirectional sensing and monitoring, and few sensors can achieve both longitudinal and transverse sensing functions simultaneously. Here, we designed an arched and stacked coupling structure triboelectric nanogenerator (AS-TENG) to harvest horizontal and vertical mechanical energy. Meanwhile, the AS-TENG can simultaneously achieve sensing effects on longitudinal pressure and transverse pulling force. According to the results, the pulling force sensing sensitivity of AS-TENG can reach 2.415 V N−1, and the pressure sensing sensitivity of AS-TENG can arrive at 2.78 V N−1. Furthermore, under longitudinal 6 Hz mechanical motion excitation, the open-circuit voltage (Voc) and short circuit current (Isc) of AS-TENG can reach 28.21 μA and 173.25 V, respectively. The transfer charge (Qsc) of AS-TENG can arrive at 169.7 nC. The AS-TENG can achieve a maximum output power of 2.02 mW at a 3 MΩ load. Moreover, the sensing signal pulse width of AS-TENG can reach 180 ms, which is higher than the traditional arch structure’s 110 ms, indicating the unique advantages of AS-TENG in future fitness monitoring. This study provides a 3D triboelectric multi-directional sensor that will serve the fields of intelligent wearable fitness and sports.
On point-ahead angle control strategies for TianQin
Authors: Wang Dezhi , Zhang Xuefeng , Duan Hui-Zong 
Published: April 2024

Abstract
Pointing-related displacement noises are crucial in space-based gravitational wave detectors, where point-ahead angle control of transmitted laser beams may contribute significantly. For TianQin that features a geocentric concept, the circular high orbit design with a nearly fixed constellation plane gives rise to small variations of the point-ahead angles within ±25 nrad in-plane and ±10 nrad off-plane, in addition to a static bias of 23 µrad predominantly within the constellation plane. Accordingly, TianQin may adopt fixed-value compensation for the point-ahead angles and absorb the small and slow variations into the pointing biases. To assess the in-principle feasibility, the far-field tilt-to-length (TTL) coupling effect is discussed, and preliminary requirements on far-field wavefront quality are derived, which have taken into account of TTL noise subtraction capability in post processing. The proposed strategy has benefits in simplifying the interferometry design, payload operation, and TTL noise mitigation for TianQin.
Cycle-to-cycle switching endurance variability in vertically aligned nanocrystalline molybdenum disulfide: computational insights
Authors: Turfanda Aykut , Ünlü Hilmi 
Published: April 2024

Abstract
We propose a model to depict abrupt transient changes in the endurance test results of a resistive switching device comprising vertically oriented layers of nanocrystalline transition metal dichalcogenide layers with respect to the substrate. We aim to relate and understand the so-called resistance drift occurring in the endurance test results with our model, which is further tested using density functional theory simulations. We conclude that the relationship between resistance drift and skin effect is dominated by alternating electric current resistance and surface resistance. These results are crucial for understanding the resistance drift occurring in several resistive switching devices operating based on defects and ions.
Flexural and acoustic phonon-drag thermopower and electron energy loss rate in silicene
Authors: Ansari Meenhaz , Ashraf S S Z , Tripathi P , Ahmad A 
Published: April 2024

Abstract
We have performed a comprehensive numerical and analytical examination of two crucial transport aspects in silicene: the phonon-drag thermopower,

S
p

, and the electron’s energy loss rate,

F

e

. Specifically, our investigation is centered on their responses to out-of-plane flexural phonons and in-plane acoustic phonons in silicene, a two-dimensional allotrope of silicon as a function of electron temperature,

T
,

and electron concentration,

n
,

upto the room temperature. It is found that the calculated quantities have a non-monotonic dependence for the phonon modes for both parameters

(

T

and

n

)

considered while analytical results predict definite dependencies up to the complete low-temperature Bloch–Gruneisen (BG) regime. To provide a more comprehensive picture, we contrast the complete numerical outcomes with the approximated analytical BG results, revealing a convergence within a specific range of temperature and carrier concentration. In light of this convergence, we put forth suggestions to elucidate the underlying factors responsible for this behavior. A comparison based on the magnitude of the calculated quantities can be made from the figures between the two considered phonon modes, which clearly shows that the out-of-plane flexural phonons are effective throughout the considered temperature range. This observation leads us to posit that the dominating contribution of the out-of-plane flexural phonon modes hinges upon the deformation potential constant and phonon energy associated with the phonon mode. Our study carries significant implications for estimating the electrical and thermal properties of silicene and provides valuable insights for the development of devices based on silicene-based technologies.
DFSA-DAN: dynamic fusion of statistical metric and adversarial learning for domain adaptation network based intelligent fault diagnosis
Authors: Shao Yining , Zheng Xiaorong , He Zhiwei , Gao Mingyu , Nie Jiahao 
Published: April 2024

Abstract
The advancement of deep transfer learning has motivated research into the realization of intelligent fault diagnosis schemes for rolling bearing. Nevertheless, existing research rarely provides further insight into the importance of statistical distance metric-based methods and adversarial learning-based methods in domain adaptation, and the commonly used feature extractors are more difficult to extract features suitable for domain transformation. In this paper, a dynamic fusion of statistical metric and adversarial learning for domain adaptation network is proposed to achieve a dynamic measure of the importance of different domain adaptation methods. This new model utilizes a local maximum mean discrepancy metric to adjust the conditional distribution and adversarial training to adjust the marginal distribution between domains. Meanwhile, to assess the importance of the two distributions, a dynamic adaptation factor is introduced for dynamic evaluation. In addition, to extract features that are more suitable for domain transformation, the model incorporates a dual depth convolutional path with an attention mechanism as a feature extractor, enabling multi-scale feature extraction. Experimental results demonstrate the model’s superior generalization capability and robustness, enabling effective cross-domain fault diagnosis in diverse scenarios.
Investigation on the fusion reliability and cluster consistency of multivariable entropy method
Authors: Guo Hang , Wang Xianzhi , Ma Hongbo , Chen Gaige , Li Yongbo 
Published: April 2024

Abstract
Recent researches have shown that the multivariable entropy based feature extraction method can obtain better diagnosis results for fault diagnosis of planetary gearbox. However, the nature properties of multivariable entropy have still not been deeply explored: the reliability of multi-source information fusion and cluster consistency for the same fault signal. These two properties will affect the accuracy of fault diagnosis based on multivariate entropy. This paper aims to reveal the nature properties of multivariate entropy. Firstly, a rigid-flexible coupling dynamic model of a planetary gearbox is conducted to establish a pure test environment. Then the generated vibration signals are used to evaluate the fusion reliability and cluster consistency of multivariable entropy. Additionally, a new multivariable entropy feature extraction method called variational embedding refined composite multiscale diversity entropy (veRCMDE) is proposed. Finally, the simulation and experiment results show that high fusion reliability and high cluster consistency enable multivariate entropy to extract more valuable features, and the proposed veRCMDE performs the best in all experiments.
Fault diagnosis of axial piston pump based on multi-source subdomain adaptation and sensor data fusion
Authors: Tang Hongbin , Gong Yangchun , Zhou Jingnan 
Published: April 2024

Abstract
The axial piston pump is the core component in hydraulic systems. Its condition monitoring and fault diagnosis are crucial to ensure the safe and reliable operation of hydraulic systems. However, most of the existing fault diagnosis methods for axial piston pumps use the same working condition data. In actual operation, axial piston pump often experience varying loads, and the collected data is typically correlated but has different distributions. Therefore, a transfer learning method of multi-source subdomain adaptation and sensor fusion (MSASF) is proposed for fault diagnosis of axial piston pump. The proposed MSASF has three modules, a shared feature extraction module, a domain-specific feature extraction module and an output decision module. Firstly, the adaptive weighted fusion of multi-sensor data features is realized by the shared feature extraction module and the common features of multi-source heterogeneous data are extracted. Secondly, in the domain-specific feature extraction module, multi-branch network is used to extract features of each pair of source and target domains and the local maximum mean difference is utilized to align the sub-domain distribution of each pair of source and target domains. Finally, in the output decision module, the distribution distance between each pair of source domain and target domain is calculated using maximum mean discrepancy to obtain its weighted score. Combined with the classification output of each source domain, the final diagnosis decision is made. A dataset was constructed for the axial piston pump design fault experiment, and four sets of transfer tasks were designed to compare with those of seven classic methods. The experimental results showed that the proposed MSASF method exhibits a superior domain adaptation effect and fault diagnosis performance.
CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s
Authors: Hao Fei , Zhang Tianqi , He Guanghong , Dou Ruofei , Meng Chao 
Published: April 2024

Abstract
To address the problem of the frequent occurrence of major casualties during construction, a lightweight multi-target detection model based on YOLOv5s, named CaSnLi-you only look once (YOLO), was proposed for the detection of multiple targets consisting of construction workers and various mechanical equipment at construction sites. In this study, the introduction of the coordinate attention mechanism along with DWConv and C3Ghost based on GhostNet, enhanced the expression and detection accuracy of the model, effectively reducing the number of parameters. Considering the environmental characteristics of construction sites, a detection box filtering strategy based on soft non-maximum suppression was employed, to effectively alleviate the issue of missed detections of occluded targets. Experimental results demonstrate the significant superiority of the proposed CaSnLi-YOLO over current mainstream detection models, such as faster region-based convolutional neural network and single-shot detector. The proposed CaSnLi-YOLO has a parameter number of 5.96 × 106, which is 15.2% less than that of the original YOLOv5s model, further improving precision, recall rate, mAP@0.5, and mAP@0.5:0.95 by 0.6%, 0.6 %, 0.2%, and 2.3%, respectively. The improved YOLOv5s model proposed in this study achieved significant improvements in multi-target detection at construction sites. The model demonstrated effective enhancements while significantly reducing parameter count and is expected to be deployed in small-edge devices for real-time security monitoring at construction sites.
Magnetic targeting and pH-microwave dual responsive Janus mesoporous silica nanoparticles for drug encapsulation and delivery
Authors: He Yuhai , Wang Chen , Wu Qiuhua , Zhang Guolin 
Published: April 2024

Abstract
In this paper, a new Janus-structured nano drug delivery carrier Fe3O4@TiO2&mSiO2 was designed and synthesized, which consisted of a spherical head and a closely connected rod. The head was a nanocomposite of core/shell structure with magnetic spinel ferric tetraoxide core and anatase titanium dioxide shell (Fe3O4@TiO2), and the rod was ordered mesoporous silica (mSiO2). The nanocarriers showed excellent magnetic targeting capability (saturation magnetization, 25.18 emu g−1). The core/shell heads endowed the carriers with fine microwave responsiveness. The pore volume of mesoporous nanocarriers was 0.101 cm3 g−1, and the specific surface area was 489.0 m2 g−1. Anticancer drug doxorubicin could be loaded in the mesoporous of the carriers to form Fe3O4@TiO2&mSiO2-DOX. The drug loading capacity was 10.4%. Fe3O4@TiO2&mSiO2-DOX exhibited acid-sensitive and microwave-sensitive release properties along with good bio-compatibility. Fe3O4@TiO2&mSiO2 Janus nanoparticles are expected to be ideal drug carriers.
Spread-out Bragg peak measurements using a compact quality assurance range calorimeter at the Clatterbridge cancer centre
Authors: Shaikh Saad , Escribano-Rodriguez Sonia , Radogna Raffaella , Kelleter Laurent , Godden Connor , Warren Matthew , Attree Derek , Saakyan Ruben , Mortimer Linda , Corlett Peter , Warry Alison , Gosling Andrew , Baker Colin , Poynter Andrew , Kacperek Andrzej , Jolly Simon 
Published: April 2024

Abstract

Objective. The superior dose conformity provided by proton therapy relative to conventional x-ray radiotherapy necessitates more rigorous quality assurance (QA) procedures to ensure optimal patient safety. Practically however, time-constraints prevent comprehensive measurements to be made of the proton range in water: a key parameter in ensuring accurate treatment delivery. Approach. A novel scintillator-based device for fast, accurate water-equivalent proton range QA measurements for ocular proton therapy is presented. Experiments were conducted using a compact detector prototype, the quality assurance range calorimeter (QuARC), at the Clatterbridge cancer centre (CCC) in Wirral, UK for the measurement of pristine and spread-out Bragg peaks (SOBPs). The QuARC uses a series of 14 optically-isolated 100 × 100 × 2.85 mm polystyrene scintillator sheets, read out by a series of photodiodes. The detector system is housed in a custom 3D-printed enclosure mounted directly to the nozzle and a numerical model was used to fit measured depth-light curves and correct for scintillator light quenching. Main results. Measurements of the pristine 60 MeV proton Bragg curve found the QuARC able to measure proton ranges accurate to 0.2 mm and reduced QA measurement times from several minutes down to a few seconds. A new framework of the quenching model was deployed to successfully fit depth-light curves of SOBPs with similar range accuracy. Significance. The speed, range accuracy and simplicity of the QuARC make the device a promising candidate for ocular proton range QA. Further work to investigate the performance of SOBP fitting at higher energies/greater depths is warranted.
Deep-learning model for background parenchymal enhancement classification in contrast-enhanced mammography
Authors: Ripaud E , Jailin C , Quintana G I , Milioni de Carvalho P , Sanchez de la Rosa R , Vancamberg L 
Published: April 2024

Abstract

Background. Breast background parenchymal enhancement (BPE) is correlated with the risk of breast cancer. BPE level is currently assessed by radiologists in contrast-enhanced mammography (CEM) using 4 classes: minimal, mild, moderate and marked, as described in breast imaging reporting and data system (BI-RADS). However, BPE classification remains subject to intra- and inter-reader variability. Fully automated methods to assess BPE level have already been developed in breast contrast-enhanced MRI (CE-MRI) and have been shown to provide accurate and repeatable BPE level classification. However, to our knowledge, no BPE level classification tool is available in the literature for CEM. Materials and methods. A BPE level classification tool based on deep learning has been trained and optimized on 7012 CEM image pairs (low-energy and recombined images) and evaluated on a dataset of 1013 image pairs. The impact of image resolution, backbone architecture and loss function were analyzed, as well as the influence of lesion presence and type on BPE assessment. The evaluation of the model performance was conducted using different metrics including 4-class balanced accuracy and mean absolute error. The results of the optimized model for a binary classification: minimal/mild versus moderate/marked, were also investigated. Results. The optimized model achieved a 4-class balanced accuracy of 71.5% (95% CI: 71.2–71.9) with 98.8% of classification errors between adjacent classes. For binary classification, the accuracy reached 93.0%. A slight decrease in model accuracy is observed in the presence of lesions, but it is not statistically significant, suggesting that our model is robust to the presence of lesions in the image for a classification task. Visual assessment also confirms that the model is more affected by non-mass enhancements than by mass-like enhancements. Conclusion. The proposed BPE classification tool for CEM achieves similar results than what is published in the literature for CE-MRI.