bluuebunny/crossref_metadata_2025_split huggingface dataset:
A rapid identification technique for rice adulteration based on improved YOLOV8 model
Authors: Zhang Yuan , Xing Xiao , Zhu Lei , Li Xin , Wang JiaNing , Du YanPing , Han Rui 
Published: December 2024

Abstract
Rice classification and quality testing are essential to ensure its safety and quality, and can effectively reduce food economic losses associated with rice, which is currently the subject of repeated reports of rice adulteration. For this reason, according to the experimental requirements, we selected the smallest and lightest version of YOLOv8n in the YOLOv8 series, and optimised and proposed the lightweight and efficient Faster-YOLO algorithm, which is more suitable for rice adulteration classification and recognition tasks, on the basis of the YOLOv8n algorithm. Firstly, this paper introduces the Group Convolutional Hybrid Attention Mechanism, which combines channel information and spatial information, and embeds it in the last layer of the Backbone network, which enhances the feature representation capability of the model by focusing on the key information to suppress the noise. Secondly, the C2F module in the Backbone part adopts the design of combining Faster and C2F to enhance the feature fusion capability and reduce the number of parameters and computational complexity of the model. Finally, the collected data are augmented with multiple aspects to simulate different environments and compared with multiple attention mechanisms and deep learning models. The experimental results show that the method proposed in this paper is superior in classification and recognition performance, with recognition accuracy of 93.4%, precision of 96.5%, recall of 96%, and F1 score of 96.2%. In this paper, the accuracy is improved by 1.1%, 2.4%, 6.9%, 7.5%, 13.1%, 7.9%, 7.1%, and 7.1% compared to yolov8n, yolo11, yolov5, EfficientNetV2, MobileNetV3, Restnet18, and VGG19 image classification methods, respectively. It is demonstrated that Faster-YOLO provides a strong basis for the rapid identification of rice adulteration by reducing the weight of the model while improving the detection and identification capabilities.
Nonlinear sliding mode control of flying wing aircraft under wind disturbance
Authors: Song Yuecheng , Zhao Wen , Liu Zhenbao , Han Junwei , Dang Qingqing 
Published: December 2024

Abstract
For flying wing aircraft, stabilization has always played a key role, and many studies have focused on stabilization. One major problem is anti-wind disturbance performance. However, few studies have been conducted on this topic. In this paper, a modified nonlinear control strategy is introduced. First of all, the aircraft is modeled in a nonlinear form, and a modified sliding surface is applied. Then, a recurrent neural network is introduced to mitigate the wind affection in different flight situations. Finally, the simulation is applied to prove the effectiveness of the proposed method. The simulation clearly shows that the proposed method works well in a large range of windy environments and flying states, indicating that the proposed method is effective for flying wings in windy environments.
MBE-YOLOv8: enhancing building crack detection with an advanced YOLOv8 framework
Authors: Zhang Zhen , Hu Zhibo , Chen Kexin , Zhou Qi , Zhang Hongxia 
Published: December 2024

Abstract
Buildings, over prolonged periods, are susceptible to developing various types of cracks, which are often small and exhibit low contrast, leading to challenges in accurate detection. Missed detections and false positives due to these characteristics can result in delayed repairs, thereby compromising structural integrity and safety. Therefore, real-time detection of building cracks is essential to maintain the longevity and safety of infrastructures. In response to these challenges, we present an optimized version of the YOLOv8 model, referred to as MBE-YOLOv8, designed specifically for building crack detection. The core enhancement involves restructuring the backbone of YOLOv8 with the integration of the multi-dimensional collaborative attention mechanism, significantly improving feature interrelationships and the extraction capabilities of the backbone network. Additionally, we introduced a Weighted Feature Fusion Network (BiFPN) and developed a novel BiFPN-L structure to enhance feature fusion and detection accuracy, particularly for small targets. The efficient channel attention (ECA) mechanism was also incorporated into the model’s neck, leading to the design of a new EC2f structure that improves the model’s adaptability to scale variations and overall feature extraction efficiency. A comparative analysis with the original YOLOv8 model demonstrated that MBE-YOLOv8 achieved performance improvements with P, R, and mAP@0.5 values of 78.6%, 67.0%, and 73.4%, respectively. These figures represent increases of 4.8, 3.8, and 4.1 percentage points compared to the previous version of the YOLOv8 model. This advancement has significantly bolstered the capability to detect cracks in buildings. Furthermore, the enhanced model preserves a compact size of 3.0 M while sustaining a high frame rate (FPS), rendering it highly deployable for applications related to crack detection.
Deep adaptively dynamic edge graph convolution network with attention weight and high-dimension affinity feature graph for rotating machinery fault diagnosis
Authors: Jiang Zhichao , Liu Dongdong , Cui Lingli 
Published: December 2024

Abstract
The graph neural network (GNN) has emerged as an effective way to mine relationships between data due to its powerful modeling capability for graph structure data, and it has garnered significant attention from researchers for intelligent fault diagnosis tasks. However, the adjacency matrix of most GNN models with deep architecture is always fixed during the aggregation process, and the edge connection relationship cannot be adaptively adjusted, which limits their performance for feature representation. Moreover, for few-shot diagnosis scenarios, the generalization performance of deep GNN models will be further degraded due to fixed receptive fields and limited training samples. To address these issues, a deep adaptively dynamic edge graph convolution network (DADE-GCN) with attention weight and a high-dimension affinity feature graph is proposed. First, a deep adaptively dynamic edge graph convolutional module with attention weight (DADE-GCNWAW) is developed to dynamically adjust the receptive field in different graph convolution layers. Subsequently, the output features of different layers are fused by a self-attention mechanism. Second, to overcome the effect of the time-shift problem existing in vibration signals and capture accurate interdependencies between data, a high-dimension affinity feature graph construction method is proposed to construct graph structure data. The effectiveness of the proposed method is quantitatively verified by two rotating machinery datasets, which indicate that the proposed DADE-GCN model can achieve average diagnosis accuracies of 98.80% in both the two few-shot diagnosis tasks, which outperform several state-of-the-art recognition methods.
Tuning specific absorption rate of Mn ferrite nanoparticles synthesized by a thermal decomposition method with different Mn concentrations
Authors: Oroujizad Sahar , Kashi Mohammad Almasi , Montazer Amir H 
Published: December 2024

Abstract
A thermal decomposition method is used to synthesize monodisperse Mn ferrite nanoparticles (NPs) by changing Mn concentration from 0.1 to 0.8 mmol. The effects of Mn concentration on structural, compositional, morphological, magnetic, and hyperthermia properties are investigated. Transmission electron microscopic images show that the morphology of the NPs changes from flower-like to polygonal with increasing the Mn concentration. The saturation magnetization reaches a maximum value of 48.32 emu g−1 and a minimum value of 11.09 emu g−1 with changing the Mn concentration, whereas the coercivity value decreases from 12.6 to 5.3 Oe. The first-order reversal curve analysis enables the estimation of superparamagnetic contribution of the Mn ferrite NPs in the range of 21%–59.5%. The highest specific absorption rate value is obtained to be 385.37 W g−1 for Mn0.1Fe2.9O4 NPs with the maximum superparamagnetic contribution using a manganese concentration of 0.4 mmol.
Unsupervised deep learning-based medical image registration: a survey
Authors: Duan Taisen , Chen Wenkang , Ruan Meilin , Zhang Xuejun , Shen Shaofei , Gu Weiyu 
Published: December 2024

Abstract
In recent decades, medical image registration technology has undergone significant development, becoming one of the core technologies in medical image analysis. With the rise of deep learning, deep learning-based medical image registration methods have achieved revolutionary improvements in processing speed and automation, showing great potential, especially in unsupervised learning. This paper briefly introduces the core concepts of deep learning-based unsupervised image registration, followed by an in-depth discussion of innovative network architectures and a detailed review of these studies, highlighting their unique contributions. Additionally, this paper explores commonly used loss functions, datasets, and evaluation metrics. Finally, we discuss the main challenges faced by various categories and propose potential future research topics. This paper surveys the latest advancements in unsupervised deep neural network-based medical image registration methods, aiming to help active readers interested in this field gain a deep understanding of this exciting area.
Characterization and controllability of radiated power via extrinsic impurity seeding in strongly negative triangularity plasmas in DIII-D
Authors: Eldon D , Casali L , Bykov I , Chrystal C , Erickson K , Hyatt A W , Leonard A W , Moser A L , Nelson A O , Odstrčil T , Paz-Soldan C , Pederson T , Scotti F , Shen H , Thome K E , Wang H Q , Welsh A , Wilks T M 
Published: December 2024

Abstract
Experiments with extrinsic impurity seeding in strongly negative triangularity shapes in DIII-D achieved radiated power fractions (relative to input power) of up to ≈85% total radiation and ≈55% core radiation in steady-operating conditions. The relationship between core and total radiation was sensitive to impurity species and input power. Attempts to reach higher radiation levels via higher impurity flows resulted in radiative collapse disruptions. Nitrogen, neon, argon, and krypton were tested. Injection was by gas puffing, usually controlled by feeding back real-time estimates for core or total radiating fractions (

P
rad

/

P
input

). Argon and krypton were controlled by feeding back total radiating fraction, whereas neon was controlled by feeding back the core radiating fraction, due to the lower efficiency of neon as a divertor radiator. Nitrogen flows were pre-programmed. Reasonable total

P
rad

control target following was achieved with argon or krypton. Control was more challenging with the neon/core radiation configuration, which was more prone to slow response and overshooting of the control target. Poor particle removal contributed to the control challenge: neon particle inventory within the last closed flux surface was roughly constant for up to 1 s (the longest duration tested) after neon injection was halted. However, separate experiments with laser-blow off of non-recycling impurities measured a short impurity confinement time, on the scale of the energy confinement time of ∼100 ms. Modeling with the Aurora impurity transport simulation matched experimental neon densityprofiles with full recycling (R = 1.0) and weak pumping but predicted rapid decreases in neon inventory if pumping were increased or recycling decreased. This indicates that changes outside the confined plasma (adding a well-placed pump) would improve controllability for all highly recycling species.
Gyrokinetic electromagnetic particle simulations in triangular meshes with C1 finite elements
Authors: Lu Zhixin , Meng Guo , Hatzky Roman , Sonnendrücker Eric , Mishchenko Alexey , Chen Jin , Lauber Philipp , Zonca Fulvio , Hoelzl Matthias 
Published: December 2024

Abstract
The triangular mesh-based gyrokinetic scheme enables comprehensive axis-to-edge studies across the entire plasma volume. Our approach employs triangular finite elements with first-derivative continuity (C1), building on previous work to facilitate gyrokinetic simulations. Additionally, we have adopted the mixed variable/pullback scheme for gyrokinetic electromagnetic particle simulations. The filter-free treatment in the poloidal cross-section with triangular meshes introduces unique features and challenges compared to previous treatments using structured meshes. Our implementation has been validated through benchmarks using ITPA-toroidicity-induced Alfvén eigenmode parameters, showing its capability in moderate to small electron skin depth regimes. Additional examinations using experimental parameters confirm its applicability to realistic plasma conditions.
JET Active Gas Handling System–operating experience and lessons learned from recent D-T campaigns
Authors: George Robert , Bickerton Sarah , Cahill Peter , Dalgliesh Peter , Davies Sophie , Franklin Owen , Hayes Nathanya , Jackson Tamsin , Kennedy David , Knight Maddie , Lefebvre Xavier , O’Callaghan James , Olney Ross , Papadopoulos Giannakis , Peyman Mo , Sanni Fatimah , Staniec Paul , Withycombe Alex , Walker Richard , Wakeling Ben ,   The JET Operations Team
Published: December 2024

Abstract
The Active Gas Handling System (AGHS) stores, supplies and re-processes tritium for the JET fusion experiment. The plant had not operated in this capacity in recent years and required upgrade and recommissioning to enable use during the latest JET tritium campaigns. Thousands of tritium transfers and operations have now been performed with over 1 kg tritium in total supplied to JET and subsequently recovered and reprocessed. This paper summarises AGHS plant operations providing examples from the recent campaigns, and presents the knowledge gained and lessons learnt. A summary of AGHS sub-system upgrades and usage during campaigns is presented. This paper includes data captured from those operations, which have been extracted from the control system as part of a wider AGHS data capture project. Findings are presented from an initiative to capture operator knowledge from AGHS, which brings together operational team input in working groups, a targeted survey and an ideas repository. Key operations are discussed with reference to plant data presented graphically. Operational challenges and lessons learnt are presented from which improvements to design and operations have been proposed. Topics discussed include system design and limitations, process control and automation, tritium accountancy, operating instructions, and ergonomics. Many of the lessons learnt and improvements presented here are relevant to the wider fusion community planning to design and operate tritium plants.
Compression–twist coupling and mechanical performances of Kresling origami-inspired periodic lattice panels
Authors: Li Zhen , Ni Junhao , Wang Zhidong , Wang Xinhao , Kang Zhengyang 
Published: December 2024

Abstract
The design and development of periodic metamaterials are of great significance for the flexible scaling of structures and the control of their performance. This paper advances the state of the art by proposing a Kresling origami-inspired periodic lattice structure and investigating the compression–twist coupling (CTC) and mechanical properties from a single element to a multi-element structure. The lattice element is designed and fabricated using 3D printing method, and its twisting and mechanical properties are investigated both experimentally and numerically. Then, the multi-element panel is created by proper arrangement of the elements. The mechanism of CTC characteristic under multi-element conditions is revealed through force analysis, and the connection between the performance of the single element and the multi-element panel is discussed. For the multi-element panel, the compression–twist performance weakens as the scale increases. However, the contribution of each element to the overall mechanical properties of the panel stabilizes once the panel reaches a size of 3 × 3 or larger. A lightweight hollow design method is also proposed by removing the elements in the central area of the panel. Comparisons between the regular and hollow panels show that the hollow design has minimal impact on the compression–twist performance and the mechanical responses of the elements in the panel. The overall force and energy absorption properties are proportional to the number of elements. Therefore, the lightweight design can tailor the mechanical properties while maintaining consistent compression–twist performance. Additionally, the proposed structure is compared with traditional 3D chiral structure, the potential advantages in compression–twist performance and energy absorption are discussed.
K-band dual-band superconducting bandpass filter using uneven coupled SIRs and stub-loaded combining network
Authors: Chen Peng , Ren Peng , Yang Kai 
Published: December 2024

Abstract
A K-band millimeter dual-band superconducting filter with uneven coupled step-impedance resonators (SIRs) and stub-loaded combining network is proposed in this paper. The novel uneven internally coupled SIR introduces a notch to the classical internally coupled SIR, enhancing the Q-factor for narrow-band filter design. The performance of the uneven coupled SIR is theoretically analyzed, and two single-passband superconducting filters based on the proposed resonator are designed and demonstrated its high Q-factor. A novel stub-loaded combining network is then investigated theoretically to integrate the two single passband filters into a dual-band filter. To validate the design concept, a dual-band superconducting filter is designed on a magnesium oxide (MgO) substrate using the yttrium barium copper oxide superconducting film. The measured results reveal insertion losses of 0.91–1.2 dB of the first passband at 24.21–24.41 GHz with the bandwidth of 0.82% and 0.43–1.3 dB of the second passband at 24.67–25.2 GHz with the bandwidth of 2.12%, respectively. Both passbands exhibit return loss levels exceeding 8 dB, a maximum out-of-band suppression of approximately 65 dB, and a rejection level of about 35 dB between the two passbands.
Next-generation cybersecurity strategies for 3D printing in high-intensity manufacturing
Authors: Moulika G , Palanisamy Ponnusamy 
Published: December 2024

Abstract
This research examines the key factors that influence optimal results in 3D printing, including print speed, temperature, layer thickness and material selection. Specifically, it explores the impact of materials such as acrylonitrile butadiene styrene (ABS), nylon, polylactic acid (PLA), and polyethylene terephthalate glycol (PETG) on the 3D printing process and the decision-making involved in achieving the best results. It provides a comprehensive review of the impact of these variables on print quality, drawing on existing studies and experimental data. The findings indicated that print speed of 66 mm s−1, temperature of 218 °C, layer thickness of 0.2 mm and use of PLA material gave the best results. In addition, the research explores the use of Support Vector Machine (SVM) modeling to predict and improve print quality based on these parameters. Research confirms that through statistical analysis of test data, optimal print settings can be effectively identified. Beyond print quality, the research also addresses critical cybersecurity vulnerabilities in 3D printing systems, particularly related to their integration with IT networks. It highlights the importance of analyzing network traffic to detect vulnerabilities, improve system performance, and strengthen cybersecurity measures. The application of machine learning techniques for anomaly detection is explored as a means of developing risk management strategies and ensuring compliance with auditing standards. Finally, the research discusses the role of blockchain technology in securing machine-to-machine communication (M2M) within the 3D printing environment, as well as ensuring data integrity and confidentiality.
Synthesis of zinc oxide-doped reduced graphene oxide (rGO) nanocomposites on visible light-driven photocatalytic and antibacterial activity applications
Authors: Thuy Pham Thi , Hue Bui Thi Cam , Sang Nguyen Xuan , Thanh Thuy Le Thi , Lieu Phuoc Long 
Published: December 2024

Abstract
In this study, reduced graphene oxide (rGO), ZnO nanocomposites, and zinc oxide-doped reduced graphene oxide (ZnO/rGO) nanocomposites were successfully synthesized via the sol–gel method and investigated for the removal of methyl blue (MB) dye and antibacterial activity under visible light irradiation. The synthesized nanocomposites were characterized using XRD, SEM, EDX, FTIR, UV–vis DRS, and BET techniques to analyze the crystallographic structure, phase, surface morphology, and different functional groups in the prepared catalysts. The ZnO/rGO nanocomposites demonstrated a great photocatalytic performance over 98% degradation of 20 mg L−1 of methylene blue (MB) dye within just 90 min under visible light. A heterojunction material achieved a higher 20% than that of the ZnO single-phase. Among the prepared photocatalysts, the nanocomposite with 0.2 grams rGO demonstrated the best results. Reusability studies revealed that ZnO/rGO catalysts maintained over 90% efficiency up to three cycles, with rGO serving as an effective adsorbent and ZnO providing catalytic activity. In addition, the antibacterial activity of the nanocomposites was evaluated against both Gram-positive and Gram-negative bacteria, showing significant inhibition due to the photocatalytic generation of reactive oxygen species. These findings suggest that ZnO/rGO nanocomposites are promising materials for environmental remediation and antibacterial applications.
Aristotle’s account of the human capacity for thinking
Authors: Corcilius Klaus , Falcon Andrea , Roreitner Robert 
Published: December 2024

Abstract
De anima III 4–5 is narrowly focused on the definition of a highly specialized form of thinking: the structurally simple, necessarily true, non-propositional grasping of essences. For Aristotle, this form of thinking grounds the possibility of all the other, more familiar, kinds of thinking. The chapter argues that Aristotle has an explanatory account of human thinking capable of accounting for key features of human thinking such as objectivity, universality, and necessity. It is argued that Aristotle achieves this by way of two key moves: namely, an account of thinking as matterlessness of a certain kind, and the claim that episodes of human thinking of essences for their actualization depend on the existence of a self-thinking separate essence.
The principles of propositional thought
Authors: Corcilius Klaus , Falcon Andrea , Roreitner Robert 
Published: December 2024

Abstract
This chapter argues that De anima III 6 contains a unitary and coherent argument that fits tightly into the overall context of Aristotle’s account of the thinking capacity. In De anima III 6 Aristotle faces the obvious question of how the capacity of grasping essences analyzed in the previous chapters can account for the other kinds of thinking. It is argued that Aristotle makes both the truth and the unity of propositional thinking depend on the grasping of essences. The chapter’s central claim is that we can see this once we realize that in De anima III 6 Aristotle distinguishes between two kinds of “dividing” propositions (and two corresponding kinds of adiaireta): namely, either dividing them into their structural elements (subject and predicate terms) or dividing them by means of explanatory middle terms.