bluuebunny/crossref_metadata_2025_split huggingface dataset:
In-line monitoring and modelling of particle size reduction of hexagonal boron nitride (hBN) suspension in femtosecond laser processing
Authors: Muneoka Hitoshi , Koike Takeru , Ito Tsuyohito , Terashima Kazuo , Miura Eisuke 
Published: July 2024

Abstract
This study investigates the particle-size reduction of hexagonal boron nitride (hBN) suspensions under femtosecond-laser irradiation. We propose an in-line monitoring method that uses transmission spectroscopy (TS) in the visible wavelength region to evaluate the changes in particle-size distribution. The Ångström exponent (α), derived from the wavelength dependence of optical thickness, is employed as an indicator of particle-size reduction. The effectiveness of α is validated through comparison with particle-size distributions obtained by the particle tracking method. We demonstrate real-time monitoring of particle-size variation in a flow system using in-line TS measurements. The dependence of α on laser energy and hBN concentration is also investigated. Furthermore, we introduce an indicator, the average number of size-reduction events per particle (N
SR), and a model for estimating it under various experimental conditions. The N
SR indicator and the model for its derivation provide a unified understanding of the experimental results obtained under various conditions, such as treatment volume, laser energy, and hBN concentration, suggesting that they capture the essential aspects of laser propagation in the suspension and the particle-size reduction process. This study highlights the potential of α as an in-line process monitoring tool and the significance of the N
SR indicator and its model in understanding the underlying mechanisms of laser processing for particle-size reduction in suspensions.
Nuclear medium effects on the properties of Λ(1405)
Authors: Süngü J Y , Er N 
Published: July 2024

Abstract
We study the Λ(1405) resonance with I(J

P
) = 0(1/2−) in the context of the pentaquark hypothesis in the nuclear medium. To explore the influence of the nuclear medium on the physical parameters of Λ(1405), we propose a molecular-type structure involving admixtures of K
−
p and

K

¯

0

n

, correlated with the nuclear matter density. Our analysis reveals a substantial shift in mass and residue, approximately 20% and 38%, respectively. These findings have significant implications for experimental researchers aiming to identify in-medium characteristics of hyperon resonances.
Ward identities in a two-dimensional gravitational model: anomalous amplitude revisited using a completely regularization-independent mathematical strategy
Authors: Dallabona G , de Oliveira P G , Battistel O A 
Published: July 2024

Abstract
We present a detailed investigation of the anomalous gravitational amplitude in a simple two-dimensional model with Weyl fermions. We employ a mathematical strategy that completely avoids any regularization prescription for handling divergent perturbative amplitudes. This strategy relies solely on the validity of the linearity of the integration operation and avoids modifying the amplitudes during intermediate calculations, unlike studies using regularization methods. Additionally, we adopt arbitrary routings for internal loop momenta, representing the most general analysis scenario. As expected, we show that surface terms play a crucial role in both preserving the symmetry properties of the amplitude and ensuring the mathematical consistency of the results. Notably, our final perturbative amplitude can be converted into the form obtained using any specific regularization prescription. We consider three common scenarios, one of which recovers the traditional results for gravitational anomalies. However, we demonstrate that this scenario inevitably breaks the linearity of integration, leading to an undesirable mathematical situation. This clean and transparent conclusion, enabled by the general nature of our strategy, would not be apparent in similar studies using regularization techniques.
The role of defect charge, crystal chemistry, and crystal structure on positron lifetimes of vacancies in oxides
Authors: Lopez-Bezanilla Alejandro , Selim Farida A , Oskar Liedke Maciej , Uberuaga Blas P 
Published: July 2024

Abstract
Density functional theory based positron lifetime (PL) calculations for cation and oxygen monovacancies in a range of oxides—hematite, magnetite, hercynite, and alumina—have been conducted to compare the impact of defect chemistry and crystal structure on the predicted lifetimes. The role of defect charge state has also been examined. A comparison across the same type of crystalline structure but different composition shows that oxygen vacancies only induce a slight increase in the positron-electron overlap and thus barely modify the PL as compared to the bulk. A much more substantial increase of PL is observed for cation monovacancies, regardless of crystal structure or the elemental nature of the vacancy, which we ascribe to an enhanced localization of charge density around the vacant site. The structural and compositional richness of the oxide leads to longer defect PLs, with defected hercynite exhibiting the longest PLs. The charge state of cation monovacancies modifies only by a small percentage the positron localization, relegating to secondary importance the metal defect’s oxidation state in modifying the lifetime of positrons within vacancy traps.
Long-term comparison and performance study of consumer grade electronic radon integrating monitors
Authors: Daraktchieva Z , Howarth C B , Wasikiewicz J M , Miller C A , Wright D A 
Published: July 2024

Abstract
This study reports the performance of 7 types of consumer grade passive Electronic Radon Integrating Monitors, ERIM (AlphaE, AER Plus, Canary, Corentium Pro, Radon Scout Home, Ramon and Wave) and passive etched track radon detectors. All monitors and passive radon detectors were exposed side by side for 2 periods of 3 months under controlled conditions in the UKHSA radon chamber and in a stainless steel container to an average radon concentration of 4781 Bq m−3 and 166 Bq m−3, respectively. The performance of each individual monitor was compared with Atmos 12DPX and AlphaGUARD P30 reference instruments. The performance of the monitors was evaluated by estimating the biased, precision and measurement errors of each type. It was found that UKHSA passive radon detectors showed excellent performance (measurement error < 10%) at both higher and lower exposures. The AlphaE, Canary and Ramon showed excellent performance, with measurement error <10%, when they were exposed to radon concentrations between 4000 Bq m−3 and 6000 Bq m−3 in the UKHSA radon chamber. However, when the monitors were exposed to radon levels below the UK radon Action Level of 200 Bq m−3, the only ERIM which had a measurement error <10% was the Radon Scout Home. All other monitors showed a significant decrease in their performance with measurement errors ranging between 20% and 50%. The calibration factor, which is the ratio between the measured value (background is subtracted) and the reference value, was also studied. It was found that the calibration factors of individual monitors changed significantly. Calibration measurements in 2019 and in 2023 found that the percentage change varied between −46% and +63%. This shows the importance of initial and regular calibration, and maintenance of the monitors.
Numerical assessment of induced electric fields in a worker’s hand with commonly used metallic implants under exposure to low frequency magnetic fields
Authors: Schmid Gernot , Schneeweiss Pia , Hirtl Rene , Jhala Tobias , Samaras Theodoros 
Published: July 2024

Abstract
The European Union’s Workers’ Directive 2013/35/EU on the minimum health and safety requirements regarding the exposure of workers to electromagnetic fields specifies action levels (ALs) for external electric and magnetic fields, which should protect against induced tissue-internal electric field strength E
i above the exposure limit values, the latter being defined in order to prevent tissue stimulation at low frequencies. However, although 2013/35/EU explicitly calls for the protection of ‘workers at particular risk’ (including workers with metallic implants), the AL specified in the Directive have been derived under the assumption that there are no metallic parts present inside the body. Therefore, in the present work, we analysed the situation of a worker’s hand and forearm bearing metallic implants (Herbert screw and volar radius plate) used for osteosynthesis after the most common bone fractures of the hand/forearm, exposed to low frequency magnetic fields. The uniform exposure of the whole hand and forearm as well as the exposure to a specific and widely used device, a deactivator for single-use labels of acousto-magnetic electronic article surveillance systems, were considered based on numerical computations using a high-resolution anatomical hand and forearm model. The results obtained indicated that the maximum induced electric field strength averaged in a volume of 2 mm × 2 mm × 2 mm cube was higher in the presence of the metallic implants by a factor of up to 4.2 for bone tissue and 2.3 for soft tissue compared with the case without an implant. Hence, it is obvious that the local induced electric field strengths may be substantially increased by the implants. The extent of this increase, however, is highly dependent on the implant’s position inside the body, the implant’s geometry, and the field distribution and orientation with respect to the anatomical structure and the implant.
Parallel-beams magnetic actuator for in-situ transmission electron microscope observation of mechanical testing
Authors: Denoual Matthieu , Lobato-Dauzier Nicolas , Saluden Luis , Sato Takaaki , Jalabert Laurent , Fujita Hiroyuki 
Published: July 2024

Abstract
Understanding the microscopic mechanisms behind mechanical fractures is essential for enhancing material properties and increasing reliability through fatigue suppression. Conventional mechanical testing methods, such as indentation tests that press a sharp needle into a specimen or tensile tests using hydraulic pumps, are unable to capture nanoscale deformations under applied forces. As a result, the microscopic mechanisms that influence mechanical properties are often inferred indirectly, and material design largely depends on the engineer’s intuition and occasional serendipity. To overcome these challenges, in-situ observation techniques utilizing transmission electron microscopes (TEMs) have been developed to enable the observation of sample deformations at the nanoscale. However, despite their high resolution, conventional TEMs are limited by a small available space -often just a few millimeters- that restricts the application of sufficient force to fracture specimens. Traditional actuation methods, such as thermal expansion, electrostatic force, and piezoelectric actuators, fail to generate significant forces within such confined spaces. In response to these limitations, our research involved the development of a micromachine with multiple parallel beams. This device leverages the Laplace force generated by an electric current passing through the beams and the magnetic field of the TEM. We demonstrated the capability to produce significant force using the magnetic field from the microscope’s magnetic lens. The actuator developed in our study successfully generated forces exceeding 50 µN, marking a significant advancement in the in-situ observation capabilities for mechanical testing.
Hybrid algorithms in path planning for autonomous navigation of unmanned aerial vehicle: a comprehensive review
Authors: Tuyet Minh Dang , Ba Dung Nguyen 
Published: July 2024

Abstract
Path planning for unmanned aerial vehicle (UAV) is the process of determining the path that travels through each location of interest within a particular area. There are numerous algorithms proposed and described in the publications to address UAV path planning problems. However, in order to handle the complex and dynamic environment with different obstacles, it is critical to utilize the proper fusion algorithms in planning the UAV path. This paper reviews some hybrid algorithms used in finding the optimal route of UAVs that developed in the last ten years as well as their advantages and disadvantages. The UAV path planning methods were classified into categories of hybrid algorithms based on traditional, heuristic, machine learning approaches. Criteria used to evaluate algorithms include execution time, total cost, energy consumption, robustness, data, computation, obstacle avoidance, and environment. The results of this study provide reference resources for researchers in finding the path for UAVs.
Methods and research for deformation monitoring of earth and rock dams based on close-range photogrammetry
Authors: Wang Kui , Cheng Yang , Xiong Yong , Wang Qiuqi , Zhao Mingjie 
Published: July 2024

Abstract
Traditional means of monitoring deformation in earth and rock dams encounter challenges such as low monitoring efficiency and limited coverage. Despite the potential of emerging technologies such as GPS and three-dimensional laser scanning, their adoption is expensive and hard to promote. This paper presents a deformation monitoring method for earth and rock dams based on the close-range photogrammetry technique. The proposed approach focuses on analytical algorithm the design and deployment of monitoring points, photographic schemes, camera checking and calibration, as well as deformation analysis methods. Initially, based on the analysis of the parsing algorithms’ applicability, they are fused to address the shortcomings of common image parsing methods in meeting the requirements of high precision and multi-image processing for deformation monitoring of earth and rock dams. Subsequently, the fused algorithm is introduced to analyze the acquired image data for 3D reconstruction, and the deformation in earth and rock dams is assessed based on the generated dense point cloud model. The proposed deformation monitoring method is applied to Pine Bridge Reservoir Dam, and the results demonstrated its capacity to comprehensively analyze the deformation. Furthermore, the required equipment is simple and easy to operate, aligning with the requirements for deformation monitoring accuracy of earth and rock dams.
Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor
Authors: Cui Jiarui , Shi Yuyu , Huang Jian , Yang Xu , Gao Jingjing , Li Qing 
Published: July 2024

Abstract
Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.
Multi-modality hierarchical attention networks for defect identification in pipeline MFL detection
Authors: Wang Gang , Su Ying , Lu Mingfeng , Chen Rongsheng , Sun Xusheng 
Published: July 2024

Abstract
Magnetic flux leakage (MFL) testing is widely used for acquiring MFL signals to detect pipeline defects, and data-driven approaches have been effectively investigated for MFL defect identification. However, with the increasing complexity of pipeline defects, current methods are constrained by the incomplete information from single modal data, which fail to meet detection requirements. Moreover, the incorporation of multimodal MFL data results in feature redundancy. Therefore, the multi-modality hierarchical attention networks (MMHAN) are proposed for defect identification. Firstly, stacked residual blocks with cross-level attention module (CLAM) and multiscale 1D-CNNs with multiscale attention module are utilized to extract multiscale defect features. Secondly, the multi-modality feature enhancement attention module (MMFEAM) is developed to enhance critical defect features by leveraging correlations among multimodal features. Lastly, the multi-modality feature fusion attention module (MMFFAM) is designed to dynamically integrate multimodal features deeply, utilizing the consistency and complementarity of multimodal information. Extensive experiments were conducted on multimodal pipeline datasets to assess the proposed MMHAN. The experimental results demonstrate that MMHAN achieves a higher identification accuracy, validating its exceptional performance.
Noncontact measurement of rectangular splines shaft based on line-structured light
Authors: Liu Siyuan , Li Honglin , Wang Conghui , Lian Fenghui , Miao Jianwei , Hu Zhengyi 
Published: July 2024

Abstract
The rectangular spline shaft, a typical type of shaft-tooth component, plays a significant role in mechanical transmissions. Existing methods for detecting size and positional tolerance in spline shafts often rely on contact-based measurement techniques, including specialized gauges and coordinate measuring machines (CMMs). To enhance the measurement efficiency, this paper proposes a method for measuring based on line-structured light. Firstly, a classification algorithm for data points on major and minor cylindrical surfaces and keyway surface of spline shaft is established, contributing to the automatic measurement. Secondly, a coaxiality error measurement model is established based on the overall least squares method, improving the measurement accuracy. Finally, a measurement model for key width and positional tolerance is established through the rotation of the spline axis. In experiments, the size and positional tolerance of the spline shaft obtained using this method are compared with measurements by CMM, meeting the general machining accuracy requirements.
EFS-YOLO: a lightweight network based on steel strip surface defect detection
Authors: Chen Beilong , Wei Mingjun , Liu Jianuo , Li Hui , Dai Chenxu , Liu Jinyun , Ji Zhanlin 
Published: July 2024

Abstract
With the advancement of deep learning technologies, industrial intelligent detection algorithms are gradually being applied in practical steel surface defect detection. Addressing the issues of high computational resource consumption and poor detection performance faced by existing models in large-scale industrial production lines, this paper proposes an EFS-YOLO (Efficient-Fast-Shared-YOLO) model based on improved YOLOv8s architecture. Firstly, the EfficientViT is employed as the feature extraction network, effectively reducing the model’s parameters and calculations while enhancing its capability to represent defect features. Secondly, the designed lightweight C2f-Faster-EffectiveSE Block (CFE-Block) was integrated into the model neck, accelerating feature fusion and better preserving detailed defect features at lower levels. Finally, the model detection head was reconstructed using the concept of shared parameters to address the high computational cost of the original detection head. Experimental results on the NEU-DET and GC10-DET datasets demonstrate that compared to the baseline model, the proposed model achieves a reduction in parameters, calculations and size by 49.5%, 62.7% and 46.9% respectively. It also exhibits an improvement in accuracy by 2.4% and 2.3% on the two datasets. The EFS-YOLO model effectively enhances precision in steel surface defect detection while maintaining lightweight characteristics, offering superior performance.
Visual localization and quantitative detection method for thermal defects in cable terminals of high-speed trains based on a temperature derivative
Authors: Liu Kai , Jiao Shibo , Nie Guangbo , Gao Bo , Yang Zhixiang , Xin Dongli , Wu Guangning 
Published: July 2024

Abstract
Cable terminal defect detection plays an important role in ensuring the safe and stable operation of high-speed trains. In this paper, a numerical model of the electromagnetic thermal field of overheating defects of cable terminal shielding grids and a method of detecting internal defects of cable terminals—even-order temperature derivative is proposed for quantitative detection of internal defective structures of vehicle-mounted cable terminals. Firstly, a numerical model of the electromagnetic thermal field of cable terminals under the condition of leakage current is constructed, through which the temperature field distribution characteristics of different defective structures are analyzed. Then, the intuitive location of the defective region is obtained by investigating the derivative characteristics of the temperature image, and the depth and intensity of the defects are quantitatively assessed by using the main side peak (MSP) distances and the MSP values extracted from the derivative curves. Finally, the simulation and experimental results achieve the identification of defect structures and the quantitative detection of defect depth and intensity, proving the effectiveness and accuracy of the proposed method.