bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anti-capitalist subjectivity: considerations of fantasy, (in)action, and solidarity building
Authors: Malherbe Nick 
Published: April 2024

AbstractAnti-capitalist subjectivities are produced through politically generative refusals of the divisive, profit-oriented, and manageable subject positions made available by capitalism’s socio-symbolic order. Pushing back against liberal political theories which presume subjectivity to be a priori or coherent, this article employs psychoanalytic theory to grapple with the flowing, changing, patterned, and disjointed nature of anti-capitalist subject formations. Although mainstream psychoanalysis has, historically, aligned with the dictates of capital, I argue that psychoanalytic theory nonetheless offers a useful resource for understanding how anti-capitalist refusal can foster emancipatory desires and situated political commitments within and among subjects. In fleshing out these arguments, I engage with the role that fantasy plays in forming anti-capitalist subjectivities. I also consider what solidarity building and political action mean with respect to anti-capitalist subjectivity. By way of conclusion, I argue why we should make the case for anti-capitalist subjectivity, offering some directions that future work may take.
Quantifying the diffusion history of Yangmingism
Authors: Lin Xiaobiao , Wu Shidai , Wu Bowei , Wang Jiawei 
Published: April 2024

AbstractYangmingism, a crucial component of Chinese Confucianism, has profoundly impacted the development of Eastern civilization throughout history and gained new significance within modern contexts. This study employs social network and GIS spatial analyses to investigate the spatio-temporal evolution and patterns of Yangmingism during the Ming and Qing dynasties. Conclusions drawn include: (1) Yangmingism spread during the Ming and Qing dynasties followed five phases (rise-peak-decline-revival-trough), consistent with general cultural diffusion processes. (2) The spatial structure of regions accepting Yangmingism changed from polycentric to localized ribbon-polycentric, then to monocentric, polycentric, and ultimately, fragmented distribution. Diffusion began with a point-axis structure along traffic routes, transitioned to a core-periphery spatial structure, and returned to a point-axis structure due to reduced diffusion potential energy. (3) Yangmingism diffusion exhibited distance decay characteristics, with the most extensive diffusion in the Han cultural region, primarily located within the middle and lower reaches of the Ganjiang River basin and the Yangtze River Delta. (4) Yangmingism diffusion reflected influences from local administrative hierarchies and cultural development. During the rise-to-peak phase, Yangmingism reached areas with higher administrative and cultural levels, while during the peak-to-decline phase, it disseminated from higher to lower hierarchy regions. (5) The spatial diffusion of Yangmingism is mainly relocation diffusion, followed by expansion diffusion. In the process of relocation diffusion, Yangmingism may reverse the diffusion from the “target regions” to the “source regions” due to the influence of the radiation of the high-ranking regions and the cultural innovation of the “target regions”.
Evaluating effects of physical soil and water conservation measures using farmers’ perception and soil analysis in southern Ethiopia
Authors: Ermias Habtamu , Wolka Kebede , Biazin Birhanu 
Published: April 2024

Context Soil and water conservation (SWC) measures have been widely practiced on cultivated lands across regions but evidence on their effects is inadequate. Aims To examine the effects of variously aged SWC measures on cultivated land. Methods A total of 122 farm households were interviewed. At the 0–20 cm layer, soil samples (n = 59) were collected from soil bunds of <5 years, 5–10 years, and 10–15 years old, and no-bund adjacent fields and analysed in the laboratory. Key results Approximately 40% of respondents perceived soil erosion as a severe problem in their area. The respondents (70%) practiced the introduced SWC measures such as soil bunds. The introduced SWC measures are beneficial in improving soil fertility (78%) and thus 96% of the respondents were interested in repairing and sustaining it. The soil pH, soil organic carbon, total nitrogen, and available phosphorous were significantly (P < 0.05) greater in the fields with bunds of 10–15 years old than the fields with bunds of less than 10 years. The 10–15 year old bund showed greater soil organic carbon (37%), total nitrogen (40%), and available phosphorous (27%) than adjacent no-bund fields, implying that SWC can show effects on soil in the long term. Conclusions The results of the study revealed that the effects of physical SWC measures on soil properties can be observed after a decade of managing them. Implications Farmers are advised to repair and retain bunds if the erosion-prone areas are used for cultivating annual crop.
Application of a mild data-driven technique to Lippmann–Schwinger inverse scattering in variable-exponent Lebesgue spaces for microwave imaging
Authors: Estatico Claudio , Schenone Valentina , Fedeli Alessandro , Randazzo Andrea 
Published: April 2024

Abstract
A mild data-driven approach for microwave imaging is considered in this paper. In particular, the developed technique relies upon the use of a Newton-type inversion scheme in variable-exponent Lebesgue spaces, which has been modified by including a data-driven operator to enforce the available a-priori information about the class of targets to be investigated. In this way, the performance of the method is improved, and the problems related to the possible convergence to local minima are mitigated. The effectiveness of the approach has been evaluated through numerical simulations involving the detection of defects inside (partially) known objects, showing good results.
Study of 113In(α, α) elastic scattering to determine α-optical potential relevant for astrophysical γ-process
Authors: Basak Dipali , Bar Tanmoy , Sahoo Lalit Kumar , Saha Sukhendu , Rana T K , Bhattacharya C , Manna S , Kundu Samir , Sahoo J K , Meena J K , Saha A K , Mondal Ashok Kumar , Basu Chinmay 
Published: April 2024

Abstract
The α-optical potential is one of the key input parameters used to measure the reaction rate of the (γ, α)-process using the Hauser-Feshbach statistical model and the principle of detailed balance. α-elastic scattering experiment on 113In p-nucleus was carried out in the energy range E
lab = 26−32 MeV. The vacuum evaporation technique was used to prepare the 113In target (∼86 μg cm−2). An energy-dependent local optical potential parameters set was obtained by analysing the experimental elastic scattering angular distribution data. The local potential parameters are extrapolated for lower energies and are used to calculate the 113In(α, γ) reaction cross-section.
Temperature field reconstruction based on femtosecond laser prepared of multiwavelength fibre bragg grating array
Authors: Niu Xiao , Si Jinhai , Chen Tao , Li Yingjie 
Published: April 2024

Abstract
Temperature field measurements are of great significance in industrial production, scientific research, and other fields. Contact temperature measurements usually achieve high spatial resolution by increasing the layout density of the sensors. However, in practical applications, achieving high-density sensor placement is often difficult. Therefore, when the number of sensors is limited, it is necessary to perform function fitting or interpolation of the temperature of the sampling points to reconstruct the temperature field distribution. This study proposed a temperature field reconstruction method based on femtosecond laser prepared multiwavelength fibre Bragg grating (FBG) array. A multiwavelength FBG array was prepared using the femtosecond laser phase mask method combined with stress stretching, and applied for measuring the temperature field distribution in a tubular high-temperature furnace. Cubic spline interpolation and backpropagation (BP) neural networks were used to construct two-dimensional temperature field models for the temperature field distribution data measured by the FBGs, and the prediction accuracies of the two models were compared. The test results show that the root mean square error of the temperature field distribution constructed using the BP neural network is 0.7333 °C, which is approximately 23.18% of the predicted results of the cubic spline interpolation model, indicating that it is a high-precision temperature field reconstruction method.
A simultaneous measurement method for multi-directional galloping of iced bundled conductors and its application in spatial galloping behavior
Authors: Cui Fujiang , Liu Peng , Wang Han , Li Zhiqiang , Xiang Jixin , Xu Jifeng , Ren He 
Published: April 2024

Abstract
The theoretical results indicate that iced bundled conductors experience spatial galloping due to wind-induced vibration, involving in-plane, out-of-plane, and torsional movements. To better understand the dynamic response of this behavior from an experimental perspective, an innovative experimental method has been proposed. The method can simultaneously measure the in-plane, out-of-plane, and torsional vibration signals of iced bundled conductors’ galloping. A testing system was established, and the method is applied in the galloping experiment of continuous iced bundled conductors, validating some theoretical results. This paper describes the construction of a wide-aperture, low-speed wind tunnel suitable for testing the galloping of continuous iced bundled conductors. A ‘cut-bury-glue’ method was proposed to create a model of continuous iced bundled conductors effectively, along with a method for connecting subconductors to improve experimental precision. The tests utilized laser displacement sensors and wireless posture sensors, considering the installation and data collection characteristics of the sensors. Through signal conversion, error correction, and other technical methods, simultaneous measurement of in-plane, out-of-plane, and torsional vibration signals was achieved. The spatial galloping behavior, changing with wind speed, exhibits limited-amplitude and synchronous characteristics. The participation of different modes shows elliptical orbital motion in single-mode galloping and ‘8’ shaped orbital motion in coupled-mode galloping. These results are consistent with previous theoretical research, offering a new approach to studying iced bundled conductors’ galloping.
Head surface strain measurement based wireless bolt sensor with self temperature compensating
Authors: Wang Tao , Zhang Wenxin , Yang Dan , Wang Hu , Lu Guangtao , Lu Mingge 
Published: April 2024

Abstract
Bolt loosening detection is crucial for ensuring the safe operation of equipment. Loosened bolts are hard to detect, and if left undetected, it can lead to catastrophic failures, especially for numerous bolts in large-scale structures. Therefore, the development of distributed bolt monitoring method and related sensors is highly necessary. In this paper, a novel bolt preload sensor with self-temperature compensation is proposed, based on the strain distribution of the bolt head end face. This study enhances previous research by conducting a detailed analysis of strain distribution at the edge of the bolt head surface. The finite element analysis results show that the bolt preload has almost no effect on the circumferential strain in the edge region of bolt head surface. Based on this feature, the strain gauge is applied circumferentially along the edge of the bolt head face as a temperature compensation gauge. In this way, the measuring strain gauge and the temperature compensation gauge can be integrated on the surface of the bolt head, thus achieving self-temperature compensation for the sensor. An experimental device has been established and the experimental results show that the designed sensor has excellent linearity to the bolt preload and effective temperature compensation. For the monitoring of numerous bolts with a wide distribution, a wireless sensing network utilizing the proposed sensor has been designed. The proposed wireless bolt sensor is easy to install and replace, without redesigning or changing the existing structure, thus providing a simple and effective way to monitor large number of bolts with wide distribution.
High precision vertical angle measurement system for calibration of various angle sensors and instruments
Authors: Kim Jong-Ahn , Lee Jae Yong , Kang Chu-Shik , Woo Jae Heun 
Published: April 2024

Abstract
A versatile measurement system was developed for efficient calibration of vertical angle sensors or instruments. The vertical angle measurement system (VAMS) comprises a precision rotation stage, a controller, and additional components for fixing an angle sensor or detecting angular deviation. It can generate single-axis rotation within ±120° range with a resolution of 0.01″. To detect angular deviation in angle instrument calibration, a special miniature autocollimator was developed using a diffractive optical element, providing high-precision angle measurement even with the short focal length of its lens. The performance of the VAMS was evaluated by comparing face angles of two optical polygons, measured by the VAMS and other reference systems. As typical exemplary calibrations, an electronic level and vertical angle readouts of a total station were calibrated with expanded uncertainty (k = 2) of 0.19″ and 0.51″, respectively.
2DLIW-SLAM:2D LiDAR-inertial-wheel odometry with real-time loop closure
Authors: Zhang Bin , Peng Zexin , Zeng Bi , Lu Junjie 
Published: April 2024

Abstract
Due to budgetary constraints, indoor navigation typically employs two-dimensional (2D) LiDAR rather than 3D LiDAR. However, the utilization of 2D LiDAR in simultaneous localization and mapping (SLAM) frequently encounters challenges related to motion degeneracy, particularly in geometrically similar environments. To address this problem, this paper proposes a robust, accurate, and multi-sensor-fused 2D LiDAR SLAM system specifically designed for indoor mobile robots. To commence, the original LiDAR data undergoes meticulous processing through point and line extraction. Leveraging the distinctive characteristics of indoor environments, line–line constraints are established to complement other sensor data effectively, thereby augmenting the overall robustness and precision of the system. Concurrently, a tightly-coupled front-end is created, integrating data from the 2D LiDAR, inertial measurement unit, and wheel odometry, thus enabling real-time state estimation. Building upon this solid foundation, a novel global feature point matching-based loop closure detection algorithm is proposed. This algorithm proves highly effective in mitigating front-end accumulated errors and ultimately constructs a globally consistent map. The experimental results indicate that our system fully meets real-time requirements. When compared to cartographer, our system not only exhibits lower trajectory errors but also demonstrates stronger robustness, particularly in degeneracy problem. We open source our methods here: https://github.com/LittleDang/2DLIW-SLAM.
Performance degradation trend prediction of proton exchange membrane fuel cell based on GA-TCN
Authors: Zhao Ziliang , Shen Senhao , Wang Zhangu 
Published: April 2024

Abstract
To improve the prediction accuracy of the performance degradation trend of proton exchange membrane fuel cell (PEMFC), this paper proposes a temporal convolutional network (TCN) model based on genetic algorithm (GA) optimization to predict the performance degradation trend of PEMFC. Firstly, variational mode decomposition and wavelet threshold denoising algorithms are used to denoise the original data. Then the hyperparameters of the TCN model are optimized by GA, and the GA-TCN model for predicting the performance degradation trend of PEMFC is constructed. Finally, this paper uses the PEMFC stack degradation experimental dataset disclosed in the IEEE PHM 2014 Data Challenge to verify, and compares the proposed model with the backpropagation neural networks model, the long short-term memory model and the classical TCN model. The results show that the proposed method has the highest performance degradation trend prediction accuracy. In particular, when the training dataset accounts for 30%, i.e. the training samples are small, the root mean square error, mean absolute error and mean absolute percentage error of the GA-TCN model are 0.004 726, 0.003 119 and 9.62%, respectively, which are 14.48%, 20.05% and 2.42% lower than that of the classical TCN model. Consequently, this methodology can forecast the degradation trend of PEMFC with high accuracy.
A high-accuracy system model and accuracy evaluation method for transfer alignment
Authors: Yang Jie , Wang Xinlong , Wang Bin , Hu Xiaodong 
Published: April 2024

Abstract
With the increasing demand for high-accuracy navigation, distributed payloads put forward higher requirements for the initial alignment accuracy of their equipped slave inertial navigation systems (INSs). However, the existing transfer alignment models have poor compensation effect on flexible deformation, resulting in low alignment accuracy. Moreover, these models cannot accurately evaluate the accuracy of transfer alignment due to ignoring the errors of the master INS. To address these two problems, a new high-accuracy transfer alignment system model is established by comprehensively considering errors of the master and slave INSs. Further, the propagation characteristics of the master and slave inertial sensor errors in transfer alignment are analyzed, and the relationship expression between the master and slave inertial sensor accuracy and the transfer alignment accuracy is derived. Thus, the accuracy of transfer alignment can be pre-evaluated according to the master and slave inertial sensor accuracy. The experiment results show that the alignment accuracy of the proposed method is improved by more than 71.56% compared with the traditional methods, and the relative errors between the proposed accuracy evaluation method and the Monte Carlo simulations are less than 9.14%. Thus, the correctness, effectiveness and superiority of the proposed method are verified.
Remaining life prediction of rolling bearings with secondary feature selection and BSBiLSTM
Authors: Song Feng , Wang Zhihai , Liu Xiaoqin , Ren Guoai , Liu Tao 
Published: April 2024

Abstract
Rolling element bearings are critical components in rotating machinery. To tackle the problem of difficult to accurately characterize the operating state of rolling bearings caused by irrelevance and varying sensitivity of multiple features to performance degradation, and introduction of subjective errors in determination of hyperparameters of deep learning models, which can affect the accuracy and efficiency of remaining useful life (RUL) prediction. To address these challenges, this paper proposed a novel RUL prediction method for rolling bearings with secondary feature selection and Bayesian optimization of self-attention mechanisms for bidirectional long short-term memory (BSBiLSTM). Firstly, multi-domain features are extracted from noise-reduced vibration signals. Then, a three-criterion constraint-based feature selection algorithm is used and a secondary selection algorithm with Pearson correlation coefficient is proposed to improve data quality. Next, the 3σ criterion is integrated to determine the first prediction time for rolling bearings and to divide the degradation stage. Subsequently, the BiLSTM model with Bayesian optimization and self-attention mechanism is proposed to predict the RUL of rolling bearings to further improve the algorithm efficiency. Finally, experimental validation is carried out based on the PRONOSTIA platform dataset and the XJTU-SY rolling bearing dataset, and the results show that the method proposed in this paper is better than many mainstream life prediction methods for rolling bearings at present, and the prediction accuracy is higher.
Enhanced photoelectrochemical water splitting by a 3D hierarchical sea urchin-like structure: ZnO nanorod arrays on TiO2 hollow hemisphere
Authors: Lee Wei Cheat , Fang Yuanxing , Le Huyen , Hodgson Ronan , Chan Hon Wing Boaz , Qian Rong , Alsohaimi Ibrahim H , Canciani Giacomo E , Alhar Munirah Sulaiman , Chen Qiao 
Published: April 2024

Abstract
A hierarchical sea urchin-like hybrid metal oxide nanostructure of ZnO nanorods deposited on TiO2 porous hollow hemispheres with a thin zinc titanate interface layer is specifically designed and synthesized to form a combined type I straddling and type II staggered junctions. The HHSs, synthesized by electrospinning, facilitate light trapping and scattering. The ZnO nanorods offer a large surface area for improved surface oxidation kinetics. The interface layer of zinc titanate (ZnTiO3) between the TiO2 HHSs and ZnO nanorods regulates the charge separation in a closely coupled hierarchy structure of ZnO/ZnTiO3/TiO2. The synergistic effects of the improved light trapping, charge separation, and fast surface reaction kinetics result in a superior photoconversion efficiency of 1.07% for the photoelectrochemical water splitting with an outstanding photocurrent density of 2.8 mA cm−2 at 1.23 V versus RHE.
Influence of printing parameters and hinge structure on shape memory performance at the hinge in 4D origami structures
Authors: Liu Kecheng , Yang Fei , Wang Xingyu , Yu Han , Zhang Juan , Wang Xingsheng , Mei Xinliang 
Published: April 2024

Abstract
Four-dimensionally (4D) printing technology effectively demonstrates the advantages of portability, foldability, and improved mechanical properties in origami structures. This study highlights the significance of printing process parameters and origami hinge structures in ensuring rapid and complete deployment of origami structures. Polylactic acid based semicrystalline shape memory polymer was utilized to produce 4D printed origami structures. Through response surface analysis methods and bending tests, optimal printing parameters were determined, including layer thickness of 0.31 mm, extrusion speed of 17 mm s−1, and infill percentage of 71%. Additionally, the study designed a U-shaped hinge and analyzed its recovery mechanism through micromorphology of bending hinges and recovery experiments.
A novel memristor Hopfield neural network with homogeneous coexisting multi-scroll attractors
Authors: Huang Lilian , Chen Fangyi , Yang Yan , Zheng Lei 
Published: April 2024

Abstract
Compared to conventional single-scroll or double-scroll attractors, multi-scroll chaotic attractors possess wide potential for application due to their adjustability and complex topology. However, neural network models for generating multiple scrolls are often implemented using memristors with piecewise nonlinear functions. To further explore multi-scroll attractors with different working mechanisms,a unique memristor containing a group of hyperbolic tangent functions is designed and then applied in a three-dimensional Hopfield neural network (HNN). The proposed memristive Hopfield neural network (MHNN) has multi-scroll chaotic attractors, where the number and parity of the scrolls be changed by adjusting the control parameters of the memristor. The complex dynamical behaviors of MHNN are studied by utilizing diverse numerical modeling approaches like bifurcation diagrams, Lyapunov exponents and phase plot. In addition, the proposed MHNN also has a complicated offset boosting coexisting behavior. By selecting suitable parameters, multiple coexisting chaotic attractors could be obtained. Homogeneous coexisting multi-scroll attractors can be shifted in multiple directions including unidirectional, planar and spatial ones. Moreover, theoretically speaking, there could be an infinite number of coexisting attractors. Finally, experimental results are validated through numerical simulations and circuit experiments to confirm the feasibility of the proposed MHNN model.