bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comment on ‘Covariant formulation of electrodynamics in isotropic media’
Authors: Franklin Jerrold 
Published: November 2023

Abstract
A recent paper Pal (2022 Eur. J. Phys.
43 015204) puts into covariant form an extension of relativistic classical electromagnetism from free space to a linearly polarizable medium. The paper is based on the assumption that the relativistic tensor, F

μ
ν
, which is a tensor in free space, will also be a tensor in a linear medium. Pal constructs a proposed covariant formulation that gives

F

μ
ν

for a medium moving with constant velocity, v, in terms of F

μ
ν
for the medium at rest. However, although F

μ
ν
has been proven to be a relativistic tensor in free space, we show that it is not a tensor in a linear medium. This means that the covariant extension proposed by Pal fails.
Bayesian acoustic emission time-difference method for locating the collision point of sweeping robot shell
Authors: Chen Zihui , Li Zhinong , Gu Fengshou 
Published: November 2023

Abstract
The existing source localization based on acoustic emission technology often depends on the assumption of constant wave velocity inside the material. However, this assumption is hardly satisfied in actual engineering. The uncertainty of wave velocity can easily lead to low localization accuracy of sweeping robots. To overcome these deficiencies, a complete probability multi-directional measurement method based on the Bayesian inference mechanism is proposed. In the proposed method, based on the Bayesian probabilistic model, the extracted sensor time difference is subjected to probabilistic inference using the coordinate input model to determine the posterior distribution of the source’s position and wave velocity of the given arrival time. Compared with the traditional time-difference method, the proposed method achieves excellent results and outperforms the standard time-difference method in localization accuracy and anti-interference. In addition, the proposed method can conveniently, quickly, and effectively determine the location of the colliding point without considering the source emission time and wave velocity. The research in this paper provides an effective method for solving the collision localization problem of the sweeping robot shell under the acoustic emission time and wave velocity are unknown.
Enhancing measurement precision through noise analysis and fringe optimization in white light interferometry
Authors: Xu Zhenyu , Chen Zhangwei 
Published: November 2023

Abstract
White light interferometry is a highly precise non-contact measurement technology. However, in practical applications, certain noise sources such as thermal noise, shot noise, and redundant intensity noise, can introduce signal distortion. In this paper, we propose a mathematical formulation for the key metric, Quality Factor, which serves as an evaluation criterion for the quality of interference signals. The influence of different factors on the measurement is quantitatively analyzed using the signal-to-noise ratio, and various filtering methods are employed to address different types of noise. Finally, experimental results highlight the primary factors and optimization techniques that significantly impact the quality of interference signals, thereby verifing the effectiveness of the method on measurement results.
Calcium regulates cortex contraction in Physarum polycephalum
Authors: Kscheschinski Bjoern , Kramar Mirna , Alim Karen 
Published: November 2023

Abstract
The tubular network-forming slime mold Physarum polycephalum is able to maintain long-scale contraction patterns driven by an actomyosin cortex. The resulting shuttle streaming in the network is crucial for the organism to respond to external stimuli and reorganize its body mass giving rise to complex behaviors. However, the chemical basis of the self-organized flow pattern is not fully understood. Here, we present ratiometric measurements of free intracellular calcium in simple morphologies of Physarum networks. The spatiotemporal patterns of the free calcium concentration reveal a nearly anti-correlated relation to the tube radius, suggesting that calcium is indeed a key regulator of the actomyosin activity. We compare the experimentally observed phase relation between the radius and the calcium concentration to the predictions of a theoretical model including calcium as an inhibitor. Numerical simulations of the model suggest that calcium indeed inhibits the contractions in Physarum, although a quantitative difference to the experimentally measured phase relation remains. Unraveling the mechanism underlying the contraction patterns is a key step in gaining further insight into the principles of Physarum’s complex behavior.
Experimental characterization of the active and passive fast-ion H-alpha emission in W7-X using FIDASIM
Authors: Poloskei Peter Zs. , Geiger B. , Jansen van Vuuren A. , Äkäslompolo S. , Ford O.P. , Spanier A. , Neelis T.W.C. , McNeely P. , Hartmann D. ,   the W7-X Team
Published: November 2023

Abstract
This paper presents the first results from the analysis of Balmer-alpha spectra at Wendelstein 7-X which contain the broad charge exchange emission from fast-ions. The measured spectra are compared to synthetic spectra predicted by the FIDASIM code, which has been supplied with the 3D magnetic fields from VMEC, 5D fast-ion distribution functions from ASCOT, and a realistic Neutral Beam Injection geometry including beam particle blocking elements. Detailed modeling of the beam emission shows excellent agreement between measured beam emission spectra and predictions. In contrast, modeling of beam halo radiation and Fast-Ion H-Alpha signals (FIDA) is more challenging due to strong passive contributions. While about 50% of the halo radiation can be attributed to passive signals from edge neutrals, the FIDA emission—in particular for an edge-localized line of sights—is dominated by passive emission. This is in part explained by high neutral densities in the plasma edge and in part by edge-born fast-ion populations as demonstrated by detailed modeling of the edge fast-ion distribution.
Explosive dynamics of double tearing mode in Tokamak
Authors: Lu X.Q. , Ma Z.W. , Guo W. , Zhang H.W. , Guo W.F. 
Published: November 2023

Abstract
Using the CLT code, the resistivity dependence of the reconnection rate during the explosive phase at various separations of two rational surfaces of m/n = 3/1 double tearing mode is investigated quantitatively. Our study focuses on the explosive reconnection process where the exchange of island positions takes place and no secondary island forms. The negative dependence of explosive reconnection rate on resistivity in low resistivity and the systematic study of the effect of the separation on the resistivity dependence in high resistivity have been studied for the first time. The negative dependence is qualitatively different from the results in some relative studies where it usually exhibits a positive dependence on the resistivity or is independent of the resistivity. The negative dependence in two regions with a low resistivity, with a high resistivity and a large separation is caused by different reasons: one is the thickness of the current sheet, and the other is the separation.
Research on the Operation Resistance of Bulldozers Based on Joint Simulation Technology
Authors: Li Bei , Liu Mingjie , Sun Ping , Zang Xiaohui 
Published: November 2023

Abstract
With the proposal of the national double-carbon policy, the pressure of energy saving and emission reduction of construction machinery is increasing. As one of the core products of construction machinery, bulldozers are criticized for their high operation carbon emissions and large fuel consumption. How to reduce the operation resistance and resistance work of bulldozers has become a new research direction of energy saving and emission reduction of bulldozers. How to obtain the resistance and resistance work of bulldozer operation has become one of the difficulties in this research direction. In this paper, the multi-body dynamic model and the ground model of the bulldozer are built by multi-body dynamics software, the discrete element model of soil is built by discrete element software, and the simulation of the bulldozers under direct pushing conditions is completed by using multi-body dynamics and discrete element joint simulation technology. The resistance curve of the bulldozer operation is obtained, which indicates that the maximum resistance is 236.55 KN, the resistance work is 6360.42 KJ, and the soil pushing amount is 41.37. The simulation data and results will help reduce the operational resistance and resistance work of the bulldozer.
Fusion of label attention and mask attention for unsupervised and self-supervised anomaly detection
Authors: Pang Fengqian , Lei Chunyue , Zeng Jingsheng 
Published: November 2023

Abstract
Many uncontrollable factors during industrial flow lead to products’ unforeseen defects. Anomaly detection for the defects is inclined to use unsupervised frameworks to formulate one-class classification tasks, as well as segmentation tasks. The attention mechanism has proved to be effective in defect classification and segmentation. However, previous works with attention mechanisms have not investigated the combination of the two kinds of attention for the above tasks in depth. In this paper, we propose a framework that fuses Label Attention and Mask Attention (LAMA). Specifically, the LAMA is implemented by a loss prediction module and a decoder in a transform-based segmentation model. Moreover, a network based on normalizing flow is introduced as the architecture of anomaly detection, which integrates with the LAMA. The experimental results on MVTecAD demonstrate that the proposed method outperforms the contrast algorithms. The proposed framework achieves an overall image-wise AUC of 99.6% and 98.6% on pixel-wise AUC.
Construction and Verification of Two-dimensional Surface Hydraulic Calculation Model
Authors: Zheng Haiya , Fang Xuezhi , Xiao Jianhua , Liu Rongwei , Zhang Yancheng , Yu Jiangshun , Cai Yun 
Published: November 2023

Abstract
Accurate calculation and prediction of the surface water flow process in the substation and surrounding area is of great significance for on-site flood control and disaster reduction of the substation. Aiming at the dynamic process of surface water flow, this paper simulated the spatial and temporal distribution characteristics of surface water flow by establishing a two-dimensional hydrodynamic calculation model. Based on the N-S equation, a finite volume integral method is used to discrete the governing equations. On this basis, a hydrodynamic model for two-dimensional numerical simulation of surface water flow is constructed, and the basic governing equations for two-dimensional surface shallow water movement are derived. Based on the Toce River physical model and water flow control experiment, the corresponding conceptual model is established. On this basis, the measured data are used to verify the constructed two-dimensional surface hydrodynamic calculation model. The results show that the model can better simulate the process of surface water flow and describe its temporal and spatial distribution characteristics. The research results will provide effective technical support for substation site flood warning and prevention.
A two-dimensional hydrodynamics prediction framework for mantle-undulated propulsion robot using multiple proper orthogonal decomposition and long short term memory neural network
Authors: Ying Zixiang , Zhang Haozhi , Wang Linxiang , Melnik Roderick 
Published: November 2023

Abstract
In this paper, a deep learning based framework has been developed to predict hydrodynamic forces on a mantle-undulated propulsion robot (MUPRo). A multiple proper orthogonal decomposition (MPOD) algorithm has been proposed to efficiently identify fluid features near the undulating mantle of the MUPRo globally and locally. The results indicate that the

L
2

error of the solution states near the undulating boundary of the proposed MPOD algorithm converges almost linearly to 0.2%. Furthermore, a hydrodynamics prediction framework has been developed based on the proposed MPOD algorithm, where a long short-term memory neural network predicts the temporal coefficients of the MPOD spatial modes. The developed framework achieves economical and reliable predictions of hydrodynamic forces acting on the undulating boundary compared to simulations and experiments. Moreover, the

L
2

error of the developed framework is one to two orders of magnitude lower than that of the frameworks based on the classical POD algorithm when the degrees of freedom are consistent. Finally, the reliability of the proposed MPOD-NIROM is discussed through an offline parameter planning case of an aquatic-inspired robot. The model presented in this paper can provide support for the offline parameter planning of aquatic-inspired robots.
Investigating and forecasting the impact of crop production shocks on global commodity prices
Authors: Zelingher Rotem , Makowski David 
Published: November 2023

Abstract
In this study, we investigate and forecast the impact of crop production shocks on the global prices of three major international agricultural commodities: maize, soybean, and cocoa. We perform a thorough assessment of the forecasting performances of five econometric and machine learning models using 60 years of data. First, we train the models on production and price data to forecast the monthly price variations for each crop separately considering different time horizons. Next, we implement a cross-validation procedure to identify the models with the most accurate forecasting ability for each crop. After choosing the best forecaster, we identify the most influential producing areas using several local and global model-agnostic interpretation tools. Our findings indicate significant differences among commodities in terms of prediction accuracy, with cocoa exhibiting a higher level of prediction error compared to less volatile markets like maize and soybean. Our results reveal a significant influence of Northern America’s maize and soybean production on the global prices of these commodities. The effects of production on prices are asymmetrical: small decreases in US production lead to substantial price increases, while small increases in production do not systematically decrease prices. In contrast, cocoa price variations are influenced by production coming from several regions, not from a single one.
Adjustable accuracy of phase steps in a double-aperture common-path interferometer by rotating and displacing grating at Fourier plane
Authors: Castillo-Luna Marymar , Meneses-Fabian Cruz 
Published: November 2023

Abstract
Detuning errors limit the accuracy of phase extraction in phase-shifting interferometry (PSI). To improve this accuracy, the systems to control the phase shifter dispositive must be highly accurate, involving high technology, exhaustive calibration, high cost, and constant maintenance. This manuscript presents a method for improving the accuracy of the phase step generated at the Fourier plane by rotating and displacing the grating in a double-aperture common-path interferometer. It is demonstrated that both the detuning errors and the phase extraction uncertainty decrease with the grating rotation angle. The experimental implementation is very simple, as it does not require any sophisticated mechanical system to improve the accuracy, no exhaustive calibration, and no additional mechanical and optical dispositive. With the present approach, the PSI method could be achieved very simply and with high accuracy by using a low-accuracy actuator. The theoretical model and experimental results are presented to demonstrate the present proposal.
Preparation and performance of surface-modified adsorbent materials from discarded traditional chinese medicine residues
Authors: Ren Junpeng , Wang Yu , Zhou Jinkang , Hu Lanxin 
Published: November 2023

Abstract
Waste Chinese medicine residue was used as a raw material and pretreated with sodium hydroxide and hydrogen peroxide, followed by chemical modification with a silane to prepare an inexpensive and highly efficient hydrophobic biobased adsorbent material. The adsorbent was characterized with SEM, TGA and FTIR analyses. The adsorption capacities and wettabilities of the Chinese medicine residue were analyzed before and after surface modification to explore the adsorption performance and surface modification mechanism of the material. The results showed that the modified Chinese medicine residue was rough and uniformly modified, with successfully grafted hydrophobic functional groups, and it had added adsorption sites, exhibiting good hydrophobicities and oleophilicities. The contact angles between the modified materials and water reached 127°, and the modified Chinese medicine residue had an adsorption capacity of 51.7 mg g−1 for organic compounds, which was a significant improvement over the original waste Chinese medicine residue. The adsorption kinetics were best described with the pseudosecond-order kinetic model, which exhibited a higher linear correlation and was closer to the measured adsorption equilibrium value based on chemical adsorption. This study demonstrated a novel use of waste Chinese medicine residues for environmental remediation.
Mechanical, and corrosive properties of AA7075 aluminium reinforced with rice husk ash particulates
Authors: Joseph O O , Dirisu J O , Atiba J , Ante S , Ajayi J A 
Published: November 2023

Abstract
The mechanical and corrosive properties of AA7075 alloy reinforced with rice husk ash (RHA) particles were studied. AA7075 matrix composite reinforced with varying percentage weight compositions of rice husk ash particles (5%, 10%, 15% and 20% wt) were prepared using stir casting technique. The mechanical, and corrosive properties of the new material were compared with that of the matrix alloy. The mechanical properties were also improved due to the automated stirring action employed during the casting process. AA7075-RHA composites had improved mechanical properties compared to the base matrix (AA7075). In terms of tensile strength there was an increase in this mechanical property with an increase in weight fraction of RHA particle (51% at 10% RHA), hardness (25.67% at 20% RHA), while for impact resistance, the reverse was the case as there was a 10.98% decrease in the resistance of the composite as the reinforcement fraction increased from 0%–20% RHA. The progressive decreases in the impact strength of the reinforced composite were probably due to agglomeration and non-homogeneity at higher reinforcements values. Corrosion experiments carried out showed improved properties in the reinforced composite compared to the unreinforced alloy, the corrosive properties improved with an increase in weight fraction of RHA in terms of weight loss (20.63% at 10% RHA), potentio-dynamic polarization (30.7% at 10% RHA) and open circuit potential (1.60% at 20%RH). The developed composite may be suitably applied in high-speed rotating shafts and automotive engineered brake parts.
Chemical structure-aware molecular image representation learning
Authors: Xiang Hongxin , Jin Shuting , Liu Xiangrong , Zeng Xiangxiang , Zeng Li 
Published: November 2023

Abstract
Current methods of molecular image-based drug discovery face two major challenges: (1) work effectively in absence of labels, and (2) capture chemical structure from implicitly encoded images. Given that chemical structures are explicitly encoded by molecular graphs (such as nitrogen, benzene rings and double bonds), we leverage self-supervised contrastive learning to transfer chemical knowledge from graphs to images. Specifically, we propose a novel Contrastive Graph-Image Pre-training (CGIP) framework for molecular representation learning, which learns explicit information in graphs and implicit information in images from large-scale unlabeled molecules via carefully designed intra- and inter-modal contrastive learning. We evaluate the performance of CGIP on multiple experimental settings (molecular property prediction, cross-modal retrieval and distribution similarity), and the results show that CGIP can achieve state-of-the-art performance on all 12 benchmark datasets and demonstrate that CGIP transfers chemical knowledge in graphs to molecular images, enabling image encoder to perceive chemical structures in images. We hope this simple and effective framework will inspire people to think about the value of image for molecular representation learning.
Efficient prediction of peptide self-assembly through sequential and graphical encoding
Authors: Liu Zihan , Wang Jiaqi , Luo Yun , Zhao Shuang , Li Wenbin , Li Stan Z 
Published: November 2023

Abstract
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.
Rare variant association on unrelated individuals in case–control studies using aggregation tests: existing methods and current limitations
Authors: Boutry Simon , Helaers Raphaël , Lenaerts Tom , Vikkula Miikka 
Published: November 2023

AbstractOver the past years, progress made in next-generation sequencing technologies and bioinformatics have sparked a surge in association studies. Especially, genome-wide association studies (GWASs) have demonstrated their effectiveness in identifying disease associations with common genetic variants. Yet, rare variants can contribute to additional disease risk or trait heterogeneity. Because GWASs are underpowered for detecting association with such variants, numerous statistical methods have been recently proposed. Aggregation tests collapse multiple rare variants within a genetic region (e.g. gene, gene set, genomic loci) to test for association. An increasing number of studies using such methods successfully identified trait-associated rare variants and led to a better understanding of the underlying disease mechanism. In this review, we compare existing aggregation tests, their statistical features and scope of application, splitting them into the five classical classes: burden, adaptive burden, variance-component, omnibus and other. Finally, we describe some limitations of current aggregation tests, highlighting potential direction for further investigations.