bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new method for localization the residual stress distribution and enhancement wear resistance through underwater friction stir processing with stationary shoulder
Authors: arezoudar alireza fallahi , hosseini akbar 
Published: December 2023

Abstract
A new method to achieve friction stir processed parts with higher wear resistance and lower residual stress values was presented. The underwater stationary shoulder FSP process (USSFSP) was investigated using experimental and numerical methods. Also, the presented process was compared with the conventional underwater FSP method (UCFSP). An improved CEL simulation method was used to prevent errors in the contact boundary between Eulerian and Lagrangian domains. Heat input was reduced significantly and resulted in a fine microstructure with higher microhardness and lower residual stresses. Temperature field was concentrated around the pin and caused a symmetrical residual stress distribution. The distribution area of Von-Mises and longitudinal stresses during the USSFSP was significantly narrower than the UCFSP process. The stress distribution around the UCFSP tool was compressive and tensile stress was produced at further distances. However, the longitudinal stress distribution in the USSFSP was completely opposite. Tensile stress was generated in the lower part of the thickness and compressive stress in the upper part and in locations away from the centerline. This method caused the formation of a compressive residual stress region and considerably reduced the width of the tensile residual stress zone. By increasing the hardness and decreasing the residual stress level, the USSFSP proved to be instrumental in improving the material's wear resistance. The wear rate in the stir zone undergoes a substantial reduction, decreasing from 5.6x10-3 mm3/m in UCFSP to 1.4x10-3 mm3/ m in USSFSP.
Heavy rainfall concentration changes in South China during the first rainy season of 1965 - 2016
Authors: Li Hui , Zhao Liang , Hu Yamin , yu chao 
Published: December 2023

Abstract

The first rainy season in South China (SC) is a concentrated precipitation season, however, it is unclear that in such concentrated precipitation season, characteristics of heavy precipitation concentration degree in SC during the past 50 years. Based on daily precipitation observation data of 192 stations in SC during the first rainy season (April-June) for 1965–2016, the study investigated and analyzed the temporal and spatial variations of the heavy precipitation concentration index (Q index for short). Results showed that the intensity of heavy precipitation over most regions of SC exhibited an increasing trend. The multi-year mean Q index of the heavy precipitation at different stations varied between 0.3 and 0.6, with an average value of 0.375. The heavy precipitation has a concentration trend in the SC inland and western mountain areas and a dispersion trend in the SC coastal area. Notably, both heavy precipitation and the Q index in most inland and western mountain areas showed an increasing trend, denoting a higher flooding risk there. On the other hand, for the duration of heavy precipitation, the 2-day duration of heavy rainfalls had an increasing trend in inland, western Mountain and East Guangdong. The 3-day duration of rainfalls tended to increase steadily in coastal areas and East Guangzhou, and the 4-day duration of rainfalls increased only in the coastal region. In conclusion, the shorter duration heavy precipitation decreased, while the longer ones increased especially in the coast and East Guangdong, which can contribute to the decrease in the Q index but can not denote a decrease in flooding risks. The possible cause may be attributed to the long-duration heavy rainfall that happened in the West Mountain, East Guangdong and Southeast Coast. On the other hand, the frequency and intensity of ongoing heavy precipitation events in coastal cities of South China have increased significantly.
OSK: Optimal Subsampling Method Based on K-means Clustering for Imbalanced Big Data
Authors: Li Li-li , Xiao Heng , Wang Yu , Shi Haolun , Cao Jiguo 
Published: December 2023

Abstract
While existing methodologies have effectively addressed big data subsampling, there is a noticeable gap in research concerning imbalanced subsampled data. To tackle the unique challenge of low specificity in the minority class within highly imbalanced datasets, we introduce the Optimal Subsampling technique rooted in K-means clustering (OSK). This method is specifically designed for massive yet imbalanced datasets. Our proposed approach employs an optimal subsampling mechanism to extract representative subsamples and utilizes K-means clustering to transform imbal-anced subsamples into multiple balanced datasets. In the spirit of ensemble learning, the OSK method generates predictions by averaging predicted values from multiple models and subsequently derives categorical decisions. The effectiveness of the OSK method is substantiated through extensive simulation studies and a real-world data example. Its superiority is evident in terms of shorter running time and higher classification accuracy when compared to existing methods.
Chelating Effect of Alizarin-Oxalate on La3+ and Nd3+ in Acidic, Basic and Neutral Medium: A DFT study
Authors: Pati Anindita , Kundu T. K. , Pal Snehanshu 
Published: December 2023

Abstract

This study focuses on conducting a comparative study of the extraction capacities of alizarin-oxalate (AR-Ox) ligands with La3+ and Nd3+ in acidic, neutral, and alkaline mediums. Density functional theory (DFT) calculations at ωB97X-D/6-311 + + G(d,p)/SDD level have been performed for structural, thermo-chemical, frontier-orbital (highest occupied molecular orbitals (HOMO) and lowest unoccupied molecular orbitals (LUMO)), natural bond orbital (NBO), reduced density gradient (RDG), and density of state (DOS) analysis for alizarin-oxalate-La(III) (AR-Ox-La) and alizarin-oxalate-Nd(III) (AR-Ox-Nd) complexes. The bonding characteristics of La3+ & Nd3+ ions with alizarin-oxalate ligand have been analysed using the quantum theory of atoms in molecules (QTAIM), revealing the presence of an intermediate type of bond between closed-shell (CS) and shared-shell (SS) electrons in (La/Nd)-O, (La/Nd)-C. The reduced density gradient (RDG) and iso-surface generated through the Multiwfn program shows mostly hydrogen-like and Van-der-Waal interaction between La3+/Nd3+ and oxygen atoms of alizarin-oxalate ligand except for some of the complexes showing the presence of non-bonded/repulsive (La/Nd)-O interaction. Thermo-chemical, DOS, and natural bond orbital analysis reveals alizarin-oxalate-( La3+/Nd3+) complexes in the alkaline medium is more stable than in neutral and acidic medium, and the stability of AR-Ox-Nd complexes is more than AR-Ox-La complexes. This work illustrates the subtle differences in chelating properties of alizarin-oxalate ligands with La3+ and Nd3+ for designing new ligands for efficient selective lanthanide separation.
Gγ-protein GS3 function downstream of OsmiR396/GS2 module to regulate grain size in rice
Authors: Zhu Lin , Shen Yanjie , Dai Zhengyan , Miao Xuexia , Shi Zhenying 
Published: December 2023

Abstract
Rice is one of the main crops in the world which provides food for more than one half world population. Grain size is tightly associated with grain weight, one of the three components determining yield in rice. Quite a few grain size determining quantitative trait loci (QTLs) have been identified, and several grain size-regulating signaling pathways been formed. Here, we showed that in the MIM396 plants, expression of GS3 gene was influenced. Through various biochemical assays, we proved that the miR396/GS2 module could directly regulate the Gg-protein GS3 to regulate grain size. Moreover, tight genetic association existed between miR396/GS2 and GS3. Moreover, other G-protein factors might also be connected with miR396/GS2 module, and thus factors from different pathways form a network to regulate grain size. Therefore, elucidation of these crosstalk would not only further our understanding of the molecular mechanism of grain size regulation, but also help guide modern crop breeding.
A simple electrochemical way to probe the surface area of zinc electrodeposition
Authors: FREVILLE Mathieu BA , ABDELGHANI-IDRISSI Soufiane , TREGOUET Corentin , BRAHMI Youcef , COLIN Annie 
Published: December 2023

Abstract

The electrochemistry of zinc deposition is a crucial topic for the development of disruptive Zn-based energy storage technologies. As example, zinc-air battery is a promising energy-storage system in the context of energy transition: it exhibits impressive properties regarding fabrication cost, recyclability, and energy density performance. However, these systems still suffer from a low cycle life, mainly due to non-homogeneous zinc deposition (such as dendritic growth). The morphologies of deposited zinc are usually observed at the end of the battery life after the opening of the cell. They require costly equipment such as tomography. We propose in this paper an easily implementable method to probe the morphology and the effective surface area of zinc electrodes during the electrodeposition process. This method does not require the need of opening the cell and can be done directly by several electrochemical measurements between the charge phases. This in-situ characterization method allows to investigate the state of a morphology-changing electrode, and thus pave the way for controlling and regulating the morphology of deposited zinc in real-life battery applications. This outcome could lead to curative electrochemical protocols, in order to limit and prevent the short-circuit caused by non-homogeneous metallic deposition and thus extend battery life.
Enhanced accuracy through machine learning-based simultaneous evaluation: a case study of RBS analysis of multinary materials
Authors: Magchiels Goele , Claessens Niels , Meersschaut Johan , Vantomme André 
Published: December 2023

Abstract
We address the high accuracy and precision demands for analyzing large in situ or in operando spectral data sets. A newly developed dual-input artificial neural network (ANN) algorithm enables the compositional and depth-sensitive analysis of multinary materials by simultaneously evaluating spectra collected under multiple experimental conditions. To validate the developed algorithm, a case study was conducted analyzing complex Rutherford backscattering spectrometry (RBS) spectra collected under two scattering geometries. The dual-input ANN analysis excelled in providing a systematic analysis and precise results, showcasing its robustness in handling complex data and minimizing user bias. A comprehensive comparison with human supervision analysis and conventional single-input ANN analysis revealed a reduced susceptibility of the dual-input ANN analysis to inaccurately known setup parameters, a common challenge in material characterization. The developed multi-input approach can be extended to a wide range of analytical techniques, in which the combined analysis of measurements performed under different experimental conditions is beneficial for disentangling details of the material properties.
A Deep Learning Approach to ECG Classification for Atrial Fibrillation, Normal Sinus Rhythm, and Congestive Heart Failure.
Authors: Sadigale Rohit , Nale Saurabh , Kamble Pawan , Wanave Rohit 
Published: December 2023

Abstract
Deep learning approaches for medical signal processing have received a lot of interest in recent years, especially in the field of electrocardiogram (ECG) categorization. This study investigates the effectiveness of using the AlexNet architecture, a well-known convolutional neural network (CNN) model, for reliable ECG data categorization. Our suggested model has attained an accuracy of 99.21% after extensive experimentation and validation, indicating its ability to distinguish various cardiac anomalies. The research includes the creation and refinement of a unique AlexNet-based CNN model adapted to the difficulties of ECG data. Using AlexNet's deep hierarchical features, our model outperforms in capturing complicated patterns and minor fluctuations within ECG data.
RCoCo: Contrastive Collective Link Prediction across Multiplex Network in Riemannian Space
Authors: Sun Li , Li Mengjie , Yang Yong , Li Xiao , Liu Lin , Zhang Pengfei , Du Haohua 
Published: December 2023

Abstract
Link prediction typically studies the probability of future interconnection among nodes with the observation in a single social network. More often than not, real scenario is presented as a multiplex network with common (anchor) users active in multiple social networks. In the literature, most existing works study either the intra-link prediction in a single network or inter-link prediction among networks (a.k.a. network alignment), and consider two learning tasks are independent from each other, which is still away from the fact. On the representation space, the vast majority of existing methods are built upon the traditional Euclidean space, unaware of the inherent geometry of social networks. The third issue is on the scarce anchor users. Annotating anchor users is laborious and expensive, and thus it is impractical to work with quantities of anchor users. Herein, in light of the issues above, we propose to study a challenging yet practical problem of Geometry-aware Collective Link Prediction across Multiplex Network. To address this problem, we present a novel contrastive model, RCoCo, which collaborates intra- and inter-network behaviors in Riemannian spaces. In RCoCo, we design a curvature-aware graph attention network (κ−GAT), conducting attention mechanism in Riemannian manifold whose curvature is estimated by the Ricci curvatures over the network. Thereafter, we formulate intra- and intercontrastive loss in the manifolds, in which we augment graphs by exploring the high-order structure of community and information transfer on anchor users. Finally, we conduct extensive experiments with 14 strong baselines on 8 real-world datasets, and show the effectiveness of RCoCo.
The impact of mobility costs on cooperation and welfare in spatial social dilemmas
Authors: Bara Jacques , Santos Fernando P. , Turrini Paolo 
Published: December 2023

Abstract

From over-exploitation of resources to urban pollution, sustaining well-being requires solving social dilemmas of cooperation. Often such dilemmas are studied assuming that individuals occupy fixed positions in a network or lattice. In spatial settings, however, agents can move, and such movements involve costs. Here we investigate how mobility costs impact cooperation dynamics. To this end, we study cooperation dilemmas where individuals are located in a two-dimensional space and can be of two types: cooperators – or cleaners, who pay an individual cost to have a positive impact on their neighbours – and defectors – or polluters, free-riding on others effort to sustain a clean environment. Importantly, agents can pay a cost to move to a cleaner site. Both analytically and through agent-based simulations we find that, in general, introducing mobility costs increases pollution felt in the limit of fast movement (equivalently slow strategy revision). The effect on cooperation of increasing mobility costs is non-monotonic when mobility co-occurs with strategy revision. In such scenarios, low (yet non-zero) mobility costs minimise cooperation in low density environments; whereas high costs can promote cooperation even when a minority of agents initially defect. Finally, we find that heterogeneity in mobility cost affects the final distribution of strategies, leading to differences in who supports the burden of having a clean environment.
Seismic response of a cliff-attached structure based on soil-structure interaction effect
Authors: Yang ZhuoXin , Mi Yuancheng , Li Hong , Ban Tao , Liu Kai , Guo Zhongfa , Liu Dewen , Sun Weiwei 
Published: December 2023

Abstract
Cliff-attached constructions denote structures that align with and directly attach to the vertical interface of a mountain cliff. Cliff-attached constructions, compared to conventional building structures, become particularly complex when considering the interaction between soil and structure due to the unequal height of the grounded foundation, which requires deeper studies. This study investigates the influence of variable parameters in soil-structure interaction (SSI) on the seismic performance of such structures, based on which three different models of cliff-attached structures were established for analysis,naming,the cliff-attached structure without SSI effects,the model with traditional SSI effects,and the model with variable-parameter SSI effects. Seismic inputs are assessed in both transverse and down slope directions. The results indicate that the seismic response of cliff-attached structures increases as shear wave velocity decreases while considering SSI effects. SSI effects are most pronounced on the upper grounding layers of the structure and its upper layers' dynamic response. Seismic responses in the down slope direction are markedly lower than those in the transverse direction. In the transverse direction，cliff-attached structures display pronounced torsional effects, resulting in a torsional effect roughly 1.4 times greater at a shear wave velocity of 100 m/s than at 700 m/s. The upper grounding layers of cliff-attached structures experience significant deformation and stress concentration. Considering variable-parameter SSI effects in mountainous regions results in more accurate and reliable seismic responses for cliff-attached structures.
Autism spectrum disorder and the risk of supernumerary teeth: A multi-centre cohort study
Authors: Lai Yin-Chen , Chan Shu-Yen , Misra Madhusmita , Magee Jennifer A , Liu Chia-Chien , Neumeyer Ann M , Ma Kevin Sheng-Kai 
Published: December 2023

Abstract
Aim To assess whether children diagnosed with autism spectrum disorder (ASD) are at risk of having supernumerary teeth (SNT).
Methods This was a multi-centre cohort study conducted between 2008 and 2022 to recruit children under 18 years of age who were previously diagnosed with ASD. Data on age, sex, and comorbidities history were collected and adjusted in the analyses. Cox proportional hazard models and Kaplan-Meier analyses were conducted to determine the effect of ASD on SNT. Subgroup analyses based on age and sex stratification were developed to identify at-risk subpopulations.
Results A total of 3,372 cases with ASD and 25,942 controls were included. 15 children with ASD and 31 non-ASD children were diagnosed with SNT during the follow-up period. The incidence of SNT was significantly higher in the ASD group compared to the non-ASD group (1.827 vs 0.671 per 1000 person-years, adjusted hazard ratio, aHR=2.583, 95% CI=1.312-5.085). Male children with ASD (aHR=3.863, 95% CI=1.781-8.376) and children with ASD aged below 6 years (aHR= 2.655, 95% CI=1.117-6.308) presented with significant risk of having SNT.
Conclusions Male children with ASD are at a significant risk of SNT. Screening for children with ASD may facilitate early detection and management of ASD-associated SNT.
Heart Sounds Generate the Fine Structure Constant Which Turns Out to be the Gate of Entrance to a Hidden New Dimension of Time
Authors: Almohammadi Ramzi 
Published: December 2023

Abstract
Classical mechanics and quantum mechanics are two major fields of physics that describe reality through the structure of matter. Classical mechanics describes reality as deterministic, while quantum mechanics describes it as probabilistic. Since both describe reality by separate unrelated laws of physics, there is no theory fits both fields into a single framework that makes the two mathematically compatible in the context of the standard model of physics. During the advancement in both fields, a dimensionless number that has been incredibly mysterious and unbelievably important starts to appear everywhere in quantum physics. This number is called the fine structure constant. Here we show how the human heart plays an important physiological role in generating the numeric value of this mysterious number which turns out to be a port of entry to a new undiscovered dimension of time differs from the cosmological time. This finding led us to discover a mechanism in the universe that mirrors the physiological process of the heart to generate similar hidden dimension of time for the universe, which turns out to be the key that connects both classical mechanic & quantum mechanics in single framework
Study on the digital transformation of China's manufacturing and the development of real economy
Authors: Jin Haipan , Hu Zhenhua , Zhu Minxun , Wei Min , Liu Wenwen 
Published: December 2023

Abstract
At present, China's economy is in a period of shifting gears and transformation, and the original competitive advantages of the manufacturing industry are gradually declining, resulting in a continued decline in the entity investment rate. This article starts from the perspective of digital transformation that releases "entity" potential or intensifies the competitiveness of "virtual" characteristics of manufacturing, constructs a theoretical framework for digital transformation to empower manufacturing enterprises to materialize or intensify financialization, and systematically explains and reveals the digital transformation of manufacturing enterprises Impact on the real economy. Studies have shown that the digital transformation of manufacturing companies has significantly improved their adherence to their main business and entity investment and restrained them from "moving from the real to the virtual". Further research found that enhancing the core competitiveness of manufacturing enterprises' products, expanding the strategic layout of the market and operating management efficiency are important paths for the digital transformation of manufacturing enterprises to empower entity development. In addition, this study provides policy reference for effectively promoting the integration of the digital economy and the real economy, realizing the high-end transformation of the global value chain, and preventing the potential risks of "Shift from real to virtual".
OrthoFlow: phylogenomic analysis and diagnostics with one command
Authors: Turnbull Robert , Steenwyk Jacob L. , Mutch Simon J. , Scholten Pelle , Salazar Vinícius W. , Birch Joanne L. , Verbruggen Heroen 
Published: December 2023

Abstract
Species trees, which depict the evolutionary relationships among organisms, underlie many evolutionary studies. Phylogenomics, the use of genome-scale datasets for phylogenetic inference, is the current gold standard for species tree inference. The development, maintenance, and execution of phylogenomic workflows is challenging, requiring programming, data management skills, and familiarity with changing best practices. We introduce OrthoFlow, a software wherein a single command automatically conducts end-to-end phylogenomic analysis—orthology inference and identification of phylogenomic markers, quality control, data matrix construction, diagnostics, and tree inference using supermatrix and supertree methods from multiple input data formats. To demonstrate the utility of OrthoFlow, we successfully recapitulate the evolutionary relationships among 24 yeast species. OrthoFlow increases the accessibility of researchers to conduct rigorous phylogenomic analysis flexibly. OrthoFlow is freely available from PyPI (https://pypi.org/project/orthoflow/), Bioconda (https://anaconda.org/bioconda/orthoflow) and GitHub (https://github.com/rbturnbull/orthoflow) under the Apache License 2.0.