bluuebunny/crossref_metadata_2025_split huggingface dataset:
Preface
Authors: Dai Wanyang 
Published: October 2024

As an annual academic conference, ICPMS has been successfully held over the last 7 years in Shanghai, Hangzhou, Yichang and online platform, attracted delegates from more than 10 countries and regions including China, Thailand, Japan, the United States, South Korea.
During May 23-25, 2024, the 7th International Conference on Physics, Mathematics and Statistics (ICPMS2024) was successfully held in Yichang, China and online platform! This conference included keynote speeches, invited speeches, oral and poster presentations, and featured multi-topic special sessions, including: Fusion and Plasma Physics, Gravitation and Cosmology, Non-localized quantum mechanics and quantum entanglement, Engineering Thermophysics, Electrical Materials and Applications.
Prof. Lixing Zhou from Tsinghua University, Prof. Qiwei Zhan from Zhejiang University and Prof. Fang Wang from Beihang University attended the conference and delivered keynote speeches. In addition, experts and scholars from the University of Illinois Urbana Champaign, Surgut State University, Chinese Academy of Sciences, Huazhong University of Science and Technology, Harbin Institute of Technology, Northeastern University, and other universities and institutes shared more than 20 wonderful speeches.
This conference proceedings included 22 accepted articles selected from 54 submissions, all the papers have been through rigorous review and process to meet the requirements of international publication standard. We would like to express our gratitude to the reviewers of these manuscripts, who provided constructive criticism and stimulated comments and suggestions to the authors. We are extremely grateful as organizers, technical program committee and editors and extend our most sincere thanks to all the authors for their excellent contribution and work. Our sincere gratitude also goes to the IOP Publishing editors and managers for their helpful cooperation during the preparation of the proceedings.
On behalf of the Organizing Committee of ICPMS2024.
List of Technical Program Committee is available in this Pdf.
A coupling method for CRUD mass calculation based on Eulerian-Eulerian two-fluid model
Authors: Cao Chengque , Li Huaibin 
Published: October 2024

Abstract
The corrosion products occur in the pressure boundary in the pressurized water reactor primary loop which is known as CRUD. CRUD could increase the risks of crud induced power shift and crud induced localized corrosion. The mechanism of CRUD production depends on nucleate boiling. Eulerian-Eulerian two-fluid model has been currently proved as effective means to understand flow boiling character. In this research, the input data of thermo-hydraulic parameter is obtained by CFD method. The CRUD mass has been calculated based on thermo-hydraulic and chemical coupled model. The main calculation code in this article is FLUENT and CAMPSIS. The calculation results show that the CRUD does most likely occur the 3rd grid mixing vanes from the top of the fuel rod. The maximum CRUD mass is closer to the position of onset of nucleate boiling.
Evaluation of geological hazard susceptibility in Xixia Xichuan area based on support vector machine
Authors: Zhou Xin , Zhu Kun , Zhang Enjing , Lu Yaobang , Fu Yongpeng 
Published: October 2024

Abstract
Geological hazards seriously affect the personal and property safety of regional residents, in the Xichuan area, a real-time monitoring and early warning system could have provided timely alerts before the landslide, potentially averting the tragic loss of life and destruction.So it is very important to carry out geological hazard susceptibility assessment and hazard prevention. The Xixia area of Xichuan is selected as the study area, and the support vector machine model is used to evaluate the susceptibility of geological hazards in the study area. Through Kendall factor correlation analysis, the susceptibility evaluation indexes of the study area are selected, the evaluation index system of the study area is established, and the thematic layers and attribute data of the evaluation indexes are extracted through GIS technology. The MATLAB platform was used to establish linear kernel function (LN-SVM), polynomial kernel function (PL-SVM), radial basis kernel function (RBF-SVM), sigmoid kernel function (Sigmoid-SVM) based on the four support vector machine models of the study area were used to evaluate and classify the susceptibility of geological hazards. ROC curves were plotted to check the accuracy of the evaluation results of the four kernel functions, and the most suitable kernel function model for the study area was selected. The results show that the RBF-SVM model has high evaluation accuracy and is most suitable for the evaluation of geological hazard susceptibility in the study area. Finally, GIS technology was applied to partition the evaluation results into susceptibility zones. The evaluation results show that 86.14% of the geological hazards in Xixia Xichuan area are of medium and high susceptibility, and the evaluation results have certain practical significance for the prevention and control of geological hazards in Xixia Xichuan area.
Degradation mechanism of SiC diodes under thermal and irradiation stress
Authors: Chen Fan , Kong Zebin , Wang Kunshu , Qin Linsheng , Liu Yuan 
Published: October 2024

Abstract
Silicon carbide (SiC)-based diodes are widely used due to their high temperature resistance. In this paper, breakdown voltage of 4H-SiC JBS diodes and capacitance-voltage of 4H-SiC MOS capacitors with identical interfacial structure were both measured at high temperature to study their interfacial properties. By analyzing the variation of interfacial properties, the correlation between thermal stress and failure mode of 4H-SiC JBS diodes was further established to reveal their degradation mechanism. During initial high temperature storage, interfacial negative effective charge density of 4H-SiC JBS diodes may increase, leading to the decrease of breakdown voltage. As storage time increases, interfacial charge density began reducing, resulting in the increase of breakdown voltage. Avalanche luminescence verifies that the variation of interfacial charge density under thermal stress led to the degradation of 4H-SiC JBS diodes and further affected their breakdown voltage. Finally, degradation mechanism of SiC-JBS, SiC-SBD and SiC-PIN diodes under irradiation stress was investigated. After irradiation, forward current of some SiC diodes increased at a small forward voltage. However, the reverse characteristics deteriorated obviously and even failed.
Design and validation of a DMA-inspired device for in-vivo measurement of human skin mechanics: a finite element analysis approach
Authors: Yang Guyeli , Pang Xin , Liu Anchen , Li Congcong , Zhu Jun , Huang Ran , Ma Xin 
Published: October 2024

Abstract
This study introduces a novel method for measuring the mechanical properties of human skin in-vivo, inspired by dynamic mechanical analysis (DMA). We designed and applied a DMA-like device to living human skin and validated its functionality through finite element analysis (FEA) on an ideally elastic skin model. The device features multiple testing modes, including horizontal movement in both same and opposite directions with optional pre-stretching. Stress-strain behaviours obtained from FEA align with initial setup parameters, serving as powerful tools for further research into the mechanical properties of skin. This method is expected to have potential significance in biomechanical theory, clinical, and healthcare applications.
Investigation into the Suppression Effect of Water Mist on the Self-ignition and Flame Propagation of High-pressure Hydrogen Release
Authors: Yu Ke , Hu Yong , Gao Chunyan , Jiang Yong 
Published: October 2024

Abstract
The self-ignition accident of pressurized hydrogen leakage is regarded as one of the big potential risks in its wide utilization. In this work, a detailed investigation into the suppression effect of water mist on the hydrogen self-ignition and flame propagation is performed. A parametric study of the effect of water concentration and droplet size on flame dynamic is conducted. The results show that the fine water mist effectively reduces the shock-heating temperature, significantly prolonging the ignition time and distance. The water droplets have a direct contact with the fast growing flame, which leads to more intense two-phase interaction that results in the decoupling of shock wave from the leading flame and a more pronounced bimodal flame structures. The droplet size is found to have small effect when the water concentration is low, and the mist with a medium size of 50 μm shows a better inhibitory performance.
Extraction and Characterization of Cellulose from Kelp (Laminaria japonica) Residue
Authors: Liu Zhenhua , Xie Wei , Ye Lan , Yuan Yuan 
Published: October 2024

Abstract
Kelp residue is the byproduct of industrial production of sodium alginate from kelp. An in-depth utilization of kelp residue not only creates higher economic value but also opens up a new pathway for comprehensive utilization of kelp. In this study, a method of first removing acid-soluble ash and then depleting protein was employed to extract cellulose components from kelp residue. Single-factor experiments demonstrated that treating kelp residue with 1.2 mol/L hydrochloric acid at a solid-liquid ratio of 1:5 effectively removed acid-soluble ash. Subsequently, further treatment with 3% NaOH at a solid-liquid ratio of 1:10, conducted at 100 °C for 140 minutes, reduced the protein content in the extract to below 1%, with a cellulose yield of 33.5%. Fourier transform infrared spectroscopy analysis revealed that characteristic peaks attributed to cellulose existed in the extractive, while electron microscopy observations indicated that the extracted kelp residue cellulose exhibited irregular thin-sheet structures. The cellulose obtained in this study lays the foundation for its further utilization and processing into cellulose-derived products.
Numerical Investigation into Gas-Solid Fluidized Bed Reactors for Activating the Iron-based Fischer-Tropsch Synthesis Catalyst
Authors: Zhao Jianning , Bu Yifeng , Li Chengxiang , Tong Ruili , Men Zhuowu , Ma Xinbin 
Published: October 2024

Abstract
The present work conducted experimental and computational fluid dynamics simulation studies on the hydrodynamic characteristics of gas-solid fluidized bed reactors for activating the iron-based Fischer-Tropsch synthesis catalyst. In order to provide reliable guidance for the design and scale-up of the reactor, the present study applied experimental data from a pilot fluidized bed device to verify the accuracy of the drag force model used in the two-fluid model and further used the validated drag force model to simulate an industrial-scale fluidized bed activation reactor. It was found that the bubbling-EMMS drag model can accurately simulate the pilot-scale fluidized bed in the flow regimes ranging from bubbling to turbulent fluidization. Simulation studies on the industrial-scale fluidized bed activation reactor showed that the reactor was in a turbulent fluidization regime under the designed operating gas velocity, with high gas-solid dispersion efficiency, reasonable utilization of reactor space, and low gas-solid separation load.
Fabrication and optimization of parameters of high-performance polyamide thin film composite membranes for desalination
Authors: Razzaq Muhammad , Hussain Ajaz , Musaddiq Sara , Hussain Dilshad , Kanwal Javaria , Wabaidur Saikh Mohammad , Asif Hafiz Muhammad , Ahmed Muhammad Mahboob , Verpoort Francis 
Published: October 2024

Abstract
Herein, we report the synthesis of polyamide thin film composite (PA-TFC) membranes through interfacial polymerization (IP) using an aqueous solution of diaminodiphenylmethane (DDM) and terephthaloyl chloride (TPC) in n-hexane, onto polysulfone (PSF) surface. The synthesized membranes were characterized by attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), scanning electron microscope (SEM) and contact angle measurement. To optimize the conditions for better membrane function, desalination (%) and permeate flow rate (L/m2.h.bar) were evaluated as a function of synthesis conditions. The membrane efficiency for salt rejection was evaluated against NaCl using a bench-top cross-flow filtration assembly where the membrane showed up to 73.13% salt rejections of NaCl. While the salt rejection of Na2SO4 and MgSO4 showed up to 84.92% and 82.94%. The promising synthesized membranes exhibited a permeate flux of 41.7 L/m2.hr.bar. The contact angle analysis revealed that the synthesized PA-TFC membranes are hydrophilic as the value of contact angle measured as 109°–100°. The synthesized membrane offered facile and effective access for the synthesis of high-performance PA-TFC membranes.
Long-range alternative splicing contributes to neoantigen specificity in glioblastoma
Authors: Ji Mingjun , Yu Qing , Yang Xin-Zhuang , Yu Xianhong , Wang Jiaxin , Xiao Chunfu , An Ni A , Han Chuanhui , Li Chuan-Yun , Ding Wanqiu 
Published: October 2024

Abstract
Recent advances in neoantigen research have accelerated the development of immunotherapies for cancers, such as glioblastoma (GBM). Neoantigens resulting from genomic mutations and dysregulated alternative splicing have been studied in GBM. However, these studies have primarily focused on annotated alternatively-spliced transcripts, leaving non-annotated transcripts largely unexplored. Circular ribonucleic acids (circRNAs), abnormally regulated in tumors, are correlated with the presence of non-annotated linear transcripts with exon skipping events. But the extent to which these linear transcripts truly exist and their functions in cancer immunotherapies remain unknown. Here, we found the ubiquitous co-occurrence of circRNA biogenesis and alternative splicing across various tumor types, resulting in large amounts of long-range alternatively-spliced transcripts (LRs). By comparing tumor and healthy tissues, we identified tumor-specific LRs more abundant in GBM than in normal tissues and other tumor types. This may be attributable to the upregulation of the protein quaking in GBM, which is reported to promote circRNA biogenesis. In total, we identified 1057 specific and recurrent LRs in GBM. Through in silico translation prediction and MS-based immunopeptidome analysis, 16 major histocompatibility complex class I-associated peptides were identified as potential immunotherapy targets in GBM. This study revealed long-range alternatively-spliced transcripts specifically upregulated in GBM may serve as recurrent, immunogenic tumor-specific antigens.
Automated lepidopteran pest developmental stages classification via transfer learning framework
Authors: Qin Wei-bo , Abbas Arzlan , Abbas Sohail , Alam Aleena , Chen De-hui , Hafeez Faisal , Ali Jamin , Romano Donato , Chen Ri-Zhao 
Published: October 2024

Abstract
The maize crop is highly susceptible to damage caused by its primary pests, which poses considerable challenges in manually identifying and controlling them at various larval developmental stages. To mitigate this issue, we propose an automated classification system aimed at identifying the different larval developmental stages of 23 instars of 4 major lepidopteran pests: the Asian corn borer, Ostrinia furnacalis (Guenée; Lepidoptera: Crambidae), the fall armyworm, Spodoptera frugiperda (J.E. Smith; Lepidoptera: Noctuidae), the oriental armyworm, Mythimna separata (Walker; Lepidoptera: Noctuidae), and the tobacco cutworm, Spodoptera litura (Fabricius; Lepidoptera: Noctuidae). Employing 5 distinct Convolutional Neural Network architectures—Convnext, Densenet121, Efficientnetv2, Mobilenet, and Resnet—we aimed to automate the process of identifying these larval developmental stages. Each model underwent fine-tuning using 2 different optimizers: stochastic gradient descent with momentum and adaptive moment estimation (Adam). Among the array of models tested, Densenet121, coupled with the Adam optimizer, exhibited the highest classification accuracy, achieving an impressive 96.65%. The configuration performed well in identifying the larval development stages of all 4 pests, with precision, recall, and F1 score evaluation indicators reaching 98.71%, 98.66%, and 98.66%, respectively. Notably, the model was ultimately tested in a natural field environment, demonstrating that Adam_Densenet121 model achieved an accuracy of 90% in identifying the 23 instars of the 4 pests. The application of transfer learning methodology showcased its effectiveness in automating the identification of larval developmental stages, underscoring promising implications for precision-integrated pest management strategies in agriculture.
Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis
Authors: Zaka Ammar , Mutahar Daud , Gorcilov James , Gupta Aashray K , Kovoor Joshua G , Stretton Brandon , Mridha Naim , Sivagangabalan Gopal , Thiagalingam Aravinda , Chow Clara K , Zaman Sarah , Jayasinghe Rohan , Kovoor Pramesh , Bacchi Stephen 
Published: October 2024

Abstract

Aims
Accurate prediction of clinical outcomes following percutaneous coronary intervention (PCI) is essential for mitigating risk and peri-procedural planning. Traditional risk models have demonstrated a modest predictive value. Machine learning (ML) models offer an alternative risk stratification that may provide improved predictive accuracy.

Methods and results
This study was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis guidelines. PubMed, EMBASE, Web of Science, and Cochrane databases were searched until 1 November 2023 for studies comparing ML models with traditional statistical methods for event prediction after PCI. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals (CIs) between ML models and traditional methods in estimating the risk of all-cause mortality, major bleeding, and the composite outcome major adverse cardiovascular events (MACE). Thirty-four models were included across 13 observational studies (4 105 916 patients). For all-cause mortality, the pooled C-statistic for top-performing ML models was 0.89 (95%CI, 0.84–0.91), compared with 0.86 (95% CI, 0.80–0.93) for traditional methods (P = 0.54). For major bleeding, the pooled C-statistic for ML models was 0.80 (95% CI, 0.77–0.84), compared with 0.78 (95% CI, 0.77–0.79) for traditional methods (P = 0.02). For MACE, the C-statistic for ML models was 0.83 (95% CI, 0.75–0.91), compared with 0.71 (95% CI, 0.69–0.74) for traditional methods (P = 0.007). Out of all included models, only one model was externally validated. Calibration was inconsistently reported across all models. Prediction Model Risk of Bias Assessment Tool demonstrated a high risk of bias across all studies.

Conclusion
Machine learning models marginally outperformed traditional risk scores in the discrimination of MACE and major bleeding following PCI. While integration of ML algorithms into electronic healthcare systems has been hypothesized to improve peri-procedural risk stratification, immediate implementation in the clinical setting remains uncertain. Further research is required to overcome methodological and validation limitations.
Factors influencing patient engagement in decision-making for catheter ablation of atrial fibrillation: a cross-sectional survey
Authors: Dong Fengwei , Wu Yaping , Wang Qing , Huang Yan , Wu Qing 
Published: October 2024

Abstract

Aims
Patient engagement in decision-making could improve healthcare quality and health outcome, which has been emphasized in atrial fibrillation (AF) management guidelines. However, patients report relatively low level of engagement in decision-making for catheter ablation (CA). Therefore, this study aims to explore the influencing factors of AF patient engagement in decision-making for CA.

Methods and results
A cross-sectional study was conducted. A total of 836 patients were recruited from six tertiary hospitals in Shanghai. Adapted version of the Control Preferences Scale, Chinese version of the All Aspects of Health Literacy Scale, Chinese version of the Facilitation of Patient Involvement Scale, and the Atrial Fibrillation Knowledge Questionnaire were used to measure patient engagement in decision-making, health literacy, perception of physician facilitation, and AF knowledge, respectively. Of the 750 patients who returned valid questionnaires, 20.2% of the patients reported active engagement in decision-making, 39.5% reported collaborative engagement, and 40.3% reported passive engagement. Compared with patients perceiving passive engagement, those with collaborative or active engagement were more likely to be female and have higher income, moderate European Heart Rhythm Association (EHRA) class (II and III), higher perception of physician facilitation, higher health literacy, and higher AF knowledge (P &lt; 0.05 for all). The collaborative engagement group exhibited a shorter AF duration.

Conclusion
The degree of patient engagement in CA decision-making varied, with most perceiving passive engagement. Patient engagement in decision-making was influenced by gender, income, duration of AF, EHRA class, perception of physician facilitation, health literacy, and AF knowledge.