Luying Ju, Zihai Yan, Mingming Wu, Gangping Zhang, Jiajia Yan, Tianci Yu, Pan Ding and Riqing Xu
The purpose of this paper is to suggest an implicit integration method for updating the constitutive relationships in the newly proposed anisotropic egg-shaped elastoplastic…
Abstract
Purpose
The purpose of this paper is to suggest an implicit integration method for updating the constitutive relationships in the newly proposed anisotropic egg-shaped elastoplastic (AESE) model and to apply it in ABAQUS.
Design/methodology/approach
The implicit integration algorithm based on the Newton–Raphson method and the closest point projection scheme containing an elastic predictor and plastic corrector are implemented in the AESE model. Then, the integration code for this model is incorporated into the commercial finite element software ABAQUS through the user material subroutine (UMAT) interface to simulate undrained monotonic triaxial tests for various saturated soft clays under different consolidation conditions.
Findings
The comparison between the simulated results from ABAQUS and the experimental results demonstrates the satisfactory performance of this implicit integration algorithm in terms of effectiveness and robustness and the ability of the proposed model to predict the characteristics of soft clay.
Research limitations/implications
The rotational hardening rule in the AESE model together with the implicit integration algorithm cannot be considered.
Originality/value
The singularity problem existing in most elastoplastic models is eliminated by the closed, smooth and flexible anisotropic egg-shaped yield surface form in the AESE model. In addition, this notion leads to an efficient implicit integration algorithm for updating the highly nonlinear constitutive equations for unsaturated soft clay.
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Xiao Xie, Tianci Song, Li Li, Weihan Jiang, Xinyuan Gao, Liwang Shu and Yongmei Liu
This study investigates the influence of personality traits on the personal digital hoarding behaviors of college students. Emotional attachment is included as a mediating…
Abstract
Purpose
This study investigates the influence of personality traits on the personal digital hoarding behaviors of college students. Emotional attachment is included as a mediating variable, thereby enhancing the relevant theoretical frameworks associated with such behaviors.
Design/methodology/approach
A total of 370 college students were investigated using the Big Five personality scale, the digital hoarding behaviors scale, and a self-compiled emotional attachment scale. The collected data were analyzed using SPSS26.0 software.
Findings
Personality traits exerted a significant influence on individual digital hoarding behaviors, among which neuroticism (r = 0.526**), extroversion (r = 0.232**), and agreeableness (r = 0.233**) demonstrated notable effects. However, no significant correlation was found to link conscientiousness and openness with personal digital hoarding behaviors. Emotional attachment (r = 0.665**) significantly impacted personal digital hoarding behaviors. Regression analysis further showed that personality traits also affect personal digital hoarding behaviors through the partial mediating effect of emotional attachment. Dependency security was identified as a partial mediator of the effects of agreeableness and neuroticism on personal digital hoarding behaviors. Possession attachment was observed to be another partial mediator of the relationship between neuroticism and personal digital hoarding behaviors. Furthermore, fear of missing out was observed to partially mediate the effects of agreeableness and neuroticism on personal digital hoarding behaviors.
Research limitations/implications
The generalizability of the self-compiled emotional attachment scale requires further verification in future research, as the selection of participants was relatively simplistic.
Practical implications
Our study showed the distinctive personality traits of individuals and their relationship with personal digital hoarding behaviors, along with the mediating role of emotional attachment. Our findings provide valuable insights for future personal information management and digital hoarding de-cluttering.
Originality/value
This research explores the influence of personality traits on the personal digital hoarding behaviors of college students and examines the mediating role of emotional attachment.
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Tianci Wang, Yan Lu, Hao Zhang, Jianxi Liu, Yunfei Zheng and Fuquan Tu
The developed plasto-elastohydrodynamic lubrication (PEHL) model is used to demonstrate the permanent change of macro morphology by critical high local stress at micro asperities…
Abstract
Purpose
The developed plasto-elastohydrodynamic lubrication (PEHL) model is used to demonstrate the permanent change of macro morphology by critical high local stress at micro asperities in contact, which may further affect the fluid-film characteristics.
Design/methodology/approach
Geometric morphology is integrated into the PEHL model to elucidate the fluid-film properties governed by both macro- and micromorphologies.
Findings
Results show the model, accounting for combination of elastic and plastic deformations, realistically reveals fluid film distribution affected by the significant pressure highly concentrated within surface micro roughness interaction. The designed macroscopic textured surface mitigates the fluid film rupture phenomenon and prevents accumulated wear degradation from plastic deformation.
Originality/value
The PEHL model takes into account both elastic and plastic deformations and realistically reveals the fluid film distribution affected by large pressures that are highly concentrated in surface micro-roughness interactions. The macro-textured surfaces are designed to mitigate fluid film rupture phenomena and prevent cumulative wear caused by plastic deformation.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0170/
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Zhanglin Peng, Tianci Yin, Xuhui Zhu, Xiaonong Lu and Xiaoyu Li
To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method…
Abstract
Purpose
To predict the price of battery-grade lithium carbonate accurately and provide proper guidance to investors, a method called MFTBGAM is proposed in this study. This method integrates textual and numerical information using TCN-BiGRU–Attention.
Design/methodology/approach
The Word2Vec model is initially employed to process the gathered textual data concerning battery-grade lithium carbonate. Subsequently, a dual-channel text-numerical extraction model, integrating TCN and BiGRU, is constructed to extract textual and numerical features separately. Following this, the attention mechanism is applied to extract fusion features from the textual and numerical data. Finally, the market price prediction results for battery-grade lithium carbonate are calculated and outputted using the fully connected layer.
Findings
Experiments in this study are carried out using datasets consisting of news and investor commentary. The findings reveal that the MFTBGAM model exhibits superior performance compared to alternative models, showing its efficacy in precisely forecasting the future market price of battery-grade lithium carbonate.
Research limitations/implications
The dataset analyzed in this study spans from 2020 to 2023, and thus, the forecast results are specifically relevant to this timeframe. Altering the sample data would necessitate repetition of the experimental process, resulting in different outcomes. Furthermore, recognizing that raw data might include noise and irrelevant information, future endeavors will explore efficient data preprocessing techniques to mitigate such issues, thereby enhancing the model’s predictive capabilities in long-term forecasting tasks.
Social implications
The price prediction model serves as a valuable tool for investors in the battery-grade lithium carbonate industry, facilitating informed investment decisions. By using the results of price prediction, investors can discern opportune moments for investment. Moreover, this study utilizes two distinct types of text information – news and investor comments – as independent sources of textual data input. This approach provides investors with a more precise and comprehensive understanding of market dynamics.
Originality/value
We propose a novel price prediction method based on TCN-BiGRU Attention for “text-numerical” information fusion. We separately use two types of textual information, news and investor comments, for prediction to enhance the model's effectiveness and generalization ability. Additionally, we utilize news datasets including both titles and content to improve the accuracy of battery-grade lithium carbonate market price predictions.
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Xinyu Zhang and Liling Ge
A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the…
Abstract
Purpose
A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body and quality evaluation. This paper aims to discuss the aforementioned idea.
Design/methodology/approach
First, the differential body is set on a rotation platform before measuring. Then one laser sensor called as “primary sensor”, is installed on the intern of the differential body. The spherical surface and four holes on the differential body are sampled by the primary sensor when the rotation platform rotates one revolution. Another sensor called as “secondary sensor”, is installed above to sample the external cylinder surface and the planar surface on the top of the differential body, and the external cylinder surface and the planar surface are high in manufacturing precision, which are used as datum surfaces to compute the errors caused by the motion of the rotation platform. Finally, the sampled points from the primary sensor are compensated to improve the measurement accuracy.
Findings
A multi-laser sensors-based measurement instrument is proposed for the measurement of geometry errors of a differential body. Based on the characteristics of the measurement data, a gradient image-based method is proposed to distinguish different objects from laser measurement data. A case study is presented to validate the measurement principle and data processing approach.
Research limitations/implications
The study investigates the possibility of correction of sensor data by the measurement results of multiple sensors to improving measurement accuracy. The proposed technique enables the error analysis and compensation by the geometric correlation relationship of various features on the measurand.
Originality/value
The proposed error compensation principle by using multiple sensors proved to be useful for the design of new measurement device for special part inspection. The proposed approach to describe the measuring data by image also is proved to be useful to simplify the measurement data processing.