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The study delves into the influence of wear cycles on these parameters. The purpose of this paper is to identify characteristic patterns of σ_RS and ε_PEEQ that discern varying wear situations, thereby contributing to the enrichment of wear theory. Furthermore, the findings serve as a foundational basis for nondestructive and in situ wear detection methodologies, such as nonlinear ultrasonic detection, known for its sensitivity to σ_RS and ε_PEEQ.

This paper elucidates the wear mechanism through the lens of residual stress (σ_RS) and plastic deformation within distinct fretting regimes, using a two-dimensional cylindrical/flat contact model. It specifically explores the impact of the displacement amplitude and cycles on the distribution of residual stress and equivalent plastic strain (ε_PEEQ) in both gross slip regime and partial slip regimes.

Therefore, when surface observation of wear is challenging, detecting the σ_RS trend at the center/edge, region width and ε_PEEQ distribution, as well as the maximum σ_RS distribution along the depth, proves effective in distinguishing wear situations (partial or gross slip regimes). However, discerning wear situations based on ε_PEEQ along the depth direction remains challenging. Moreover, in the gross slip regime, using σ_RS distribution or ε_PEEQ along the width direction rather than the depth direction can effectively provide feedback on cycles and wear range.

This work introduces a novel perspective for investigating wear theory through the distribution of residual stress (σ_RS) and equivalent plastic strain (ε_PEEQ). It presents a feasible detection theory for wear situations using nondestructive and in situ methods, such as nonlinear ultrasonic detection, which is sensitive to σ_RS and ε_PEEQ.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0005/

Meteorological disasters pose a significant risk to people’s lives and safety, and accurate prediction of weather-related disaster losses is crucial for bolstering disaster prevention and mitigation capabilities and for addressing the challenges posed by climate change. Based on the uncertainty of meteorological disaster sequences, the damping accumulated autoregressive GM(1,1) model (DAARGM(1,1)) is proposed.

Firstly, the autoregressive terms of system characteristics are added to the damping-accumulated GM(1,1) model, and the partial autocorrelation function (PACF) is used to determine the order of the autoregressive terms. In addition, the optimal damping parameters are determined by the optimization algorithm.

The properties of the model were analyzed in terms of the stability of the model solution and the error of the restored value. By fitting and predicting the losses affected by meteorological disasters and comparing them with the results of four other grey models, the validity of the new model in fitting and prediction was verified.

The dynamic damping trend factor is introduced into the grey generation operator so that the grey model can flexibly adjust the accumulative order of the sequence. On the basis of the damping accumulated grey model, the autoregressive term of the system characteristics is introduced to take into account the influence of the previous data, which is more descriptive of the development trend of the time series itself and increases the effectiveness of the model.

The purpose of this paper is to reveal the distributional characteristics and evolutional patterns in source periodicals, topics, authors, funding, and institutes of research papers in Chinese Agricultural Economics so as to understand the current situations and developmental tendency of Chinese agricultural economics research over the past decade.

Using the citation analysis method, this paper analyzed the distributional characteristics and evolution of source periodicals, fields, authors and topics of 2,203 highly cited journal papers from the database of China National Knowledge Infrastructure (CNKI) and 189 cited journal papers from database of Social Sciences Citation Index (SSCI) in agricultural economics first-authored by Chinese scholars from 2006 to 2015.

First, over the past decade, agricultural economics research in China has seen a rapid development. Specially, 103 scholars and 42 institutes have played key roles in the development, and 12 Chinese periodicals and 3 international journals have been the most influential outlets. Second, the coverage of the topics in Chinese agricultural economics research is broad and has expanded over the past decade. The rural land issue has been the most popular topic, while the issues regarding rural institutional arrangements and industrialization in rural areas have been explored extensively. However, issues in other fields, such as agricultural markets and trade, rural labor, food safety, etc. have to be further studied. Third, the improvements of economic theory and quantitative analytic techniques, the supports from research funding, and an increase in the collaboration between Chinese scholars and those from other countries have made great contribution to the rapid development of Chinese agricultural economics research over the past decade.

This paper is an original work that identifies the most influential journal papers including highly cited journal papers from CNKI and cited journal papers from SSCI, using citation frequency and standard Essential Science Indicators method. This is a contribution relative to the methods used by previous studies, which did not account for frequency of citation of a paper. Moreover, this study is based on data from two databases, CNKI and SSCI, suggesting that the coverage of sample papers is broader compared to those of previous studies.