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The purpose of this paper is to present the author’s method of conservative load spectrum (LS) derivation and close-proximity LS extrapolation applying a correction for measurement uncertainty caused by too low sampling frequency or signal noise, which may affect the load histories collected during the flying session and cause some recorded load increments to be lower than the actual values.

Having in mind that the recorded load signal is burdened with some measurement error, a conservative approach was applied during qualification of the recorded values into 32 discrete load-level intervals and derivation of 32 × 32 half-cycle arrays. A part of each cell value of the half-cycle array was dispersed into the neighboring cells placed above by using a random number generator. It resulted in an increase in the number of load increments, which were one or two intervals higher than those resulting from direct data processing. Such an array was termed a conservative clone of the actual LS. The close-proximity approximation consisted of multiplication of the LSs clones and their aggregation. This way, the LS for extended time of operation was obtained. The whole process was conducted in the MS Excel environment.

Fatigue life calculated for a chosen element of aircraft structure using conservative LS is about 20%–60% lower than for the actual LS (depending on the applied value of dispersion coefficients used in the procedure of LSs clones generation). It means that such a result gives a bigger safety margin when operational life of the aircraft is estimated or when the fatigue test for an extended operational period is programed based on a limited quantity of data from a flying session.

This paper presents a proposal for a novel, conservative approach to fatigue life estimation based on the short-term LS derived from the load signal recorded during the flying session.

The purpose of this paper is to assess whether China’s public sector can continue to generate advanced genetically modified (GM) technologies that will be competitive in the market.

The authors investigated all the research teams that have been conducting research projects under the variety development special program. The data collected include detail information on research capacity, research areas, performance, and process of their research projects. Based on the survey data, the authors assessed the innovations and progress of the variety development special program.

Unlike other countries, most GM products in China are developed by public research institutes. There is rising concern on the ability of China’s public sector to continuously generate indigenous GM technology that can compete with multinational companies. The study surveyed 197 research institutes and 487 research teams and found that the GM program in China lacks coordination: researchers do not want to share their research materials with others. Due to the lack of coordination, most of the hundreds of research teams often worked independently in the year 2008-2010. Moreover, the authors found the lack of coordination may be due to the reason that the interests of researchers are not well protected. This paper also provided the recent progress and policy changes of GM program in China, and it found that the efficiency in the later three years improved a lot. In order to establish a competitive national public GM research system, China should continuously consolidate and integrate the upstream, midstream, and downstream activities of the whole GM innovation process. China’s public sector may also need to work more closely with both the domestic and international private sectors.

This paper is a comprehensive analysis on the development of transgenic technology in China. The results of this paper can provide evidence for the dynamic adjustment of the policies in the variety development special program and can also provide reference for the future assessment of the variety development special program.

The purpose of this study is to examine the effects of nitrogen (N) deposition on clonal growth in a rhizome clonal plant, Leymus chinensis (Trin.) Tzvel.

The study established seven N concentration gradients (0, 2, 4, 8, 16, 32 and 64 g N m⁻²) to simulate the continuous increase in N deposition for the cultivation of L. chinensis seedlings and assess the response mechanism of the cloned L. chinensis plant at different N levels by analyzing the aboveground and belowground plant appearance traits, parent ramets and daughter ramets of resource allocation and biomass allocation.

The results of this study showed that the different N treatment levels could promote clonal growth and had certain regularity under the seven treatments. The addition of N could significantly increase the ramet number, rhizome length, rhizome spacer length, biomass of mother ramets, daughter ramets and belowground L. chinensis population when the N addition was greater than 4 g m⁻²; however, the clonal growth ability of L. chinensis decreased and the rhizome length, ramet number, stem and leaf biomass of daughter ramets and stem biomass of mother ramets significantly decreased when the N addition was greater than 32 g N m⁻².

With global warming, atmospheric N deposition is increasing and it is of great significance to explore the response mechanism of different N levels for the growth of clone plants. This study provides basic data and a theoretical basis for the survival prediction of cloned plants under the background of a global climate change strategy and has important theoretical and practical significance for the scientific management of grasslands in the future.

The purpose of this paper is to examine the role of graduate recruitment in the professional socialisation and subjectification of Big Four professionals.

The paper draws on documentary data and interviews conducted at one British university. It adopts an interpretive perspective and is informed by Foucault’s work on technologies of power and technologies of the self.

The paper argues that the graduate recruitment practices of Big Four firms represent a series of examinations which produce the category of ideal recruits. It moreover suggests that this category serves as the ultimate objective of an ethical process whereby aspiring accountants consciously and deliberately seek to transform themselves into the type of subjects they aspire to be – ideal recruits.

The findings of the paper are primarily based on interviews conducted at one university. Future research could explore if students at other universities experience graduate recruitment in similar or different ways.

The paper highlights the constitutive role of graduate recruitment practices and shows that they can construct ideal recruits as much as they select them. It also shows that graduate recruitment is an important anticipatory socialisation mechanism that can compel aspiring accountants to learn how to look, sound and behave like Big Four professionals long before they join such organisations. Finally, the paper discusses its implications for the future of the profession, social mobility and the use of Foucault’s work on technologies of power and the self in studying subjectivity at elite professional service firms.

The purpose of this paper is to assess the impacts on production of five fruit crops from 1961 to 2018 of energy use, CO₂ emissions, farming areas and the labor force in China.

This analysis applied the autoregressive distributed lag-bound testing (ARDL) approach, Granger causality method and Johansen co-integration test to predict long-term co-integration and relation between variables. Four machine learning methods are used for prediction of the accuracy of climate effect on fruit production.

The Johansen test findings have shown that the fruit crop growth, energy use, CO₂ emissions, harvested land and labor force have a long-term co-integration relation. The outcome of the long-term use of CO₂ emission and rural population has a negative influence on fruit crops. The energy consumption, harvested area, total fruit yield and agriculture labor force have a positive influence on six fruit crops. The long-run relationships reveal that a 1% increase in rural population and CO₂ will decrease fruit crop production by −0.59 and −1.97. The energy consumption, fruit harvested area, total fruit yield and agriculture labor force will increase fruit crop production by 0.17%, 1.52%, 1.80% and 4.33%, respectively. Furthermore, uni-directional causality is correlated with the growth of fruit crops and energy consumption. Also, the results indicate that the bi-directional causality impact varies from CO₂ emissions to agricultural areas to fruit crops.

This study also fills the literature gap in implementing ARDL for agricultural fruits of China, used machine learning methods to examine the impact of climate change and to explore this important issue.