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Textile reinforced concrete (TRC) has excellent bearing capacity and anti-crack and corrosion resistance capacity, which are suitable for strengthening concrete structure under harsh environments.

In this thesis, flexural properties of RC beams strengthened with TRC under chloride wet–dry cycles were studied and the effects of the concentration of the salt solution, number of wet–dry cycles, bending stress level and TRC form were considered. Four-point bend loading mode was adopted for the step-loading procedure.

As the number of wet–dry cycles was relatively few, the trend of the yield and ultimate load with the increasing concentration of salt solution and wet–dry cycles were not obvious. However, the beams under high sustained bending stress level (0.5) had a decrease in the bearing capacity and an increase in mid-span deflection because of the larger degree of the corrosion of steel bars and the weaker bond capacity between the steel bar and concrete. Besides, there was little difference between the precast TRC plate and the casting TRC on beams in terms of the capacity of anti-crack, bearing and deflection.

In this paper, preliminary work has been carried out, but some of the factors were not comprehensive considered, which are inevitable. As the time of dry–wet cycles was short and TRC layer had good anti-crack and anti-permeability performance, smaller chloride ions’ penetration resulted in the corrosion ratio of steel bars to be lower.

It should be noted that under high corrosion rates of the reinforcement, the whole TRC strengthening layer might be spalled off if only the strengthening form at the beam bottom is used, and thus the U-type strengthening form could be considered, which means that the beam is strengthened at both the bottom and side surfaces.

This research only considers the flexural performance of the beams strengthened with TRC in conventional environment, and there is little research on the TRC-strengthened beam under corrosion environment. On the basis of previous research, this paper carried out the experimental study on beams strengthened with TRC under chloride wet–dry cycle environment, and the effects of the concentration of the salt solution, number of wet–dry cycles, bending stress level and TRC form were considered.

This paper aims to quantify the dependence relationship of bat algorithm’s (BA) behaviour on the factors that could possibly affect the outputs, and rank the importance of the various uncertain factors thus suggesting research priorities.

This paper conducts a sensitivity analysis based on variance decomposition of factors in both of original and improved BA. The data sets for sensitivity analysis are generated by optimal Latin hyper sampling in the design of experiment. The optimal factor sets are screened by stochastic error bar measures for the effective and robust implementation of BA.

The paper reveals the inner dependent relationship between factors and output in both of original and improved BA. It figures out the weakness in original BA and improves that. It suggests that uncertainty brought about by factors are mainly caused by the interaction effect and all the higher-order term in sensitivity indices for both of original and improved BA. It ranks the main effect and the total effect of factors and screens out some optimal factor sets for BA.

This paper quantifies the dependence relationship of BA’s behaviour on the factors that could affect outputs using sensitivity analysis based on variance decomposition.

This paper applied grey wave forecasting in a decomposition–ensemble forecasting method for modelling the complex and non-linear features in time series data. This application aims to test the advantages of grey wave forecasting method in predicting time series with periodic fluctuations.

The decomposition–ensemble method combines empirical mode decomposition (EMD), component reconstruction technology and grey wave forecasting. More specifically, EMD is used to decompose time series data into different intrinsic mode function (IMF) components in the first step. Permutation entropy and the average of each IMF are checked for component reconstruction. Then the grey wave forecasting model or ARMA is used to predict each IMF according to the characters of each IMF.

In the empirical analysis, the China container freight index (CCFI) is applied in checking prediction performance. Using two different time periods, the results show that the proposed method performs better than random walk and ARMA in multi-step-ahead prediction.

The decomposition–ensemble method based on EMD and grey wave forecasting model expands the application area of the grey system theory and graphic forecasting method. Grey wave forecasting performs better for data set with periodic fluctuations. Forecasting CCFI assists practitioners in the shipping industry in decision-making.

The aim of this study is to (1) construct a standard framework for assessing the capability of bioenergy enterprises' digital green innovation partners; (2) quantify the choice of partners for digital green innovation by bioenergy enterprises; (3) propose based on a dual combination empowerment niche digital green innovation field model.

Fuzzy set theory is combined into field theory to investigate resource complementarity. The successful application of the model to a real case illustrates how the model can be used to address the problem of digital green innovation partner selection. Finally, the standard framework and digital green innovation field model can be applied to the practical partner selection of bioenergy enterprises.

Digital green innovation technology of superposition of complementarity, mutual trust and resources makes the digital green innovation knowledge from partners to biofuels in the enterprise. The index rating system included eight target layers: digital technology innovation level, bioenergy technology innovation level, bioenergy green level, aggregated digital green innovation resource level, bioenergy technology market development ability, co-operation mutual trust and cooperation aggregation degree.

This study helps to (1) construct the evaluation standard framework of digital green innovation capability based on the dual combination empowerment theory; (2) develop a new digital green innovation domain model for bioenergy enterprises to select digital green innovation partners; (3) assist bioenergy enterprises in implementing digital green innovation practices.

This study aims to analyze those factors affecting the rural resident’s willingness to adopt solar photovoltaic (PV) which is important for accelerating the popularization of clean energy in China.

This study contained a sample of 653 households in 8 provinces/regions by stratified, and random sampling in rural China. Descriptive analysis, exploratory factor analysis and confirmatory factor analysis techniques have been used for analytical purposes.

The empirical results indicate that financial incentive and social interaction have positive effects on rural residents’ adoption willingness, while village leaders’ engagement can indirectly influence their adoption willingness through social interaction and residents’ cognition.

This study mainly considers external and internal factors but ignores the effect of technical factors. In addition, the samples are just selected from the residents who have adopted solar PV.

This study is expected to be useful for the government, regulators, village leaders, village leaders and other institutions.

This study conducts a systematic analysis and clarifies the relationship between factors (external and internal) and rural residents’ adoption willingness. The village leaders’ engagement is first added to the conceptual model as an external factor, which is very essential in rural residents’ adoption in China.

In the Western world, Carl Jung was the first to posit a theory of synchronicity to explain the startling divinatory power of the I Ching. Yet long before his time and unbeknownst to the West, the Chinese had already institutionalized as their tradition, simple practices for enabling decisions grounded on the synchronous concept. The purpose of this paper is to explain the process from within the context of Chinese Buddhist spirituality.

The approach here is to provide the background of Han Chinese Buddhism in modern China and within it, the wide practices of Kuan Yin spirituality and introducing the tools that the Chinese devised for consulting the Goddess of Mercy, as Kuan Yin is otherwise known. Then a discussion is made, for the first time, of the underlying mechanics as well as the mind and energy aspects. Having so introduced the religious background, spirituality, tools and mechanics, the processes of temple consultations for decisions are then explained. In the discussion, a framework for classifying decisions is outlined along with probability concepts. There is also the requirement for the inquirer to seek a metaphorical interpretation of the poetic imagery as contained in the Qian (a slip of paper).

Through writing this paper, the author wishes readers, both managerial and those in research, to understand what is still the approach (even more widely in China now than before) in how the Chinese – in and outside of China – approach the task of making major, complex decisions. These practices which date from antiquity clearly suggest the Chinese had gone beyond Jungian synchronicity in translating the theory into practice for decision making. In other words, they had long recognized the need for tools, techniques and approaches to help them make complex, difficult decisions: decisions that often go beyond the rational boundaries of the mind.

With the rising impact of the Chinese on the global economy and society, there is clearly a need for works that explain major Chinese processes such as the making of decisions. The art of decision making by the Chinese on the basis of what Jung theorized as synchronicity should become much better understood by researchers and managers.

There are very few academic papers exploring the process of Kuan Yin consultation in decision making by the Chinese. Yet this can be seen across many temples in everyday China and overseas Chinese communities as well as in Japan, Korea and Vietnam. The understanding of such processes is necessary for anyone, who wishes to grasp the minds of the Chinese as regards the process involved in the making of major decisions.

Based on signaling theory, this paper aims to explore the impact of supply chain transparency (SCT) on firms' bank loan (BL) and supply chain financing (SCF) in the context of voluntary disclosure of supplier and customer lists.

Based on panel data collected from Chinese-listed firms between 2012 and 2021, fixed-effect models and a series of robustness checks are used to test the predictions.

First, improving SCT by disclosing major suppliers and customers promotes BL but inhibits SCF. Specifically, customer transparency (CT) is more influential in SCF than supplier transparency (ST). Second, supplier concentration (SC) weakens SCT’s positive impact on BL while reducing its negative impact on SCF. Third, customer concentration (CC) strengthens the positive impact of SCT on BL but intensifies its negative impact on SCF. Last, these findings are basically more pronounced in highly competitive industries.

This study contributes to the SCT literature by investigating the under-explored practice of supply chain list disclosure and revealing its dual impact on firms' access to financing offerings (i.e. BL and SCF) based on signaling theory. Additionally, it expands the understanding of the boundary conditions affecting the relationship between SCT and firm financing, focusing on supply chain concentration. Moreover, it advances signaling theory by exploring how financing providers interpret the SCT signal and enriches the understanding of BL and SCF antecedents from a supply chain perspective.

This paper inspects in detail the seasonality (deterministic) in container freight rates, and compares seasonality patterns in different freight rate indices. A deterministic seasonality unit root test is performed to achieve set objectives. This study concludes that all the indices (tested in this paper) exhibit significant deterministic seasonality. For January and August, there is no seasonal effect observed in all five series. At the same time, all the indices except Exports from Europe Rate Index (EEI) exhibit significant seasonal patterns in February, September, and December. All five indices exhibit significant seasonality during May, and the coefficient sign shows a drop in the freight rates. During March, October, and November; it is observed that only EEI exhibit significant seasonal patterns. The results could be beneficial for carriers and agents who are involved in the containerised freight transport business. Also, shippers could get a clear idea about the freight rates' nature across various trade routes.

The purpose of this paper is to investigate factors influencing intent to adopt unmanned aerial vehicles for use in agricultural operations in Jilin Province, China.

This paper uses probit analysis to estimate the influence of factors on intent to adopt unmanned aerial vehicles for plant protection application operations in Jilin Province, China based on the Technology Acceptance Model.

UAV technology is acceptable to most participating farmers. Gender (male), agricultural income ratio, understanding of new technology, perceived ease-of-use and perceived usefulness have a positive relationship with farmers’ willingness to adopt UAVs.

For application, crop monitoring and other farm operations, UAVs have clear advantages. There remain challenges associated with the development of applicable technology and facilitating adoption among farmers. Understanding factors affecting adoption will contribute to the latter, and brings forth the following recommendations: conduct research on the cost effectiveness of UAVs for agriculture; accelerate development of the information service system; and increase financial support.

This paper is among the first to consider adoption intent among Chinese farmers of use of UAVs for agricultural operations.

The purpose of this study is to investigate the deformation characteristics and failure modes of the right bank slope of Xiluodu Hydropower Station after excavation.

Micro-seismic monitoring technology is applied to obtain the microfracture information and study the internal damage evolution law of the slope rock mass. A numerical model for discontinuous deformation analysis (DDA) is established to analyse the deformation characteristics and failure mode of the slope. Micro-seismic monitoring and DDA can verify and supplement each other's results in the investigation of slope failure.

The results show that the slope has a downhill displacement along the weathered zone under natural conditions; the maximum resultant displacement at the monitoring point is 380 mm. The micro-seismic events are concentrated in an area located 30–100 m horizontally away from the slope surface and at an elevation of 390–470 m. The distribution of these micro-seismic events is consistent with the location of the unloading and weathered zones; it is the same as the DDA simulation result.

The study is anticipated to be used as reference for the stability analysis of rock slopes. By combining the continuous (micro-seismic monitoring technology) and discontinuous (DDA) methods, the entire process starting from the gradual accumulation of internal rock micro-damage to the macroscopic discontinuous deformation and failure of the slope can be investigated.