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The purpose of this research is to provide a theoretical framework for complex equipment quality risk evaluation. The primary aim of the framework is to enhance the ability to identify risks and improve risk control efficiency during the development phase.

A novel framework for quality risk evaluation in complex equipment is proposed, which integrates probabilistic hesitant fuzzy set-quality function deployment (PHFS-QFD) and grey clustering. PHFS-QFD is applied to identify the quality risk factors, and grey clustering is used to evaluate quality risks in cases of poor quality information during the development stage. The unfolding function of QFD is applied to simplify complex evaluation problems.

The methodology presents an innovative approach to quality risk evaluation for complex equipment development. The case analysis demonstrates that this method can efficiently evaluate the quality risks for aircraft development and systematically trace back the risk factors through hierarchical relationships. In comparison to traditional failure mode and effects analysis methods for quality risk assessment, this approach exhibits superior effectiveness and reliability in managing quality risks for complex equipment development.

This study contributes to the field by introducing a novel theoretical framework that combines PHFS-QFD and grey clustering. The integration of these approaches significantly improves the quality risk evaluation process for complex equipment development, overcoming challenges related to data scarcity and simplifying the assessment of intricate systems.

To predict the passenger volume reasonably and accurately, this paper fills the gap in the research of quarterly data forecast of railway passenger volume. The research results can also provide references for railway departments to plan railway operation lines reasonably and efficiently.

This paper intends to establish a seasonal cycle first order univariate grey model (GM(1,1) model) combing with a seasonal index. GM (1,1) is termed as the trend equation to fit the railway passenger volume in China from 2014 to 2018. The railway passenger volume in 2019 is used as the experimental data to verify the forecasting effect of the proposed model. The forecasting results of the seasonal cycle GM (1,1) model are compared with the traditional GM (1,1) model, seasonal grey model (SGM(1,1)), Seasonal Autoregressive Integrated Moving Average (SARIMA) model, moving average method and exponential smoothing method. Finally, the authors forecast the railway passenger volume from 2020 to 2022.

The quarterly data of national railway passenger volume have a clear tendency of cyclical fluctuations and show an annual growth trend. According to the comparison of the modeling results, the authors know that the seasonal cycle GM (1,1) model has the best prediction effect with the mean absolute percentage error of 1.32%. It is much better than the other models, reflecting the feasibility of the proposed model.

As the previous grey prediction model could not solve the series prediction problem with seasonal fluctuation, and there are few research studies on quarterly railway passenger volume forecasting, GM (1,1) model is taken as the trend equation and combined with the seasonal index to construct a combination forecasting model for accurate forecasting results in this study. Besides, considering the impact of the epidemic on passenger volume, the authors introduce a disturbance factor to deal with the forecasting results in 2020, making the modeling results more scientific, practical and referential.

As the carrier of knowledge, intellectual capital plays a crucial role in technology capability. However, most of the previous studies focus on technological capability from a static perspective, rather than take dynamic technology capability into consideration. Based on this research gap, the purpose of this paper is to investigate the effects of intellectual capital and its sub-dimensions on dynamic technology capability, measuring by the factor scores of five technological input and output variables.

The authors combine the system dynamic method and empirical study to guarantee the internal and external validity. Specifically, the authors design the system dynamic model and simulation to analyze the system mechanism of intellectual capital and its sub-dimensions on dynamic technology capabilities from four cause and effect feedback loops. Then, the authors propose eight hypotheses based on this system dynamic model. In the empirical test phase, the authors employed a panel data set pertaining to Chinese manufacturing firms from 2007 to 2017, and adopted the fixed effect panel model according to Hausman test.

The authors find that intellectual capital efficiency (ICE) and its sub-dimensions (i.e. human capital efficiency, organizational capital efficiency and capital employed efficiency (CEE) have significantly positive impacts on dynamic technology capability. The results also show that the positive effects of ICE and OC on dynamic technology capability would be strengthened in state-owned enterprises compared with non-state-owned enterprises, while this moderation effect is weakened on the relationship between CEE and dynamic technology capability.

In this study, the authors first introduce the system dynamic method to explore the relationship of intellectual capital and dynamic technology capability, which is a valuable trial on combining system science and empirical study. Additionally, the authors continue to expand the dynamic technology capability from the intellectual capital perspective, and also find the moderating effect from the ownership aspect. It is beneficial to the theoretical development of intellectual capital and dynamic technology capability. Furthermore, the authors provide significant inspirations and implications for enterprise’s managers.

This study seeks to explore the intricate relationship among supply chain transparency, digitalization and idiosyncratic risk, with a specific focus on newly public firms. The objective is to determine whether supply chain transparency effectively mitigates idiosyncratic risk within this context and to understand the potential impact of digitalization on this dynamic interplay.

The study utilizes data from Initial Public Offerings (IPOs) on China’s Growth Enterprise Board (ChiNext) over the last five years, sourced from the CSMAR database and firms’ annual reports. The research covers the period from 2009 to 2021, observing each firm for five years post-IPO. The final sample comprises 2,645 observations from 529 firms. The analysis employs the Hausman test, considering the panel-data structure of the sample and favoring fixed effects over random effects. Additionally, it applies the high-dimensional fixed effects (HDFE) estimator to address unobserved heterogeneity.

The analysis initially uncovered an inverted U-shaped relationship between supply chain transparency and idiosyncratic risk, indicating a delicate equilibrium where detrimental effects diminish and beneficial effects accelerate with increased transparency. Moreover, this inverted U-shaped relationship was notably more pronounced in newly public firms with a heightened level of firm digitalization. This observation implies that firm digitalization amplifies the impact of transparency on a firm’s idiosyncratic risk.

This study distinguishes itself by providing distinctive insights into supply chain transparency and idiosyncratic risk. Initially, we introduce and substantiate an inverted U-shaped correlation between supply chain transparency and idiosyncratic risk, challenging the conventional linear perspective. Secondly, we pioneer the connection between supply chain transparency and idiosyncratic risk, especially for newly public firms, thereby enhancing comprehension of financial implications. Lastly, we pinpoint crucial digital conditions that influence the relationship between supply chain transparency and idiosyncratic risk management, offering a nuanced perspective on the role of technology in risk management.

Coal and power generation are related upstream and downstream industries. Coal price marketization and electricity price regulation have caused the price of coal to be sensitive to the benefits of generators. The paper aims to discuss these issues.

As a financial tool, contracts for differences can both help balance interests and reduce risks caused by spot price fluctuation. This thesis regards coal demand as a triangular fuzzy stochastic variable while directing a levelling consideration towards risk returns for coal and power enterprises that are involved in coal generation contracts for differences. Risk and benefit measurement models were established between coal suppliers and power generators, and risk and benefit balance optimization models for contract negotiation were constructed.

A numerical example showed that the above models can be effectively used to avoid the risks of coal-electricity parties.

This thesis regards coal demand as a triangular fuzzy random variable while directing a levelling consideration towards the risk return to coal and power enterprises that are involved with coal generation contracts for differences. The features of this thesis are the following: demand information is regarded as a fuzzy random variable instead of a random variable. With historical data, sales experience and increasingly clear macro-economic conditions, coal and power enterprises are able to make a fuzzy decision – to a certain extent – when the transaction approaches. Accurate market information enables the supply chain system to satisfy the clients’ needs better, improve the profit level or avoid severe financial damages; by developing a feasible set of contracts for different parameters, it is possible to estimate whether the price difference enables supply chain coordination, requires changes or gives accounts to all involved parties of the supply chain; and without the assumption that the traditional M-V rule is unfavourable to decision makers, this thesis proposes the prospect M-V rule, which involves decision makers’ projections of future coal generation prices and enables wide applicability of the response method to contracts for differences.

The aim of this paper is to synthesize graphene-modified titanium dioxide (GR-TiO₂) nanorod arrays nanocomposite films, so that these can enhance the photocatalytic properties of titanium dioxide and overcome the problem of difficult separation and recovery of photocatalysts.

The GR-TiO₂ nanocomposite films were synthesized via hydrothermal method and spin-coating. The obtained samples were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), ultraviolet–visible (UV-Vis) diffuse reflectance spectrum and Raman spectrum. The photocatalytic performance of the GR-TiO₂ nanocomposite films for degrading Rhodamin B under ultraviolet (UV) was studied by a UV-Vis spectrophotometer. The photocatalytic enhancement mechanism of graphene was studied by photoelectrochemical analysis.

The introduction of graphene expanded the range of the optical response of TiO₂ nanorod arrays, improving the separation efficiency of the photogenerated electron-hole pairs, and thus dramatically increasing its photocatalytic performance.

A simple and novel way for synthesizing GR-TiO₂ nanocomposite films has enhanced the photocatalytic performance of TiO₂.

The photocatalyst synthesized is easy to separate and recycle in the process of photocatalytic reaction, so it is possible to achieve industrialization.

The purpose of this paper is to derive a new method for the hydrophilic finishing of wool fabric.

A new biological catalyst, microbial transglutaminase (mTGase), was used to catalyze the grafting of ε‐poly‐L‐lysine (ε‐PLL) onto the wool fabric.

The K/S value, SEM morphology and DSC analysis proved that the grafting reaction occurred. The hydrophilic properties of the ε‐PLL‐grafted wool fabrics were studied. The results showed that the grafted ε‐PLL could increase the hydrophilicity, which was demonstrated in terms of the obvious shortening in the wetting time and the process of water absorption and moisture absorption. The grafted wool also achieved better antistatic property.

Future work could be focused on the application of this biological method on other protein fabric which was designed to change the performance.

The biological approach is safe, eco‐friendly and effective relative to the conventional methods.

The article's aim is to provide an overview of articles in this issue.

The article gives summary information and perspectives on the articles that appear in the issue and provides information helping readers.

The article first discusses three articles showing what three journal editors see as topics and find acceptable as research methods. The other three articles appear because they have important implications that receive limited attention in the literature. These articles address innovative treatment of problems with information commonly collected on return (repurchase), vague units of count and ineffective data collection.

This research provides insights on what three journal editors research, and the priorities and innovative work on the need for better return data, for better terms for units (e.g. of analysis) and for more effective data collection.

This paper has two main aims: to show responses like yes or very likely for inbound visitors returning to a destination can lead to misleading and unreliable information; and to clarify the kind of information that should be collected.

Responses from Taiwan's inbound visitors relating to returning are examined to see what can be learned. Modeling is used to extract meaningful quantitative information from data.

Modeling shows that survey responses about return are inconsistent. Although 95 percent of non‐visiting‐friends‐and‐relations (VFR) leisure visitors indicate returning, this is not consistent with a retention rate of 90 percent. A retention rate of 33 percent is consistent with the observation that 70 percent of person‐visits are first‐visits. However, 33 percent retention is not consistent with over 95 percent of visitors returning. Conventional questions are yielding highly unreliable information and, therefore, data collection should be changed.

Relations between vague questions and return trips have been established. This research provides new evidence of the need for return data to include information allowing estimation of volume and timing of return.