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The study’s purpose is to investigate the constraints and facilitators influencing skiing participation in Beijing. This research includes three segments based on the frequency of skiing participation (i.e. non-, low-frequency-, and high-frequency skiers). By doing so, the study offers an enhanced understanding of the Chinese skiing market and unveils insights assisting industry professionals to effectively address their customers' diverse needs and expectations.

An online survey was developed based on prior research and consisted of four sections: (1) skiing participation; (2) constraints; (3) facilitators; (4) demographics. Items in the constraint and facilitator scale were measured using a 7-point Likert scale. A total of 409 participants completed the survey. The participants included 137 non-skiers, 134 low-frequency skiers, and 138 high-frequency skiers.

Through an exploratory factor analysis, three constructs emerged: general constraints, facilitators and learning constraints. As expected, facilitators were a positive predictor of skiing participation. Importantly, the emergent construct of learning constraints was a negative predictor of skiing and yet, the construct of general constraints was insignificant. Furthermore, the three segments differ significantly in household status, income, and education level.

These results support previous research noting the relevance in skiing participation of the dimensions: facilitators and learning constraints. The findings point to the need for ski resorts in Beijing to offer instructional sessions for beginners so they may become familiar with skiing fundamentals and enhance their confidence, particularly among nonskiers and low-frequency skiers.

Industry 4.0 has an inimitable potential to create competitive advantages for the apparel industry by enhancing productivity, production, profitability, efficiency and effectiveness. This study, thus, aims to assess the digitalisation level of the Tanzanian apparel industry through the Industry 4.0 perspectives.

A mixed-methods-based approach was deployed. This study deployed semi-structured interviews, document review and observation methods for the qualitative approach. For the quantitative approach, closed-ended questionnaires were used to ascertain the digitalisation levels and maturity level of the textiles and apparel (T&A) factories and small and medium-sized textile enterprises in Tanzania. The sample size was 110, with participants engaged through the purposive sampling technique.

Industry 4.0 frameworks evolved into practices mainly since 2011 in several service and manufacturing industries globally. For Tanzania, the findings indicate that the overall maturity level of the T&A industries is 2.5 out of 5.0, demonstrating a medium level of adoption. Thus, the apparel industries are not operating under the industry 4.0 framework; they are operating within the third industrial revolution – Industry 3.0 – framework. For such industries to operate within the fourth industrial revolution – Industry 4.0 – that is only possible if there is significantly well-developed industrial infrastructure, availability of engineering talent, stable commercial partnerships, demand from the marketplace and transactional relationship with customers.

This study’s limitations include: firstly, Industry 4.0 is an emerging area; this resulted in limited theoretical underpinnings in the Tanzanian perspectives. Secondly, the studied industries may not suffice the need to generalise the findings for the entire country, thus needing another study.

Although Industry 4.0 conceptual frameworks have been on trial in several industries since 2011, this is amongst the first empirical research on Industry 4.0 in the Tanzanian apparel industry that assesses the digitalisation levels.

Smart contracts are written in high-level programming languages, compiled into Ethereum Virtual Machine (EVM) bytecode, deployed onto blockchain systems and called with the corresponding address by transactions. The deployed smart contracts are immutable, even if there are bugs or vulnerabilities. Therefore, it is critical to verify smart contracts before deployment. This paper aims to help developers effectively and efficiently locate potential defects in smart contracts.

GethReplayer, a smart contract testing method based on transaction replay, is proposed. It constructs a parallel transaction execution environment with two virtual machines to compare the execution results. It uses the real existing transaction data on Ethereum and the source code of the tested smart contacts as inputs, conditionally substitutes the bytecode of the tested smart contract input into the testing EVM, and then monitors the environmental information to check the correctness of the contract.

Experiments verified that the proposed method is effective in smart contract testing. Virtual environmental information has a significant effect on the success of transaction replay, which is the basis for the performance of the method. The efficiency of error locating was approximately 14 times faster with the proposed method than without. In addition, the proposed method supports gas consumption analysis.

This paper addresses the difficulty that developers encounter in testing smart contracts before deployment and focuses on helping develop smart contracts with as few defects as possible. GethReplayer is expected to be an alternative solution for smart contract testing and provide inspiration for further research.

This study aims to investigate the design and optimization of landing gear buffers to improve the landing-phase comfort of civil aircraft.

The vibration comfort during the landing and taxiing phases is calculated and evaluated based on the flight-testing data for a type of civil aircraft. The calculation and evaluation are under the guidance of the vibration comfort standard of GB/T13441.1-2007 and related files. The authors establish here a rigid-flexible coupled multibody dynamics finite element model of one full-size aircraft. Furthermore, the authors also implement a dynamic simulation for the landing and taxiing processes. Also, an analysis of how the main parameters of the buffers affect the vibration comfort is presented. Finally, the optimization of the single-chamber and double-chamber buffers in the landing gear is performed considering vibration comfort.

The double-chamber buffer with optimized parameters in landing gear can improve the vibration comfort of the aircraft during the landing and taxiing phases. Moreover, the comfort index can be increased by 25.6% more than that of a single-chamber type.

To the best of the authors’ knowledge, this study first investigates the evaluation methods and evaluation indexes on the aircraft vibration comfort, then further conducts the optimization of the parameters of landing gear buffer with different structures, so as to improve the comfort of aircraft passengers during landing process. Most of the current studies on aircraft landing gear have focused on the strength and safety of the landing gear, with very limited research on cabin vibration comfort during landing and subsequent taxiing because of the coupling of runway surface unevenness and airframe vibration.

This paper aims to prepare Poly(Styrene-Butyl acrylate-Methacrylic acid) @Poly Gallic acid-Fe³⁺ photonic crystal composite inks [P(St-BA-MAA)@PGA-Fe³⁺ PCCI, @ means the PGA-Fe³⁺ is loaded on the microspheres] and construct noniridescent structural colors on fabric substrates, with the goal of improving the visibility of structural colors.

P(St-BA-MAA)@PGA-Fe³⁺ PCCI were prepared by coating P(St-BA-MAA) microspheres with a metal-polyphenol network formed by gallic acid (GA, C₇H₆O₅) and Fe³⁺. The assembly effects of the inks were explored under different conditions, including pH, temperature, concentration and surface tension. The optimal self-assembly conditions of the inks were determined using the controlled variable method.

The results demonstrated the successful preparation of P(St-BA-MAA)@PGA-Fe³⁺ PCCI. The metal polyphenol network film composed of GA and Fe³⁺ was successfully coated on the surface of P(St-BA-MAA) seed microspheres. The assembly mechanism of the inks was investigated, indicating that at a diethylene glycol (DEG, C₄H₁₀O₃) concentration of 0.3 wt% and pH of 7, bright noniridescent structural colors could be formed on fabric surfaces after self-assembly by PCCI at 60 °C for 10 min. Furthermore, the mechanical fastness of the structural colors was enhanced due to the adherence of the soft shell composed of P(St-BA-MAA) and GA.

Utilizing a cost-effective approach and a diverse array of readily available raw materials, we have successfully prepared P(St-BA-MAA)@PGA-Fe³⁺ PCCI, which boasts superior performance and offers fabrics a range of unique coloring styles. This innovation paves the way for potential applications of structural colors in practical production, thereby broadening their realm of utility.

Drawing upon the theory of communication visibility, this research intends to investigate the direct effect of enterprise social media (ESM) usage on team members’ knowledge creation capability (KCC) and the mediating effects of psychological safety and team identification. In addition, it aims to untangle how the efficacy of ESM usage varies between pre- and post-COVID-19 periods.

Using two-wave survey data from 240 members nested within 60 teams, this study utilizes a multilevel approach to test the proposed hypotheses.

We discover that ESM usage enhances team members’ KCC. More importantly, the results show that psychological safety and team identification mediate the ESM–KCC linkage. Interestingly, we further find that the impacts of ESM usage on team members’ KCC, psychological safety, and team identification are stronger in the pre-COVID-19 period than those in the post-COVID-19 period.

This research sheds light on the ESM literature by unraveling the mechanisms of psychological safety and team identification underlying the linkage between ESM usage and team members’ KCC. Moreover, it advances our understanding of the differential efficacy of ESM usage in pre- and post-COVID-19 periods.

The study aims to test prospect theory (PT) predictions in the cryptocurrency (CC) market. It proposes a new asset pricing model that explores the potential of prospect theory value (PTV) as a significant predictor of CC returns.

The study comprehensively analyses a large sample set of 1,629 CCs, representing more than 95% of the CC market. The study uses a portfolio analysis approach, employing univariate and bivariate sorting techniques with equal-weighted and value-weighted portfolios. The study also employs ordinary least squares (OLS) regression, panel data methods and quantile regression (QR) to estimate the models.

This study demonstrates an average inverse relationship between PTV and CC returns. However, this relationship exhibits asymmetry across different quantiles, indicating that investor reactions vary based on market conditions. Moreover, PTV provides more robust predictions for smaller CCs characterized by high volatility and illiquidity. Notably, the findings highlight the dominant role of the probability weighting (PW) component in PT for predicting CC behaviors, suggesting a preference for lottery-like characteristics among CC investors.

The study is one of the early studies on CC price dynamics from the PT perspective. The study is the first to apply a QR approach to analyze the cross-section of CCs using a PT-based asset pricing model. The results shed light on CC investors' decision-making processes and risk perception, offering valuable insights to regulators, policymakers and market participants. From a practical perspective, a trading strategy centered around the PTV effect can be implemented.

Under the background of coexistence of information overload and information fragmentation, it is of great significance to identify influencing factors and reveal the evolution logic of public opinion for public opinion governance.

Taking 24 hot social events as research cases, firstly, the evolution process of public opinion was divided into initial stage and response stage. Secondly, eight antecedent variables were extracted for qualitative comparative analysis of fuzzy sets. Finally, the configuration path of public opinion evolution results was summarized.

The research showed that compared with the initial stage, the influencing factors in the reaction stage played a key role in the continuous evolution of public opinion. The influencing factors in the initial stage and response stage played an indispensable role in promoting the evolution of public opinion to calm down.

This research can provide reference for regulators to timely grasp the initiative, discourse power and leadership of public opinion development.

Research on the two-stage configuration path of public opinion evolution is helpful to clarify the key factors affecting the evolution trend of online public opinion of hot events.

Despite the increasing studies concerning external knowledge for innovative firms or for innovations, little research demonstrates as what are those capabilities that enable firms to explore and assess high-valuable knowledge. The aim of this study is to examine those capabilities that aid innovative firms to search and identify the potential knowledge for open innovation. This study is an empirical and theoretical examination of knowledge search and identification capabilities of the UK-based biotechnology firms. The literature has been drawn from the research streams into knowledge management, open innovation and dynamic capabilities.

Forty-five semi-structured interviews with senior-level innovation and R&D managers were completed, and thematic analysis using NVivo 12 was performed.

Evidence from 45 interviews with senior-level innovation and R&D managers in the UK-based biotechnology firms and thematic analysis was used to develop a process framework capturing the key capabilities. By identifying the role and contribution of each of the capabilities in knowledge identification in the practices of open innovation, this study contributes to open innovation and knowledge management literature.

Managerial implications suggest knowledge-oriented leads greatly influence the development of identification capabilities. Hence, firms with knowledge-oriented leads are better crafted approaches to achieve competitive advantages in the practices of open innovation.

This study offers a new theoretical perspective and empirical evidence to view knowledge identification capabilities as the dynamic capabilities. This study offers a process framework of identification capabilities.

This study aims to assess the oxidative stability of avocado oil (AO) at various temperatures, using butylated hydroxytoluene (BHT) as an artificial antioxidant and different concentrations of ultrasonic extract of Chlorella vulgaris.

Extracts of C. vulgaris were obtained using four solvents: water, acetone, ethanol and 80% ethanol-aqueous. Standard techniques were used to conduct qualitative phytochemical screening of the extracts. The extracted samples were analyzed for total phenolics, total flavonoids, antioxidant activity and phenolic compound fractionation. Some physicochemical parameters of AO treated with various concentrations of C. vulgaris ultrasonic extract compared to a 200 ppm BHT and exposed to different temperatures were measured.

The highest phenolic, flavonoids content and antioxidant activity was achieved by 80% ethanolic extract of C. vulgaris . The results showed that exposure of AO to high temperatures led to significant changes in the oil's physicochemical properties. These changes increased as the temperature increased. On the other hand, adding 80% ethanolic extract of C. vulgaris into AO reduced the effect of heat treatment on the change in physicochemical properties.

Adding 80% ethanolic extract of C. vulgaris into AO can potentially reduce the impact of heat treatment on the alteration of physicochemical properties.