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The purpose of this paper is to provide an overview of the computational progress in the development of hydrogen-fired gas turbines. This review aims to identify suitable combustion models, appropriate NO_x chemistry mechanisms and NO_x emission levels for effective utilization of hydrogen as an alternative fuel in gas turbines.

Hydrogen is recognized as a potential alternative fuel for achieving exceptionally low emissions in gas turbines. The developments in conventional, trapped vortex combustor and micromix combustors are discussed, along with various computational models aimed at accurately predicting combustion and emission characteristics. The results of numerical simulations were then discussed with emphasis on their role in optimizing the combustor geometry.

Computational studies that were used to optimize the combustor geometry to reduce NO_x emissions and the flashback phenomenon are discussed. To retrofit existing gas turbines for hydrogen fuel, minor modifications that are required were discussed by analyzing extensive literature. The influence of key design and geometrical parameters on NO_x emissions and the appropriate selection of combustion models for numerical simulations in optimizing various combustion systems are elaborated.

The review emphasizes the computational studies in the progress of hydrogen-fired gas turbine developments. The previous reviews were primarily focused on the combustion technologies for hydrogen-fired gas turbines. This comprehensive review focuses on the key design parameters, flame structure, selection of combustion models, combustion efficiency improvement and impact of parametric studies on NO_x formation of various combustion systems, in particular hydrogen combustion for gas turbine applications.

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.

This study aims to develop a scale using item response theory (IRT) to assess the entrepreneurial potential for digital transformation in Brazilian companies.

IRT was used to develop a scale for entrepreneurial potential in digital transformation. This scale was constructed from a questionnaire, covering the domains: (1) data-driven culture, (2) openness to knowledge and adaptation to change, (3) connectivity and (4) creativity and innovation. The questionnaire was administered to a sample of 216 entrepreneurs from small business enterprise (SBE) and startups in Brazil.

A questionnaire was developed and validated to assess the latent trait of entrepreneurial potential within the context of digital transformation. Additionally, a three-level scale of entrepreneurial potential was established: low (level I), intermediate (level II) and high (level III). The interpretation of this scale provides valuable information on which domains, such as data-driven culture, innovation, among others, can be enhanced to improve the potential of entrepreneur for digital transformation.

The sample was limited to small Brazilian companies and startups, which may restrict the applicability of the results to other business or geographic contexts. Additionally, the items evaluated in the scale may not fully capture all nuances of entrepreneurial potential for digital transformation. Future research should consider including new items that cover a broader range of entrepreneurial characteristics.

The findings of this study have significant practical implications for the Brazilian entrepreneurial ecosystem, the entrepreneurs themselves, public policy makers and entrepreneurship support institutions. These results can guide digital transformation strategies, adjustments in public policies and investments, thereby promoting economic development and innovation in the country.

This study stands out for using IRT as a robust methodology to develop an interpretative scale to assess entrepreneurial potential in the digital transformation era. By focusing on Brazilian SBEs and startups, the study offers an original contribution on how these companies are handling the challenges of digitalization and identifying areas for improvement to further promote digital transformation among entrepreneurs.

Organisations using advanced technology, like ChatGPT, for executing their marketing practices are proliferating, but such fast growth also comes with different adverse impacts of ChatGPT. This interaction of ChatGPT with the humanly implemented marketing 5.0 approach complements the marketing effectiveness. However, while considering the brighter aspects of this techno-marketing integration, marketers should also keep its dark side in mind. Therefore, this chapter investigates the integration of AI-enabled ChatGPT into marketing 5.0 practices. However, both the concepts under study are growing in terms of literature, and the research gap is even more extended when considering their associated views. Furthermore, significantly less literature is available emphasising the negative aspects of this advanced technology. This chapter bridges these gaps by reviewing the literature and presenting the gold-plating effect of ChatGPT usage while implementing marketing 5.0 practices. It also proposes a framework for showing the relationship between ChatGPT utilisation and practicing marketing 5.0, depicting the dark side of this techno-marketing integration. It also emphasised the need for conscious and learned associations between the concepts under study.

The purpose of this study is to examine the relation between product market competition and audit fees by using firm-level product market competition measures and mitigating the endogeneity issues.

This study uses 12,136 US firms from 2004 and 2019. To ensure the robustness of the main findings, this study uses three firm-level product market competition measures and import trade tariff rate reductions of the USA as a quasi-natural experiment. This study also performs three cross-sectional tests and validation tests.

This study demonstrates that there is a negative relation between product market competition and audit fees and establishes a causal relation. Moreover, it reveals that the findings become more pronounced when auditors possess industry-specific expertise, when client firms are younger, and when operating within more homogeneous industries. Additionally, a validation analysis supports the findings.

This study offers significant insights for regulators by highlighting how product market competition plays a constructive role in overseeing firm management.

The authors contribute to the existing literature by showing that there is a negative association between product market competition and audit fees after controlling external monitoring mechanisms. The authors also find the causal relation. These findings indicate that competitive pressures originating from product markets exert a significant influence on disciplining a client firm’s management.

The purpose of this study is to comprehensively analyze the research article retractions in social sciences over the past decade (2014–2023).

The study used scientometric methods to evaluate the prevalence, patterns and factors contributing to social sciences article retractions. Bibliographic data of retracted articles were collected from the Retraction Watch Database under an agreement signed with the database. Further, citations of the retracted articles were collected from Scopus and Google Scholar. The analysis encompasses performance assessment and citation-based analysis to reveal the trend of retraction and scrutinize the impact of retracted articles.

Over the past decade, article retractions have shown dynamic trends, with notable fluctuations in recent years. Further, investigating the time taken for article retraction reveals the urgency of addressing issues identified soon after publication. Scientific misconduct and publication-related concerns emerge as primary factors leading to retractions. Countries such as Russia, the USA, China and publishers such as Elsevier and Taylor and Francis led in the retractions of social science articles. A significant portion of retracted works had garnered academic attention prior to retraction and even after retraction.

This study can contribute to a better understanding among scholars and stakeholders of the trends and reasons for retractions of research articles in the social sciences.

While the application of blockchain technology in the organic food supply chain has been increasingly recognized, the extant knowledge of how blockchain-driven traceability influences consumer perceptions and purchase intentions remains underexplored. Grounded in the stimulus-organism-response theory, this study aims to construct a moderated mediation model to examine blockchain-enabled traceability’s direct and indirect impacts on organic food purchase intention through perceived blockchain-related information transparency, considering the moderating role of blockchain-based trust.

A purposive sample of 5,326 Vietnamese consumers was surveyed using the PROCESS macro to test the proposed hypotheses.

The findings indicate that blockchain-enabled traceability significantly enhances perceived blockchain-related information transparency, which positively influences organic food purchase intention. Furthermore, blockchain-based trust was found to positively moderate both the direct effect of transparency on purchase intention and the indirect impact of traceability on purchase intention through transparency.

Practical and managerial insights for stakeholders in the organic food sector are also discussed.

These results contribute to the literature by extending the stimulus-organism-response model to the context of blockchain technology in supply chains and highlighting the critical role of trust in moderating the effectiveness of technological innovations.

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.

The purpose of this paper is to demonstrate the efficacy of machine learning (ML) in managing natural language processing tasks, specifically by developing two ML models to systematically classify a substantial number of food waste interventions.

A literature review was undertaken to gather global food waste interventions. Subsequently, two ML models were designed and trained to classify these interventions into predefined supply chain-related groups and intervention types. To demonstrate the use of the models, a meta-analysis was performed to uncover patterns amongst the interventions.

The performance of the two classification models underscores the capabilities of ML in natural language processing, significantly enhancing the efficiency of text classification. This facilitated the rapid and effective classification of a large dataset consisting of 2,469 food waste interventions into six distinct types and assigning them to seven involved supply chain stakeholder groups. The meta-analysis reveals the most dominant intervention types and the strategies most widely adopted: 672 interventions are related to “Process and Operations Optimisation”, 457 to “Awareness and Behaviour Interventions” and 403 to “Technological and Engineering Solutions”. Prominent stakeholder groups, including “Processing and Manufacturing”, “Retail” “Government and Local Authorities” and “NGOs, Charitable Organisations and Research and Advocacy Groups”, are actively involved in over a thousand interventions each.

This study bridges a notable gap in food waste intervention research, a domain previously characterised by fragmentation and incomprehensive classification of the full range of interventions along the whole food supply chain. To the best of the authors’ knowledge, this is the first study to systematically classify a broad spectrum of food waste interventions while demonstrating ML capabilities. The study provides a clear, systematic framework for interventions to reduce food waste, offering valuable insight for practitioners in the food system, policymakers and consumers. Additionally, it lays the foundation for future in-depth research in the food waste reduction domain.

The purpose of this paper is the study of flexural properties of architected lattice beams composed of modified body-centered cubic (BCC) structures when such are additively manufactured with the liquid crystal display method. The BCC topology was modified by grading the dimensions of the cross-sections of the struts that compose them and their targeted distribution within the lattice beam.

Six gradations of strut cross-sections were proposed, and their effective stiffness was evaluated in compression finite element (FE) simulations. These were compared and categorized according to their stiffness. Then, these were distributed and arranged in a targeted manner, following two approaches: longitudinal and transversal. Experimental three-point bending tests and FE simulations were performed to characterize their effective flexural properties. The properties of targeted distributions were contrasted with those of uniform distributions.

Although the structures with longitudinal and transverse distribution presented the same relative density, they demonstrated different stiffness and strength. Beams with longitudinal distribution were 77% stiffer than those with transverse distribution. The method proposed here demonstrates how the effective mechanical properties and failure modes can be tailored by modifying the material arrangement in engineered structures while keeping the amount of material used constant.

The flexural properties of lattice beams with two types of grading and unit cell arrangements were studied. The literature has not deeply studied such a double degree of matter distribution and arrangement in structures.