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This chapter focuses on the conservative Heritage Foundation's “Project 2025” and especially its comprehensive Mandate for Leadership, which provides a detailed plan for fundamental policy and administrative changes to be instituted in a Trump second term. It advocates an unparalleled concentration of executive power, elimination of the independence of the civil service and Department of Justice from the office of the president, and institution of permanent dominance of Trumpian conservatism. The specific focus is on the Mandate's proposed antienvironmental policies, which are weaved throughout the document and are designed to roll back sweepingly previous climate-change and environmental protection policies. Stressing maximal usage, production, and export of fossil fuel, the Trumpian “energy dominance agenda” is in polar contradiction to climate science policy aimed at decarbonizing the economy and society and averting catastrophic climate change and a “Hothouse Earth.” The Mandate's postfactual discourse combined with its advocacy of an all-powerful president and conspiratorial vision of the “woke” left as public enemy has definite protofascist overtones.

The purpose of this study is to achieve a comprehensive understanding of how the intricate interconnections between oil price fluctuations, supply chain disruptions and shifting demand patterns collectively shape inflation dynamics within the Chinese economy, especially during critical periods such as the Covid-19 pandemic and geopolitical events like the Russia–Ukraine conflict. The importance of assessing the impact of oil price volatility on China’s inflation becomes particularly pronounced amidst these challenging circumstances.

This study uses the Markov Regime-Switching generalized autoregressive conditional heteroskedasticity (MRS-GARCH) family of models under student’s t-distributions to measure the uncertainty of oil prices and the inflation rate during the period spanning from 1994 to 2023 in China.

The results indicate that the MRS-GJR-GARCH-in-mean (MRS-GARCH-M) models, when used under student’s t-distributions, exhibit superior performance in modeling the volatility of both oil prices and the inflation rate. This finding underscores the effectiveness of these models in capturing the intricacies of volatility dynamics in the context of oil prices and inflation. The study has identified compelling evidence of regime-switching behavior within the oil price market. Subsequently, the author conducted an analysis by extracting the forecastable component, which represents the expected variation, from the best-fitted models. This allowed us to isolate the time series of oil price uncertainty, representing the unforecastable component. With this unforecastable component in hand, the author proceeded to estimate the impact of oil price fluctuations on the inflation rate. To accomplish this, the author used an autoregressive distributed lag model, which enables us to explore the dynamic relationships and lags between these crucial economic variables. The study further reveals that fluctuations in oil prices exert a noteworthy and discernible influence on the inflation rate, with distinct patterns observed across different economic regimes. The findings indicate a consistent positive impact of oil prices on inflation rate uncertainty, particularly within export-oriented and import-oriented industries, under both of these economic regimes.

This study offers original value by analyzing the impact of crude oil price volatility on inflation in China. It provides unique insights into the relationship between energy market fluctuations and macroeconomic stability in one of the world’s largest economies. By focusing on crude oil – a critical but often overlooked component – this research enhances understanding of how energy price dynamics influence inflationary trends. The findings can inform policymakers and stakeholders about the significance of energy market stability for maintaining economic stability and guiding inflation control measures in China.

In this chapter, I unpack the sociotechnical frontier of Web3, anchoring its analysis in the historical intertwining of computational histories and cryptographic cultures. It explores the case study of cryptomarkets to illustrate the originating culture of a laissez-faire-valuing techno-elite. The chapter traces the computational and social origins of Web3, from Robert Axelrod’s work on cooperation to the cypherpunk movement and the development of digital currencies. The Silk Road cryptomarket serves as a microcosm of the larger Web3 ecosystem, offering insights into its potential to disrupt established systems and the complex policy considerations this disruption gives rise to. The chapter examines the community dynamics, social connection, and identity-building practices within cryptomarkets, revealing the tension between experimental energy and outsider perspectives. It situates the emergent form of the Web3 community within its history of technological development, moving beyond community dynamics to larger shifts in computational process and power. The chapter concludes by offering policy recommendations that address trust in decentralised systems, adapt to borderless digital communities, and empower innovative forms of activism through technology. The chapter maintains a critical stance throughout, acknowledging the diverse and sometimes contradictory nature of the Web3 community while exploring its potential to reshape digital interactions and challenge existing power structures.

There is a dearth of knowledge regarding how user dependency offers valuable resources to develop the intellectual capital of social streaming apps (SSAs) companies. This study aims to integrate major conceptual components of the UandD model, identify contextualized goal-oriented SSA dependency and empirically evaluate their interrelated user-dependency relationships in the SSA context.

A mixed-methods approach was utilized in this study. First, user gratifications were elicited through a qualitative approach, considering the exploratory stage of the SSA phenomenon. Second, statistical methods were applied to investigate and extract the sub-dimensions of SSA dependency. At last, a research model was developed grounded on the UandD model and empirically validated using the quantitative approach.

The results validated the gratification-dependency-attitude-behavior relationships hypothesized by the UandD framework in SSA. The role of user-SSA dependency in enhancing intellectual capital in the social media industry has been highlighted in this study.

This research not only provides an opportunity for the UandD model to realize its theoretical potential as envisioned by scholars but also contributes to the scholarship on social streaming apps and media dependency theory by conceptualizing goal-oriented dependency in SSAs.

The research results will guide digital media practitioners to a more nuanced understanding of the relationships between their users and modern digital media apps and thus empower the practitioners to better manage their intellectual capital based on the facilitation of their users’ dependency.

This work is one of the pioneers in contextualizing the UandD model in the SSA field, refining and measuring the SSA dependency and its distinct subdimensions and employing mixed-methods to offer a comprehensive understanding of how user dependency boosts intellectual capital in the SSA industry.

Performance management systems (PMSs) must remain dynamic and maintain the ability to withstand the high degrees of complexity brought about by digital technologies (DTs). Academics and practitioners have explored DTs and PMSs separately. This study aims to bridge the academic and practitioner discourse surrounding PMSs and DTs to promote knowledge diffusion and collaboration.

The authors adopted a mixed method approach, combining quantitative and qualitative content analysis, which were validated through semi-structured interviews with academics and practitioners. The authors first conducted a content analysis of the academic literature contained in the Scopus database and the practitioners' literature featured in the Nexis Uni database.

The combined academic-practitioners’ body of knowledge underscores the risk and environmental dimension, as well as the theoretical frameworks employed to explore digitally empowered PMSs. These findings were corroborated by 17 confirmatory interviews conducted with scholars and practitioners.

Due to its scope, the research is limited to publications listed on Scopus and Nexis Uni. Additionally, its qualitative and interpretative nature presents some generalizability issues.

This study connects the intentions of scholars and practitioners concerning PMSs and DTs to promote collaborative efforts and knowledge diffusion. Moreover, considering the broader spectrum of insights, this manuscript postulates new avenues of research that address the key issues and concerns of both academics and practitioners. Finally, this article showcases PMs- and DT-appropriate theoretical frameworks that can inform practitioners’ discourse and work.

This study aims to enhance heat transfer efficiency while minimizing friction factor and entropy generation in the flow of Nickel zinc ferrite (NiZnFe₂O₄) nanoparticles suspended in multigrade 20W-40 motor oil (as specified by the Society of Automotive Engineers). The investigation focuses on the effects of the melting process, nonspherical particle shapes, thermal dispersion and viscous dissipation on the nanofluid flow.

The fundamental governing equations are transformed into a set of similarity equations using Lie group transformations. The resulting set of equations is numerically solved using the spectral local linearization method. Additionally, sensitivity analysis using response surface methodology (RSM) is conducted to evaluate the influence of key parameters on response function.

Higher dispersion reduces entropy production. Needle-shaped particles significantly enhance heat transfer by 27.65% with melting and reduce entropy generation by 45.32%. Increasing the Darcy number results in a reduction of friction by 16.06%, lower entropy by 31.72% and an increase in heat transfer by 17.26%. The Nusselt number is highly sensitive to thermal dispersion across melting and varying volume fraction parameters.

This study addresses a significant research gap by exploring the combined effects of melting, particle shapes and thermal dispersion on nanofluid flow, which has not been thoroughly investigated before. The focus on practical applications such as fuel cells, material processing, biomedicine and various cooling systems underscores its relevance to sectors such as nuclear reactors, tumor treatments and manufacturing. The incorporation of RSM for friction factor analysis introduces a unique dimension to the research, offering novel insights into optimizing nanofluid performance under diverse conditions.