Search results

This study aims to evaluate blast loads on and the response of submerged structures.

An arbitrary Lagrangian–Eulerian method is developed to model fluid–structure interaction (FSI) problems of close-in underwater explosions (UNDEX). The “fluid” part provides the loads for the structure considers air, water and high explosive materials. The spatial discretization for the fluid domain is performed with a second-order vertex-based finite volume scheme with a tangent of hyperbola interface capturing technique. The temporal discretization is based on explicit Runge–Kutta methods. The structure is described by a large-deformation Lagrangian formulation and discretized via finite elements. First, one-dimensional test cases are given to show that the numerical method is free of mesh movement effects. Thereafter, three-dimensional FSI problems of close-in UNDEX are studied. Finally, the computation of UNDEX near a ship compartment is performed.

The difference in the flow mechanisms between rigid targets and deforming targets is quantified and evaluated.

Cavitation is modeled only approximately and may require further refinement/modeling.

The results demonstrate that the proposed numerical method is accurate, robust and versatile for practical use.

Better design of naval infrastructure [such as bridges, ports, etc.].

To the best of the authors’ knowledge, this study has been conducted for the first time.

Artificial intelligence (AI) is vastly impacting the digital transformation of societies, economies, businesses, markets and enterprises, at a very fast pace, mostly after the global success of the generative algorithms. In this respect, this study, with an exploratory intention, aims to provide evidence about the fundamental issues of AI, particularly if generative, when adapted to humanism, with a specific focus on the wine business.

An exploratory analysis, conducted on a convenience sample of wine business operators, has been performed to investigate AI applications when connected with the conceptual platform of the “Industry 5.0” framework.

The results of the survey provide evidence about the success of AI in the wine business. Specifically, the research outcomes highlight that the interviewees (wine business operators) recognized the high relevance of the potential use of AI in the strategic and operating management of wine firms.

This study aims to provide new empirical evidence with regard to the application of AI in real business contexts. More specifically, in this exploratory investigation, a potential interaction between AI and sustainability has been highlighted in the wine industry, especially from an environmental point of view, i.e. for respectfully governing and managing the business impact on the planet and also for increasing the general efficiency of the process, with peculiar applications on the managerial, economic and financial side of the wine business.

Previous research focused on open innovation (OI) suggests that enterprises benefit from adopting the journey; however, the relationship among OI, marketing journey and knowledge-intensive innovation marketing activities (KIIMA) remains unclear. The present study proposes a conceptual model of the marketing journey linking heterogeneous modes of marketing collaboration to knowledge-intensive activities.

The conceptual model was tested via ordinary least squares (OLS) linear regression based on a sample of data drawn from the Eurostat database.

The results indicate that strategies are a robust proxy for evaluating KIIMA, and partnerships, heterogeneous sources of knowledge and different marketing modes for collaboration among European knowledge-intensive firms are core antecedents of KIIMA, such as new-product development and marketing innovation, as well as firms' sustainable competitive advantage.

This study fills the gap by tracking the role of the journey within marketing collaborations on KIIMA, and it intervenes in the debate about interactive marketing innovation mechanisms. The study contributes to OI, knowledge management and the marketing literature by identifying the heterogeneous modes for marketing collaborations under which the marketing journey enhances knowledge-intensive activities such as those for marketing innovation.

Within the last 15 years, law enforcement agencies have increased their collection of data on vehicle stops. A variety of resource guides, research reports, and peer‐reviewed articles have outlined the methods used to collect these data and conduct analyses. This literature is spread across numerous publications and can be cumbersome to summarize for practical use by practitioners and academics. This article seeks to fill this gap by detailing the current best practices in vehicle stop data collection and analysis in state police agencies.

The article summarizes the data collection techniques used to assist in identifying racial/ethnic disparities in vehicle stops. Specifically, questions concerning why, when, how, and what data should be collected are addressed. The most common data analysis techniques for vehicle stops are offered, including an evaluation of common benchmarking techniques and their ability to measure at‐risk drivers. Vehicle stop outcome analyses are also discussed, including multivariate analyses and the outcome test. Within this summary, strengths and weaknesses of these techniques are explored.

In summarizing these approaches, a body of best practices in vehicle stop data collection and analysis is developed.

Racial profiling continues to be a contentious issue for law enforcement and the community. A considerable body of research has developed to assess the prevalence of racial profiling. This article offers social scientists and practitioners a comprehensive, succinct, peer‐reviewed summary of the best practices in vehicle stop data collection and analysis.

This study aims to provide a comprehensive review and bibliometric analysis of the literature in the field of science, technology, engineering and mathematics (STEM) education over the past 15 years, with a specific focus on global distribution and research trends.

This study collected 1,718 documents from the Web of Science (WOS) database and analyzed their timeline distribution, geographical distribution, research topics, subject areas, learning stages and citation burst using a bibliometric approach with VOSviewer and Citespace.

Results indicated that: overall, STEM education has increasingly gained scholarly attention and is developing diversely by emphasizing interdisciplinary, cross-domain and regional collaboration. In terms of global collaboration, a collaborative network with the USA in the center is gradually expanding to a global scope. In terms of research themes, four key topics can be outlined including educational equity, pedagogy, empirical effects and career development. Social, cultural and economic factors influence the way STEM education is implemented across different countries. The developed Western countries highlighted educational equity and disciplinary integration, while the developing countries tend to focus more on pedagogical practices. As for research trends, eastern countries are emphasizing humanistic leadership and cultural integration in STEM education; in terms of teachers’ professional development, teachers’ abilities of interdisciplinary integration, technology adoption and pedagogy application are of the greatest importance. With regards to pedagogy, the main focus is for developing students’ higher-order abilities. In terms of education equity, issues of gender and ethnicity were still the hottest topics, while the unbalanced development of STEM education across regions needs further research.

This study provides a global landscape of STEM education along the timeline, which illustrates the yearly progressive development of STEM education and indicates the future trends.