MHD flow in a vertical channel under the effect of temperature dependent physical parameters

Mixed convective flow in a vertical channel filled with electrically conducting viscous fluid with isothermal wall conditions is investigated for variable properties. The combined effects of temperature dependent viscosity and temperature dependent thermal conductivity are analyzed. The solutions are obtained both analytically by perturbation method and numerically by Runge–Kutta method with shooting technique. The dimensionless governing parameters affecting velocity and temperature fields are variable viscosity parameter (−0.5 ≤ bν ≤ 0.5), variable thermal conductivity parameter (−0.5 ≤ bk ≤ 0.5), Hartmann number (1 ≤ M ≤ 3), applied electric field parameter (E0 = ±1, 0), wall temperature ratio parameter (−2 ≤ m ≤ 2) and buoyancy parameter (0 < N ≤ 1.5). For some limiting cases, the obtained results are validated by comparing with those available from the existing literature. Correlations for skin friction and Nusselt number in terms of governing parameters are developed

The k-means algorithm: A comprehensive survey and performance evaluation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. The k-means clustering algorithm is considered one of the most powerful and popular data mining algorithms in the research community. However, despite its popularity, the algorithm has certain limitations, including problems associated with random initialization of the centroids which leads to unexpected convergence. Additionally, such a clustering algorithm requires the number of clusters to be defined beforehand, which is responsible for different cluster shapes and outlier effects. A fundamental problem of the k-means algorithm is its inability to handle various data types. This paper provides a structured and synoptic overview of research conducted on the k-means algorithm to overcome such shortcomings. Variants of the k-means algorithms including their recent developments are discussed, where their effectiveness is investigated based on the experimental analysis of a variety of datasets. The detailed experimental analysis along with a thorough comparison among different k-means clustering algorithms differentiates our work compared to other existing survey papers. Furthermore, it outlines a clear and thorough understanding of the k-means algorithm along with its different research directions

Assessing Traffic Performance: Comparative Study of Human and Automated HGVs In Urban Intersections and Highway Segments

This study conducts a comparative analysis of traffic dynamics at urban signalized intersections and on highways, incorporating both human-operated and automated heavy goods vehicles (HGVs) using the PTV VISSIM simulation model. It examines the impacts of automated driving technologies on critical traffic performance metrics such as queue length, travel time, vehicle delay, emissions, and fuel consumption. Initial findings indicate that automation in HGVs enhances traffic flow, particularly by reducing queue lengths and vehicle delays. However, varying levels of automation from cautious to aggressive reveal complex trade-offs between operational efficiency and environmental impacts. On highways, automated HGVs demonstrate superior performance by reducing travel times and delays while increasing throughput compared to human-driven HGVs. These results underscore the operational benefits of automated HGVs under diverse traffic conditions and highlight their significant implications for transportation planning and policy-making. This research contributes valuable insights into the integration of automated technologies in transportation systems, facilitating informed decision-making for stakeholders considering the adoption of these advancements in the current infrastructure

Brexit or Brand it? The effects of attitude towards Brexit and reshored brands on consumer purchase intention

Brexit has caused a seismic shift in the British socio-economic and political landscapes, creating widespread uncertainties, while simultaneously giving hope and national pride to millions. The decision by a number of organizations to reshore their production has opened a new era for business management that challenges the axiomatic assumption of the benefits of offshored production. Although reshoring predates Brexit, the link between the two in the British context is not just serendipitous and they are argued to have reasonable interlinkages. However, there is inadequate empirical evidence to suggest that British consumers’ attitude towards Brexit has any effect on their intention to purchase reshored brands. Through a mixed-methods study comprising a survey of 415 respondents and 20 in-depth interviews, this paper addresses this research gap. Findings suggest that corporate social responsibility (CSR) and consumer reshoring sentiment (CRS) have positive effects on consumers’ attitude towards reshored brands. Despite CRS's positive influence on attitude towards Brexit, the latter does not have any significant effects on the intention to purchase a reshored brand, which is positively influenced by the attitude towards the same brand. As such, companies should enhance the image of their brands and CSR in order to harness the benefits of reshoring