Universidade Federal do Rio Grande

Brazil holds approximately 40 % of its territory in the sea, an area known as the "Blue Amazon" due to its dimension, and strategic, economic and biological significance. Despite this importance, much of Brazil's marine biodiversity remains poorly mapped and quantified. Therefore, the National Institute of Science and Technology for the Blue Amazon Biodiversity (INCT-BAA) was established to expand knowledge on this region. One of INCT-BAA's primary objectives is to develop an integrated database that combines historical and contemporary information on marine biodiversity. To support this objective, a state-of-the-art systematic literature review was conducted to synthesize existing knowledge on species occurrence, spatial and temporal distribution, natural and anthropogenic threats, ecological scope of studies, and knowledge gaps and trends. Given the broad scope of the review, a specific protocol was developed, requiring adaptations to traditional systematic review methodologies (e.g., elaboration of a robust glossary of categories and methods to extract species locations from papers), which are detailed in this document. The review was conducted over one year using 1,034 search strings across Web of Science, ScienceDirect, and Google Scholar and retrieving over 200,000 species occurrences from 7,687 studies, covering more than 27,000 species from 1917 to 2023.

This research distinguishes itself by integrating machine learning algorithms to assess the impact of confined magnetic fields on vortex dynamics in hybrid nanofluid flow, through the inclusion of both vertical and horizontal magnetic field strips. Specifically, the study focuses on a vertical cavity with an aspect ratio of 1:5, where a bottom lid moves horizontally from left to right to drive the flow. We have applied a limited magnetic field consisting of vertical, and horizontal strips. The authors have developed MATLAB codes to implement an algorithm for solving the governing equations of the nanofluid flow and heat transfer. The algorithm is based on the Stream-Vorticity formulation and uses a finite difference method. The algorithm can examine how several parameters, such as magnetic field strength (0–300), nanoparticle volume fraction (0–20%), and Reynolds number (Re) (1–50), impact the characteristics of nanofluids in terms of flow and thermal properties. The results demonstrate that magnetic fields influence the stress distribution of the flow pattern and the temperature distribution. Further, the presence of a magnetic field also affects stress distribution. Moreover, it has been determined that the Nusselt number (Nu) experiences a 60% increase due to the magnetic field, while there is a remarkable rise attributed to the Re. Similarly, significant changes are observed in skin friction under both parameters. These findings carry implications for designing and operating devices.

The medical industry extensively uses nanoparticles for applications such as wound dressing, artificial organ components, drug delivery, tissue engineering, and cardiovascular disease treatment. The incorporation of nanoparticles into the base fluid enhances the rate of heat transmission and additionally decreases blood pressure. This study aims to examine the effects of a new tetra-hybrid nanofluid model, which includes nanoparticles, on the flow of blood through a stenosed artery with a circular shape. Model partial differential equations (PDEs) incorporate phenomena such as thermal radiation and viscous dissipation. Furthermore, we transform these modeled PDEs into dimensionless ordinary differential equations (ODEs) using self-similarity variables and numerically solve the proposed ODEs using the well-established Lobatto IIIa numerical technique. The impact of several dimensionless parameters on the heat transfer rate, skin friction, velocity, and temperature fields has been computed and analyzed using figures and tables. Moreover, we conducted a computational fluid dynamics (CFD) investigation on a tetra-hybrid nanofluid, using blood as the base fluid. The results indicate that heat transmission is higher in tetra-hybrid nanoparticles compared to tri-hybrid and di-hybrid nanofluids. The volume fraction of nanoparticles in the base fluid increases, resulting in a decrease in the surface drag coefficient and a decrease in the heat transport phenomenon. Amplification in the thermal radiation parameter improves heat transfer, helping to remove toxins and plaque from blood flowing through arteries. An increase in thermal radiation generates excess heat that dilates inflexible arteries, facilitating blood flow. From CFD analysis, it is observed that thermal conduction k and heat transfer coefficient h amplify by improving Reynolds number. Pressure at the outlet interface of the tube decreases from 3058 Pa to 2996 Pa for the case of a 10% volume fraction of nanoparticles. Velocity boundary layer thickness decreases from 1.46 to 1.37 with an increase in the volume fraction of nanoparticles from 1% to 10%. Heat deliverance rate amplifies in the case of tetrahybrid nanofluid 2.5394–2.6147 in contrast to trihybrid nanofluid 2.4008 to 2.4711 by amplifying Rd from 0.7 to 1.1. The surface drag coefficient amplifies by magnifying Re but the Nusselt number diminishes by improving the volume fraction of nanoparticles.