Abstract
We present new Very Large Telescope Interferometer (VLTI)/GRAVITY near-infrared interferometric measurements of the angular size of the innermost hot dust continuum for 14 type 1 active galactic nuclei (AGNs). The angular sizes are resolved on scales of similar to 0.7 mas and the inferred ring radii range from 0.028 to 1.33 pc, comparable to those reported previously and a factor of 10-20 smaller than the mid-infrared sizes in the literature. Combining our new data with previously published values, we compiled a sample of 25 AGNs with bolometric luminosity ranging from 10(42) to 10(47) erg s(-1), with which we studied the radius-luminosity (R - L) relation for the hot dust structure. Our interferometric measurements of radius are offset by a factor of 2 from the equivalent relation derived through reverberation mapping. Using a simple model to explore the dust structure's geometry, we conclude that this offset can be explained if the 2 mu m emitting surface has a concave shape. Our data show that the slope of the relation is in line with the canonical R proportional to L-0.5 when using an appropriately non-linear correction for bolometric luminosity. In contrast, using optical luminosity or applying a constant bolometric correction to it results in a significant deviation in the slope, suggesting a potential luminosity dependence on the spectral energy distribution. Over four orders of magnitude in luminosity, the intrinsic scatter around the R - L relation is 0.2 dex, suggesting a tight correlation between the innermost hot dust structure size and the AGN luminosity.

Abstract
Geophysical studies on the lunar surface have, in the past, used various methods that contribute not only towards the knowledge of the lunar subsurface but also contribute towards the design of future lunar missions, namely those that will, in the near future, take humans to the Moon’s surface. This work analyzes a specific set of ground penetrating radar (GPR) data, collected during the Chang’E-4 mission of the Chinese Space Agency, using theYutu-2 rover within the von Kármán crater, on the far-side of the Moon. From this dataset two electrical parameters were estimated. The approach uses the backscatter of the electromagnetic wavefield in order to obtain estimates of the real component of the complex relative permittivity as well as the electrical resistivity. © 2024 International Multidisciplinary Scientific Geoconference. All rights reserved.

Abstract
The classic literature about innovation conveys innovation strategy the leading and starting role to generate business growth due to technology development and more effective managerial practices. The advent of Artificial Intelligence (AI) however reverts this paradigm in the context of Industry 5.0. The focus is moving from how innovation fosters AI to how AI fosters innovation. Therefore, our research question can be stated as follows: What factors influence the effect of AI on Innovation Capacity in the context of Industry 5.0? To address this question we conduct a scoping review of a vast body of literature spanning engineering, human sciences, and management science. We conduct a keyword-based literature search completed by bibliographic analysis, then classify the resulting 333 works into 3 classes and 15 clusters which we critically analyze. We extract 3 hypotheses setting associations between 4 factors: company age, AI maturity, manufacturing strategy, and innovation capacity. The review uncovers several debates and research gaps left unsolved by the existing literature. In particular, it raises the debate whether the Industry5.0 promise can be achieved while Artificial General Intelligence (AGI) remains out of reach. It explores diverging possible futures driven toward social manufacturing or mass customization. Finally, it discusses alternative AI policies and their incidence on open and internal innovation. We conclude that the effect of AI on innovation capacity can be synergic, deceptive, or substitutive depending on the alignment of the uncovered factors. Moreover, we identify a set of 12 indicators enabling us to measure these factors to predict AI's effect on innovation capacity. These findings provide researchers with a new understanding of the interplay between artificial intelligence and human intelligence. They provide practitioners with decision metrics for a successful transition to Industry 5.0.

Abstract
This work provides a method that combines graphene oxide coating and self-image theory to improve the sensitivity of optical sensors. The sensor is designed specifically to measure the amount of glucose present quantitatively in aqueous solutions that replicate the range of glucose concentrations found in human saliva. COMSOL Multiphysics 6.0 was used to simulate the self-imaging phenomenon using a coreless silica fiber (CSF). For high-quality self-imaging, the second and fourth self-imaging points are usually preferred because of their higher coupling efficiency, which increases the sensor sensitivity. However, managing the fourth self-image is more difficult because it calls for a longer CSF length. As a result, the first and second self-image points were the focus of the simulation in this work. After the simulation, using the Layerby-Layer method, the sensor was constructed to a length that matched the second self-image point (29.12 mm) and coated with an 80 mu m/mL graphene oxide layer. When comparing uncoated and graphene oxide-covered sensors to measure glucose in liquids ranging from 25 to 200 mg/dL, one bilayer of polyethyleneimine/graphene demonstrated an eight-fold improvement in sensitivity. The final sensor, built on graphene oxide, showed stability with a low standard deviation of 0.6 pm/min. It also showed sensitivity at 10.403 +/- 0.004 pm/(mg/dL) with a limit of detection of 9.15 mg/dL.

Abstract
Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.

Abstract
This research aims to analyze the positioning theory and discourse within Sao Paulo's Place Branding from 2014 to 2019, investigating the symbolic representations employed by the Sao Paulo Tourism Bureau to emphasize its branding endeavors. The methodology employed a framework based on Semprini's [10] Project/Manifestation approach and Discourse Analysis. The impetus behind this study arises from the substantial investments made by cities to craft comprehensive disclosure strategies and establish place branding for their respective regions. We observed aspects of Communication and DigitalMarketing in the three promotional videos produced by SPTuris in 2014, 2017, and 2019, which underwent meticulous analysis. Our findings unveiled a consistent thematic discourse despite shifts in political administration. The 2014 video accentuated multiculturalism and cosmopolitanism, while the 2017 edition highlighted experiential marketing, business, consumption, and cosmopolitan elements. Remarkably, the 2019 presentation featured images emphasizing receptivity. Themes such as Culture, Arts, and Gastronomy were recurrent across all videos. The scrutinized discourse reaffirms Sao Paulo's capital as a trendsetter within Brazil.