The expanding adoption of artificial intelligence systems across high-impact sectors has catalyzed concerns regarding inherent biases and discrimination, leading to calls for greater transparency and accountability. Algorithm auditing has emerged as a pivotal method to assess fairness and mitigate risks in applied machine learning models. This systematic literature review comprehensively analyzes contemporary techniques for auditing the biases of black-box AI systems beyond traditional software testing approaches. An extensive search across technology, law, and social sciences publications identified 22 recent studies exemplifying innovations in quantitative benchmarking, model inspections, adversarial evaluations, and participatory engagements situated in applied contexts like clinical predictions, lending decisions, and employment screenings. A rigorous analytical lens spotlighted considerable limitations in current approaches, including predominant technical orientations divorced from lived realities, lack of transparent value deliberations, overwhelming reliance on one-shot assessments, scarce participation of affected communities, and limited corrective actions instituted in response to audits. At the same time, directions like subsidiarity analyses, human-cent
Rising fuel prices can affect driver behavior and thus the number of accidents, which is a key road safety issue. The aim of this paper was to assess and quantify the relationship between fuel prices (FP) and the number of road accidents in Europe. Content analysis of statistics from the countries was used to collect data, which were examined using Ramsey resets and Poisson distributions and then processed using negative binomial regression (NB), cluster analysis and visualization using contour plots. The results show that in Germany and Poland there is a statistically significant low negative correlation between fuel price and the number of traffic accidents, while in the Czech Republic and Denmark the relationship is weaker and statistically insignificant. In Iceland, no significant correlation was found. The contribution of this paper is to provide important insights that can be used in the development of transport policies and regulations to improve road safety. The main limitations include the difficulty of data collection, as many countries do not publish detailed statistics, and the low number of accidents in Iceland, which makes it impossible to perform a robust analysis for this country and may cause generalization of the results.
Given the increasing demand for sustainable energy sources and the challenges associated with the limited efficiency of solar cells, this review focuses on the application of gold quantum dots (AuQDs) in enhancing solar cell performance. Gold quantum dots, with their unique properties such as the ability to absorb ultraviolet light and convert it into visible light expand the utilization of the solar spectrum in solar cells. Additionally, these quantum dots, through plasmonic effects and the enhancement of localized electric fields, improve light absorption, charge carrier generation (electrons and holes), and their transfer. This study investigates the integration of quantum dots with gold plasmonic nanoparticles into the structure of solar cells. Experimental results demonstrate that using green quantum dots and gold plasmonic nanoparticles as intermediate layers leads to an increase in power conversion efficiency. This improvement highlights the significant impact of this technology on solar cell performance. Furthermore, the reduction in charge transfer resistance and the increase in short-circuit current are additional advantages of utilizing this technology. The findings of this research emphasize the high potential of gold quantum dots in advancing next-generation solar cell technology.
Molybdenum (Mo) is considered and described as an essential element for living organisms’ development. Until now, no studies have been performed on genes involved in the Mo transporter in ancestral Ipomoea species. This study aimed to identify potential Mo genes in Ipomoea trifida and I. triloba genomes using bioinformatics tools. We identified four Mo transporter genes, two in I. trifida and two in I. triloba. Based on the RNA-seq datasets, we observed that Mo genes are expressed (in silico) and present different mechanisms between the tissues analyzed. The information generated in this study fills missing gaps in the literature on the Mo gene in an important agronomic crop.
With modern society and the ever-increasing consumption of polymeric materials, the way we look at products has changed, and one of the main questions we have is about the negative impacts caused to the environment in the most diverse stages of the life cycle of these materials, whether in the acquisition of raw materials, in manufacturing, distribution, use or even in their final disposal. The main methodology currently used to assess the environmental impacts of products from their origin to their final disposal is known as Life Cycle Assessment (LCA). Thus, the objective of this work is to evaluate how much the biodegradable polymer contributes to the environment in relation to the conventional polymer considering the application of LCA in the production mode. This analysis is configured through the Systematic Literature Review (SLR) method. In this review, 28 studies were selected for evaluation, whose approaches encompass knowledge on LCA, green biopolymer (from a renewable but non-biodegradable source), conventional polymer (from a non-renewable source) and, mainly, the benefits of using biodegradable polymers produced from renewable sources, such as: corn, sugarcane, cellulose, chitin and others. Based on the surveys, a comparative analysis of LCA applications was made, whose studies considered evaluating quantitative results in the application of LCA, in biodegradable and conventional polymers. The results, based on comparisons between extraction and production of biodegradable polymers in relation to conventional polymers, indicate greater environmental benefits related to the use of biodegradable polymers.
Using individual- and panel country-level data from 118 countries for the period 1981–2020, this study investigates the effects of national- and individual-level economic and environmental factors on subjective well-being (SWB). Two individual SWB indicators are selected: the feeling of happiness and life satisfaction. Additionally, two environmental factors are also considered: CO2 emissions by country level and personal perspective on environmental protection. The ordered probit estimation results show that CO2 emissions have a significant negative effect on SWB, and a higher perspective on environmental protection has a significant and positive effect. Compared with the average marginal effect of national income, CO2 emissions are a more important determinant of SWB when considering a personal perspective on protecting the environment. The estimation results are robust to various estimation model specifications: inclusion of additional air pollutants (CH4 and N2O), PM 2.5 and various sample groupings. This study makes a novel contribution by providing comprehensive insights into how both individual environmental attitudes and national pollution levels jointly influence subjective well-being.
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