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While genetic variation in any species is potentially shaped by a range of processes, phylogeography and landscape genetics are largely concerned with inferring how environmental conditions and landscape features impact neutral intraspecific diversity. However, even as both disciplines have come to utilize SNP data over the last decades, analytical approaches have remained for the most part focused on either broad-scale inferences of historical processes (phylogeography) or on more localized inferences about environmental and/or landscape features (landscape genetics). Here we demonstrate that an artificial intelligence model-based analytical framework can consider both deeper historical factors and landscape-level processes in an integrated analysis. We implement this framework using data collected from two Brazilian anurans, the Brazilian sibilator frog (Leptodactylus troglodytes) and granular toad (Rhinella granulosa). Our results indicate that historical demographic processes shape most the genetic variation in the sibulator frog, while landscape processes primarily influence variation in the granular toad. The machine learning framework used here allows both historical and landscape processes to be considered equally, rather than requiring researchers to make an a priori decision about which factors are important., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The transition to a low-carbon economy is one of the main challenges of our time. In this context, solar energy, along with many other technologies, has been developed to optimize performance. For example, solar trackers follow the sun's path to increase the generation capacity of photovoltaic plants. However, several factors need consideration to further optimize this process. Important variables include the distance between panels, surface reflectivity, bifacial panels, and climate variations throughout the day. Thus, this paper proposes an artificial intelligence-based algorithm for solar trackers that takes all these factors into account—mainly weather variations and the distance between solar panels. The methodology can be replicated anywhere in the world, and its effectiveness has been validated in a real solar plant with bifacial panels located in northeastern Brazil. The algorithm achieved gains of up to 7.83% on a cloudy day and obtained an average energy gain of approximately 1.2% when compared to a commercial solar tracker algorithm. [ABSTRACT FROM AUTHOR]

This Article investigates the feasibility and consequences of granting legal personality to Artificial Intelligences (AIs) in the context of Brazilian law, with a special focus on copyright law. It conducts a thorough analysis of how such a grant can enhance legal security and encourage innovation in AI technologies. Through an integrative review of the literature and a comparative analysis of national and international legislation and jurisprudence, the study explores the implications of this legislative innovation. This Article highlights the importance of legal clarity for companies and investors in the AI sector, emphasizing that granting legal personality to AIs can simplify the identification of the copyright holder and protect investments. However, this Article also recognizes challenges, such as the complexity of assigning authorship and evaluating the originality of works created by AIs. A careful debate is proposed on criteria for determining which AIs should be considered legal persons and how to balance the rights and duties of AIs and their creators. This Article suggests adapting the legal structure of the limited liability company (LTDA) to incorporate AIs as operational entities, aiming for an effective legal framework for managing risks associated with AI. This Article concludes that granting legal personality to AIs in Brazil is a promising strategy, requiring careful consideration and forward-looking vision, emphasizing the need for Brazilian law to prepare for the opportunities and challenges of the AI era. [ABSTRACT FROM AUTHOR]

This article investigates the historical uses and types of artificial intelligence (AI) systems and resources in Brazilian journalistic products. It is a work anchored in critically analyzing the literature on the subject, mapping and observing cases, seeking to identify uses and innovative processes, and analyzing AI projects for journalism. A search was carried out in web repositories, specifically Google, Google Scholar, and Scopus, using the terms: "inteligência artificial" + "jornalismo", "bot + jornalismo", "Geração de linguagem natural [NLG] + jornalismo", "aprendizado de máquina [machine learning] + jornalismo", and "algoritmos + jornalismo". The corpus analysis (N = 45) includes the evaluation of the impacts of AI on the production and distribution of news in the context of Brazilian digital journalism. We try to answer questions about the uses of databases, approximation with platforms, uses of shared codes, connections with other Ais, and sources of funding, and whether they are backend or frontend initiatives. In a parallel investigation, we try to identify if Brazilian newsrooms are officially using ChatGPT, a generative AI. The findings point to advances in using low-cost and low-impact AI, with the predominance of bots. The great availability of this kind of AI in web repositories is believed to facilitate native digital media to incorporate innovative processes in using these technologies. [ABSTRACT FROM AUTHOR]