A data breach is the unauthorized disclosure of sensitive personal data, and it impacts millions of individuals annually in the United States, as reported by Privacy Rights Clearinghouse. These breaches jeopardize the physical safety of the individuals whose data are exposed and result in substantial economic losses for the affected companies. To diminish the frequency and severity of data breaches in the future, it is imperative to research their causes and explore preventive measures. In pursuit of this goal, this study considers a dataset of data breach incidents affecting companies listed on the New York Stock Exchange and NASDAQ. This dataset has been augmented with additional information regarding the targeted company. This paper employs statistical visualizations of the data to clarify these incidents and assess their consequences on the affected companies and individuals whose data were compromised. We then propose mitigation controls based on established frameworks such as the NIST Cybersecurity Framework. Additionally, this paper reviews the compliance scenario by examining the relevant laws and regulations applicable to each case, including SOX, HIPAA, GLBA, and PCI-DSS, and evaluates the impacts of data breaches on stock market prices. We also review guidelines for appropriately responding to data leaks in the U.S., for compliance achievement and cost reduction. By conducting this analysis, this work aims to contribute to a comprehensive understanding of data breaches and empower organizations to safeguard against them proactively, improving the technical quality of their basic services. To our knowledge, this is the first paper to address compliance with data protection regulations, security controls as countermeasures, financial impacts on stock prices, and incident response strategies. Although the discussion is focused on publicly traded companies in the United States, it may also apply to public and private companies worldwide.

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability.

This manuscript explores the role of green hydrogen produced through ethanol reforming in accelerating Brazil’s transition to a low-carbon economic framework. Despite ongoing efforts to lessen carbon dependence, Brazil’s reliance on biofuels and other renewable energy sources remains inadequate for fully achieving its decarbonization objectives. Green hydrogen presents a vital opportunity to boost energy sustainability, especially in sectors that are challenging to decarbonize, such as industry and transportation. By analyzing Brazil’s input–output (I-O) table, using data from the Brazilian Institute of Geography and Statistics (IBGE), this study evaluates the macroeconomic potential of green hydrogen, focusing on GDP growth and employment generation. Furthermore, the research explores green hydrogen systems’ economic feasibility and potential impact on future energy policies, offering valuable insights for stakeholders and decision-makers. In addition, this investigation highlights Brazil’s abundant renewable resources and identifies the infrastructural investments necessary to support a green hydrogen economy. The findings aim to strengthen Brazil’s national decarbonization strategy and serve as a model for other developing nations transitioning to clean energy.

This article presents an innovative approach to video steganography called Stego-STFAN, as by using a cheap model process to use the temporal and spatial domains together, they end up presenting fine adjustments in each frame, the Stego-STFAN had a $PSN{R}_c$ metric of 27.03 and $PSN{R}_S$ of 23.09, which is close to the state-of-art. Steganography is the ability to hide a message so that third parties cannot perceive communication between them. Thus, one of the precautions in steganography is the size of the message you want to hide, as the security of the message is inversely proportional to its size. Inspired by this principle, video steganography appears to expand channels further and incorporate data into a message. To improve the construction of better stego-frames and recovered secrets, we propose a new architecture for video steganography derived from the Spatial-Temporal Adaptive Filter Network (STFAN) in conjunction with the Attention mechanism, which together generates filters and maps dynamic frames to increase the efficiency and effectiveness of frame processing, exploiting the redundancy present in the temporal dimension of the video, as well as fine details such as edges, fast-moving pixels and the context of secret and cover frames and by using the DWT method as another feature extraction level, having the same characteristics as when applied to an image file.

The use of biofuels represents a promising means of achieving a sustainable future and offers considerable economic and environmental benefits. Since they are derived from organic sources, such as vegetable oils and animal fats, biofuels can mitigate the effects of greenhouse gas emissions, improve air quality, support local agriculture, create employment opportunities, and enhance energy security by reducing dependence on fossil fuels. However, introducing these alternative fuels to the aviation sector remains a significant challenge. Thus, it is vital to investigate the potential of sustainable aviation fuel (SAF) and discover how to overcome the technological obstacles to its integration into mainstream aviation to attain broader decarbonization objectives. This article seeks to contribute to a discussion about SAF by examining how it has evolved and its connections to related patents. This article is a comprehensive study of biofuel innovation, highlighting the complex relationships between academia, industry, and other stakeholders. It is hoped that the findings from this study will provide a clearer understanding of the catalysts involved in SAF innovation and provide valuable insights for policymakers, academics, and professionals in the field who are committed to shaping the trajectory of sustainable energy technologies in the future.

Passwords are a common user authentication method, and must be safeguarded by effective security measures. However, there are many cases of compromised user credentials in data breaches. This work studies RockYou2024, a massive data breach that occurred in July 2024 and exposed over 9 billion passwords. We investigate the passwords with regard to their lengths, entropy, use of personal information and common strings, and evaluation from zxcvbn, as well as making a comparative assessment of the results with previous password databases, namely RockYou2021 and RockYou, which was leaked in 2009. This analysis found that the passwords from RockYou2021 and RockYou2024 are significantly more secure than those from RockYou, which suggests an improvement in password creation awareness and policies. It was also noted that RockYou2021 and RockYou2024 have similar statistical distributions in all the analyses conducted. We have also found that the country of origin for most passwords within these databases is most likely to be the United States of America. These datasets were searched for passwords that are often used in industrial systems, which pose potential security risks in critical infrastructure sectors. Finally, we also propose passBiRVAE, a contextualized Bidirectional Recurrent Neural Network , used to generate passwords based on the RockYou2024 database. Future works should make further improvements to the results obtained from this model. However, there is a risk of threats to the validity of these analyses.