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There is limited evidence that extended exposure to an electromagnetic field (EMF) has negative health effects on humans. The authors measured the power density and strength of EMF at different distances and directions in front of a microwave oven, and they discuss the safety of different distances.

Additive manufacturing (AM) is transforming the production of complex metal parts, but challenges like internal cracking can arise, particularly in critical sectors such as aerospace and automotive. Traditional methods to assess cracking susceptibility are costly and time-consuming, prompting the use of machine learning (ML) for more efficient predictions. This study developed a multi-model ML pipeline that predicts solidification cracking susceptibility (SCS) more accurately by considering secondary alloy properties alongside composition, with Random Forest models showing the best performance, highlighting a promising direction for future research into SCS quantification.

The authors looked at differences in water quality between Chinatown and Bayside. They wanted to look at the racial and economic demographics of each region and how that correlated to access to clean drinking water. Ultimately they did not find any significant differences in water quality, but identified important future directions for this work.

Seeking to investigate the effects of ambient pollutants on human respiratory health, here the authors used machine learning to examine asthma in Lost Angeles County, an area with substantial pollution. By using machine learning models and classification techniques, the authors identified that nitrogen dioxide and ozone levels were significantly correlated with asthma hospitalizations. Based on an identified seasonal surge in asthma hospitalizations, the authors suggest future directions to improve machine learning modeling to investigate these relationships.

The advent of quantum computing will pose a substantial threat to the security of classical cryptographic methods, which could become vulnerable to quantum-based attacks. In response to this impending challenge, the field of post-quantum cryptography has emerged, aiming to develop algorithms that can withstand the computational power of quantum computers. This study addressed the pressing concern of classical cryptographic methods becoming vulnerable to quantum-based attacks due to the rise of quantum computing. The emergence of post-quantum cryptography has led to the development of new resistant algorithms. Our research focused on four quantum-resistant algorithms endorsed by America’s National Institute of Standards and Technology (NIST) in 2022: CRYSTALS-Kyber, CRYSTALS-Dilithium, FALCON, and SPHINCS+. This study evaluated the security, performance, and comparative attributes of the four algorithms, considering factors such as key size, encryption/decryption speed, and complexity. Comparative analyses against each other and existing quantum-resistant algorithms provided insights into the strengths and weaknesses of each program. This research explored potential applications and future directions in the realm of quantum-resistant cryptography. Our findings concluded that the NIST algorithms were substantially more effective and efficient compared to classical cryptographic algorithms. Ultimately, this work underscored the need to adapt cryptographic techniques in the face of advancing quantum computing capabilities, offering valuable insights for researchers and practitioners in the field. Implementing NIST-endorsed quantum-resistant algorithms substantially reduced the vulnerability of cryptographic systems to quantum-based attacks compared to classical cryptographic methods.

The authors use the unique movements of the jumping gall wasp to study momentum transfer with potential applications in robotics and extraterrestrial research.

The study discusses the relationship between bacterial species and the human gut microbiome, emphasizing the role of quorum sensing molecules in bacterial communication and its implications for health. Authors investigated the impact of bacterial supernatants from Escherichia coli (E. coli) on the growth of new E. coli and Enterobacter aerogenes (E. aerogenes) cultures.

Here, recognizing the need to improve the efficiency of the conversion of solar energy to electrical energy, the authors used MATLAB to mathematically simulate a multi-layered thin film with an without an antireflective coating. They found that the use of alternating ZnO-SiO₂ multilayers enhanced the transmission of light into the solar cell, increasing its efficiency and reducing the reflectivity of the Si-Air interface.

Using the data provided by the University of Twente High School Project on Astrophysics Research with Cosmics (HiSPARC), an analysis of locations for possible high-energy cosmic ray air showers was conducted. An example includes an analysis conducted of the high-energy rain shower recorded in January 2014 and the use of Stellarium™ to discern its location.

The authors look at using publicly available data and machine learning to see if they can develop a criminal activity index for counties within the state of California.