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A meta-analysis on NIST post-quantum cryptographic primitive finalists

Benny et al. | Sep 21, 2024

A meta-analysis on NIST post-quantum cryptographic primitive finalists
Image credit: Benny et al. 2024

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.

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The effect of bioenhancers on ampicillin-sulbactam as a treatment against A. baumannii

Balaji et al. | Sep 21, 2024

The effect of bioenhancers on ampicillin-sulbactam as a treatment against <i>A. baumannii<i>

This article explores the potential of piperine, a bioenhancer from black pepper, to improve antibiotic efficacy against antibiotic-resistant Acinetobacter baumannii. By combining piperine with ampicillin-sulbactam, the study demonstrated a significant reduction in bacterial growth for most strains tested, showcasing the promise of bioenhancers in combating resistant pathogens. This research highlights the possibility of reducing the required antibiotic dosage, potentially offering a new strategy in the fight against drug-resistant bacteria.

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Using broad health-related survey questions to predict the presence of coronary heart disease

Chavda et al. | Aug 23, 2024

Using broad health-related survey questions to predict the presence of coronary heart disease

Coronary heart disease (CHD) is the leading cause of death in the U.S., responsible for nearly 700,000 deaths in 2021, and is marked by artery clogging that can lead to heart attacks. Traditional prediction methods require expensive clinical tests, but a new study explores using machine learning on demographic, clinical, and behavioral survey data to predict CHD.

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Intra and interspecies control of bacterial growth through extracellular extracts

Howe et al. | Jun 07, 2024

Intra and interspecies control of bacterial growth through extracellular extracts

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.

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Automated classification of nebulae using deep learning & machine learning for enhanced discovery

Nair et al. | Feb 01, 2024

Automated classification of nebulae using deep learning & machine learning for enhanced discovery

There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.

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The effect of neuroinflammation and oxidative stress on the recovery time of seizures

Kantipudi et al. | Jul 31, 2023

The effect of neuroinflammation and oxidative stress on the recovery time of seizures

Neuroinflammation and oxidative stress are both known to play a role in the occurrence and severity of seizures. This study tested effects of oxidative stress from seizures by evaluating the longevity, egg-laying, and electroshock resilience of C. elegans. Results revealed that oxidative stress and neuroinflammation diminish longevity and reproductivity while also increasing recovery time after seizures in C. elegans. This research can help lead to future studies and may also lead to finding new therapeutics for epilepsy.

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siRNA-dependent KCNMB2 silencing inhibits lung cancer cell proliferation and promotes cell death

Jeong et al. | Nov 01, 2022

siRNA-dependent KCNMB2 silencing inhibits lung cancer cell proliferation and promotes cell death

Here, seeking to better understand the genetic associations underlying non-small cell lung cancer, the authors screened hundreds of genes, identifying that KCNMB2 upregulation was significantly correlated with poor prognoses in lung cancer patients. Based on this, they used small interfering RNA to decrease the expression of KCNMB2 in A549 lung cancer cells, finding decreased cell proliferation and increased lung cancer cell death. They suggest this could lead to a new potential target for lung cancer therapies.

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Impact of light intensity and electrolyte volume on performance of photo-electrochemical (PEC) solar cell

Patel et al. | Mar 14, 2022

Impact of light intensity and electrolyte volume on performance of photo-electrochemical (PEC) solar cell

Here, seeking to develop more efficient solar cells, the authors investigated photo-electrochemical (PEC) solar cells, specifically molybdenum diselenide (MoSe2) based on its high resistance to corrosion. They found that the percentage efficiency of these PEC solar cells was proportional to light intensity–0.9 and that performance was positively influenced by increasing the electrolyte volume. They suggest that studies such as these can lead to new insight into reaction-based solar cells.

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Discovery of the Heart in Mathematics: Modeling the Chaotic Behaviors of Quantized Periods in the Mandelbrot Set

Golla et al. | Dec 14, 2020

Discovery of the Heart in Mathematics: Modeling the Chaotic Behaviors of Quantized Periods in the Mandelbrot Set

This study aimed to predict and explain chaotic behavior in the Mandelbrot Set, one of the world’s most popular models of fractals and exhibitors of Chaos Theory. The authors hypothesized that repeatedly iterating the Mandelbrot Set’s characteristic function would give rise to a more intricate layout of the fractal and elliptical models that predict and highlight “hotspots” of chaos through their overlaps. The positive and negative results from this study may provide a new perspective on fractals and their chaotic nature, helping to solve problems involving chaotic phenomena.

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Effects of Prolonged Azithromycin Therapy on Bacterial Resistance to Functionally Analogous Antibiotics

Gibbs et al. | Dec 04, 2020

Effects of Prolonged Azithromycin Therapy on Bacterial Resistance to Functionally Analogous Antibiotics

In this study, the authors investigate a potential case of cross antibiotic-resistance. Using swabs from an individual who received long-term treatments of azithromycin, they addressed the question of whether any bacteria in this individual might develop resistance to not only azithromycin, but also other antibiotics with similar structures. This study cleverly addresses the important issue of antibiotic resistance from a new and thoughtful approach.

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