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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.

Noise pollution negatively impacts the health and behavioral routines of humans and other animals, but the production of synthetic sound-absorbing materials contributes to harmful gas emissions into the atmosphere. The authors of this paper investigated the effectiveness of environmentally-friendly, cheap natural-fiber materials, such as jute, as replacements for synthetic materials, such as gypsum and foam, in soundproofing.

At a crime scene, the presence and pattern of gunshot residue can help forensic scientists piece together the events that occurred. To assist this, the authors of this paper determined the relationship between shooting distance and nitrite residue patterns left on fabric targets.

Semiconductors are the center of the fourth industrial revolution as they are key components for all electronics. Exposed wafers made of silicon (Si), which can easily oxidize, convert to silicon dioxide (SiO₂). The surface of SiO₂ wafers consists of many Si-OH bonds, allowing them to easily bond with water, resulting in a “wet” or hydrophilic condition. We sought to determine a way to modify the surface of SiO₂ wafers to become hydrophobic to ensure safe wet cleaning.

With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.

Building on previous work by earlier astronomers, the authors investigate the correlation between the density and size of celestial objects in the universe, including neutron stars, galaxies, and galaxy clusters.

Investigating the green valley hypotheses of galaxy evolution

Investigation of resonant vibration using cello model

The authors test how the size-segregation theory applies to the behavior of hollow and irregular-shaped objects.

The authors measure changes in the viscosity of glycerol with increasing temperature using the falling ball approach.