Here, seeking a better solution to produce downforce that keeps a vehicle grounded at high speeds than wings which tend to result in degraded car performance due to increased air resistance, the authors considered using the Magnus effect as a replacement. The authors found that a spinning cylinder generated significantly more downforce through the Magnus effect than a standard wing at all wind speeds as simulated through the use of a leaf blower. They suggest that a cylinder could be a potential replacement for a wing when downforce is a priority.
Here, the authors considered the stretching behavior of rubber bands by exposing the rubber bands to increasing loads and measuring their stretch response. They found that a linear stretch response was observed for intermediate loading steps, but this behavior was lost at lower or higher loads, deviating from Hooke's Law. The authors suggest that studies such as these can be used to evaluate other visco-elastic structures.
In this study, the authors test whether a gravity vehicle, which is a vehicle powered by its own gravity on a ramp, could be designed to move faster when mathematical calculations for optimal mass and center of gravity were applied in the design.
Here, the authors investigated the most efficient way to position magnets to hold the most pieces of paper on the surface of a refrigerator. They used a regression model along with an artificial neural network to identify the most efficient positions of four magnets to be at the vertices of a rectangle.
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.
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.
Reinforcement learning (RL) is a form of machine learning that can be harnessed to develop artificial intelligence by exposing the intelligence to multiple generations of data. The study demonstrates how reply buffer reward mechanics can inform the creation of new pruning methods to improve RL efficiency.
Image credit: Chunduri, Srinivas and McMahan, 2024.
Collisions of heavy ions, such as muons result in jets and noise. In high-energy particle physics, researchers use jets as crucial event-shaped observable objects to determine the properties of a collision. However, many ionic collisions result in large amounts of energy lost as noise, thus reducing the efficiency of collisions with heavy ions. The purpose of our study is to analyze the relationships between properties of muons in a dimuon collision to optimize conditions of dimuon collisions and minimize the noise lost. We used principles of Newtonian mechanics at the particle level, allowing us to further analyze different models. We used simple Python algorithms as well as linear regression models with tools such as sci-kit Learn, NumPy, and Pandas to help analyze our results. We hypothesized that since the invariant mass, the energy, and the resultant momentum vector are correlated with noise, if we constrain these inputs optimally, there will be scenarios in which the noise of the heavy-ion collision is minimized.
