Earthquakes pose a significant threat to urban areas, necessitating accurate prediction models to mitigate their impact. This study focuses on earthquake prediction in Los Angeles, a region with high seismic activity and limited research. We established a feature matrix for predicting earthquakes within a 30-day period by analyzing the most predictive patterns from recent studies. Our model developed a subset of features capable of predicting the highest magnitude of an earthquake. Using advanced machine learning algorithms and neural networks, our model achieved an accuracy of 69.14% in predicting the maximum magnitude earthquake as one of the 5 categories. We aim to provide a useful guideline for future researchers.

What makes a wine exceptional enough to score a perfect 10 from experts? This study explores a data-driven approach to identify the ideal physicochemical composition for wines that could achieve this highest possible rating. Using a dataset of 11 measurable attributes, including alcohol, sulfates, residual sugar, density, and citric acid, for wines rated up to a maximum quality score of 8 by expert tasters, we sought to predict compositions that might enhance wine quality beyond current observations. Our methodology applies a second-degree polynomial ridge regression model, optimized through an exhaustive evaluation of feature combinations. Furthermore, we propose a specific chemical and physical composition of wine that our model predicts could achieve a quality score of 10 from experts. While further validation with winemakers and industry experts is necessary, this study aims to contribute a practical tool for guiding quality exploration and advancing predictive modeling applications in food and beverage sciences.

This research investigates a novel hybridization strategy between Convolutional Neural Networks (CNNs) and gradient-boosted decision trees to enhance image classification accuracy. While conventional approaches focus on optimizing either CNN architectures or machine learning algorithms independently, we propose that intervening in the architecture itself—by strategically replacing the dense classifier portion of the CNN with a tree-based learner—can yield superior results. In our study, we construct a CNN composed of three convolutional blocks, each followed by ReLU activation, max-pooling, and dropout layers. Instead of proceeding through the final dense layers, we extract features immediately after the Flatten layer and input them into an XGBoost classifier. Our experiments reveal that applying XGBoost to these flattened features results in a higher classification accuracy than the fully optimized CNN. Although other datasets were examined during initial testing, this paper focuses exclusively on CIFAR-10 for clarity and reproducibility. The findings suggest that performance gains can be achieved through structural interventions in model architecture, challenging the prevailing emphasis on end-to-end optimization.

We present a probabilistic modeling framework for quantifying decryption-failure probability (DFP) in CRYSTALS-Kyber, the lattice-based key encapsulation mechanism standardized by NIST as ML-KEM. Our method combines exact tail computation of Kyber's centered-binomial noise distribution using FFT-based convolution with principled comparisons to classical concentration inequalities such as Hoeffding and Bernstein. This hybrid analytical-computational approach yields implementation-independent upper bounds on DFP that are exponentially small in the security parameter. Specifically, we compute two-sided tails for aggregated noise variables, translate those into per-ciphertext failure probabilities through a transparent union bound, and determine the minimal reconciliation margins required to ensure DFP ≤ 2 -λ for λ ∈ 128, 192, 256. Across Kyber-like parameter regimes, Bernstein-type inequalities consistently overestimate risk compared to the exact probabilistic tails, which are several orders of magnitude smaller. The resulting gap highlights the conservatism of inequality-based analyses and clarifies the quantitative safety margins inherent to Kyber's design. These findings contribute to the broader effort of modeling reliability in post-quantum cryptographic primitives using probabilistic and computational methodologies.