Mathematics and computing

Understanding Diatomic Hydrides: Vibrational Thermodynamics and Molecular Structure

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Analytical molecular potentials achieve peak physical validity when researchers examine the bound spectrum, thermal response, and eigenstate structure inside a unified framework, according to recent quantum mechanics studies. By determining complete vibrational branches for molecular systems like \(\mathrm{H_2}\), LiH, and ScH using the Newing potential, physicists connect spectral organization directly to finite-level thermodynamics and reduced … Read more

Enhanced Tomato Leaf Disease Detection with YOLOv11n-DeiT

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Optimizing Real-Time Object Detection: The Shift to YOLOv11n and Transformer Backbones The latest advancements in the YOLO (You Only Look Once) architecture, specifically the integration of the YOLOv11n model with the Distilled Vision Transformer (DeiT), have set a new benchmark for accuracy and efficiency in real-time object detection. According to research evaluating performance on the … Read more

Optimizing User Engagement in Virtual Reality Using Ensemble Deep Learning

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Adaptive Virtual Reality Design using Immersion Levels and User Engagement (AVRDIL-UEGE) achieves a classification accuracy of 98.40% and a Matthews correlation coefficient of 0.95 on benchmark datasets, according to recent technical research addressing the persistent challenge of recognizing user immersion and involvement in virtual reality environments. The Immersion Bottleneck in Modern Virtual Reality Virtual reality … Read more

Synergistic Coordinate and Attention Module for Pipeline Weld Defect Detection

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An enhanced YOLOv8n-based detection method achieves a Precision of 88.3% and an mAP@0.5 of 79.2% on the ROC-DET dataset, improving industrial pipeline inner-wall defect inspection according to study data. By integrating a synergistic coordinate and attention module alongside a hybrid CIoU–NWD loss, the upgraded model boosts spatial localization and accurately identifies small, low-contrast defects that … Read more

Cross-Validation Bias in Predictive Maintenance: A Leakage-Resilient Benchmark

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Predictive maintenance for urban electric transport fleets operates under severe class imbalance, where equipment failures are rare but individually costly. According to a benchmarking study of thirty classical, boosting, and deep tabular models conducted on an anonymised multi-modal fleet dataset comprising trams, trolleybuses, and electric buses, reported optimization gains are frequently artifacts of cross-validation optimism … Read more

Continual Graph Learning for Fraud Detection Under Adversarial Drift

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Financial transaction networks face a persistent threat from strategic adversarial drift, a phenomenon where sophisticated threat actors manipulate graph structures to bypass conventional fraud detection systems. According to researchers, traditional temporal graph neural networks frequently fail in these scenarios because they discard historical patterns during retraining and struggle to generalize against novel structural perturbations. Understanding … Read more

Ancient Maya Mathematical Discovery Rivals Classical Masters

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Archaeologists have identified a specific Maya mathematician-astronomer named Sak Tahn Waax, or “White-Chested Fox,” after analyzing a complex mathematical formula inscribed on the walls of a chamber in Xultun, Guatemala. Published in the journal Antiquity, the findings confirm that Maya scholars were recognized for their intellectual contributions in the mid-eighth century AD, using advanced astronomical … Read more

Improving Brain Tumor Detection with Deep Learning and Explainable AI

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New deep learning frameworks for brain tumor detection are increasingly utilizing stratified patient-wise cross-validation and quantitative explainability (XAI) metrics to bridge the gap between algorithmic performance and clinical reliability. By integrating architectures like InceptionV3 with rigorous testing on independent datasets, researchers are addressing critical hurdles in medical AI, specifically data scarcity and the “black box” … Read more

Human-AI Co-Design for Clinical Prediction Models

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HACHI is an iterative human-in-the-loop framework that utilizes AI agents to accelerate the development of fully interpretable clinical prediction models (CPMs) from unstructured clinical notes. By alternating between AI-driven statistical exploration and expert human feedback, the system optimizes for transparency and steerability, demonstrably outperforming traditional modeling approaches in tasks like acute kidney injury and traumatic … Read more

Lyapunov-PINN Framework for SEIR Epidemic Model Stability

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For decades, epidemiologists have relied on mathematical models like the SEIR (Susceptible-Exposed-Infectious-Recovered) framework to predict how viruses move through populations. While these models are foundational, they often struggle with the messy, unpredictable nature of human behavior and the massive computational power required to process real-time data. However, a new paradigm is emerging: the integration of … Read more

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