Single-molecule detection (SMD) has reformed analytical science by enabling the direct observation of individual molecular events, thus overcoming the limitations of ensemble-averaged measurements. This review presents a comprehensive analysis of the principles, devices, and emerging materials that have shaped the current landscape of SMD. We explore a wide range of sensing mechanisms, including surface plasmon resonance, mechanochemical transduction, transistor-based sensing, optical microfiber platforms, fluorescence-based techniques, Raman scattering, and recognition tunneling, which offer distinct advantages in terms of label-free operation, ultrasensitivity, and real-time responsiveness. Each technique is critically examined through representative case studies, revealing how innovations in device architecture and signal amplification strategies have collectively pushed the detection limits into the femtomolar to attomolar range. Beyond the sensing principles, this review highlights the transformative role of advanced nanomaterials such as graphene, carbon nanotubes, quantum dots, MnO₂ nanosheets, upconversion nanocrystals, and magnetic nanoparticles. These materials enable new transduction pathways and augment the signal strength, specificity, and integration into compact and wearable biosensing platforms. We also detail the multifaceted applications of SMD across biomedical diagnostics, environmental monitoring, food safety, neuroscience, materials science, and quantum technologies, underscoring its relevance to global health, safety, and sustainability. Despite significant progress, the field faces several critical challenges, including signal reproducibility, biocompatibility, fabrication scalability, and data interpretation complexity. To address these barriers, we propose future research directions involving multimodal transduction, AI-assisted signal analytics, surface passivation techniques, and modular system design for field-deployable diagnostics. By providing a cross-disciplinary synthesis of device physics, materials science, and real-world applications, this review offers a comprehensive roadmap for the next generation of SMD technologies, poised to impact both fundamental research and translational healthcare.

Accurate predictions of earthquakes are crucial for disaster preparedness and risk mitigation. Conventional machine learning models like Random Forest, SVR, and XGBoost are frequently used for seismic forecasting; however, capturing the intricate spatiotemporal relationships in earthquake data remains a challenge. To overcome this issue, we propose SeismoQuakeGNN, a novel Graph Neural Network (GNN) and Transformer-based hybrid framework that integrates spatial and temporal learning for improved seismic forecasting. Unlike existing GNN-based models, SeismoQuakeGNN introduces an optimized spatial encoding mechanism to dynamically learn seismic interdependencies, coupled with a Transformer-driven attention module to capture long-range temporal correlations. Furthermore, initial experiments with XGBoost demonstrated its limitations in learning earthquake patterns, reinforcing the need for deep spatial–temporal modeling. The new SeismoQuakeGNN method is capable of substantial and efficient data processing of relationships in both space and time, as well as providing superior transfer to different seismic areas, thereby qualifying as a dependable starting point to extensive earthquake forecasting and hazard evaluation.

In Formula 1, which is among the most competitive motorsports in the world, the timing of a pit stop can make the difference between winning and losing a race. Conventional methods based on human judgment can be erratic, especially in rapidly changing race conditions. This work proposes a datadriven framework based on deep learning models to predict optimal pit stop timings using raw telemetry data extracted from FastF1 API. To improve the robustness of the models, advanced preprocessing techniques such as normalization, imputation, and class balancing with Synthetic Minority Over-sampling Technique (SMOTE) were implemented. Five different deep learning architectures, including Bi-LSTM, TCN-GRU, GRU, InceptionTime, and CNN-BiLSTM, were trained and evaluated employing precision, recall, and F1-score as metrics. Of these, the Bi-LSTM model achieved the overall best performance which can be explained by its capability to model long-range dependencies in both forward and backward temporal directions. The Bi-LSTM achieved a precision of 0.77, recall of 0.86, and an F1-score of 0.81 on the test set, demonstrating strong predictive accuracy under real-race conditions. Additionally, a historical race visualization interface was developed to visualize the model's predictions.

Skin cancer is the abnormal growth of skin cells. It occurs mostly in skin exposed to sunlight. To prevent the occurrence of skin cancer, avoid exposing skin to ultraviolet radiation. Skin cancer can be very harmful if found very late. Traditional convolutional neural networks (CNNs) face challenges in fine-grained lesion classification due to their limited ability to extract detailed features. To overcome such limitations, we introduced a novel approach in the form of a lightweight multi-scale vision transformer (LMS-ViT) application for the automated detection of skin cancer using dermoscopic images and the HAM10000 dataset. Unlike CNNs, LMS-ViT employs a multi-scale attention mechanism to capture both global lesion structures and fine-grained textural details, improving classification accuracy. This study combines skin images from the HAM10000 dataset with pictures taken using a smartphone. It uses a compact method to mix important features, which makes the system faster and suitable for real-time use in medical apps. The proposed system enables real-time skin cancer classification via a smartphone camera, making it portable and platform-independent. Experimental results show that LMS-ViT surpasses CNN-based models across all skin lesion categories, achieving 90% accuracy, an 18% improvement over CNN, while reducing computational cost by 30%. LMS-ViT also improves precision, recall, and F1-score, particularly in complex categories such as Vasc (0.96 to 1.01) and Nv (0.94 to 1.01), demonstrating superior classification power. With real-time android implementation, LMS-ViT offers accessible, mobile-friendly diagnostics for early skin cancer detection.