This innovative study analyzes the emotional reaction of 27 individuals from Generation X to flirtatious behaviors exhibited by conversational artificial intelligence. Using a quantitative methodology based on Sentiment Analysis, testimonies and experiences with three models—ChatGPT, Grok, and Gemini—were collected, focusing on phrases or linguistic gestures that could be considered seductive, empathetic, or emotionally warm. The results show that, although there is a clear awareness that no person is behind the AI, several responses generated feelings of companionship, affective validation, and even mild attachment, especially in moments of emotional vulnerability—very difficult to ex... More >

Accurate traffic flow prediction plays a critical role in intelligent transportation systems, providing essential support for urban planning, traffic control, and congestion mitigation. To address the challenges of spatial heterogeneity and temporal dynamics inherent in traffic data, this paper proposes AST-GNNFormer, an adaptive spatio-temporal graph neural network that integrates graph attention mechanisms with temporal convolution. The model introduces three key components to enhance predictive accuracy and generalization: (1) a Layer-aware Information Preservation mechanism that mitigates over-smoothing in deep GNNs by retaining original node features across layers; (2) an Inter-Layer At... More >

Object detection is a fundamental problem in computer vision, with applications spanning self-driving cars, surveillance systems, medical imaging, robotics, and smart cities. Among the myriad of algorithms developed for this task, the You Only Look Once (YOLO) family stands out for its ability to perform real-time and accurate object detection. This article provides a comprehensive analysis of the YOLO algorithm series, from YOLOv1 to YOLOv8, evaluating them across key performance metrics, including precision, recall, mean Average Precision (mAP), frames per second (FPS), and overall effectiveness. Unlike traditional two-stage detectors such as R-CNN, YOLO formulates object detection as a si... More >

Accurate forecasting of reference evapotranspiration (ETo) is crucial for sustainable water resource management and precision agriculture. The present study evaluates three ETo prediction methods: Random Forest (RF), Cartesian Genetic Programming (CGP), and Convolutional Neural Network-Graphics Processing Unit (CNN-GPU) across time intervals of 1 to 364 days. Using dispersion analysis (scatter/violin plots) and accuracy metrics (RMSE, MAE, R^2, SI), it was seen that the RF and CNN-GPU models consistently outperform CGP, particularly at extended horizons. At 364 days, CNN-GPU achieved the highest accuracy (RMSE: 0.678 mm/day, R^2: 0.874), while RF maintained robust performance (RMSE: 0.683 mm... More >

The growing prominence of prompt engineering as a means of controlling large language models has given rise to a diverse set of methods, ranging from handcrafted templates to embedding-level tuning. Yet, as prompts increasingly serve not merely as input scaffolds but as adaptive interfaces between users and models, the question of how to systematically optimize them remains unresolved. Reinforcement learning, with its capacity for sequential decision-making and reward-driven adaptation, has been proposed as a possible framework for discovering effective prompting strategies. This survey explores the emerging intersection of RL and prompt engineering, organizing existing research along three... More >