Real-time and accurate cattle behavior detection is crucial for improving animal welfare and optimizing livestock management. To address this, this study comprehensively evaluates six community-developed lightweight YOLO (You Only Look Once) models-YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n, and YOLOv13n-for recognizing five key cattle behaviors (standing, lying, foraging, drinking, and rumination) using the custom CBVD-5 dataset. The dataset includes diverse lighting conditions and natural barn environments to reflect real-world monitoring scenarios. All models were assessed using standard object detection metrics (precision, recall, mAP@50, and mAP@50-95) and deployment-oriented indicators including inference time, frames per second (FPS), model size, and training duration to provide a balanced evaluation of accuracy and efficiency. YOLOv13n achieved the highest mAP@50-95 (0.712), while YOLOv11n reached the highest mAP@50 (0.967) and exhibited stable convergence with a good trade-off between detection accuracy and inference speed. YOLOv10n demonstrated the fastest inference speed (1.2 ms/frame, ~833 FPS), making it well-suited for latency-sensitive applications such as continuous barn surveillance. All models maintained compact sizes below 6 MB and real-time throughput exceeding ~300 FPS, confirming their practicality for edge deployment. The results establish a reproducible benchmarking framework for evaluating modern YOLO variants, offering insights into model selection for efficient, scalable, and welfare-oriented livestock monitoring systems.

Seamless multimodal human-robot communication has become essential as social, assistive, and educational robots move into everyday settings like homes, healthcare facilities, and classrooms, where they need to integrate speech, gesture, gaze, facial expressions, and tactile cues with high precision, low latency, and real-world robustness. This review systematically examines how current techniques achieve real-time, reciprocal multimodal human-robot interaction (HRI), focusing on fusion strategies, system architectures, and applications in specific domains. We searched the Web of Science Core Collection for English-language empirical studies from 2015 to 2025, selecting 148 papers that address communicative integration. The most important insights include a clear shift toward hybrid and attention-based fusion since 2022 (about 43% of approaches), which better handles temporal asynchrony and embodied interactions than earlier methods; widespread inconsistency in evaluation metrics that hinders cross-study comparisons; and a persistent gap between strong laboratory performance and weaker real-world robustness under noise, occlusion, or user variability. Key challenges include real-time processing, semantic alignment, data efficiency, deployment robustness, and the under-explored integration of tactile signals with affect. Looking ahead, the review suggests prioritizing adaptive fusion policies, standardized benchmarks for synchronization and fluency, and continual learning to enable user-personalized adaptation. Ultimately, it aims to guide the development of more human-centered robotic agents that can engage collaboratively, meaningfully, and are socially acceptable in daily life.

Coronary X-ray angiography is the clinical reference for visualizing coronary anatomy, but automated instance-level segment labeling remains difficult because of low contrast, overlapping branches, class imbalance across 25 SYNTAX-defined segments, and heterogeneous published protocols. This protocol therefore provides a reproducible, image-level comparison of five You Only Look Once (YOLO) segmentation models-YOLOv8-seg, YOLOv9c-seg, YOLO11-seg, YOLO12-seg, and YOLO26-seg-on the public Roboflow v1 ARCADE release of 3,587 expert-annotated frames divided into 3,138 training, 299 validation, and 150 test frames. Models were initialized from official segmentation checkpoints, trained only on the training split with shared stochastic gradient descent (SGD) settings and validation-based early stopping and checkpoint selection, and evaluated once post hoc on the untouched test split; mask mean average precision (mAP) at intersection over union (IoU) thresholds of 0.50-0.95 (mAP@50-95) was the primary accuracy metric and A100 model-level latency at 32-bit floating-point precision (FP32) was a secondary efficiency descriptor. On validation, the larger YOLOv9c-seg obtained the highest observed mask mAP@50-95 of 0.210; on the test split, compact YOLO11-seg obtained the highest observed mask mAP@50-95 of 0.241, followed by YOLO26-seg (0.239) and YOLOv9c-seg (0.235). Major proximal segments were consistently easier to segment than rare distal variants, while recorded inference times of 0.6 ms versus 3.0 ms per frame describe model-level throughput rather than end-to-end clinical latency. These single-seed, image-level results do not establish patient-level independence, statistical superiority, or clinical utility; they are intended as a methodological benchmark that others can reimplement, with prospective, hardware-specific, and patient-level validation still required before any workflow claim.

Real-time and accurate cattle behavior detection is crucial for improving animal welfare and optimizing livestock management. To address this, this study comprehensively evaluates six community-developed lightweight YOLO (You Only Look Once) models-YOLOv8n, YOLOv9t, YOLOv10n, YOLOv11n, YOLOv12n, and YOLOv13n-for recognizing five key cattle behaviors (standing, lying, foraging, drinking, and rumination) using the custom CBVD-5 dataset. The dataset includes diverse lighting conditions and natural barn environments to reflect real-world monitoring scenarios. All models were assessed using standard object detection metrics (precision, recall, mAP@50, and mAP@50-95) and deployment-oriented indicators including inference time, frames per second (FPS), model size, and training duration to provide a balanced evaluation of accuracy and efficiency. YOLOv13n achieved the highest mAP@50-95 (0.712), while YOLOv11n reached the highest mAP@50 (0.967) and exhibited stable convergence with a good trade-off between detection accuracy and inference speed. YOLOv10n demonstrated the fastest inference speed (1.2 ms/frame, ~833 FPS), making it well-suited for latency-sensitive applications such as continuous barn surveillance. All models maintained compact sizes below 6 MB and real-time throughput exceeding ~300 FPS, confirming their practicality for edge deployment. The results establish a reproducible benchmarking framework for evaluating modern YOLO variants, offering insights into model selection for efficient, scalable, and welfare-oriented livestock monitoring systems.