Digital livestock farming faces a critical deployment challenge: bridging the gap between cutting-edge AI algorithms and practical implementation in resource-constrained agricultural environments. While deep learning models demonstrate exceptional accuracy in laboratory settings, their translation to operational farm systems remains limited by computational constraints, connectivity issues, and user accessibility barriers. Dairy DigiD addresses these challenges through a novel edge-cloud AI framework integrating YOLOv11 object detection with DenseNet121 physiological classification for cattle monitoring. The system employs YOLOv11-nano architecture optimized through INT8 quantization (achieving 73% model compression with <1% accuracy degradation) and TensorRT acceleration, enabling 24 FPS real-time inference on NVIDIA Jetson edge devices while maintaining 94.2% classification accuracy. Our key innovation lies in intelligent confidence-based offloading: routine detections execute locally at the edge, while ambiguous cases trigger cloud processing for enhanced accuracy. An entropy-based active learning pipeline using Roboflow reduces the annotation overhead by 65% while preserving 97% of the model performance. The Gradio interface democratizes system access, reducing technician training requirements by 84%. Comprehensive validation across ten commercial dairy farms in Atlantic Canada demonstrates robust performance under diverse environmental conditions (seasonal, lighting, weather variations). The framework achieves mAP@50 of 0.947 with balanced precision-recall across four physiological classes, while consuming 18% less energy than baseline implementations through attention-based optimization. Rather than proposing novel algorithms, this work contributes a systems-level integration methodology that transforms research-grade AI into deployable agricultural solutions. Our open-source framework provides a replicable blueprint for precision livestock farming adoption, addressing practical barriers that have historically limited AI deployment in agricultural settings.

Dense video captioning involves identifying, localizing, and describing multiple events within a video. Capturing temporal and contextual dependencies between events is essential for generating coherent and accurate captions. To effectively capture temporal and contextual dependencies between events, we propose Dense Video Captioning with Dual Contextual, Systematic, and Linguistic Modules (DVC-DCSL), a novel dense video captioning model that integrates contextual, semantic, and linguistic modules. The proposed approach employs two uni-directional LSTMs (forward and backward) to generate distinct captions for each event. A caption selection mechanism then processes these outputs to determine the final caption. In addition, contextual alignment is improved by incorporating visual and textual features from previous video segments into the captioning module, ensuring smoother narrative transitions. Comprehensive experiments conducted using the ActivityNet dataset demonstrate that DVC-DCSL increases the Meteor score from 11.28 to 12.71, representing a 12% improvement over state-of-the-art models in the field of dense video captioning. These results highlight the effectiveness of the proposed approach in improving dense video captioning quality through contextual and linguistic integration.

Background: As intelligent systems increasingly operate in high-risk environments, understanding how they perceive and respond to hazards is critical for ensuring safety. Methods: In this study, we conduct a comparative analysis of 60 real-world accident reports, 30 from process control systems (PCSs) and 30 from autonomous vehicles (AVs), to examine differences in risk triggers, perception paradigms, and interaction failures between humans and artificial intelligence (AI). Results: Our findings reveal that PCS risks are predominantly internal to the system and detectable through deterministic, rule-based mechanisms, whereas AVs’ risks are externally driven and managed via probabilistic, multi-modal sensor fusion. More importantly, despite these architectural differences, both domains exhibit recurring human–AI interaction failures, including over-reliance on automation, mode confusion, and delayed intervention. In the case of PCSs, these failures are historically tied to human–automation interaction; this article extrapolates these patterns to anticipate potential human–AI interaction challenges as AI adaptation increases. Conclusions: This study highlights the need for a hybrid risk perception framework and improved human-centered design to enhance situational awareness and responsiveness. While AI has not yet been implemented in PCS incident studies, this work interprets human–automation failures in these cases as indicative of potential challenges in human–AI interaction that may arise in future AI-integrated process systems. Implications extend to developing safer intelligent systems across industrial and transportation sectors.

This survey provides a comprehensive review of the integration of large language models (LLMs) into autonomous robotic systems, organized around four key pillars: locomotion, navigation, manipulation, and voice-based interaction. We examine how LLMs enhance robotic autonomy by translating high-level natural language commands into low-level control signals, supporting semantic planning and enabling adaptive execution. Systems like SayTap improve gait stability through LLM-generated contact patterns, while TrustNavGPT achieves a 5.7% word error rate (WER) under noisy voice-guided conditions by modeling user uncertainty. Frameworks such as MapGPT, LLM-Planner, and 3D-LOTUS++ integrate multi-modal data—including vision, speech, and proprioception—for robust planning and real-time recovery. We also highlight the use of physics-informed neural networks (PINNs) to model object deformation and support precision in contact-rich manipulation tasks. To bridge the gap between simulation and real-world deployment, we synthesize best practices from benchmark datasets (e.g., RH20T, Open X-Embodiment) and training pipelines designed for one-shot imitation learning and cross-embodiment generalization. Additionally, we analyze deployment trade-offs across cloud, edge, and hybrid architectures, emphasizing latency, scalability, and privacy. The survey concludes with a multi-dimensional taxonomy and cross-domain synthesis, offering design insights and future directions for building intelligent, human-aligned robotic systems powered by LLMs.

Background/Objectives: Knowledge Graphs (KGs) are often incomplete, which can significantly impact the performance of downstream applications. Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC. Link prediction serves as a fundamental task in this domain. The semantic correlation among entity features plays a crucial role in determining the effectiveness of link-prediction models. Notably, the human brain can often infer information using a limited set of salient features. Methods: Inspired by this cognitive principle, this paper proposes a lightweight Bi-level routing attention mechanism specifically designed for link-prediction tasks. This proposed module explores a theoretically grounded and lightweight structural design aimed at enhancing the semantic recognition capability of language models without altering their core parameters. The proposed module enhances the model’s ability to attend to feature regions with high semantic relevance. With only a marginal increase of approximately one million parameters, the mechanism effectively captures the most semantically informative features. Result: It replaces the original feature-extraction module within the KGML framework and is evaluated on the publicly available WN18RR and FB15K-237 dataset. Conclusions: Experimental results demonstrate consistent improvements in standard evaluation metrics, including Mean Rank (MR), Mean Reciprocal Rank (MRR), and Hits@10, thereby confirming the effectiveness of the proposed approach.

Background/Objectives: While machine learning has made substantial strides in pronunciation detection in recent years, there remains a notable gap in the literature regarding research on improvements in the acquisition of speech sounds following a training intervention, especially in the domain of perception. This study addresses this gap by developing a deep learning algorithm designed to identify perceptual gains resulting from second language (L2) phonetic training. Methods: The participants underwent multiple sessions of high-variability phonetic training, focusing on discriminating challenging L2 vowel contrasts. The deep learning model was trained on perceptual data collected before and after the intervention. Results: The results demonstrated good model performance across a range of metrics, confirming that learners’ gains in phonetic training could be effectively detected by the algorithm. Conclusions: This research underscores the potential of deep learning techniques to track improvements in phonetic training, offering a promising and practical approach for evaluating language learning outcomes and paving the way for more personalized, adaptive language learning solutions. Deep learning enables the automatic extraction of complex patterns in learner behavior that might be missed by traditional methods. This makes it especially valuable in educational contexts where subtle improvements need to be captured and assessed objectively.

Background: Rapid advancements in large language models (LLMs) have significantly enhanced Retrieval-Augmented Generation (RAG) techniques, leading to more accurate and context-aware information retrieval systems. Methods: This article presents the creation of a RAG-based chatbot tailored for university course catalogs, aimed at answering queries related to course details and other essential academic information, and investigates its performance by testing it on several locally deployed large language models. By leveraging multiple LLM architectures, we evaluate performance of the models under test in terms of context length, embedding size, computational efficiency, and relevance of responses. Results: The experimental analysis obtained by this research, which builds on recent comparative studies, reveals that while larger models achieve higher relevance scores, they incur greater response times than smaller, more efficient models. Conclusions: The findings underscore the importance of balancing accuracy and efficiency for real-time educational applications. Overall, this work contributes to the field by offering insights into optimal RAG configurations and practical guidelines for deploying AI-powered educational assistants.

Background/Objectives: The resilience of safety-critical systems is gaining importance due to the rise in cyber and physical threats, especially within critical infrastructure. Traditional static resilience metrics may not capture dynamic system states, leading to inaccurate assessments and ineffective responses to cyber threats. This work aims to develop a data-driven, adaptive method for resilience metric learning. Methods: We propose a data-driven approach using inverse reinforcement learning (IRL) to learn a single, adaptive resilience metric. The method infers a reward function from expert control actions. Unlike previous approaches using static weights or fuzzy logic, this work applies adversarial inverse reinforcement learning (AIRL), training a generator and discriminator in parallel to learn the reward structure and derive an optimal policy. Results: The proposed approach is evaluated on multiple scenarios: optimal communication network rerouting, power distribution network reconfiguration, and cyber–physical restoration of critical loads using the IEEE 123-bus system. Conclusions: The adaptive, learned resilience metric enables faster critical load restoration in comparison to conventional RL approaches.

Due to range of factors in the development stage, generative artificial intelligence (AI) models cannot be completely free from bias. Some biases are introduced by the quality of training data, and developer influence during both design and training of the large language models (LLMs), while others are introduced in the text-to-image (T2I) visualization programs. The bias and initialization at the interface between LLMs and T2I applications has not been examined to date. This study analyzes 770 images of librarians and curators generated by DALL-E from ChatGPT-4o prompts to investigate the source of gender, ethnicity, and age biases in these visualizations. Comparing prompts generated by ChatGPT-4o with DALL-E’s visual interpretations, the research demonstrates that DALL-E primarily introduces biases when ChatGPT-4o provides non-specific prompts. This highlights the potential for generative AI to perpetuate and amplify harmful stereotypes related to gender, age, and ethnicity in professional roles.

(1) Acute pancreatitis (AP) is a medical emergency associated with high mortality rates. Early and accurate prognosis assessment during admission is crucial for optimizing patient management and outcomes. This study seeks to develop robust radiomics-based machine learning (ML) models to classify the severity of AP using contrast-enhanced computed tomography (CECT) scans. (2) Methods: A retrospective cohort of 287 AP patients with CECT scans was analyzed, and clinical data were collected within 72 h of admission. Patients were classified as mild or moderate/severe based on the Revised Atlanta classification. Two radiologists manually segmented the pancreas and peripancreatic regions on CECT scans, and 234 radiomic features were extracted. The performance of the ML algorithms was compared with that of traditional scoring systems, including Ranson and Glasgow-Imrie scores. (3) Results: Traditional severity scoring systems produced AUC values of 0.593 (Ranson, Admission), 0.696 (Ranson, 48 h), 0.677 (Ranson, Cumulative), and 0.663 (Glasgow-Imrie). Using LASSO regression, 12 radiomic features were selected for the ML classifiers. Among these, the best-performing ML classifier achieved an AUC of 0.876 in the training set and 0.777 in the test set. (4) Conclusions: Radiomics-based ML classifiers significantly enhanced the prediction of AP severity in patients undergoing CECT scans within 72 h of admission, outperforming traditional severity scoring systems. This research is the first to successfully predict prognosis by analyzing radiomic features from both pancreatic and peripancreatic tissues using multiple ML algorithms applied to early CECT images.