The core function of quantum networks is to establish high-fidelity quantum entanglement for long-distance communication. However, the main challenge is to efficiently allocate resources under limited conditions, maximize throughput, satisfy end-to-end (E2E) fidelity requirements, and prevent quantum decoherence caused by inefficient routing algorithms. Current research focuses on optimizing either throughput or fidelity, with a lack of approaches that optimize both simultaneously; furthermore, existing algorithms suffer from high computational complexity. To tackle these challenges, this study proposes a Satisfying Fidelity Threshold Routing and Adaptive Purification Strategy (SFTRAP). SFTRAP maximizes throughput for each request by selecting multiple paths and dynamically choosing links for entanglement purification based on the current state of link resources, thus minimizing throughput loss while satisfying fidelity threshold. The strategy also adaptively adjusts the number of purification rounds according to the fidelity threshold, thereby optimizing the time required for deep purification and enhancing algorithmic efficiency. For multi-request scenarios, SFTRAP employs a priority sorting mechanism that takes into account both path cost and path freedom, which refines request scheduling and path selection to create more efficient request combinations, thus further boosting the overall network throughput. Simulation results indicate that SFTRAP surpasses state-of-the-art methods in terms of both throughput and algorithmic efficiency, highlighting its potential for optimizing resources in quantum networks.

Multimodal data have emerged as a cornerstone for understanding and analyzing complex human behaviors, particularly in mental health monitoring. In this study, we propose a deep learning-driven behavioral modeling framework for intelligence of social things (IoST)-based mental health monitoring and intervention, designed to integrate and analyze multimodal data—including text, speech, and physiological signals—captured from interconnected IoST devices. The framework incorporates an adaptive attention-based fusion mechanism that dynamically adjusts the contribution of each modality based on contextual relevance, enhancing the robustness of multimodal integration. Additionally, we employ a temporal-aware recurrent neural network with an attention mechanism to capture long-term dependencies and evolving behavioral patterns, ensuring precise mental health state prediction. To validate the framework, extensive experiments were conducted using three publicly available datasets: DAIC-WOZ, SEED, and MELD. Comparative experiments demonstrate the superior performance of the proposed framework, achieving state-of-the-art accuracy of 93.5%, F1-scores of 92.9%, and AUC-ROC of 0.95 values. Ablation studies highlight the critical roles of attention mechanisms and multimodal integration, showcasing significant performance improvements over single-modality and simplified fusion approaches. These findings underscore the framework’s potential as a reliable and efficient tool for real-time mental health monitoring in IoST environments, paving the way for scalable and personalized interventions.

With the rapid development of Artificial Intelligence Generated Content (AIGC), single data centers are increasingly unable to meet the growing demands for data and computational resources in distributed large-scale language model (LLM) training. In this context, distributed training across heterogeneous data centers has become a necessary choice to enhance computational power and flexibility. However, the networks in heterogeneous data centers are polymorphic, with diverse communication protocols and network architectures. This heterogeneity renders traditional routing devices ineffective in recognizing and processing gradient data. Moreover, frequent copying and excessive parsing of gradient data by routing devices across heterogeneous data centers significantly increase model training time. To address these challenges, we propose XAForward, a method for accelerating distributed LLM in heterogeneous data centers using eXpress Data Path (XDP). Specifically, XAForward introduces a polymorphic-compatible protocol that reconstructs the header of gradient data packets to enable efficient data forwarding across different communication protocols in heterogeneous data centers. Additionally, to accelerate distributed LLM computing and reduce gradient data copying and excessive parsing during training, XAForward leverages kernel-bypass techniques based on XDP for packet processing and kernel-level data forwarding using network index identifiers. Experimental results show that, compared to state-of-the-art methods, XAForward reduces the distributed LLM training time by approximately 35% to 40%.

The rise of Embodied AI, including autonomous robots, cooperative autonomous driving, and augmented reality agents, is driving a deep integration of intelligent systems with the physical world, imposing stringent demands on the underlying network for real-time, low-latency interaction. However, these Embodied AI systems typically operate in complex polymorphic network environments, simultaneously handling heterogeneous identifiers such as content, IP, and geographic location. This causes traditional routing mechanisms to suffer from significant latency overhead and compatibility bottlenecks due to protocol conversion and adaptation, severely limiting the performance and responsiveness of Embodied AI applications. To address this challenge, we propose the Polymorphic Compatible Segment Routing (PCSR) protocol. PCSR adopts an innovative paradigm of decoupling the protocol from the infrastructure. It dynamically maps native protocol semantics to lightweight 8 -byte identifiers and utilizes compatibility logic encapsulated at the packet tail to achieve smooth compatibility with traditional networks without requiring large-scale modification of existing equipment. Furthermore, we built a zero-copy forwarding engine using the kernel eXpress Data Path (XDP) technology to fundamentally optimize data transmission efficiency. Experimental validation on a 10-node heterogeneous testbed shows that PCSR reduces end-toend latency by 32.7% and maintains a high throughput rate in hybrid network environments. This work demonstrates that PCSR provides an efficient and deployable routing solution for latency-critical, cross-domain collaborative services required by Embodied AI.

The growing concern regarding the environment and the compounded nature of healthcare-related emergencies has impacted the effectiveness of medical supply chain management. As a rule, demand forecasting techniques do not synchronize the altitude of medical need with the level of environmental care throughout a public health crisis. This study presents MedSC, a deep learning framework for dynamically forecasting emergency medical supply requirements within an AIoT green supply chain environment. The framework features advanced fuzzy clustering with long short-term memory) prediction models in medical supply prioritization using AIoT sensor data and integrating metrics of environmental care. MedSC determines the relevance and importance of medical supply through eco-friendly hierarchical clustering for prioritized grouping. It then devises unique forecasting models for non-interconnected and interconnected supply chain subsystems. The results of experiments conducted at three major medical distribution centers validate the claims in the hypothesis, proving that the framework outperforms traditional forecasting methods and even sophisticated contemporary approaches. These performed exceedingly well on ultra-low latency real-time predictive bounds, claiming an industry record in fast predictive bounds, holding double the precision while throttling emissions by 28.5%. The outstanding attribute of the framework is the balance of healthcare needs and environmental care, which improved energy per unit work by 91.3% while 89.7% improved resource per unit work.

The optimization of modern healthcare facilities presents unique challenges at the intersection of medical service quality, energy efficiency, and environmental impact. By integrating carbon-intelligent Industrial Internet of Things (IIoT) technologies with healthcare operations, our approach enables real-time monitoring and optimization of carbon emissions while maintaining medical service quality. Specifically, this paper presents a novel deep reinforcement learning-based multi-objective optimization algorithm (HC-DMOPSO) for IIoT-enabled healthcare building management. By integrating healthcare-specific constraints with an enhanced swarm intelligence framework, our approach optimizes building operations while considering medical device power demands, patient comfort, and environmental requirements. The proposed algorithm combines dual-distance metrics -population average distance and crowding distance -with deep Q-networks to effectively explore the complex solution space. Experimental results demonstrate HC-DMOPSO’s superior performance across multiple metrics: The integration of carbon intelligent IIoT sensors and actuators enables HC-DMOPSO to achieve 24.8% reduction in energy consumption while maintaining 99.92% medical power reliability, 32.5% decrease in peak load with only 0.38∘C average temperature deviation, and 28.7% improvement in carbon reduction compared to baseline methods.