CATD: Unified Representation Learning for EEG-to-fMRI Cross-Modal Generation.

Multi-modal neuroimaging analysis is crucial for a comprehensive understanding of brain function and pathology, as it allows for the integration of different imaging techniques, thus overcoming the limitations of individual modalities. However, the high costs and limited availability of certain modalities pose significant challenges. To address these issues, this paper proposed the Condition-Aligned Temporal Diffusion (CATD) framework for end-to-end cross-modal synthesis of neuroimaging, enabling the generation of functional magnetic resonance imaging (fMRI)-detected Blood Oxygen Level Dependent (BOLD) signals from more accessible Electroencephalography (EEG) signals.

SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems.

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems.

Retraction Notice: iMetaTown: A Metaverse System with Multiple Interactive Functions Based on Virtual Reality.

This work aims to pioneer the development of a real-time interactive and immersive Metaverse Human-Computer Interaction (HCI) system leveraging Virtual Reality (VR). The system incorporates a three-dimensional (3D) face reconstruction method, grounded in weakly supervised learning, to enhance player-player interactions within the Metaverse. The proposed method, two-dimensional (2D) face images, are effectively employed in a 2D Self-Supervised Learning (2DASL) approach, significantly optimizing 3D model learning outcomes and improving the quality of 3D face alignment in HCI systems.

Autonomously Adjusting Multi-Relational Hypergraphs Structure for Predicting circRNA-MiRNA Associations.

Identifying circRNA-miRNA associations is critical for understanding gene regulatory mechanisms, discovering new biomarkers, and developing therapeutic strategies. The ongoing advancement of autonomous artificial intelligence (AI) technology, particularly in relational and graph learning, enables researchers to develop autonomous AI prediction models to process and analyze existing associations. These models can autonomously extract meaningful patterns and relationships, thereby accurately predicting unknown associations and providing efficient auxiliary tools for traditional experimental methods.

A Semantic Conditional Diffusion Model for Enhanced Personal Privacy Preservation in Medical Images.

Deep learning has significantly advanced medical image processing, yet the inherent inclusion of personally identifiable information (PII) within medical images-such as facial features, distinctive anatomical structures, rare lesions, or specific textural patterns-poses a critical risk to patient privacy during data transmission. To mitigate this risk, we introduce the Medical Semantic Diffusion Model (MSDM), a novel framework designed to synthesize medical images guided by semantic information, synthesis images with the same distribution as the original data, which effectively removes the PPI of the original data to ensure robust privacy protection. Unlike conventional techniques that combine semantic and noisy images for denoising, MSDM integrates Adaptive Batch Normalization (AdaBN) to encode semantic information into high-dimensional latent space, embedding it directly within the denoising neural network.

Diabetes Status and Cardiovascular Complications Risk in Noncardiac Surgery: A Population-Based Cohort Study.

Objective: Patients with diabetes are considered to be at high surgical risk due to the potential occurrence of cardiovascular and diabetes-related complications. Limited research exists on the cardiovascular risk profiles of patients with prediabetes and undiagnosed diabetes in noncardiac surgery. In this population-based cohort study, we investigated different glycated hemoglobin levels and their associated postoperative cardiovascular risks.

U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision.

Forest fires pose a serious threat to the global ecological environment, and the critical steps in reducing the impact of fires are fire warning and real-time monitoring. Traditional monitoring methods, like ground observation and satellite sensing, were limited by monitoring coverage or low spatio-temporal resolution, making it difficult to meet the needs for precise shape of fire sources. Therefore, we propose an accurate and reliable forest fire monitoring segmentation model U3UNet based on UAV vision, which uses a nested U-shaped structure for feature fusion at different scales to retain important feature information.

Association between stress hyperglycemia ratio and postoperative major adverse cardiovascular and cerebrovascular events in noncardiac surgeries: a large perioperative cohort study.

Background: There has been a concerning rise in the incidence of major adverse cardiovascular and cerebrovascular events (MACCE) following noncardiac surgeries (NCS), significantly impacting surgical outcomes and patient prognosis. Glucose metabolism abnormalities induced by stress response under acute medical conditions may be a risk factor for postoperative MACCE. This study aims to explore the association between stress hyperglycemia ratio (SHR) and postoperative MACCE in patients undergoing general anesthesia for NCS.

Rethinking max-min planning on energy-efficient software-defined networking for 5G networks.

Energy efficiency plays an important role in intelligent networking for 5G networks, which concerns environmental, financial, and performance aspects of intelligent networking for 5G networks. To this end, network designers propose energy-efficient approaches to reduce energy consumption of networks and to raise network performance by switching off the links/nodes with low loads or at idle status. The existing energy-efficient approaches can be formulated as a max-min optimal problem, namely maximizing network/node/port throughput via minimum energy consumption.

Retracted: iMetaTown: A Metaverse System with Multiple Interactive Functions Based on Virtual Reality.

This work aims to pioneer the development of a real-time interactive and immersive Metaverse Human-Computer Interaction (HCI) system leveraging Virtual Reality (VR). The system incorporates a three-dimensional (3D) face reconstruction method, grounded in weakly supervised learning, to enhance player-player interactions within the Metaverse. The proposed method, two-dimensional (2D) face images, are effectively employed in a 2D Self-Supervised Learning (2DASL) approach, significantly optimizing 3D model learning outcomes and improving the quality of 3D face alignment in HCI systems.

Toward the Internet of Medical Things: Architecture, trends and challenges.

In recent years, the growing pervasiveness of wearable technology has created new opportunities for medical and emergency rescue operations to protect users' health and safety, such as cost-effective medical solutions, more convenient healthcare and quick hospital treatments, which make it easier for the Internet of Medical Things (IoMT) to evolve. The study first presents an overview of the IoMT before introducing the IoMT architecture. Later, it portrays an overview of the core technologies of the IoMT, including cloud computing, big data and artificial intelligence, and it elucidates their utilization within the healthcare system.

Digital twins for building industrial metaverse.

Introduction: The concept of the metaverse, a virtual world where users can interact with a computer-generated environment, has received significant attention recently.

Objectives: This study aims to investigate the application and fundamental technologies of Digital Twins (DT) in the development of the industrial metaverse, to enhance factory production efficiency.

Methods: The study adopts a literature review approach to explore the architecture and key technologies of the industrial metaverse, including DT, cloud rendering, virtual-real interaction, big data visualization, and the Internet of Things.