The growing reliance on satellite-based infrastructures for communication, navigation, defense, and environmental monitoring has magnified the urgency of securing Space Information Networks (SINs) against cyber threats. This paper presents a comprehensive review of the vulnerabilities, threat vectors, and advanced countermeasures impacting SINs. Key vulnerabilities, including system complexity, use of Commercial Off-the-Shelf (COTS) components, lack of standardized security frameworks, and emerging quantum threats, are critically analyzed. This paper classifies cyber threats into active and passive categories, highlighting real-world case studies such as Denial-of-Service attacks, message modification, eavesdropping, and satellite transponder hijacking. A detailed survey of countermeasures follows, focusing on AI-driven intrusion detection, federated learning approaches, deep learning techniques, random routing algorithms, and quantum-resistant encryption. This study emphasizes the pressing need for integrated, resilient, and proactive security architectures tailored to the unique constraints of space systems. It concludes by identifying research gaps and recommending future directions to enhance the resilience of SINs against evolving cyber threats in an increasingly contested space environment.

The accurate identification of medicine vials is crucial for emergency medical services, especially for vials that resemble one another but have different labels, volumes, and concentrations. This study introduces a method to detect vials in real-time using mixed reality technology through Microsoft HoloLens 2. The system is also equipped with an SQL server to manage barcode and vial information. We conducted a comparative analysis of the barcode detection capabilities of the HoloLens 2 camera and an external scanner. The HoloLens 2 effectively identified larger barcodes when they were 20–25 cm away in normal lighting conditions. However, it faced difficulties in detecting smaller barcodes that were consistently detected by the external scanner. The frame rate investigation revealed performance fluctuations: an average of 10.54 frames per second (fps) under standard lighting conditions, decreasing to 10.10 fps in low light and further reducing to 10.05 fps when faced with high barcode density. Resolution tests demonstrated that a screen resolution of 1920 × 1080 yielded the best level of accuracy, with a precision rate of 98%. On the other hand, a resolution of 1280 × 720 achieved a good balance between accuracy 93% and speed. The HoloLens 2 demonstrates satisfactory performance under ideal circumstances; however, enhancements in detecting algorithms and camera resolution are required to accommodate diverse surroundings. This approach seeks to help paramedics make quick and accurate decisions during critical situations and tackle common obstacles such as reliance on networks and human mistakes. Our new approach of a hybrid method that integrates an external Bluetooth scanner with the MR device gives optimal results compared to the scanner-only approach.

Deepfakes, created using deep learning methods such as Generative Adver-sarial Networks (GANs) and diffusion models, have revolutionized digitalmedia synthesis by enabling highly realistic image, video, and audio ma-nipulation. While these technologies support creative applications in en-tertainment, accessibility, and education, their misuse for misinformation,fraud, and identity theft has raised serious ethical and security concerns.This survey provides a comprehensive review of the evolution of deepfakegeneration and detection techniques across image and video domains. Itexplores early encoder–decoder and GAN-based pipelines, diffusion-drivenframeworks, and recent advances in controllable and identity-preserving syn-thesis. On the detection side, spatial, frequency, and multi-domain fusionmethods are discussed alongside advanced video detection frameworks thatintegrate convolutional and recurrent neural networks (CNN–RNN hybrids)and transformers for spatio-temporal analysis. Emerging approaches such asthe Frequency Self-Blended Image (FSBI) and Spatio Frequency Cross Fusion(SFCF) models are highlighted for their improved cross dataset generaliza-tion and robustness. Furthermore, the paper analyzes benchmark datasets,evaluation metrics, and cross-domain robustness issues that remain major re-search challenges.The paper further outlines open challenges and emerging di-rections learnable frequency filters, diffusion-based forensics, explainable AI,and lightweight deployment for edge devices. Integrating these approacheswithin a cohesive framework can accelerate the development of robust, gen-eralizable, and trustworthy detection systems.

This study addresses two key challenges in computational pharmacology: identifying novel therapeutic uses for existing drugs and modeling drug safety-related characteristics. We propose a multi-relational biomedical knowledge graph that integrates gene, drug, and disease associations with adverse effect data, enabling joint modeling of therapeutic and safety-related properties. A Relational Graph Convolutional Network (R-GCN) is employed to learn relationally aware embeddings that capture complex biological interactions across heterogeneous entities. The framework is evaluated on two tasks: (1) drug–disease link prediction for drug repurposing and (2) prediction of drug side-effect burden based on adverse event patterns. The experimental results demonstrate that the R-GCN model outperforms baseline methods, achieving 94.63% accuracy in drug–disease link prediction, while embedding-based classifiers attain up to 97.14% F1-score in side-effect burden classification. Additionally, multi-hop relational reasoning enables the discovery of biologically plausible connections between drugs, genes, and diseases. These findings highlight the effectiveness of knowledge graph-based representation learning in jointly supporting therapeutic discovery and safety-related analysis. While side-effect burden is used as a surrogate measure rather than a direct indicator of drug quality, the proposed framework provides a scalable foundation for integrating real-world pharmacovigilance and regulatory data in future studies.