Abstract
This article introduces a comprehensive frame-work for Post-Disaster Search and Rescue (PDSR), aiming to optimize search and rescue operations leveraging Unmanned Aerial Vehicles (UAVs). The primary goal is to improve the precision and availability of sensing capabilities, particularly in various catastrophic scenarios. Central to this concept is the rapid deployment of UAV swarms equipped with diverse sensing, communication, and intelligence capabilities, functioning as an integrated system that incorporates multiple technologies and approaches for efficient detection of individuals buried beneath rubble or debris following a disaster. Within this framework, we investigate an architectural solution and address the associated challenges to ensure superior performance in real-world disaster scenarios. The proposed framework is designed to provide comprehensive coverage of affected areas by utilizing a multi-tier swarm architecture with multi-modal sensing capabilities. By integrating data from var-ious sensors and applying machine learning for data fusion, the framework enhances detection accuracy and supports precise survivor identification, even in complex environments.

Abstract
The forthcoming 6G networks will embrace a new realm of AI-driven services that requires innovative network slicing strategies, namely slicing for AI, which involves the creation of customized network slices to meet Quality of Service (QoS) requirements of diverse AI services. This poses challenges due to time-varying dynamics of users' behavior and mobile networks. Thus, this paper proposes an online learning framework to determine the allocation of computational and communication resources to AI services, to optimize their accuracy as one of their unique key performance indicators (KPIs), while abiding by resources, learning latency, and cost constraints. We define a problem of optimizing the total accuracy while balancing conflicting KPIs, prove its NP-hardness, and propose an online learning framework for solving it in dynamic environments. We present a basic online solution and two variations employing a pre-learning elimination method for reducing the decision space to expedite the learning. Furthermore, we propose a biased decision space subset selection by incorporating prior knowledge to enhance the learning speed without compromising performance and present two alternatives of handling the selected subset. Our results depict the efficiency of the proposed solutions in converging to the optimal decisions, while reducing decision space and improving time complexity. Additionally, our solution outperforms State-of-the-Art techniques in adapting to diverse environmental dynamics and excels under varying levels of resource availability.

Abstract
In the smart grid, data communication between smart meters and utility servers should be authentic, private, have integrity while being accessible. To mitigate the risks of potential attacks, securing these two-way communications is crucial. Equally important is maintaining near real-time communication and avoiding significant delays when extra security levels are involved. Existing research on smart grids has not simultaneously tackled the issues of security, communication speed, and network scalability. In this work, we propose a novel delay-optimized blockchain solution for securing cryptographic communication between consumers and the utility in a smart grid. Our solution, based on EOS smart contracts, Edge computing, asymmetric Cryptographic functions, and Group signatures (EECG), treats data communication as transactions that are asymmetrically encrypted and signed in groups before being stored on the EOS blockchain, ensuring confidentiality, privacy, availability, and low cost. The use of edge computing reduces the computational burden of smart meters, increases transaction speed, enhances data privacy, and improves scalability. Furthermore, an optimization problem for associating smart meters with edge nodes is formulated to minimize data exchange and processing delays over the blockchain, facilitating near real-time secure data access.

Abstract

Abstract

Abstract
Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder that significantly affects public health, contributing to cardiovascular and metabolic impairments. Previous studies highlight the heterogeneity of OSA, which is manifested in different phenotypes, complicating personalized treatment strategies. Current phenotyping methods primarily rely on traditional clustering techniques, such as k-means, which may fail to capture complex relationships among features. This study introduces a novel approach based on subject-based SpO(2) weighted correlation networks and modularity analysis to identify clinically relevant subgroups within the OSA population. Using a subset of 2,641 subjects from the Sleep Heart Health Study (SHHS), we extracted 43 SpO(2) features from polysomnography to build correlation networks from them. A bootstrap procedure ensured robustness, while Blondel's modularity algorithm identified subgroups without requiring a predefined number of clusters. Comparison with k-means revealed that the correlation network method identified subgroups with more significantly different sociodemographic, clinical, and anthropometric characteristics (35 variables vs. 28 for k-means). These 35 features effectively revealed hidden SpO2 patterns, suggesting that subject-based correlation networks can identify distinct OSA phenotypes and enhance personalized treatment strategies. This approach improves clinical decisionmaking and patient care. Future research should validate these findings in longitudinal studies and explore integrating multimodal data to refine OSA phenotyping.