Niyaz Ahmad Wani

The evolution of sixth‐generation (6G) wireless networks demands ultra‐reliable low‐latency communication (URLLC), massive connectivity, and high‐capacity data transmission in highly dynamic environments. Space–Air–Ground Integrated Networks (SAGINs) have emerged as a promising architecture by seamlessly integrating satellites, unmanned aerial vehicles (UAVs), and terrestrial infrastructure to provide ubiquitous connectivity. However, stochastic traffic arrivals, UAV mobility, time‐varying wireless channels, and the coexistence of enhanced Mobile Broadband (eMBB) and URLLC services make traffic offloading and resource allocation highly challenging. These factors transform the optimization task into a stochastic mixed‐integer nonlinear programming (MINLP) problem. To address this challenge, this paper proposes a Quantum Federated Reinforcement Learning (QFRL)‐based traffic offloading framework for RSMA‐enabled SAGINs. The optimization problem is formulated as a constrained Markov decision process (CMDP), allowing distributed small cells to jointly optimize traffic offloading ratios, bandwidth allocation, RSMA power distribution, and UAV trajectory planning while satisfying stringent delay and reliability requirements. A variational quantum circuit (VQC)‐based actor‐critic architecture is developed to improve learning efficiency and policy representation in high‐dimensional continuous action spaces. In addition, a federated aggregation mechanism enables privacy‐preserving distributed learning and scalable coordination across the space, air, and ground segments. The proposed framework employs temporal‐difference learning and parameter‐shift gradient optimization to ensure stable convergence under stochastic network dynamics. Simulation results demonstrated that the proposed QFRL framework reduces traffic dropping probability by 28%–35%, decreases URLLC delay by 22%–30%, improves network availability by 18%–25%, and enhances traffic offloading efficiency by 20%–27% compared with Differentiated Federated Soft Actor‐Critic (DFSAC), Double Q‐Learning delay sensitive replay memory (DSRPM), and Nash Equilibrium Iteration Offloading (NEIO‐G) schemes, respectively.

The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) associated protein 9 (Cas9) system has revolutionized and changed the genome editing by enabling precise and efficient genetic modifications across various diverse biological applications. Despite its ability, the broader therapeutic adoption of CRISPR-Cas9, particularly in vivo settings, is constrained drastically by immunogenicity risks arising from guide RNA (gRNA) target interactions and unintended host immune recognition patterns. Accurate prediction of these T-cell epitope reactivity is essential to ensure safety, mitigate adverse immune responses in application, and guide the rational design of genome-editing therapies. In this work, we propose CRISPR-ImmunoPred, a phased hybrid prediction framework that integrates gradient boosting and deep learning for immunogenicity-aware CRISPR design framework. The framework comprises of (i) a Particle Swarm Optimization (PSO) optimized XGBoost based on gRNA Viability Profiler for assessing CRISPR editing fitness and (ii) an Attentive Immunogenicity Inference model which employs cross-attention Transformer architectures to explicitly capture guide target sequence interactions alongside numerical biological features. Comprehensive sensitivity analysis, systematic ablation studies, and group-wise generalization experiments finally demonstrate that each architectural component contributes meaningfully to predictive performance and that the proposed framework degrades gracefully under biologically meaningful distributional shifts. On combining high predictive accuracy with partial interpretability through feature attribution and architectural analysis, CRISPR-ImmunoPred provides a robust and modular approach for balancing editing efficiency and immune safety, supporting safer CRISPR-based therapeutic development and rational genome editing design.

Data transactions in healthcare are steadily increasing across various platforms, aiming to improve patient care and increase data transparency. Blockchain technology will serve as a catalyst in healthcare data transactions, ensuring data security and privacy for various stakeholders. Improving data security, transparency, and interoperability, blockchain technology’s application in healthcare has demonstrated considerable promise. However, healthcare applications that rely on real-time data transaction settlement face obstacles caused by Layer1 blockchains’ poor transaction throughput and excessive latency. In this work, we adopt established consensus and a zk-Rollup verification workflow, specifying healthcare-oriented configurations for security, auditability, and throughput. This paper integrates the smart contracts, zero knowledge proof and off chain data storage to increase the efficiency, and security and reduce transaction costs. The usefulness of the suggested algorithm in healthcare applications is demonstrated by thorough literature research, comparative analysis, and experimental data. Transaction throughput increases very high, latency improved by 57%, and decrease the transaction cost to 96% in healthcare data transactions which are all greatly improved by the proposed system. Unlike existing zk-Rollup–based healthcare frameworks, the proposed model integrates cross-chain identity validation and verifiable data provenance to achieve secure interoperability across multi-chain healthcare systems.

Edge computing is increasingly being identified as an essential tool for enabling real-time processing of data on resource-constrained Internet of Things (IoT) devices, especially within consumer electronics. Current deep learning architectures are less adaptable to resource fluctuations, as they fail to dynamically adapt and remain less efficient on resource-constrained devices. This research introduces as it improves decision support on resource-constrained Internet of Things devices by adapting complexity levels for real-time resource profiling. The proposed architecture integrates collaborative computing and peer-to-peer networking, while resource management is achieved through the context-aware component, which utilizes behavior analytics and environmental observations. The experiment showed efficiency improvement by 28% on the proposed architecture compared to other architectures, improving inference accuracy by 94.3%, and improving resource management and latency by 10-15% and 30%, respectively.

Cardiovascular prediction and therapy planning require high diagnostic fidelity, identifiable causal structure, patient-specific adaptation, and quantifiable privacy. NeuroTwin is a neurosymbolic digital twin that integrates four computational modules into a unified clinical decision framework. The adaptive diffusion transformer (ADViT) performs modality-specific denoising of ECG and PCG signals, followed by patch-level feature encoding and cross-modal fusion that preserves temporal–spectral structure. The symbolic causal discovery network (SCDN) constructs a sparse directed acyclic graph through a differentiable acyclicity constraint and converts stable edges into executable rules. The neural federated digital twin (NFDT) performs distributed optimization with differentially private Gaussian aggregation and incorporates online patient-state updates for personalized modeling under heterogeneous institutional data distributions. A hierarchical meta-reinforcement learner (HMRL) governs treatment recommendations through a bi-level policy that balances symptom reduction, adverse-effect mitigation, and longitudinal stability. NeuroTwin achieves 98.5% diagnostic precision, 96.2% success in treatment optimization, a 0.942 causal explainability score and a 0.032 privacy leakage rate.