Rajesh Sharma Rajendran

Introduction: This computational modeling study introduces a novel Explainable Artificial Intelligence (XAI) framework for optimizing single-dose psilocybin treatment protocols through personalized intervention modeling using publicly available mental health datasets. All results presented are derived from novel simulated data and predictive modeling only, not from real-time clinical implementations or actual patient treatments. Methods: The mathematical optimization model integrates digital twin technologies, multimodal depression detection systems, and Bayesian optimization algorithms to create comprehensive computational patient profiles with temporal resolution processing capabilities at 250 Hz sampling frequency. Validation employed three publicly available datasets: (1) the Psilocybin Precision Functional Mapping dataset from OpenNeuro containing neuroimaging data from 7 participants, (2) the MODMA multimodal mental disorder dataset with 53 participants including electroencephalography and audio signals, and (3) a meta-analytic psilocybin therapy outcomes dataset containing aggregated results from 10 clinical trials. The framework incorporates pharmacokinetic modeling with an absorption rate constant of 0.45 per hour and an elimination rate constant of 0.23 per hour, receptor occupancy dynamics based on a dissociation constant of 6.3 nanomolar, and simulated real-time monitoring protocols processing physiological parameters including heart rate variability, blood pressure measurements, and cortisol levels at a 1 Hz frequency. Results: The simulated computational model demonstrates significant improvements in prediction accuracy, reaching 94.7%, and therapeutic transparency, achieving 89.3% explainability scores. Simulated validation demonstrates computational precision of 92.8% in predicting treatment response patterns across diverse patient populations in silico . The proposed computational methodology addresses key challenges in psychedelic-assisted therapy modeling through interpretable artificial intelligence models, achieving 96.2% computational safety index scores and 91.5% algorithmic compliance metrics in simulation environments. Energy-efficient computational architecture achieves 73.4% carbon footprint reduction through optimized algorithm design and sparse matrix representations. Discussion: This study presents a theoretical computational framework for modeling therapeutic outcomes through simulation and prediction, establishing a foundation for future clinical validation through prospective randomized controlled trials. The framework supports sustainable digital mental healthcare delivery systems compatible with renewable energy infrastructure. All findings represent computational predictions and simulated scenarios requiring extensive clinical validation before any practical application.

Background: Modern cities are complex, making urban disaster management harder. Traditional frameworks often have decision‐making delays, resource allocation issues, and data privacy issues. Federated learning (FL), deep neural networks (DNNs), and optimization methodologies can alter urban resilience, sustainability, data security, and operational scalability. Objective: This research intends to improve catastrophe management by using a hybrid AI framework using FL, DNNs, and optimization. They want to improve energy efficiency, scalability, data privacy, and real‐time decision‐making. The ultimate goal is inclusive, durable, and resilient urban systems that can handle various disasters. Methods: The hybrid AI system uses FL for decentralized privacy‐preserving data processing, DNNs for multimodal data analysis, and optimization algorithms for energy efficiency. Data collection, AI feature extraction, cloud–edge decision‐making, and secure federated model updates are the steps. The proposed framework is evaluated using a simulation‐based environment that emulates real‐world urban sensor networks and edge–cloud systems. The results demonstrate improved accuracy, response speed, and energy efficiency under controlled experimental conditions. Results: It outperforms standard models in classification accuracy (98%), response time (25%), and energy efficiency (20%). Hybrid risk assessments and resource optimization improve decision‐making by 15%. Simulations confirm the scalability and adaptability of this framework to design resilient responses to different urban catastrophe management scenarios. Conclusion: A privacy‐preserving, scalable, energy‐efficient hybrid AI platform for urban disaster management. The convergence of FL, DNNs, and optimization algorithms creates a new standard for robust urban systems. It presents a comprehensive, inclusive, and sustainable disaster management theoretical structure.