The proliferation of electronic health records necessitates secure and privacy-preserving data sharing frameworks to combat escalating cybersecurity threats in healthcare. Current systems face critical limitations including centralized data repositories vulnerable to breaches, static consent mechanisms, and inadequate audit capabilities. This paper introduces an integrated blockchain and federated learning framework that enables privacy-preserving collaborative AI across healthcare institutions without centralized data pooling. The proposed approach combines federated distillation for heterogeneous model collaboration with dynamic differential privacy that adapts noise injection to data sensitivity levels. A novel threshold key-sharing protocol ensures decentralized access control, while a dual-layer Quorum blockchain establishes immutable audit trails for all data sharing transactions. Experimental evaluation on clinical datasets (Mortality Prediction and Clinical Deterioration from eICU-CRD) demonstrates that our framework maintains diagnostic accuracy within 3.6% of centralized approaches while reducing communication overhead by 71% and providing formal privacy guarantees. For Clinical Deterioration prediction, the framework achieves 96.9% absolute accuracy on the Clinical Deterioration task with FD-DP at $ϵ$ = 1.0, representing only 0.14% degradation from centralized performance. The solution supports HIPAA-aligned technical safeguards, mitigates inference and membership attacks, and enables secure cross-institutional data sharing with real-time auditability. This work establishes a new paradigm for privacy-preserving healthcare AI that balances data utility, regulatory requirements, and protection against emerging threats in distributed clinical environments.

This study presents an experimental investigation of open- and closed-loop speed control applied to a 2.2 kW brushless DC (BLDC) motor integrated with a planetary gearbox and evaluated under variable-speed and fixed-load conditions. The aim is to characterize the dynamic response, energy-conversion efficiency, and battery state-of-charge (SOC) behavior of the powertrain under real-time control strategies. A complete laboratory platform was developed using a commercial BLDC motor controller, a 60 V–17.4 Ah lithium-ion battery pack, an optical speed sensor, and an eddy-current dynamometer capable of applying load torques from 1.5 to 7.5 N·m. Open-loop operation regulated speed through direct voltage modulation, whereas closed-loop control was implemented using a proportional–integral–derivative (PID) algorithm executed in real time on a microcontroller. Real-time measurements of voltage, current, torque, and speed were used to compute electrical power, mechanical power, and system efficiency, while battery SOC variation was estimated using the Coulomb-counting method. The results indicate that open-loop control exhibits substantial speed-tracking errors, pronounced transient power surges during speed transitions, reduced efficiency, and an increased SOC depletion rate over the investigated speed range of 1000–2600 rpm. In contrast, the closed-loop PID controller maintains steady-state speed errors within approximately 1 % across all load levels, suppresses current fluctuations, mitigates transient losses, and enhances overall system efficiency by approximately 14–19 percentage points. Closed-loop operation further reduces SOC depletion by 0.49–0.75 percentage points, depending on the applied load torque. These findings demonstrate the effectiveness of feedback control in improving stability, energy utilization, and electric powertrain performance in BLDC-based propulsion systems, with direct relevance to electric-vehicle and hybrid powertrain systems.

Healthcare analytics involving sensitive patient data demand robust statistical safeguards alongside verifiable compliance mechanisms that allow regulators to independently reconstruct query operations, applied policies, and reported outcomes without accessing raw medical information.We present a governance-first, upgradeable on-chain framework that anchors complete request lifecycles to public blockchain using keccak256 cryptographic commitments. This approach delineates a process wherein researchers submit structured queries, a controller captures active policy states, and hospitals provide result digests. A comprehensive and immutable event trail for this entire process is maintained on the Ethereum Sepolia blockchain. External auditors can then reconstruct complete timelines and verify consistent binding between requests, policies, and outcomes. The proposed modular verifier interface, currently implemented via configurable MockVerifier, maintains stable Application Binary Interface compatibility for future production verifier integration while validating end-to-end governance. We demonstrate the framework’s practicality through a detailed diabetes prevalence analytics case study. We provide detailed gas consumption profiles, request latency measurements, and observable failure modes that demonstrate how governance rules translate into enforceable reversions. The architecture maintains minimal on-chain state with Layer-2 readiness, offering immediate regulatory accountability while preserving straightforward upgrade paths to advanced cryptographic components like zk verifiers (Groth16), verifiable differential privacy through VRF-seeded randomness, content-addressed artifact storage, and versioned policy management. This approach effectively separates initial deployment feasibility from computationally intensive cryptography while delivering immediately actionable, externally verifiable evidence of policy compliance.

Flapping-wing robots (FWRs) are highly sensitive to atmospheric gusts due to their low mass and strong aerodynamic coupling. One way to address this challenge is through active wing folding inspired by natural flyers. This paper presents the design, modeling and simulation of an active bio-inspired folding wing (BFW) for FWRs, where gust-induced effects are alleviated through controlled geometric folding of the wing. The proposed BFW consists of an electromechanical folding mechanism comprising a servomotor-driven gear and lead-screw assembly, articulated feather elements, and elastic components. A bond graph modeling approach is adopted to model the coupled electrical and mechanical dynamics of the BFW in an energy-consistent manner. From the developed bond graph formulation, a thirteenth-order state-space model is derived, representing the inertial and compliant dynamics of the folding mechanism. Numerical simulations are performed under step gust profiles of different intensities to examine the transient response of the folding wing. The results show that partial and full wing folding lead to noticeable reductions in gust-induced wing angular displacement when compared to the unfolded configuration. The presented modeling and simulation results demonstrate the potential of bio-inspired wing folding as an effective mechanism for mitigating gust-induced effects and provide a foundation for future studies on control design and experimental validation of FWRs.

Unsteady atmospheric disturbances significantly compromise the stability of ornithopters, necessitating advanced turbulence-mitigation strategies. In contrast, natural flyers display remarkable aerodynamic adaptability through dynamic flow-control mechanisms such as covert feathers, enabling stability across unsteady flow regimes. Drawing inspiration from this biological phenomenon, this study presents the modeling and hybrid control of a kestrel-based ornithopter equipped with a Nature-Inspired Flow Control Device (NFCD) that replicates the adaptive feather deployment mechanism observed in kestrels. A reduced-order multibody bond-graph model (BGM) of the full ornithopter is developed, incorporating the main body, propulsion system, rigid wings, and the NFCD subsystem. The model captures key fluid-structure-interaction (FSI) effects between morphing feathers and surrounding airflow. A Linear Quadratic Regulator (LQR) ensures optimal performance under nominal gust conditions (≤3 m/s), while an H₂ controller activates during high-intensity gusts (≥4 m/s) to enhance disturbance rejection through electromechanical feather actuation. A gain-scheduled transition is employed in the intermediate gust range (3–4 m/s) to ensure a smooth transition between controllers. Simulations indicate up to 70% reduction in gust-induced oscillations and 32% gust-mitigation efficiency, achieved through feather actuation in the NFCD combined with hybrid control, stabilizing the ornithopter in less than 1.4 s under higher gust conditions. The close correspondence between simulated responses and previously reported findings validates the proposed approach. Overall, by merging biomimetic aerodynamics, nature-inspired flow control, and advanced control design, the LQR-H₂ governed NFCD provides a promising pathway toward gust-tolerant ornithopters capable of resilient and stable flight in unsteady atmospheric environments.