Privacy-preserving synthesis of electronic health records is difficult because clinical data combine continuous measurements, categorical diagnoses, class imbalance, missingness, and multiple trust boundaries. PrivMedSynth is presented as a conditional diffusion framework for mixed type tabular health records in Medical Internet of Things edge cloud environments. The framework applies a local randomizer before transmission and trains a conditional diffusion model with differentially private stochastic gradient descent in the cloud. Continuous features are perturbed with a calibrated Gaussian mechanism, categorical variables are protected with generalized randomized response, and clinical conditions are used as explicit generation controls. Rényi differential privacy accounts for repeated cloud updates and is converted to an approximate differential privacy guarantee. The revised formulation distinguishes the formal privacy guarantee from empirical membership inference performance and makes the privacy unit, adjacency relation, modality budget, and noise parameterization explicit. The experiments report a total variation distance of 0.041, a downstream AUROC of 0.884, and a membership inference AUROC of 0.503 on MIMIC III. These utility and attack results remain conditional on the stated preprocessing and require reproduction under the corrected accounting before being interpreted as a final end to end guarantee. The framework provides a principled basis for studying the privacy and utility tradeoff in distributed synthetic clinical data generation.
The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for navigating this complexity, enabling rapid prediction of electrochemical properties, de novo design of polymer electrolytes, and precise optimization of nanostructures for supercapacitors and batteries. This review systematically examines the application of ML techniques, including graph neural networks, Bayesian optimization, variational autoencoders, and transformer-based language models, for the discovery of energy storage polymer composites. The discussion critically evaluates ML-driven advancements across lithium-ion batteries, flexible energy storage devices, and solar energy materials, drawing on quantitative performance benchmarks reported in the primary literature. Emerging strategies such as active learning, multi-fidelity data fusion, physics-informed neural networks, and polymer-specific foundation models are discussed alongside persistent challenges related to data scarcity, model interpretability, and the translation gap between computational prediction and experimental synthesis. The review further addresses the landscape of open polymer property databases, the role of autonomous closed-loop experimentation in accelerating materials discovery, and the importance of reproducible, well-documented machine learning pipelines for the field to mature beyond proof-of-concept demonstrations. By consolidating evidence from verified primary sources and presenting original comparative analyses across methods and application domains, this review provides researchers, materials scientists, and computational chemists with an actionable, evidence-based perspective on the current state and future trajectory of AI-accelerated, sustainable energy storage polymer composite discovery.
Manas Kumar Yogi, D. Uma, Yamuna Mundru et al.· Journal of Polymer & Composi...· 0 citations
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