Variable stiffness concepts enable structural adaptation to diverse environments, categorized as smart materials or specialized configuration designs. Multi-layered jamming (MLJ) provides a rapid, reversible, and easily controlled actuationmethod. This study examinesMLJ-based variable stiffness components for rapid construction and energy dissipation in civil engineering, focusing on an MLJreinforced Tensairity beam and construction procedure. The numerical model shows the structure's enhanced load-bearing capability post-vacuuming. During large deformation, energy dissipation via interlayer friction produces hysteresis loops, which may benefit to mitigate dynamic responses. While these techniques show promise, challenges exist concerning material limits, application boundaries, quantification, and precise shape control. They could also find utility in environments with confinement pressures like soil or water, expanding the potential applications.

Deep learning (DL) models are increasingly built on federated edge participants holding local data. To enable insight extractions without the risk of information leakage, DL training is usually combined with differential privacy (DP). The core theme is to tradeoff learning accuracy by adding statistically calibrated noises, particularly to local gradients of edge learners, during model training. However, this privacy guarantee unfortunately degrades model accuracy due to edge learners' local noises, and the global noise aggregated at the central server. Existing DP frameworks for edge focus on local noise calibration via gradient clipping techniques, overlooking the heterogeneity and dynamic changes of local gradients, and their aggregated impact on accuracy. In this article, we present a systematical analysis that unveils the influential factors capable of mitigating local and aggregated noises, and design PrivateDL to leverage these factors in noise calibration so as to improve model accuracy while fulfilling privacy guarantee. PrivateDL features on: (i) sampling-based sensitivity estimation for local noise calibration and (ii) combining large batch sizes and critical data identification in global training. We implement PrivateDL on the popular Laplace/Gaussian DP mechanisms and demonstrate its effectiveness using Intel BigDL workloads, i.e., considerably improving model accuracy by up to 5X when comparing against existing DP frameworks.