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We synthesized and characterized the advanced multifunctional features of mycelium–coir-based composites as a replacement for fossil-based foams used in building insulation. 

Superparamagnetic tunnel junctions (sMTJs) are emerging as promising components for stochastic units in neuromorphic computing owing to their tunable random switching behavior. Conventional MTJ control methods, such as spin-transfer torque (STT) and spin–orbit torque (SOT), often require substantial power. Here, we introduce the voltage-controlled exchange coupling (VCEC) mechanism, enabling the switching between antiparallel and parallel states in sMTJs with an ultralow power consumption of only 40 nW, approximately 2 orders of magnitude lower than conventional STT-based sMTJs. This mechanism yields a sigmoid-shaped output response, making it ideally suited to neuromorphic computing applications. Furthermore, we validate the feasibility of integrating VCEC with SOT current control, offering an additional dimension for magnetic state manipulation. This work marks the first practical demonstration of the VCEC effect in sMTJs, highlighting its potential as a low-power control solution for probabilistic bits in advanced computing systems. 

Uncertainty Quantification (UQ) is vital for decision makers as it offers insights into the potential reliability of data and model, enabling more informed and risk-aware decision-making. Graphical models, capable of representing data with complex dependencies, are widely used across domains. Existing sampling-based UQ methods are unbiased but cannot guarantee convergence and are time-consuming on large-scale graphs. There are fast UQ methods for graphical models with closed-form solutions and convergence guarantee but with uncertainty underestimation. We propose LinUProp, a UQ method that utilizes a novel linear propagation of uncertainty to model uncertainty among related nodes additively instead of multiplicatively, to offer linear scalability, guaranteed convergence, and closed-form solutions without underestimating uncertainty. Theoretically, we decompose the expected prediction error of the graphical model and prove that the uncertainty computed by LinUProp is the generalized variance component of the decomposition. Experimentally, we demonstrate that LinUProp is consistent with the sampling-based method but with linear scalability and fast convergence. Moreover, LinUProp outperforms competitors in uncertainty-based active learning on four real-world graph datasets, achieving higher accuracy with a lower labeling budget.