4D flow magnetic resonance imaging (MRI) is a reliable, non-invasive approach for estimating blood flow velocities, vital for cardiovascular diagnostics. Unlike conventional MRI focused on anatomical structures, 4D flow MRI requires high spatiotemporal resolution for early detection of critical conditions such as stenosis or aneurysms. However, achieving such resolution typically results in prolonged scan times, creating a trade-off between acquisition speed and prediction accuracy. Recent studies have leveraged physics-informed neural networks (PINNs) for super-resolution of MRI data, but their practical applicability is limited as the prohibitively slow training process must be performed for each patient. To overcome this limitation, we propose PINGS-X, a novel framework modeling high-resolution flow velocities using axes-aligned spatiotemporal Gaussian representations. Inspired by the effectiveness of 3D Gaussian splatting (3DGS) in novel view synthesis, PINGS-X extends this concept through several non-trivial novel innovations: (i) normalized Gaussian splatting with a formal convergence guarantee, (ii) axes-aligned Gaussians that simplify training for high-dimensional data while preserving accuracy and the convergence guarantee, and (iii) a Gaussian merging procedure to prevent degenerate solutions and boost computational efficiency. Experimental results on computational fluid dynamics (CFD) and real 4D flow MRI datasets demonstrate that PINGS-X substantially reduces training time while achieving superior super-resolution accuracy.

Whole-body hydration (WBH) assessment is crucial in reducing the risk of dehydration, which can have significant health impacts when left untreated. Existing methods for monitoring WBH are invasive or require bulky equipment, making them impractical for everyday or continuous use. In this work, we introduce an arm-wearable electronic tattoo (e-tattoo) utilizing bioimpedance (Bio-Z) sensing for noninvasive, continuous, and mobile WBH monitoring. Although whole-body Bio-Z is a proven method for indicating WBH status, the effectiveness of using arm Bio-Z for this purpose has been unclear. Our IRB-approved study, which involved diuretic-induced dehydration, demonstrated a strong positive linear correlation between cross-arm Bio-Z and percent body weight loss, with Pearson's r = 0.967 ± 0.031 achieved in six human participants. Furthermore, results indicated that arm Bio-Z outperformed traditional whole-body Bio-Z recordings, likely due to the stretchable, flexible, and bodyconformal nature of the proposed e-tattoo. These findings suggest that the upper arm Bio-Z can serve as a reliable, continuous proxy for WBH, offering a cost-effective and highly accessible solution. The potential uses of this wearable technology range from improving personal wellness to helping professional sports, healthcare, and occupational safety.

With the emergence of information technologies, an overwhelming amount of data and information is generated everyday. Storing and processing this huge volume of data is named by a ubiquitous term: big data management. Cloud storage systems enhance reliability and availability of data by introducing redundancy, i.e., data replication, in the system, thereby protecting the data integrity from node failures which occur frequently in any large-scale storage system. However, efficiently determining the level of redundancy, i.e., number of data replicas, is not a trivial task for a cloud service provider (CSP). Traditional methods, which use a fixed number of replicas for all users regardless of the user's budget, do not achieve efficiency in terms of financial benefit of CSPs. This paper presents an efficient replication scheme that allows a CSP to determine the optimal number of replicas for each user depending on the user's budgetary constraint and the CSP's resource capacity while maximizing the financial benefit of the CSP. Numerical simulations were performed to assess the validity of our approach. The results show the scalability of the proposed scheme which can apply to real systems with an arbitrary number of users.