A Second Order Fully-discrete Linear Energy Stable Scheme for a Binary Compressible Viscous Fluid Model

We present a linear, second order fully discrete numerical scheme on a staggered grid for a thermodynamically consistent hydrodynamic phase field model of binary compressible fluid flow mixtures derived from the generalized Onsager Principle. The hydrodynamic model not only possesses the variational structure, but also warrants the mass, linear momentum conservation as well as energy dissipation. We first reformulate the model in an equivalent form using the energy quadratization method and then discretize the reformulated model to obtain a semi-discrete partial differential equation system using the Crank-Nicolson method in time. The numerical scheme so derived preserves the mass conservation and energy dissipation law at the semi-discrete level. Then, we discretize the semi-discrete PDE system on a staggered grid in space to arrive at a fully discrete scheme using the 2nd order finite difference method, which respects a discrete energy dissipation law. We prove the unique solvability of the linear system resulting from the fully discrete scheme. Mesh refinements and two numerical examples on phase separation due to the spinodal decomposition in two polymeric fluids and interface evolution in the gas-liquid mixture are presented to show the convergence property and the usefulness of the new scheme in applications

Bio-inspired Design and Fabrication of Super-Strong and Multifunctional Carbon Nanotube Composites

Carbon nanotubes (CNTs) are ideal scaffolds to design and architect high-performance composites at high CNT volume fractions. In these composites, the CNT alignment determines the level of aggregation and the structure morphology, and thus the load transfer efficiency between neighboring CNTs. Here, we discuss two major solutions to produce high-volume fraction CNT composites, namely the layer-by-layer stacking of aligned CNT sheets and the stretching of entangled CNT webs (networks). As inspired by the growth procedure of natural composites, the aggregation of CNTs can be well controlled during the assembling process. As a result, the CNTs can be highly packed, aligned, and importantly unaggregated, with the impregnated polymers acting as interfacial adhesion or mortars to build up the composite structure. The CNT/bismaleimide composites can yield a super-high tensile strength up to 6.27–6.94 GPa and a modulus up to 315 GPa

Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study

Background: Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients\u27 privacy at each site. Objective: This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. Methods: We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. Results: We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. Conclusions: Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain

Thermodynamics of wetting, prewetting and surface phase transitions with surface binding

In living cells, protein-rich condensates can wet the cell membrane and surfaces of membrane-bound organelles. Interestingly, many phase-separating proteins also bind to membranes leading to a molecular layer of bound molecules. Here we investigate how binding to membranes affects wetting, prewetting and surface phase transitions. We derive a thermodynamic theory for a three-dimensional bulk in the presence of a two-dimensional, flat membrane. At phase coexistence, we find that membrane binding facilitates complete wetting and thus lowers the wetting angle. Moreover, below the saturation concentration, binding facilitates the formation of a thick layer at the membrane and thereby shifts the prewetting phase transition far below the saturation concentration. The distinction between bound and unbound molecules near the surface leads to a large variety of surface states and complex surface phase diagrams with a rich topology of phase transitions. Our work suggests that surface phase transitions combined with molecular binding represent a versatile mechanism to control the formation of protein-rich domains at intra-cellular surfaces

A Reliable Data Provenance and Privacy Preservation Architecture for Business-Driven Cyber-Physical Systems Using Blockchain

Cyber-physical systems (CPS) including power systems, transportation, industrial control systems, etc. support both advanced control and communications among system components. Frequent data operations could introduce random failures and malicious attacks or even bring down the whole system. The dependency on a central authority increases the risk of single point of failure. To establish an immutable data provenance scheme for CPS, the authors adopt blockchain and propose a decentralized architecture to assure data integrity. In business-driven CPS, end users are required to share their personal information with multiple third parties. To prevent data leakage and preserve user privacy, the authors isolate and feed different information retrieval requests using tokens specifically generated for each type of request. Providing both traceability of data operations, and unlinkability of end user activities, a robust blockchain-based CPS is prototyped. Evaluation indicates the architecture is capable of assured data provenance validation and user privacy preservation at a low overhead

Clarifying the mechanisms of the light-induced color formation of apple peel under dark conditions through metabolomics and transcriptomic analyses

Many studies have demonstrated that anthocyanin synthesis in apple peel is induced by light, but the color of bagged apple peel continues to change under dark conditions after light induction has not been characterized. Here, transcriptional and metabolic changes associated with changes in apple peel coloration in the dark after different light induction treatments were studied. Apple pericarp can achieve a normal color under complete darkness followed by light induction. Metabolomics analysis indicated that the expression levels of cyanidin-3-O-galactoside and cyanidin-3-O-glucoside were high, which might be associated with the red color development of apple peel. Transcriptome analysis revealed high expression levels of MdUFGTs, MdMYBs, and MdNACs, which might play a key role in light-induced anthocyanin accumulation under dark conditions. 13 key genes related to dark coloring after light induction was screened. The results of this study provide new insights into the mechanism of anthocyanin synthesis under dark conditions