Speeding up biphasic reactions with surface nanodroplets

Biphasic chemical reactions compartmentalized in small droplets offer advantages, such as streamlined procedures for chemical analysis, enhanced chemical reaction efficiency and high specificity of conversion. In this work, we experimentally and theoretically investigate the rate for biphasic chemical reactions between acidic nanodroplets on a substrate surface and basic reactants in a surrounding bulk flow. The reaction rate is measured by droplet shrinkage as the product is removed from the droplets by the flow. In our experiments, we determine the dependence of the reaction rate on the flow rate and the solution concentration. The theoretical analysis predicts that the life time $\tau$ of the droplets scales with Peclet number $Pe$ and the reactant concentration in the bulk flow $c_{re,bulk}$ as $\tau\propto Pe^{-3/2}c_{re,bulk}^{-1}$, in good agreement with our experimental results. Furthermore, we found that the product from the reaction on an upstream surface can postpone the droplet reaction on a downstream surface, possibly due to the adsorption of interface-active products on the droplets in the downstream. The time of the delay decreases with increasing $Pe$ of the flow and also with increasing reactant concentration in the flow, following the scaling same as that of the reaction rate with these two parameters. Our findings provide insight for the ultimate aim to enhance droplet reactions under flow conditions

Efficient Fully Bayesian Approach to Brain Activity Mapping with Complex-Valued fMRI Data

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be computationally efficient via image partitioning and parallel computation while being competitive with state-of-the-art methods. We support these claims with both simulated numerical studies and an application to real cv-fMRI data obtained from a finger-tapping experiment

Experimental Evaluation of Water Control for Continuous Packer in Buried Hill Fractured Gas Reservoir: A Case Study of HZ 26-6 Condensate Gas Field

AbstractAlthough research into water control completion techniques is now relatively mature, there are still difficulties in controlling water in fractured gas reservoirs in the sea. Unlike oil reservoirs, submerged gas reservoir development still faces some problems. To explore the water control mechanism of buried hill fractured gas reservoir, based on the parameters of well HZ26-6-2 in HZ 26-6 condensate gas field, the water control experiment of the continuous packer in the bottom water gas reservoir was completed through the design of experimental parameters, the design of buried hill fractured formation, and the process design of continuous packer. Studies have shown that the continuous packer water control technology delays the water coning rate and makes the water propulsion more uniform. The water breakthrough time and gas production are slightly different for different fractured gas reservoirs, and the network fracture model has a short gas recovery period and fast gas production speed. Continuous packer water control can improve natural gas recovery by 14.3% in fractured gas reservoir. Based on fracture dredging ability, sand accumulation and filling degree, and drilling and production strategy, the influencing factors of horizontal well water control effect in buried hill fractured gas reservoir are discussed and summarized. The research results are aimed at establishing a development model of water control for buried hill gas reservoirs and providing good technical support for rational and scientific water control of horizontal Wells in offshore condensate gas fields. The findings of this study can help for a better understanding of water control of horizontal wells in offshore condensate gas fields

Privacy-preserving federated deep learning for cooperative hierarchical caching in fog computing

Over the past few years, Fog Radio Access Networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities towards the edge of networks, closer to users. In F-RANs, distributed edge caching among Fog Access Points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatio-temporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyse the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a Federated Learning based Cooperative Hierarchical Caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbour F-APs and vertical cooperation between the BaseBand Unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH