A Hybrid Multiscale Framework for Subsurface Flow and Transport Simulations

AbstractExtensive research is aimed at improving predictive ability of biogeochemical earth and environmental system simulators, with applications ranging from contaminant transport and remediation to impacts of carbon and nitrogen cycling on local ecosystems and climate. Most process-based numerical models are designed for a single characteristic length and time scale. For application-relevant scales, it is necessary to introduce approximations and empirical parameterizations to describe complex systems because of limitations on process understanding, system characterization and computation. Using emerging understanding of biological and environmental processes at fundamental scales to advance predictions of the larger system behavior requires the development of multiscale simulators, and there is strong interest in coupling microscale and macroscale models together in a hybrid multiscale simulation. A limited number of hybrid multiscale simulations have been developed for biogeochemical systems, mostly using application-specific approaches for model coupling. We are developing a generalized approach to hierarchical model coupling designed for high-performance computational systems, based on the Swift computing workflow framework. In this presentation we will describe the generalized approach and provide two use cases: 1) simulation of a mixing-controlled biogeochemical reaction coupling pore- and continuum-scale models, and 2) simulation of biogeochemical impacts of groundwater–river water interactions coupling fine- and coarse-grid model representations. This generalized framework can be customized for use with any pair of linked models (microscale and macroscale) with minimal intrusiveness to the at-scale simulators. It combines a set of python scripts with the Swift workflow environment to execute a complex multiscale simulation utilizing an approach similar to the well-known Heterogeneous Multiscale Method. User customization is facilitated through user-provided input and output file templates and processing function scripts, and execution within a high-performance computing environment is handled by Swift, such that minimal to no user modification of at-scale codes is required

Whisper-MCE: Whisper Model Finetuned for Better Performance with Mixed Languages

Recently Whisper has approached human-level robustness and accuracy in English automatic speech recognition (ASR), while in minor language and mixed language speech recognition, there remains a compelling need for further improvement. In this work, we present the impressive results of Whisper-MCE, our finetuned Whisper model, which was trained using our self-collected dataset, Mixed Cantonese and English audio dataset (MCE). Meanwhile, considering word error rate (WER) poses challenges when it comes to evaluating its effectiveness in minor language and mixed-language contexts, we present a novel rating mechanism. By comparing our model to the baseline whisper-large-v2 model, we demonstrate its superior ability to accurately capture the content of the original audio, achieve higher recognition accuracy, and exhibit faster recognition speed. Notably, our model outperforms other existing models in the specific task of recognizing mixed language

Three Dimensional Quantitative Structure-Activity Relationships of Sulfonamides Binding Monoclonal Antibody by Comparative Molecular Field Analysis

The three-dimensional quantitative structure-activity relationship (3D-QSAR) model of sulfonamide analogs binding a monoclonal antibody (MabSMR) produced against sulfamerazine, was carried out by comparative molecular field analysis (CoMFA). The affinities of MabSMR, expressed as Log10IC50, for 17 sulfonamide analogs were determined by competitive fluorescence polarization immunoassay (FPIA). Removal of two outliers from the initial set of 17 sulfonamide analogs improved the predictability of the models. The 3D-QSAR model of 15 sulfonamides resulted in q2cv values of 0.600, and r2 values of 0.995, respectively. This novel study combining FPIA with CoMFA demonstrates that multidisciplinary research can be used as a useful tool to investigate antigen-antibody interactions and provide information required for design of novel haptens, which may result in new antibodies with properties already optimized by an antibody-based immunoassay

Catalytic Removal of Ozone by Pd/ACFs and Optimal Design of Ozone Converter for Air Purification in Aircraft Cabin

Ozone in aircraft cabin can bring obvious adverse impact on indoor air quality and occupant health. The objective of this study is to experimentally explore the ozone removal performance of flat-type catalyst film by loading nanometer palladium on the activated carbon fibers (Pd/ACFs), and optimize the configuration of ozone converter to make it meet the design requirements. A one-through ozone removal unit with three different Pd/ACFs space was used to test the ozone removal performance and the flow resistance characteristic under various temperature and flow velocity. The results show that the ozone removal rate of the ozone removal unit with the Pd/ACFs space of 1.5 mm can reach 99% and the maximum pressure drop is only 1.9 kPa at the reaction temperature of 200℃. The relationship between pressure drop and flow velocity in the ozone removal unit has a good fit to the Darcy-Forchheimer model. An ozone converter with flat-type reactor was designed and processed based on the one-through ozone removal experiment, its ozone removal rate and maximum pressure drop were 97% and 7.51 kPa, separately, with the condition of 150℃ and 10.63 m/s. It can meet the design requirements of ozone converter for air purification and develop a healthier aircraft cabin environment

A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple competing objectives whose relative importance (preferences) is unknown to the agent. While this alleviates dependence on scalar reward design, the expected return of a policy can change significantly with varying preferences, making it challenging to learn a single model to produce optimal policies under different preference conditions. We propose a generalized version of the Bellman equation to learn a single parametric representation for optimal policies over the space of all possible preferences. After an initial learning phase, our agent can execute the optimal policy under any given preference, or automatically infer an underlying preference with very few samples. Experiments across four different domains demonstrate the effectiveness of our approach.Comment: Accepted in NeurIPS 201

Comparative pharmacokinetics of free doxorubicin and a liposomal formulation in cats following intravenous administration

Doxorubicin, a potent chemotherapeutic agent used extensively in cancer treatment, displays complex pharmacokinetic behavior, especially across various formulations. With a rising incidence of cancer cases in cats, understanding the drug’s pharmacokinetics in feline subjects remains a critical yet unexplored area. Hence, this study investigated the pharmacokinetic profile of doxorubicin after slow intravenous administration of doxorubicin hydrochloride (DOX·HCl) or doxorubicin hydrochloride pegylated liposome (DOX·HCl-PLI) in twelve cats at a single dose of 20 mg/m2. Blood samples collected at pretreatment time (0 h) and over 192 h were analyzed using ultra-performance liquid chromatography-mass spectrometry (UPLC-MS/MS). The obtained pharmacokinetic parameters of doxorubicin revealed significant differences between the two formulations and were as follows: elimination half-life (T1/2λz) of 5.00 ± 3.20 h (DOX·HCl) and 17.62 ± 8.13 h (DOX·HCl-PLI), area under the concentration/time curve from 0 to last point (AUClast) of 0.67 ± 0.12 μg hr./mL (DOX·HCl) and 783.09 ± 267.29 μg hr./mL (DOX·HCl-PLI), and total body clearance (CL_obs) of 27098.58 ± 5205.19 mL/h/m2 (DOX·HCl) and 28.65 ± 11.09 mL/h/m2 (DOX·HCl-PLI). Additionally, differences were also detected in the apparent volume of distribution (Vz_obs) with 178.56 ± 71.89 L/m2 (DOX·HCl) and 0.64 ± 0.20 L/m2 (DOX·HCl-PLI), and the maximum plasma concentration (Cmax) with 2.25 ± 0.30 μg/mL (DOX·HCl) and 24.02 ± 5.45 μg/mL (DOX·HCl-PLI). Notably, low concentration of doxorubicinol, the metabolite of doxorubicin, was detected in plasma after administration of DOX·HCl, with even less present when DOX·HCl-PLI was administered. This investigation provides valuable insights into the distinct pharmacokinetic behaviors of DOX·HCl and DOX·HCl-PLI in cats, contributing essential groundwork for future studies and potential clinical applications in feline oncology

From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion

With the rapid progression of deep learning technologies, multi-modality image fusion has become increasingly prevalent in object detection tasks. Despite its popularity, the inherent disparities in how different sources depict scene content make fusion a challenging problem. Current fusion methodologies identify shared characteristics between the two modalities and integrate them within this shared domain using either iterative optimization or deep learning architectures, which often neglect the intricate semantic relationships between modalities, resulting in a superficial understanding of inter-modal connections and, consequently, suboptimal fusion outcomes. To address this, we introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. This method capitalizes on the complementary characteristics of diverse modalities, bolstering both the accuracy and robustness of object detection. The codebook is utilized to enhance a streamlined and concise depiction of the fused intra- and inter-domain dynamics, fine-tuned for optimal performance in detection tasks. We present a bilevel optimization strategy that establishes a nexus between the joint problem of fusion and detection, optimizing both processes concurrently. Furthermore, we introduce the first dataset of paired infrared and visible images accompanied by text prompts, paving the way for future research. Extensive experiments on several datasets demonstrate that our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.Comment: 10 pages, 12 figures, 3 tables, conferenc