Improved Operator Learning by Orthogonal Attention

Neural operators, as an efficient surrogate model for learning the solutions of PDEs, have received extensive attention in the field of scientific machine learning. Among them, attention-based neural operators have become one of the mainstreams in related research. However, existing approaches overfit the limited training data due to the considerable number of parameters in the attention mechanism. To address this, we develop an orthogonal attention based on the eigendecomposition of the kernel integral operator and the neural approximation of eigenfunctions. The orthogonalization naturally poses a proper regularization effect on the resulting neural operator, which aids in resisting overfitting and boosting generalization. Experiments on six standard neural operator benchmark datasets comprising both regular and irregular geometries show that our method can outperform competing baselines with decent margins.Comment: 14 pages, 5 figure

PharmacyGPT: The AI Pharmacist

In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists. Our methodology encompasses the utilization of LLMs to generate comprehensible patient clusters, formulate medication plans, and forecast patient outcomes. We conduct our investigation using real data acquired from the intensive care unit (ICU) at the University of North Carolina Chapel Hill (UNC) Hospital. Our analysis offers valuable insights into the potential applications and limitations of LLMs in the field of clinical pharmacy, with implications for both patient care and the development of future AI-driven healthcare solutions. By evaluating the performance of PharmacyGPT, we aim to contribute to the ongoing discourse surrounding the integration of artificial intelligence in healthcare settings, ultimately promoting the responsible and efficacious use of such technologies

Optimal design of step-stress accelerated degradation test oriented by nonlinear and distributed degradation process

International audienceThe Step-stress accelerated degradation test (SSADT) is an efficient tool to assess the reliability of products. To ensure an accurate evaluation of reliability-related indexes with limited cost, the optimal design for SSADTs is widely applied. Conventional optimal design methods ideally assume the degradation process to be linear and distribution-free, which may cause the designed test plans to be drastically different from the optimal one. To overcome the drawbacks of the unreasonable assumption, this paper develops a new optimal design method for SSADTs, including a generalized Wiener process-based degradation model (GWPDM) considering the distribution feature and a corresponding optimization criterion aiming at accurate prediction of degradation distributions. The GWPDM is first discussed, where the influence of the nonlinearity, unit-to-unit variability, measurement error and stress-related temporal variability on the degradation process is clarified. The statistical inference of the developed GWPDM is conducted to deduce the proposed optimization criterion. Thus, the optimization model is established to determine the optimal SSADT plan. The proposed method is used to design a SSADT plan for the metal-film resistor. Finally, the necessity and the robustness of the proposed method are verified

Degradation of Chloroquine by Ammonia-Oxidizing Bacteria: Performance, Mechanisms, and Associated Impact on N₂O Production

Since the mass production and extensive use of chloroquine (CLQ) would lead to its inevitable discharge, wastewater treatment plants (WWTPs) might play a key role in the management of CLQ. Despite the reported functional versatility of ammonia-oxidizing bacteria (AOB) that mediate the first step for biological nitrogen removal at WWTP (i.e., partial nitrification), their potential capability to degrade CLQ remains to be discovered. Therefore, with the enriched partial nitrification sludge, a series of dedicated batch tests were performed in this study to verify the performance and mechanisms of CLQ biodegradation under the ammonium conditions of mainstream wastewater. The results showed that AOB could degrade CLQ in the presence of ammonium oxidation activity, but the capability was limited by the amount of partial nitrification sludge (∼1.1 mg/L at a mixed liquor volatile suspended solids concentration of 200 mg/L). CLQ and its biodegradation products were found to have no significant effect on the ammonium oxidation activity of AOB while the latter would promote N2O production through the AOB denitrification pathway, especially at relatively low DO levels (≤0.5 mg-O2/L). This study provided valuable insights into a more comprehensive assessment of the fate of CLQ in the context of wastewater treatment