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Public transportation infrastructure is a typical, complex, coupled network that is usually composed of connected bus lines and subway networks. This study proposes an entropy-based node importance identification method for this type of coupled network that is helpful for the integrated planning of urban public transport and traffic flows, as well as enhancing network information dissemination and maintaining network resilience. The proposed method develops a systematic entropy-based metric based on five centrality metrics, namely the degree centrality (DC), betweenness centrality (BC), closeness centrality (CC), eigenvector centrality (EC), and clustering coefficient (CCO). It then identifies the most important nodes in the coupled networks by considering the information entropy of the nodes and their neighboring ones. To evaluate the performance of the proposed method, a bus–subway coupled network in Chengdu, containing 10,652 nodes and 15,476 edges, is employed as a case study. Four network resilience assessment metrics, namely the maximum connectivity coefficient (MCC), network efficiency (NE), susceptibility (S), and natural connectivity (NC), were used to conduct group experiments. The experimental results demonstrate the following: (1) the multi-functional fitting analysis improves the analytical accuracy by 30% as compared to fitting with power law functions only; (2) for both CC and CCO, the improved metric's performance in important node identification is greatly improved, and it demonstrates good network resilience. [ABSTRACT FROM AUTHOR]

Distributed generators (DGs) have a high penetration rate in distribution networks (DNs). Understanding their impact on a DN is essential for achieving optimal power flow (OPF). Various DG models, such as stochastic and forecasting models, have been established and are used for OPF. While conventional OPF aims to minimize operational costs or power loss, the "Dual-Carbon" target has led to the inclusion of carbon emission reduction objectives. Additionally, state-of-the-art optimization techniques such as machine learning (ML) are being employed for OPF. However, most current research focuses on optimization methods rather than the problem formulation of the OPF. The purpose of this paper is to provide a comprehensive understanding of the OPF problem and to propose potential solutions. By delving into the problem formulation and different optimization techniques, selecting appropriate solutions for real-world OPF problems becomes easier. Furthermore, this paper provides a comprehensive overview of prospective advancements and conducts a comparative analysis of the diverse methodologies employed in the field of optimal power flow (OPF). While mathematical methods provide accurate solutions, their complexity may pose challenges. On the other hand, heuristic algorithms exhibit robustness but may not ensure global optimality. Additionally, machine learning techniques exhibit proficiency in processing extensive datasets, yet they necessitate substantial data and may have limited interpretability. Finally, this paper concludes by presenting prospects for future research directions in OPF, including expanding upon the uncertain nature of DGs, the integration of power markets, and distributed optimization. The main objective of this review is to provide a comprehensive understanding of the impact of DGs in DN on OPF. The article aims to explore the problem formulation of OPF and to propose potential solutions. By gaining in-depth insight into the problem formulation and different optimization techniques, optimal and sustainable power flow in a distribution network can be achieved, leading to a more efficient, reliable, and cost-effective power system. This offers tremendous benefits to both researchers and practitioners seeking to optimize power system operations. [ABSTRACT FROM AUTHOR]

Stacked plate heat exchangers are widely used in thermal energy storage systems and a comprehensive and accurate analysis is necessary for their application and optimization. The fluid flow distribution between the plates is important to ensure even and full usage of the thermal energy storage potential. However, due to the complex topography of the plate surface, it would be computationally expensive to simulate the flow distribution in the multiple channels using a full three-dimensional model, so this work applies a reduced-dimensional model to significantly reduce the computational cost of the simulation and provides a comprehensive analysis of the effect of the internal structure on the internal flow distribution. The work extends a previously presented model to consider transient flow and a multichannel height distribution strategy to allow for simulating multiple channels between stacks of plates. Based on fully-developed flow assumptions, the three-dimensional model is reduced to a planar model, thus obtaining simulation results with satisfactory accuracy at a significantly lower computational cost. The model is verified by a three-dimensional simulation of a sliced two-channel model representing the considered system. The reduced-dimensional model gives similar results to the three-dimensional model for different geometrical and physical parameters. Lastly, the extended reduced-dimensional model is used to simulate the flow of a full two-channel model and the influence of the plate topography on the internal flow distribution is investigated through a comprehensive parametric analysis. The analysis shows that the complex topography of the plate surface eliminates the variation in inlet velocity and significantly changes the internal fluid flow, eventually resulting in a consistent velocity distribution. [ABSTRACT FROM AUTHOR]

Although farrerol, a characteristically bioactive constituent of Rhododendron dauricum L., exhibits extensive biological and pharmacological activities (e.g., anti-oxidant, anti-immunogenic, and anti-angiogenic) as well as a high drug development potential, its metabolism remains underexplored. Herein, we employed ultra-high performance liquid chromatography/quadrupole time-of-flight mass spectrometry coupled with multiple data post-processing techniques to rapidly identify farrerol metabolites produced in vivo (in rat blood, bile, urine and feces) and in vitro (in rat liver microsomes). As a result, 42 in vivo metabolites and 15 in vitro metabolites were detected, and farrerol shown to mainly undergo oxidation, reduction, (de)methylation, glucose conjugation, glucuronide conjugation, sulfate conjugation, N-acetylation and N-acetylcysteine conjugation. Thus, this work elaborates the metabolic pathways of farrerol and reveals the potential pharmacodynamics forms of farrerol. [ABSTRACT FROM AUTHOR]

Eupatorin is the major bioactive component of Java tea (Orthosiphon stamineus), exhibiting strong anticancer and anti-inflammatory activities. However, no research on the metabolism of eupatorin has been reported to date. In the present study, ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS) combined with an efficient online data acquisition and a multiple data processing method were developed for metabolite identification in vivo (rat plasma, bile, urine and feces) and in vitro (rat liver microsomes and intestinal flora). A total of 51 metabolites in vivo, 60 metabolites in vitro were structurally characterized. The loss of CH2, CH2O, O, CO, oxidation, methylation, glucuronidation, sulfate conjugation, N-acetylation, hydrogenation, ketone formation, glycine conjugation, glutamine conjugation and glucose conjugation were the main metabolic pathways of eupatorin. This was the first identification of metabolites of eupatorin in vivo and in vitro and it will provide reference and valuable evidence for further development of new pharmaceuticals and pharmacological mechanisms. [ABSTRACT FROM AUTHOR]