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In a “low-carbon” context, the power load is affected by the coupling of multiple factors, which gradually evolves from the traditional “pure load” to the generalized load with the dual characteristics of “load + power supply.” Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads. From the perspective of image processing, this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition. First, the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting, gradient boosted decision tree, and random forest algorithms. Subsequently, the generalized load data are decomposed into three sets of modalities by modal decomposition, and red, green, and blue (RGB) images are generated using them as the pixel values of the R, G, and B channels. The generated images are diversified, and an optimized DenseNet neural network was used for training and prediction. Finally, the base load, wind power, and photovoltaic power generation data are selected, and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm. Based on the proposed graphical forecasting method, the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Abstract Nanjing Yunjin, a traditional Chinese silk weaving craft, is celebrated globally for its unique local characteristics and exquisite workmanship, forming an integral part of the world's intangible cultural heritage. However, with the advancement of information technology, the experiential knowledge of the Nanjing Yunjin production process is predominantly stored in text format. As a highly specialized and vertical domain, this information is not readily convert into usable data. Previous studies on a knowledge graph-based Nanjing Yunjin Question-Answering System have partially addressed this issue. However, knowledge graphs need to be constantly updated and rely on predefined entities and relationship types. Faced with ambiguous or complex natural language problems, knowledge graph information retrieval faces some challenges. Therefore, this study proposes a Nanjing Yunjin Question-Answering System that integrates Knowledge Graphs and Retrieval Augmented Generation techniques. In this system, the ROBERTA model is first utilized to vectorize Nanjing Yunjin textual information, delving deep into textual semantics to unveil its profound cultural connotations. Additionally, the FAISS vector database is employed for efficient storage and retrieval of Nanjing Yunjin information, achieving a deep semantic match between questions and answers. Ultimately, related retrieval results are fed into the Large Language Model for enhanced generation, aiming for more accurate text generation outcomes and improving the interpretability and logic of the Question-Answering System. This research merges technologies like text embedding, vectorized retrieval, and natural language generation, aiming to overcome the limitations of knowledge graphs-based Question-Answering System in terms of graph updating, dependency on predefined types, and semantic understanding. System implementation and testing have shown that the Nanjing Yunjin Intelligent Question-Answering System, constructed on the basis of Knowledge Graphs and Retrieval Augmented Generation, possesses a broader knowledge base that considers context, resolving issues of polysemy, vague language, and sentence ambiguity, and efficiently and accurately generates answers to natural language queries. This significantly facilitates the retrieval and utilization of Yunjin knowledge, providing a paradigm for constructing Question-Answering System for other intangible cultural heritages, and holds substantial theoretical and practical significance for the deep exploration and discovery of the knowledge structure of human intangible heritage, promoting cultural inheritance and protection.

Regenerative fuel cells can operate alternately as an electrolyzer and as a fuel cell, frequently involving water as a reactant or product. Modifying the electrode surface to manipulate water can prevent electrode flooding and enhance the electrode's mass transfer efficiency by facilitating better contact with gaseous reactants. However, conventional electrodes face difficulties in allowing water droplets to penetrate in a single direction leaving electrodes. In this work to address this issue, a wettability gradient electrode is designed and fabricated for efficient water manipulation in regenerative fuel cells. The findings demonstrate that the water removal strategy in the electrolyzer mode yields the highest ammonia yield and Faradaic efficiency of 3.39 × 10-10 mol s-1 cm-2 and 0.49 %, respectively. Furthermore, in the fuel cell mode, the discharging process sustains for approximately 20.5 h, which is six times longer than the conventional strategy. The ability to sustain the discharging process for extended periods is particularly advantageous in regenerative fuel cells, as it enables the cells to operate for longer periods without the need for regeneration.

Abstract Although there are many methods to improve the fault ride‐through (FRT) capability of doubly‐fed induction generator (DFIG) systems at present, each method has its shortcomings, especially the applicability under different voltage dips (VDs), so an improved system structure with a dynamic switching topology and a corresponding control scheme is proposed. Based on the mechanism analysis that the series impedance of the stator can effectively reduce the overcurrent on the rotor side, and considering the feasibility of the FRT scheme in engineering, the dynamic switching topology is designed. The selection of theoretical parameters in different cases is also analysed and designed. Simultaneously, to cooperate with the hardware measures, the control scheme of the rotor side converter (RSC) under different conditions is also improved. The RSC can use the control scheme of active flux attenuation to effectively and quickly reduce the overcurrent on the rotor side, and use reactive power support to accelerate the voltage recovery. The novelty of the FRT scheme is that the scheme can dynamically adjust the topology structure and control scheme under different voltage dips. Thus, its ride‐through performance during fault is better under different conditions. A simulation model of the improved system structure and control scheme is built on the MATLAB/Simulink platform. Through the comparison of simulation data, the validity and correctness of the proposed FRT scheme are verified.

Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer (PEMWE), deteriorating the cell performance. In order to address this issue, the dual-layer flow field design has been proposed in our previous study. In this study, the VOF (volume of fluid) method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode surface. However, it is very time-consuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases, which needs to be calculated through computation-intensive physical model. Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. Based on the highly efficient surrogate, genetic algorithm (GA) is further utilized to determine the optimal heights of base layer and degassing layer. Finally, the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.

BackgroundIncreasing evidence emphasizes the potential relationship between diabetes and OAB (overactive bladder). However, large population epidemiology is still lacking.MethodsThis cross-sectional study included six cycle NHANES surveys, with a total of 23863 participants. Logistic regression models were constructed to analyze the association between diabetes mellitus, diabetes-related markers, and inflammatory biomarkers with OAB. Restricted cubic splines were used to analyze the non-linear associations. Mediating analysis was performed to test the effect of inflammatory biomarkers on the relationship between diabetes-related markers and OAB. Finally, machine learning models were applied to predict the relative importance and construct the best-fit model.ResultsDiabetes mellitus participants’ OAB prevalence increased by 77% compared with non-diabetes. As the quartiles of diabetes-related markers increased, the odds of OAB monotonically increased in three models (all p for trend < 0.001). Glycohemoglobin exhibited a linear association with OAB (p for nonlinearity > 0.05). White blood cells significantly mediated the associations between diabetes-related markers (glycohemoglobin, fasting glucose, and insulin) with OAB, and the proportions were 7.23%, 8.08%, and 17.74%, respectively (all p < 0.0001). Neutrophils partly mediated the correlation between (glycohemoglobin, fasting glucose, and insulin) and OAB at 6.58%, 9.64%, and 17.93%, respectively (all p < 0.0001). Machine learning of the XGBoost model constructs the best fit model, and XGBoost predicts glycohemoglobin is the most important indicator on OAB.ConclusionOur research revealed diabetes mellitus and diabetes-related markers were remarkably associated with OAB, and systemic inflammation was an important mediator of this association.

To address the broadband oscillation problem caused by isomerized power electronic devices in microgrids, an oscillation characteristic and stability region analysis method was proposed for hierarchical control structure microgrids based on the dynamic time-varying fast and slow scale analysis method. From the perspective of control, the controller was combined with the main circuit, and a nonlinear dynamic model of the microgrid hierarchical control grid-connected inverter was established. Then, the dynamic time-varying fast and slow scale analysis method was used to obtain the Hopf bifurcation oscillation trajectory. Furthermore, and combining with three-dimensional and Hopf bifurcation, considering the correlation between the main circuit parameters and controller parameters as the starting point, the trajectory instability region analysis method is proposed. Finally, the instability mechanism of the hierarchical control structure microgrid is revealed using the above methods. These methods provide a theoretical basis for the correct selection of the main circuit structure parameters and controller parameters of the microgrid.

Abstract Opioid-alcohol polysubstance use is prevalent and worsens treatment outcomes. Here we assessed whether co-consumption of oxycodone and alcohol influence the intake of one another, demand for oxycodone, and the neurocircuitry underlying cue-primed reinstatement of oxycodone-seeking. Male and female rats underwent oxycodone intravenous self-administration (IVSA) with homecage access to alcohol (20% v/v) and/or water immediately after the IVSA session. Next, economic demand for intravenous oxycodone was assessed while access to alcohol and/or water continued. Control rats self-administered sucrose followed by access to alcohol and/or water. Rats underwent a cue-primed reinstatement test and brains were processed for c-fos mRNA expression. While both sexes decreased oxycodone intake if they had access to alcohol, and decreased alcohol intake if they had access to oxycodone, only female oxycodone + alcohol rats exhibited decreased demand elasticity and increased cue-primed reinstatement. Alcohol consumption increased the number of basolateral and central amygdala neurons activated during sucrose and oxycodone reinstatement and the number of ventral and dorsal striatum neurons engaged by sucrose reinstatement. Nucleus accumbens shell dopamine 1 receptor expressing neurons displayed activation patterns consistent with oxycodone reinstatement. Thus, alcohol alters the motivation to seek oxycodone in a sex-dependent manner and the neural circuitry engaged by cue-primed reinstatement of sucrose and oxycodone-seeking.

Background: Graves’ disease is a common autoimmune disease. Cytokines and their signalling pathways play a major part in the pathogenesis of Gr aves’ disease; however, the underlying mechanism needs to be clarified. Aims: The aim of this study was to explore whether circular RNAs par ticipate in the immunological pathology of Graves’ disease via cytokine-related signalling pathways. Methods: Bioinformatics analysis was performed to identify differentially expressed circular RNAs and their targets and associated pathways. A tota l of three patients with Graves’ disease and three sex- and age-matched healthy controls were enrolled for validation with microarray analysis and real-time quantitative PCR (qPCR). An additional 24 patients with Graves’ disease and 24 gender- and age-matched controls were included for validation by real-time fluorescent qPCR. Flow cyto metry and CCK8 assays were used to detect the apoptotic and proliferative levels of J urkat cells (T lymphocytes) with the silenced expression of circRNA. ELISA was performed to detect the growth and apoptosis-related proteins. The competition mechanism of en dogenous RNA was explored by real-time fluorescence qPCR. Results: A total of 366 significantly differentially expressed circular RN As were identified in the Graves’ disease group compared to healthy cont rols. The level of hsa_circ_0090364 was elevated in Graves’ disease patients and p ositively correlated with thyroid-stimulating hormone receptor antibodies. Further analyses suggested that hsa_ circ_0090364 may regulate the JAK-STAT pathway via the hsa-miR- 378a-3p/IL-6ST/IL21R axis to promote cell growth. Conclusions: These results provide novel clues into the pathophysiological m echanisms of Graves’ disease and potential targets for drug treatment.

DNA sensing through the cGAS-STING pathway plays an important role in cancer immunosurveillance. Pharmaceutical activation of STING in the tumor environment is considered an attractive approach to induce anti-tumor immunity, but had limited efficacy in the clinic. Several studies have found that STING is epigenetically silenced in many tumors, including colon cancer. This suggests that STING silencing in tumor cells contributes to immune escape and may limit the application of STING agonists. We previously found that inhibition of the KDM5 family histone demethylases restored STING expression in human breast cancer cells and activated the cGAS-STING pathway. In this study, we used MC38 and CT26 syngeneic mouse colorectal cancer models to show that loss of STING in tumor cells accelerates tumor growth. KDM5 inhibitors activate STING expression in mouse colorectal cancer cells and suppress colon cancer growth in immune competent mice in a STING-dependent manner. This study highlights KDM5 inhibitors as novel immune modulators in cancer therapies.

Path planning using handcrafted waypoints is inefficient for a multi-task UAV operating in dynamic environments with potential risks such as bad weather, obstacles, or forbidden zones, among others. In this paper, we propose an automatic path planning method through natural language that instructs the UAV with compound commands about the tasks and the corresponding regions in a given map. First, we analyze the characteristics of the tasks and we model each task with a parameterized zone. Then, we use deep neural networks to segment the natural language commands into a sequence of labeled words, from which the semantics are extracted to select the waypoints and trajectory patterns accordingly. Finally, paths between the waypoints are generated using rapidly exploring random trees (RRT) or Dubins curves based on the task requirements. We demonstrate the effectiveness of the proposed method using a simulated quadrotor UAV that follows sequential commands in four typical tasks with potential risks.

In this study, a 3-D multiphase model of the proton exchange membrane (PEM) electrolyzer is developed on an open-source computational fluid dynamics (CFD) platform, OpenFOAM. A pseudo-coupling method is presented to consider the detailed two-phase distribution conditions at the interface between the flow channel and liquid/gas diffusion layer (L/GDL) in anode flow channel of a 3-D PEM electrolyzer model. The detailed two-phase distribution conditions can be obtained by high-speed optical images or volume of fluid (VOF) simulation results. Through this method, it is possible to predict the effect of two-phase flow existing in the anode flow channel on the transport phenomena in the porous electrode, and thus cell performance. The 3-D model is validated with experimental test results at three current density conditions (0-1, 0-2, 0-3 A cm−2) considering the existence of two-phase flow. It is found that if the two-phase flow in the anode flow channel is neglected, the simulation polarization curves will deviate from the experimental test results, especially at high current density conditions. In contrast, the simulated polarization curves manage to fit the experimental data accurately with the pseudo-coupling method, indicating that it is necessary to consider the effect of two-phase flow. In addition, this pseudo-coupling model can not only take the multi-phase mass transport into consideration but also the distribution of temperature and current density inside a PEM electrolyzer. With the help of this model, a novel double-layer flow field is proposed to enhance the performance of electrolyzer. It is found that the cell performance of the proposed flow field is 0.171 V better that of the traditional parallel flow field at 3 A cm−2. Also, the temperature distribution and current density distribution of the new flow field are more uniform, which could benefit the durability and performance of PEM electrolyzer.

Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integral, Derivative) controller is a choice for the UAV landing task, but it suffers the problem of manual parameter tuning, which becomes intractable if the initial landing condition changes or the mobile platform keeps moving. In this paper, we design a novel learning-based controller that integrates a standard PID module with a deep reinforcement learning module, which can automatically optimize the PID parameters for velocity control. In addition, corrective feedback based on heuristics of parameter tuning can speed up the learning process compared with traditional DRL algorithms that are typically time-consuming. In addition, the learned policy makes the UAV landing smooth and fast by allowing the UAV to adjust its speed adaptively according to the dynamics of the environment. We demonstrate the effectiveness of the proposed algorithm in a variety of quadrotor UAV landing tasks with both static and dynamic environmental settings.

As a clean electrochemical energy conversion device, proton exchange membrane (PEM) fuel cell will play great effect in the carbon neutral society. It is widely recognized as a challenge to couple the multi-phase flow in gas flow channel and the electrochemical activities in the electrodes in PEM fuel cell modeling. In this study, a two-fluid model is developed to describe the multi-phase flow in the whole cell, in conjunction with the transport phenomena and electrochemical reaction kinetics. We assume the analogy between micro channel and porous media, and derive the gas/liquid velocity ratio for multi-phase flow in channel through Darcy's law for two-phase flow, which is expressed as a function of gas/liquid viscosity ratio and liquid saturation. Integration of the two-fluid model makes it able to predict the two-phase pressure drop and local liquid saturation in gas flow channels in an operating fuel cell. A sub-model of liquid water coverage at the GDL (gas diffusion layer) surface is also developed and incorporated into the model to study the impact of various contact angles at GDL surface on cell performance. The liquid saturation profiles along the flow direction are compared with literature data with reasonable agreement achieved. The model is employed to study a flow field of five parallel channels connected with an inlet/outlet manifold. It shows that multi-phase flow in the gas flow channel leads to more non-uniform flow and oxygen distributions, and hence a larger concentration loss under high current densities than the single-phase channel flow model.

Multi-UAV cooperative systems are highly regarded in the field of cooperative multi-target localization and tracking due to their advantages of wide coverage and multi-dimensional perception. However, due to the similarity of target visual characteristics and the limitation of UAV sensor resolution, it is difficult for UAVs to correctly distinguish targets that are visually similar to their associations. Incorrect correlation matching between targets will result in incorrect localization and tracking of multiple targets by multiple UAVs. In order to solve the association problem of targets with similar visual characteristics and reduce the localization and tracking errors caused by target association errors, based on the relative positions of the targets, the paper proposes a globally consistent target association algorithm for multiple UAV vision sensors based on triangular topological sequences. In contrast to Siamese neural networks and trajectory correlation, the relative position relationship between targets is used to distinguish and correlate targets with similar visual features and trajectories. The sequence of neighboring triangles of targets is constructed using the relative position relationship, and the feature is a specific triangular network. Moreover, a method for calculating topological sequence similarity with similar transformation invariance is proposed, as well as a two-step optimal association method that considers global objective association consistency. The results of flight experiments indicate that the algorithm achieves an association accuracy of 84.63%, and that two-step association is 12.83% more accurate than single-step association. Through this work, the multi-target association problem with similar or even identical visual characteristics can be solved in the task of cooperative surveillance and tracking of suspicious vehicles on the ground by multiple UAVs.

In this paper, we first study the event-triggered continuous-discrete Kalman filtering problem to control the MSE under a given triggering threshold, where if the mean squared error (MSE) is smaller than this threshold, then an event is triggered to let the system take a new measurement to reduce the MSE. Previously, since the accurate relationship between the sampling period and the MSE is unknown, it is difficult to control the MSE under a certain system requirement by tuning the sampling period. With our proposed event-triggered continuous-discrete Kalman filter, we are able to upper bound the MSE as expected. Specifically, we strictly prove that whenever a measurement is taken, and the decrease in MSE is lower bounded by a constant determined by the system parameters as well as the triggering threshold if the output matrix has no zero columns. Based on this important property, we provide a rigorous proof that the MSE is finally upper bounded by the triggering threshold, as time goes to infinity. Also, we prove that the event-triggered continuous-discrete Kalman filter can be implemented in a self-triggered manner. Finally, our simulation results corroborate the effectiveness and accuracy of the theoretical results.

We have found that anodized amorphous TiO2 nanotubes can be transformed into rutile phase TiO2, resulting in the formation of a flower-like structure, when soaked in H3PO4 solution at room temperature for 2–4 days. The presence of PO43− ions in the soaking solution is believed to play a key role in the formation of the flower-like TiO2 structure. The resulting TiO2 flower-like material can be used as a binder-free electrode and exhibits obvious redox activity in AlCl3 solution and superior supercapacitive performance compared to conventional TiO2 nanotubes. The presented technique offers a simple, scalable, energy-saving, and cost-effective way to tailor the morphology and properties of anodized TiO2 nanotubes for different applications. Keywords: TiO2 nanotubes, H3PO4 soaking, Flower-like structure, Dissolution-recrystallization, Supercapacitors

The LLCL filter is widely used in grid-tied inverter systems due to its small size, low cost and good attenuation for high-frequency harmonics. However, the LLCL filter resonance phenomenon and the wide range of impedance may compromise the system stability. In order to solve these problems, a coordination control strategy for LLCL-filter-based grid-tied inverter with indirect sliding mode power control and virtual impedance was presented. The LLCL filter design parameter has been carefully adjusted to ensure the system stable operation under a wide range of grid impedance variations. Comparing with the traditional proportional integral control system through simulation experiment, the output current harmonics of grid-tied inverter are analysed. The proposed strategy is feasible with good control accuracy and tracking performance.

Considering the problem of cooperative operation of highly intermittent renewable generations (photovoltaic and wind power) and flexible load in active distribution network, a distributed cooperative optimisation operation strategy based on combine-then-adapt diffusion algorithm is proposed in this study. Distributed agents of distributed generators are connected by the distributed sparse network through distributed hierarchical control method. As an agent node of networked system, it interchanges information with its neighbours. In order to make the distribution generator meet the equal incremental cost principle, the adaptive adjacency matrix and fusion matrix with the weighted factor are introduced to update the state value of nodes to obtain the same convergence value. In this strategy, the coordinated source and load is applied by the active power optimisation dispatching for active distribution network. Stimulate analysis and experimental results show that the proposed combine-then-adapt diffusion algorithm can effectively solve the problem of real-time detection signal, can quickly learn and adapt to environmental changes. Finally, the simulation analysis and experimental results of IEEE14-bus distribution test system is carried out to verify the effectiveness and feasibility of the proposed method.

The interconnection of multiple microgrids can form a microgrid cluster (MGC). The economic benefit and operation reliability of the whole system can be improved through the energy mutual-aid and coordination control between microgrids. This paper proposes a bi-level coordinated optimal dispatching strategy for microgrid clusters. The upper layer aims to minimize the operating costs of the microgrid cluster system and the power fluctuation of the tie lines. The exchange power and generation power of each sub-microgrid are coordinated and controlled, which are sent to the lower level model as scheduling instructions. The lower layer takes a single microgrid as the research object. By optimizing the output of power generation unit, the operation cost of single microgrid can be minimized. Iterative solution is adopted between the bi-level optimization models. In order to avoid the premature and local convergence of the algorithm in the optimization process, the improved artificial bee colony algorithm (ABC) is used to solve the objective function. Finally, simulation examples verify the effectiveness and feasibility of the proposed strategy.

Cooperative standoff tracking of moving targets is an important application of fixed-wing unmanned aerial vehicles (UAVs). To cope with the problem of long convergence time and unstable tracking in cooperative target tracking, traditional Lyapunov vector field guidance (LVFG) is modified. The guidance parameter c is discussed, and the gradient descent method is utilized to develop the optimal guidance parameter search algorithm. As for tracking moving targets, an interacting multiple model-based unscented Kalman filter (IMM-UKF) estimator is built for predicting the target state, and the result is used for correcting the guidance law. Meanwhile, a speed-based controller is developed for faster convergence to the desired intervehicle phase, and the stability of the controller is proved using the Lyapunov stability theory. Numerical simulation results indicate the proposed guidance converges faster to the standoff circle without intersecting the orbit. The state estimator reduces the estimate error and the intervehicle phase converges faster to the desired phase than the traditional control method. Furthermore, extensive hardware-in-the-loop simulations are carried out to verify the feasibility of the algorithm.

Cyclic GMP-AMP (cGAMP) synthase (cGAS) stimulator of interferon genes (STING) senses pathogen-derived or abnormal self-DNA in the cytosol and triggers an innate immune defense against microbial infection and cancer. STING agonists induce both innate and adaptive immune responses and are a new class of cancer immunotherapy agents tested in multiple clinical trials. However, STING is commonly silenced in cancer cells via unclear mechanisms, limiting the application of these agonists. Here, we report that the expression of STING is epigenetically suppressed by the histone H3K4 lysine demethylases KDM5B and KDM5C and is activated by the opposing H3K4 methyltransferases. The induction of STING expression by KDM5 blockade triggered a robust interferon response in a cytosolic DNA-dependent manner in breast cancer cells. This response resulted in resistance to infection by DNA and RNA viruses. In human tumors, KDM5B expression is inversely associated with STING expression in multiple cancer types, with the level of intratumoral CD8+ T cells, and with patient survival in cancers with a high level of cytosolic DNA, such as human papilloma virus (HPV)-positive head and neck cancer. These results demonstrate a novel epigenetic regulatory pathway of immune response and suggest that KDM5 demethylases are potential targets for antipathogen treatment and anticancer immunotherapy.

In this study, a 3D + 1D (three-plus-one dimensional) multiphase PEM (proton exchange membrane) fuel cell model is developed, in which the detailed channel two-phase flow is also considered based on the two-phase Darcy's law. Compared with conventional 3D fuel cell models, the 3D + 1D model improves calculation efficiency and stability. Utilizing this model, the simulation work is conducted on an automobile PEM fuel cell (309.12 cm2) with the full flow field morphology considered. Its accuracy is comprehensively validated at this scale against the experimentally measured polarization curves and HFR (high frequency resistance) under different anode and cathode stoichiometric ratios and pressures and operating temperatures, indicating the model reliability. Subsequently, we numerically investigate the influence of four flow field configurations on cell performance characteristics, including the wave-like, dotted, baffled, and straight flow fields. It is found that the dotted and baffled configuration effectively improved cell performance, but the wave-like flow field has little influence on cell performance compared with straight flow field. Moreover, it is found that decreasing the channel/rib width decreases the concentration loss, but it also increases the ionic ohmic loss, resulting in an optimal channel/rib width for cell performance improvement. [ABSTRACT FROM AUTHOR]

With increasing reforms related to integrated energy systems (IESs), each energy subsystem, as a participant based on bounded rationality, significantly influences the optimal scheduling of the entire IES through mutual learning and imitation. A reasonable multiagent joint operation strategy can help this system meet its low-carbon objectives. This paper proposes a bilayer low-carbon optimal operational strategy for an IES based on the Stackelberg master-slave game and multiagent joint operation. The studied IES includes cogeneration, power-to-gas, and carbon capture systems. Based on the Stackelberg master-slave game theory, sellers are used as leaders in the upper layer to set the prices of electricity and heat, while energy producers, energy storage providers, and load aggregators are used as followers in the lower layer to adjust the operational strategy of the system. An IES bilayer optimization model based on the Stackelberg master-slave game was developed. Finally, the Karush-Kuhn-Tucker (KKT) condition and linear relaxation technology are used to convert the bilayer game model to a single layer. CPLEX, which is a mathematical program solver, is used to solve the equilibrium problem and the carbon emission trading cost of the system when the benefits of each subject reach maximum and to analyze the impact of different carbon emission trading prices and growth rates on the operational strategy of the system. As an experimental demonstration, we simulated an IES coupled with an IEEE 39-node electrical grid system, a six-node heat network system, and a six-node gas network system. The simulation results confirm the effectiveness and feasibility of the proposed model. [ABSTRACT FROM AUTHOR]