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Abstract The rapid expansion of low‐voltage distributed photovoltaic (PV) systems with decentralized layouts poses significant data collection challenges. The scarcity of data amplifies prediction complexities, affecting the operational security and stability of distribution networks. To enhance predictive accuracy for distributed regional PV power generation, including unmonitored low‐voltage systems, this paper proposes a novel prediction approach that combines weight optimization and transfer learning. Firstly, to more accurately extrapolate from neighbouring monitored PV to estimate unmonitored PV outputs, a novel SSA‐MLP‐XGBoost model is proposed for monitored PV power plants, in which the hyperparameters of multilayer perceptron (MLP) and (extreme gradient boosting) XGBoost models are fine‐tuned using the sparrow search algorithm (SSA), and the optimized models are synergistically integrated to heighten prediction precision for monitored plants. Secondly, based on the predicted power curves of monitored PV power plants, a weight optimization model is presented to optimize the combined weights between unmonitored and monitored power stations. Furthermore, the power generation model from monitored facilities is transplanted to unmonitored plants for power generation estimation. Ultimately, the authors combine the weight optimization and transfer learning model to get more accurate and robust model. Validation across three regional distributed photovoltaic clusters demonstrates a noteworthy improvement in prediction accuracy—20%, 7.5%, and 25% for the respective clusters compared to the existing methods.

Abstract To achieve carbon neutrality, renewable energy‐based power systems and hydrogen are increasingly being promoted. A novel electricity‐thermal‐hydrogen integrated energy system that combines new energy generation, multi‐source load, and multiple energy storage is proposed by the authors. To address uncertainties in new energy output, and issues of untimely unit regulation response and large planning tracking errors, a multi‐scale scheduling method based on model predictive control (MPC) was proposed. In the day‐ahead dispatching stage, an optimal economic dispatching model was established with the lowest system operation cost as the optimisation objective. The model considers equipment investment, operation, maintenance, and peak‐to‐valley differences in electricity prices. In the intraday dispatching stage, an MPC‐based intraday rolling optimisation correction strategy was proposed to cope with contact line power fluctuations caused by prediction errors of new energy and multi‐source load. This strategy combines time‐domain rolling and feedback correction of the real‐time system state to eliminate the influence of uncertainty factors in the microgrid. The MPC‐based intraday rolling optimal scheduling model was established in the form of a discrete state space and transformed into a quadratic planning problem to improve the efficiency and accuracy of the model solution. Finally, a typical microgrid was used as an example to verify the effectiveness of the proposed method. Results show that the contact line tracking error can be within 0.025 kW, and the single scheduling time was within 0.14 s.

Abstract Tumor microenvironment (TME)‐triggered phototheranostic platform offers a feasible strategy to improve cancer diagnosis accuracy and minimize treatment side effects. Developing a stable and biocompatible molecular phototheranostic platform for TME‐activated second near‐infrared (NIR‐II) fluorescence imaging‐guided multimodal cascade therapy is a promising strategy for creating desirable anticancer agents. Herein, a new NIR‐II fluorescence imaging‐guided activatable molecular phototheranostic platform (IR‐FEP‐RGD‐S‐S‐S‐Fc) is presented for actively targeted tumor imaging and hydrogen sulfide (H2S) gas‐enhanced chemodynamic‐hypothermal photothermal combined therapy (CDT/HPTT). It is revealed for the first time that the coupling distance between IR‐FE and ferrocene is proportional to the photoinduced electron transfer (PET), and the aqueous environment is favorable for PET generation. The part of Cyclic‐RGDfK (cRGDfk) peptides can target the tumor and benefit the endocytosis of nanoparticles. The high‐concentration glutathione (GSH) in the TME will separate the fluorescence molecule and ferrocene by the GSH‐sensitive trisulfide bond, realizing light‐up NIR‐II fluorescence imaging and a cascade of trimodal synergistic CDT/HPTT/gas therapy (GT). In addition, the accumulation of hydroxyl radicals (•OH) and down‐regulation of glutathione peroxidase 4 (GPX4) can produce excessive harmful lipid hydroperoxides, ultimately leading to ferroptosis.

Abstract With the development of the electricity market, peer‐to‐peer (P2P) transaction plays an important role in promoting local consumption of renewable energy and arousing the enthusiasm of prosumers. However, due to the diversification of prosumers, the confidentiality of the information and the interaction between prosumers, there are increasing challenges for the traditional model‐based optimisation methods in both P2P modelling and model solution accuracy. Therefore, this paper proposes a novel P2P transaction method based on deep learning under a two‐stage market environment, which uses a data‐driven approach to build a transaction behaviour model based on public information. The neural network model based on Long Short‐Term Memory (LSTM) is utilised to characterise the behaviour of prosumers in P2P transactions effectively. Based on this model, the energy consumption plans and P2P bids of prosumers are optimised accordingly. Through the simulation test of an example system with six prosumers, the results show that the model established can well represent the P2P transaction behaviour of prosumers, and the proposed method can effectively improve the efficiency of P2P transactions and the economic benefits of prosumers, providing a reference for the decision‐making of P2P transactions.

Abstract To improve the space‐time adaptive processing (STAP) performance of airborne radar in complex environment, a generalised eigenvalue reweighting covariance matrix estimation algorithm called GERCM is proposed here. First, the interference plus noise (IPN) covariance matrix of cell under test (CUT) data is estimated by the selected target‐free training samples around the CUT with the sample covariance matrix method. Then, with the component decompositions of the selected training samples and the assumption of approximately equal subspace, the IPN covariance matrix of CUT data is reformulated by the eigenvector matrix, eigenvalue matrix, and the eigenvalue reweighting vector. Subsequently, based on the modified covariance matching estimation criterion, the eigenvalue reweighting vector is estimated by solving the redesigned convex optimisation problem with the Lagrange dual method. Finally, the STAP weight vector is calculated to process the CUT data. The proposed algorithm can obtain a relatively accurate IPN covariance matrix of CUT data by sufficiently utilising the non‐homogeneous training samples and can effectively protect the moving targets in CUT data, which can be applied to airborne radar with arbitrary array structure and antenna configuration. Simulation results and performance analyses based on the multi‐channel airborne radar measurement data demonstrate the effectiveness of the proposed GERCM algorithm.

Abstract To improve the clutter suppression performance of airborne radar in heterogeneous environment, a robust refined training sample reweighting space–time adaptive processing (STAP) method called RRSRW is proposed here. First, some target‐free training samples around the cell under test (CUT) are selected and the corresponding clutter dictionary matrices are constructed using the radar system parameters. Then, the clutter patch amplitudes and array error for selected training samples are simultaneously estimated through the formulated constrained least squares problem. Subsequently, based on the covariance matching estimation criterion, the local weighting coefficients for selected training samples are estimated by the redesigned convex optimisation problem. Finally, the STAP weight vector is calculated to process the CUT data. The proposed method is robust to array error and can effectively protect the moving targets in CUT data, which is free of hyper‐parameters and has global convergence properties. Simulation results demonstrate that the proposed RRSRW method can effectively suppress the strong ground clutter and greatly improve the detection performance of moving targets in heterogeneous environment.

The AC/DC hybrid distribution network takes advantage of both AC and DC technologies. It is one of the most important forms of future distribution systems. However, currently few work has been done on the topology determination of this network, and its fault behaviour of DC short-circuit fault has not been clearly investigated yet. First, the tree, radial, and ring topologies of a three-terminal AC/DC distribution network are compared. Also the characteristics of fault currents in DC circuit breakers during a DC short-circuit fault are analysed in detail. Then the equivalent circuit models during the fault are established and accurate mathematical expressions of transient fault currents are deduced. Based on this, the influence of network topologies on the characteristics of fault currents is investigated thoroughly. Moreover, a method to determine the peak values and peak times of fault currents is presented which can simplify the selection of circuit components. The simulation results show that the fault currents of the radial topology have the lowest peak values and longest peak times, and the proposed method meets the requirements of both simplicity and accuracy. Finally, the influence of the key parameters on the proposed method is investigated comprehensively.

DC voltage oscillation occurs more and more frequently in voltage source converter (VSC)-based AC/DC hybrid distribution system (AC/DC HDS). However, currently, the explanation of the oscillation mechanism is not clear enough. One reason is the traditional state-space model of the system is too complicated, and the information in the model is too dispersed. This research aims at researching the small-signal stability of AC/DC HDS and revealing the mechanism of DC voltage oscillation. Firstly, a method to reduce the order of the state-space model is proposed. A three-order VSC model with definitude physical meaning is obtained by utilising instantaneous power theory. According to this model, the potential instability factors in VSC are identified. Then, based on this, the interaction behaviour between VSC and DC network is researched by frequency domain analysis. The instability mechanism is ultimately revealed. The analysis result shows that the VSC's phase roll-off characteristic is the root cause of instability, the inherent oscillation characteristics of DC network has negative interactions with VSC and will narrow the system stability boundary. Finally, the correctness of the theory is verified by electromagnetic transient simulation.

We report a case of a previously well 58-year-old man, who presented with delirium and low GCS, and was found to have extreme hypernatraemia (Na+ = 191 mmol/L) and hyperglycaemia (glucose = 31 mmol/L). This resulted in a corrected serum sodium of 202 mmol/L. He was treated with fluid and electrolyte replacement in the intensive care unit, and had returned to essentially normal function by hospital discharge. The aetiology was believed to be due to severe dehydration and a new diagnosis of diabetes mellitus. Extreme hypernatraemia (serum sodium level greater than 190 mmol/L) is rare and associated with a high mortality. The mainstay of treatment is careful fluid and electrolyte management. Most recommendations advise to reduce the serum sodium by 0.5 mmol/L/hour, due to concerns over cerebral oedema; however, there are reports that slower correction is associated with higher mortality. In this case, the initial corrected sodium of 202 mmol/L was steadily corrected to 160 mmol/L over 91 hours, at a rate of 0.46 mmol/L/hour. This demonstrates the safety of the recommended approach.