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Optimally reconfiguring an active distribution network (ADN) during power outages has been regarded as a reasonable approach to facilitate system secure operation and reliability. Nevertheless, most existing studies for the reconfiguration virtually focus on taking actions from the generation- and network-side, in which the potential achievement from the demand-side is underestimated. Moreover, the phase-unbalance and voltage violation in ADNs should be restricted to avoid extreme conditions of distributed generators (DGs) that jeopardize system reliability. To bridge the gap, a new approach to reconfigure ADNs under multiple faults is proposed in this paper, incorporating a phase demand balancing (PDB) model to improve dispatch performance. The model regulates asymmetrical loads to mitigate the phase-unbalance issue from the demand-side, co-optimized with step voltage regulators (SVRs) and DG dispatching to enhance reliability and flexibility in reconfiguring ADNs. The derived optimization is a challenging mixed-integer non-convex programming (MINCP), which is reformulated as an efficiently solvable mixed-integer second-order cone programming (MISOCP) via exact equivalence and accurate approximation techniques. Case studies based on modified IEEE benchmark systems validate the effectiveness and advantages of the proposed method.

As the marine industry progresses, copper-containing metallic materials that possess both high strength and ductility are demanding. Traditional copper alloys used in marine environment face challenges regarding inadequate strength and corrosion resistance. In the present work, cold rolling and subsequent high-temperature annealing processes were employed to the Cu1.2NiMnFe0.5Cr0.5Al0.2high-entropy alloy. This alloy exhibited remarkable mechanical properties, including a yield strength (YS) of 530 MPa, ultimate tensile strength (UTS) of 913 MPa, and a relatively high fracture elongation (EL) of 37.7%. After subsequent aging treatment at 400 °C, spinodal decomposition occurred within the matrix, leading to enhanced strength. The decomposed microstructure was comprehensively characterized, and the strengthening mechanism was analyzed. Notably, the sample aged for 6 h exhibited a YS of 701 MPa and an EL of 21.8%. The present HEAs exhibit superior mechanical properties compared to traditional copper alloys and other Cu-containing HEAs, when considering both strength and ductility. Furthermore, these alloys demonstrated exceptional corrosion resistance and a favorable copper ion release rate in 3.5 wt% NaCl solution. This work introduces an innovative class of advanced alloys and offers a strategic approach to alloy design for marine applications.

This paper studies limit behaviors of stationary measures for stochastic ordinary differential equations with nondegenerate noise and presents a criterion to guarantee that a repeller with zero Lebesgue measure is a null set of any limit measure. Using this criterion, we first provide a series of nontrivial concrete examples to show that their repelling limit cycles or quasi-periodic orbits are null sets for all limit measures, which deduces that all their limit measures are concentrated on stable equilibria and stable limit cycles or quasi-periodic orbits, and saddles. Interesting open questions on exact supports of limit measures are proposed.

False data injection attacks (FDIAs) significantly disrupt the secure operation of smart grids by manipulating the measured values collected by intelligent instruments. Existing studies have utilized deep learning techniques to enhance the detection of FDIAs, however, these studies often overlook the spatial correlation between power grid topology and measurement data. Meanwhile, the high complexity of deep neural network severely impedes the interpretability of detection models, resulting in the incredibility of detection results. To address the above challenges, this paper proposes an interpretable deep learning FDIAs detection method, named dual-attention multi-head graph attention network, DAMGAT. The DAMGAT introduces a dual-attention mechanism that incorporates both node feature attention and spatial topology attention into a multi-head graph attention network. This mechanism efficiently aggregates attack characteristics and spatial topology information by dynamically capturing the potential correlations between FDIAs detection and measurement data. Furthermore, the proposed model can provide clear and credible interpretations for high-accuracy detection results via analyzing features and spatial topology attention weights. Extensive simulations are performed using the IEEE 14-bus and 118-bus test systems. The experimental results demonstrate that the proposed model outperforms state-of-the-art FDIA detection methods in terms of accuracy, while also providing reasonable interpretability for features and spatial dimensions.

This article presents a data-driven economic model predictive control (EMPC) framework for maximizing the output power of the nonlinear wave-energy converter (WEC). The linear representation of the nonlinear WEC is realized by the Koopman operator theory, and extended dynamic mode decomposition is used to approximate the infinite-dimensional Koopman operator to a finite-dimensional linear model. The lifted linear model plays a role in designing controllers for the nonlinear WEC system using model-based linear controller strategies. The EMPC method is adopted to realize the economic optimization and process control of the WEC. The designed EMPC controller is capable of translating the optimal control of the Koopman linear system to the original nonlinear WEC system. The asymptotic stability of the system is achieved by using the stage-cost function and auxiliary optimization problem. The simulations show the economy and effectiveness of the proposed method.

Applying large language models (LLMs) to modern power systems presents a promising avenue for enhancing decision-making and operational efficiency. However, this action may also incur potential security threats, which have not been fully recognized so far. To this end, this article analyzes potential threats incurred by applying LLMs to power systems, emphasizing the need for urgent research and development of countermeasures.

This article explores the interaction between active distribution networks (ADN) and mobile energy, specifically mobile charging robots (MCRs) and electric vehicles (EVs). We introduce the concept of MCR operator, who acts as a mediator between EVs and the ADN. To realize the interaction among the three parties, a cloud-edge-terminal control framework is formulated considering the coupling factors with the transportation systems. On this basis, the interaction between MCRs and EVs is achieved based on the game theory, which is implemented in a fully distributed manner to protect the data privacy. To reduce the computation complexity, the game-based interactive problem is further decomposed into a charging price bargaining problem and a charging decision and power regulation problem. Furthermore, the power quality of the ADN is guaranteed through incentivizing the charging/discharging behaviors of MCRs, which is formulated as a coordination between the ADN operator and the MCR operator, and the topology switching is also taken into consideration, thus the voltage regulation ability of the ADN and the economics of the MCR operator can be enhanced at the same time. Simulation results confirm the effectiveness and superiority of the proposed approach, which also indicate that mobile energy and the ADN can achieve a win-win situation by benefiting all parties: Improving the utility for the EV aggregator by 35.48% (reducing the charging cost for EV owners), increasing the charging revenue for the MCR operator by 12%, and addressing the voltage issue for the ADN with a 100% voltage security rate.

This letter proposes two novel effective transfer learning (TL) methods for power system stability assessment (SA) under distinct scenarios: cross-fault, where different types of faults are considered, and cross-scale, which accounts for varying system knowledge levels. Addressing the challenges faced in scenarios with few limited labeled SA data, our proposed data-driven SA models aim to transfer to the different but related scenarios by leveraging numerous instances from fully knowledge database and few labeled instances from the limited knowledge database. Moreover, a significant feature of our approach is the incorporation of the Extreme Learning Machine, a rapid neural network-based learning algorithm. Preliminary testing showcases an improvement of more than 24% in SA accuracy, especially for large-scale cross-scale transfer, demonstrating the efficacy of our TL techniques while maintaining computational efficiency.

To improve the operational security and reliability of distribution networks, distribution network operators will encourage customers to modify their demand profiles and then give them financial compensation. The compensation is always settled based on customer baseline load (CBL). The estimation of CBL can be achieved using both traditional historical data methods and non-participant data methods. However, relying solely on a single method for estimating baseline load for some residential customers may result in significant estimation errors. To estimate baselines for residential loads under different conditions, this paper proposes a novel deep learning method that intelligently selects historical data-based CBL and non-participant data-based CBL. This method provides a special way for taking advantage of the historical load data of demand response (DR) participant and the concurrent load data of nonparticipants. Moreover, to preserve the privacy of residential customers, a horizontal federated learning framework is proposed to train local neural networks in residential units. In the federated learning framework, a novel adaptive learning rate method is proposed to improve the performance of local neural networks under low global aggregation frequency. Compared with the state-of-the-art CBL estimation methods, the improved federated learning method can significantly reduce the seriously inaccurate estimation and improve the overall performance of CBL estimation under various condition. The effectiveness and superiority of the proposed method have been verified on a real-world load dataset.

Developing the deep learning (DL) technique is a promising way to improve renewable energy forecasting accuracy and offset the negative impacts of renewable energy on the power system. However, the application of the DL technique brings novel cyberthreats to the renewable energy forecast, and its cybersecurity has not received enough attention in previous literatures. To fill the gap, the vulnerability of renewable energy forecasting is, among the first, studied in-depth in this article. First, a novel cyberattack named adversarial learning attack (ALA) is proposed. The ALA is achieved by tampering with the meteorological data obtained by online weather forecasts from external application programming interfaces to undermine the renewable energy forecasting performance, which jeopardizes the power system operation. Then, an iterative algorithm is proposed to solve the ALA-based optimization problem. As the DL model is involved as optimization constraints, the optimization problem is nonconvex and NP-hard, which is unable to be solved by traditional approaches. The proposed algorithm utilizes the proximal gradient descent principle and is effective in iteratively exploring the near-optimal solution. At last, the impact of the ALA strategy on the power system operation is assessed, which considers the economic loss incurred and the potential hazards. The feasibility and efficacy of the ALA strategy are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus benchmarks. The simulation results reveal that the ALA is able to impose severe economic losses on the operation and even induces disastrous hazards, such as power system collapse.

In this article, a multiple time-scale voltage regulation is proposed based on the three-level coordinated control approach. This approach aims at solving the voltage issues in different scopes of space-time for active distribution networks. First, to address the global voltage issue of the entire network on a relatively slow-time scale, a multimode switching control is designed based on the Petri-net, which can effectively switch the on-load tap changer to extend its service life. Second, a multiobjective optimization considering the voltage differences with the security boundary and the transmission loss of the entire network is proposed. As such the global voltage issue can be handled by cooperating with the first-level switching control. Third, to solve the local voltage issue on a fast-time scale, a fully distributed optimal control integrating the active/reactive power sharing of all the distributed units is proposed. This control facilitates the control efficiency and active power consumption for renewable energy sources. Finally, the efficiency and effectiveness of the proposed method are validated under different scenarios in case studies.

Based on ABAQUS mechanical simulation software and different types of falling ball test jigs, this paper analyzes in detail the influence of different falling ball test jig on test results from the factor of the stress size of the display layer and the distribution of falling ball energy. The final conclusion is as follows: Increasing the gap between the AMOLED display module and the falling ball test jig can effectively reduce the force on the display layer and reduce the damage effect of the ball on the display layer.

The shared community energy storage system (CESS) can reduce energy storage costs by exploiting the complementarity of end-users and economies of scale. To further improve the economic feasibility of the CESS, we propose a novel business model and pricing method for the virtual storage rental service (VSRS). In this model, the rental users aim to minimize the electricity bill by renting the virtual capacity and optimizing its operation, while the CESS operator seeks to maximize the revenue from the combination of energy arbitrage and the VSRS. The pricing problem and the optimal operation problems of the CESS and users’ virtual batteries are modeled as a bi-level optimization problem. Next, the proposed problem is solved through transformer-based deep deterministic policy gradient (TDDPG) method and mixed-integer linear programming (MILP) due to the non-convexity and non-continuity of the original problem. The post-hoc interpretability of the policy network is provided based on the Shapley value to reveal the importance of different input features for decision-making. Numerical simulations suggest that the proposed VSRS could benefit the CESS operator and users. Moreover, the explanation based on the Shapley value could effectively generate an implicit solution for understanding the policy network.

An aggregation scheme is an effective transactive manner of Distributed Energy Resources (DER) spreading across distribution networks. Distributed approach locally achieves cost minimization of an aggregator and customers. The uncertainties of wholesale market price and rooftop PV output will impact on aggregator's scheduling decision and each customer's cost, while solar energy fluctuation can cause an overvoltage problem in distribution networks. However, the probability distributions of these uncertainties always have errors, even in emerging data-based methods. There is no stochastic method using real data with an out-of-sample guarantee suitable for this distributed approach so far to help an aggregator avoid price risk and manage customers’ energy against solar energy fluctuation. To address these unsolved issues, we propose a data-driven Wasserstein distributionally robust formulation of the aggregator's agent and customer's agent respectively. The Wasserstein metric is employed to construct the Wasserstein ambiguity set. The mathematical models are then reformulated equivalently to convex programming respectively so that the operating model can be solved by the off-the-shelf solver. To improve the efficiency of the distributed solving framework, an alternating optimization procedure (AOP) process is proposed to overcome the issue caused by binary variables in the alternating direction method of multipliers (ADMM). The proposed operation framework is verified on the modified IEEE 33-bus distribution network and realistic single-feeder LV network.

Designing customized dynamic pricing is a promising way to incent consumers to adjust their daily energy consumption behaviors. It helps manage flexible demand response resources on peak load. However, it is insufficiently investigated in previous studies from the individual behavior perspective. To tackle the gap, this paper proposes a graph deep learning-based retail dynamic pricing mechanism. First, a graph attention network-based temporal price elasticity perceptron model is proposed. It explores a novel path to learn price elasticity by using graph deep learning, and can accurately assess consumers’ energy consumption behaviors under different prices. Then, to avoid unfair evaluation of demand response, two indexes are proposed as auxiliary measures to assess energy consumption behavior learning models. At last, a customized dynamic pricing model based on the temporal price elasticity perceptron model is proposed. It can develop consumer’s time-varying demand response potential. This potential is first defined in this paper to measure what potentials of shifting/curtailing energy during a period a consumer has. By the pricing, the consumer could be incented to engage in demand response. The numerical studies validate the feasibility and superiority of the proposed methods, meanwhile price risks from the price change can be hedged effectively.

Dear Editor, This letter presents a novel and efficient adversarial robustness verification method for tree-based smart grid dynamic security assessment (DSA). Based on tree algorithms technique, the data-driven smart grid DSA has received significant research interests in recent years. However, the well-trained tree-based DSA models with high accuracy are always vulnerable caused by some physical noises or attacks, which can misclassify the DSA results. Only with the accuracy index is not enough to represent the performance of the tree-based DSA models. To provide formal robustness guarantee and select the trusted tree-based DSA models, this letter proposes an efficient adversarial robustness verification strategy with a sound robust index to quantify the ability of tree-based DSA models against any adversarial attack. Analysis results verifies the proposed strategy can achieve up to ∼564X speedup.

Second-life battery energy storage systems (SL-BESSs) have potential to be used as an economic and affordable energy storage solution for supporting a variety of applications, such as energy backup in residential sector. This article proposes a new capacity determination framework for SL-BESSs that are utilized in household environment. Despite the capital cost is significantly reduced, the degradation characteristics of retired batteries exhibits two-stage decline characteristics. The proposed framework first establishes the SL-BESSs model and estimates its remaining useful lifetime by identifying the “knee point” in the SL-BESSs capacity degradation curve; based on this, a two-level SL-BESS capacity determination model is formulated, which sizes the SL-BESS based on short-term home energy management under different scenarios. In particular, this article considers the dual-service modes of an SL-BESS in its capacity determination process: first, provide local energy supply to the household, and second, provide cloud energy storage service to external users. Extensive numerical simulations are conducted to validate the proposed framework.

Developing the deep learning (DL) technique is a promising way to enhance smart grid (SG) cybersecurity. However, previous DL methods require massive attack samples for cyberattack correlation learning, whilst the real-world SG is incapable of providing such a large dataset. Moreover, existing work commonly focuses on extracting temporal features from power grid data for cyberattack detection, while the spatial features are insufficiently investigated. To address these limitations, a spatiotemporal graph deep learning (STGDL)-based scheme is proposed to detect cyberattacks without requiring attack samples. First, the graph convolution and temporal gated convolution are orchestrated to extract spatiotemporal features jointly. Then, a quantile regression training strategy is adopted to give normally operational bounds of state variables in state estimation (SE). It gets rid of limitations on needing attack samples, and the state bounds can indicate cyberattack anomalies. At last, a super-resolution perception (SRP) network is proposed. The SRP network is able to reconstruct the high-frequent data of estimated states from low-frequent SE results, so as to improve the temporal learning ability in the STGDL model. The feasibility and effectiveness of the proposed scheme are validated by conducting comprehensive and extensive experiments on the IEEE 30-bus and 118-bus benchmarks.

Recently, severe wildfires occurred in some countries like Australia and U.S., and wildfires usually threaten the security of the electricity grids. Therefore, it is crucial to identify the transmission interconnections that have insufficient power transfer capacity and take preventative actions to prevent the overload problem. In this paper, we have proposed a graph theoretic-based network expansion and transportable energy storage system (TESS) planning strategy. Through graph-theoretic analysis, the critical asset and limiting critical cut-set can be identified. Two novel criteria, namely transfer margin ratio (TMR) and adequacy ratio (AR), are proposed in this paper to assess the vulnerability and the reliability of the network based on graph theory. To reduce the vulnerability and to enhance the reliability of the grid, network expansion and TESS planning are proposed as two feasible methods. Therefore, this paper presents a risk-based network expansion and TESS planning strategy, and the investment plans are assessed by TMR and AR to make a final decision. The proposed method is verified in the IEEE 30-bus and IEEE 118-bus systems. The simulation results reveal that a graph theoretic-based planning strategy saves the total cost and reduces the shed loads in contingency.

Enhanced by machine learning (ML) techniques, data-driven dynamic security assessment (DSA) in smart cyber-physical grids has attracted significant research interest in recent years. However, the current centralized ML architectures have limited scalability, are vulnerable to privacy exposure, and are costly to manage. To resolve these limitations, we propose a novel effective and secure distributed DSA method based on horizontal federated learning (HFL) and differential privacy (DP), namely EFedDSA. It leverages local system operating data to predict and estimate the system stability status and optimize the power systems in a decentralized fashion. In order to preserve the privacy of the distributed DSA operating data, EFedDSA incorporates Gaussian mechanism into DP. To reduce the computational burden from multiple transmission communication rounds, a discounting method for the total communication round is proposed to reduce the total transmission rounds. Theoretical analysis on the Gaussian mechanism of EFedDSA provides formal DP guarantees. Extensive experiments conducted on the New England 10-machine 39-bus testing system and the synthetic Illinois 49-machine 200-bus testing system demonstrate that the proposed EFedDSA method can achieve advantageous DSA performance with fewer communication rounds, while protecting the privacy of the local model information compared to the state of the art.

Purpose: Mechanical products usually require deterministic finite element analysis in the design phase to determine whether their structures meet the requirements. However, deterministic design ignores the influence of uncertainties in the design and manufacturing process of mechanical products, leading to the problem of a lack of design safety or excessive redundancy in the design. In order to improve the accuracy and rationality of the design results, a robust design method for structural reliability based on an active-learning marine predator algorithm (MPA)–backpropagation (BP) neural network is proposed. Design/methodology/approach: The MPA was used to obtain the optimal weights and thresholds of a BP neural network, and an active-learning function applicable to neural networks was proposed to efficiently improve the prediction performance of the BP neural network. On this basis, a robust optimization design method for mechanical product reliability based on the active-learning MPA-BP model was proposed. Random moving quadrilateral sampling was used to obtain the sample points required for training and testing of the neural network, and the reliability sensitivity corresponding to each sample point was calculated by subset simulated significant sampling (SSIS). The total mass of the mechanical product and the structural reliability sensitivity of the trained active-learning MPA-BP model output were taken as the optimization objectives, and a multi-objective reliability-robust optimization design model was constructed, which was solved by the second-generation non-dominated ranking genetic algorithm (NSGA-II). Then, the dominance function was used in the obtained Pareto solution set to make a dominance-seeking decision to obtain the final reliability-robust optimization design solution. The feasibility of the proposed method was verified by a reliability-robust optimization design example of the bogie frame. Findings: The prediction error of the active-learning MPA-BP neural network was smaller than those of the particle swarm optimization (PSO)-BP, marine predator algorithm (MPA)-BP and genetic algorithm (GA)-BP neural networks under the same basic parameter settings of the algorithm, which indicated that the improvement strategy proposed in this paper improved the prediction accuracy of the BP neural network. To ensure the reliability of the bogie frame, the reliability sensitivity and total mass of the bogie frame were reduced, which not only realized the lightweight design of the bogie frame, but also improved the reliability and robustness of the bogie. Originality/value: The MPA algorithm with a higher optimization efficiency was introduced to find the weights and thresholds of the BP neural network. A new active-learning function was proposed to improve the prediction accuracy of the MPA-BP neural network.

The spread of electric vehicles (EVs) could reduce greenhouse gas emissions and achieve sustainable travel patterns. However, the rapidly increasing charging demand will bring challenges to the operation of charging stations and power systems. Therefore, a two-stage EV management scheme is introduced in this article to overcome these challenges and promote sustainable transport. A charging dispatch model based on fuzzy multicriteria decision making is proposed in the first stage, where users' preferences are in the form of intuitionistic fuzzy sets to address the fuzziness and uncertainty of subjective factors and human judgment. A $\sigma$-cut similarity matrix is proposed to increase the users' satisfaction by excluding options with lower similarity. In the second stage, a noncooperative game model is proposed to incentive EVs to participate in supplementary frequency regulation (SFR). A fuzzy set is employed to reflect users' willingness to adjust charging power. The existence and uniqueness of the Nash equilibrium are investigated. Moreover, a distributed proximal best response algorithm with linear convergence is employed to find Nash equilibrium. Numerical simulations indicate that the proposed method can reduce charging costs while meeting users' preferences and facilitate EVs to participate in SFR.

The thermal flexibility of buildings in integrated energy systems (IES) has great potential to improve the operational economy and wind power consumption. To incentivize the thermal flexibility of buildings under uncertainties, we propose a robust nodal pricing (RNP) model for thermal loads based on the Stackelberg game approach. The RNP model adopts a bilevel framework in which the IES operator plays the leader at the upper level while the thermal load aggregators (TLA) play the followers at the lower level. The IES operator problem optimizes the heat prices and dispatch plan, which is modeled as a two-stage robust optimization problem to address the uncertainties in the renewables and the loads. The TLA problem optimizes the thermal loads of buildings to minimize the energy cost and thermal comfort loss, which is modeled as a distributionally robust chance-constrained optimization problem to address the uncertainty in outdoor temperature. Then, we convert the TLA model into deterministic quadratic programming and prove that Slater’s condition holds under a mild assumption. Using it, the TLA model is equivalently converted into Karush-Kuhn-Tucker conditions, leading to a reformulation of a classical two-stage robust optimization model for the RNP model. Case studies compare different pricing methods and verify the superiority of the proposed method.

Due to large thermal inertia of buildings and flexibility of interruptible loads, smart buildings pose a remarkable potential for developing virtual energy storage systems (VESSs). However, current literature lacks advanced models to quantify and thus properly optimize available capacity of VESS for power system ancillary services, especially frequency regulation services (FRS). This paper, firstly, presents a novel probabilistic model for explicitly quantifying the VESS capacity in charging and discharging modes, which can further be optimized by scheduling building loads. While the optimized capacity is fully available for FRS, building customer economic benefit and comfort could considerably be impaired by various uncertainties including weather and user related parameters, as well as FRS requests. Due to limited information of the uncertainties, common model-based stochastic and robust optimization approaches are inefficient, leading to either poor out-of-sample performance or over-conservative solutions. Thus, secondly, this paper develops a data-driven distributionally robust optimization (DRO) method to robustly optimize the capacity of VESS against the worst distribution of the uncertainties with a finite training dataset. The proposed data-driven DRO-based model can well match modern data-driven operation environment. Numerical simulations validate the high efficiency and out-of-sample performance of the proposed VESS capacity optimization method under the uncertainties.

Anomaly detection (AD) is of great importance to wind turbine (WT) prognostic health management systems. With the rising application of deep learning (DL), both deep regression- and deep reconstruction-based normal behavior modeling (NBM) methods have shown great promise for WT AD. However, massive missing and false alarms still could be witnessed in existing methods due to the lack of effective mining of interdependent relationships between normal and anomalies. Hence, this article proposes a generalized Siamese NBM scheme that can be applied to most existing backbones. By considering fault samples, a parameter-shared backbone and two auxiliary regularization terms are designed to explore characteristics between anomaly and normal. To alleviate the dependence on manual annotation for fault instances, a density-based clustering algorithm is adopted for the predefined outliers chosen. Furthermore, to enhance the trustworthiness of the proposed scheme, we implement a label correction based on temporal neighbor consistency. The experimental results show that the proposed Siamese NBM scheme improves state-of-the-art studies greatly. The outliers filtered by clustering can work as manually labeled samples without large fluctuation, also providing discriminative information. The label correction method can not only improve the reliability of the proposed Siamese NBM scheme but also the comparative deep methods, especially for those weak anomaly detectors.

The magnetization of a quantum magnet can be pinned at a fraction of its saturated value by collective effects. One example of such a plateau phase is found in spin-1/2 triangular-lattice antiferromagnets. They feature strong geometrical frustration and the plateau phase therein is often interpreted as arising from an order-by-disorder mechanism driven by quantum fluctuations. Here we observe a one-third magnetization plateau under an applied magnetic field in the spin-1 antiferromagnet Na3Ni2BiO6with a honeycomb lattice, which, with conventional magnetic interactions, would not be geometrically frustrated. Based on our elastic neutron scattering measurements, we propose the spin structure of the plateau phase to be an unusual partial spin-flop ferrimagnetic order. Our theoretical calculations indicate that bond-anisotropic Kitaev interactions are the source of frustration that produces the plateau. These results suggest that Kitaev interactions provide a different route to frustration and phases driven by quantum fluctuations in high-spin magnets.

In the transition to a two-sided electricity market, energy users are turning into prosumers who own the flexible distributed energy resources (DERs) and have the potential to provide services to the power system. Virtual power plants (VPPs) aggregate DERs to join the electricity market and respond to system signals. It is urgent to develop a new pricing mechanism for VPPs to allocate the payoff from the electricity market to prosumers. This paper proposes a customized rebate package pricing mechanism for a VPP retailer to reward prosumers for supporting the power system. The retailer’s pricing strategies are determined based on a Stackelberg game, considering the heterogeneous prosumers’ dynamic selecting process based on an evolutionary game. The extended replicator dynamics is proposed to take the future payoff into account and guarantee the evolutionary equilibrium. Moreover, a new reinforcement learning algorithm based on the Cross learning model is developed to solve the evolutionary game with less computational effort. The simulation results verify the effectiveness of the proposed customized rebate package pricing mechanism, which can efficiently reward prosumers’ flexible resources in supporting the system while maximizing the retailer’s utility to achieve a win-win outcome.

A modern electricity market is essentially a complex network, characterized by complicated interactions among cyber communications, physical systems, and social agents. Trading behavior modeling has always been complicated in the physical system-based market. In this paper, trading behavior modeling in the electricity market is solved by a data-driven method combining experimental economics and machine learning, called Hybrid Experimental Learning (HEL). Based on the historical and experiment simulated data, HEL models the trading behavior by a machine learning generative model which will be interpreted by a post hoc interpretation approach. Taking a simulated electricity market based on the trial spot market rule in Guangdong, China as an example, a generative adversarial network (GAN) is employed to generate the offering strategies of a gas generator. Local interpretable model-agnostic explanation (LIME) as a post hoc interpretation approach is applied to explain the relationship between the output of GAN and some of the inputs of HEL, which can be described as offering mechanisms for the gas generator.

The local market can promote more self-generated electricity consumed locally. However, the unstable and intermittent generation of renewable energy sources brings uncertainty and overbidding risk to the local market. We proposed a competitive local energy market (LEM) where renewable energy aggregators (REAs) can submit bids instead of acting as price takers. A data-driven joint chance-constrained game of REAs is proposed for the optimal bidding problem under the network constraints and the risk-averse chance constraint. We consider the case that the maximum outputs of REAs are random variables whose probability distributions are unknown, but the decision-maker has access to finite samples. Each player’s strategy set is defined in a data-driven distributionally robust framework with the Wasserstein ambiguity set. The exact reformulation of the distributionally robust chance constraint and the corresponding inner approximation version are derived. We prove the existence of the Nash equilibrium in the proposed distributionally robust chance-constrained game. Finally, an efficient best response seeking algorithm is developed to find the normalized Nash equilibrium, and the convergence proof of the proposed algorithm is also provided. Numerical simulations demonstrate that the proposed game model can effectively increase the revenue of reliable players and reduce the overbidding rate of REAs.

A direct production method of high-strength titanium alloy from upgraded titania slag (UGS) was developed. First, UGS was reduced into alloy powder with 1.3 wt.% oxygen using magnesium powder. Subsequently, the alloy powder was compacted at 600 MPa to produce a pellet. Lastly, the pellet was sintered to produce titanium alloy. The reduced powder was characterized, and the effect arising from the sintering temperature on the sintered density, compressive strength, microstructure, and hardness of the alloys was studied. The results showed that the density tended to increase with the temperature increasing from 900 to 1200 °C, whereas the porosity decreased. A significant sintering densification was achieved at the temperature above 1100 °C (98.65% at 1100 °C and 99.41% at 1200 °C). At 1100 °C, the hardness and compressive strength reached the maximal values of the alloy, HV 655.7 and 1563 MPa, respectively.

Lung cancer is the most common malignancy and the leading cause of cancer death worldwide, and lung adenocarcinoma (LUAD) is the most prevalent subtype. Considering the emergence of resistance to therapies, it is urgent to develop more effective therapies to improve the prognosis. Here we reported that pancreatic progenitor cell differentiation and proliferation factor (PPDPF) deficiency inhibited LUAD development both in vitro and in vivo. Mechanistically, PPDPF induces hyperactive STAT3 by interfering STAT3-PTPN1 interaction. Activated STAT3 promoted BMPR2 transcription, which further inhibited apoptosis. Moreover, PPDPF reduced NK cell infiltration and activation to develop an immunosuppressive microenvironment, which was also mediated by STAT3. Furthermore, we identified that the expression of PPDPF was positively correlated with the malignant features of LUAD, as well as BMPR2 and p-STAT3 level in clinical samples. Therefore, our study suggests that PPDPF positively regulates BMPR2 expression and facilitates immune escape via regulating STAT3 activity, providing a potential therapy target for LUAD.

A high-purity Ti–48Al–2Nb–2Cr alloy powder with an oxygen content as low as 0.0572 wt.% and a particle size of <150 μm was produced from a mixture of TiO2, Al2O3, Nb2O5, and Cr2O3powders through reduction with magnesium and deoxidation with calcium. The phase and composition of the products were analyzed. The final product mainly included γ-TiAl and minor α2-Ti3Al phases, and Ti, Al, Cr, and Nb were homogenously distributed in the powder with a mole ratio of 49.73:43.51:2.05:1.98. The reduction and deoxidation mechanisms were investigated by thermodynamic modeling using the HSC Chemistry software and Pandat software based on the Ti alloy database.

The ultrahard X-ray multifunctional application beamline (BL12SW) is a phase-II beamline project at the Shanghai Synchrotron Radiation Facility. The primary X-ray techniques used at the beamline are high-energy X-ray diffraction and imaging using white and monochromatic light. The main scientific objectives of ultrahard X-ray beamlines are focused on two research areas. One is the study of the structural properties of Earth’s interior and new materials under extreme high-temperature and high-pressure conditions, and the other is the characterization of materials and processes in near-real service environments. The beamline utilizes a superconducting wiggler as the light source, with two diamond windows and SiC discs to filter out low-energy light (primarily below 30 keV) and a Cu filter assembly to control the thermal load entering the subsequent optical components. The beamline is equipped with dual monochromators. The first was a meridional bending Laue monochromator cooled by liquid nitrogen, achieving a full-energy coverage of 30–162 keV. The second was a sagittal bending Laue monochromator installed in an external building, providing a focused beam in the horizontal direction with an energy range of 60–120 keV. There were four experimental hutches: two large-volume press experimental hutches (LVP1 and LVP2) and two engineering material experimental hutches (ENG1 and ENG2). Each hutch was equipped with various near-real service conditions to satisfy different requirements. For example, LVP1 and LVP2 were equipped with a 200-ton DDIA press and a 2000-ton dual-mode (DDIA and Kawai) press, respectively. ENG1 and ENG2 provide in situ tensile, creep, and fatigue tests as well as high-temperature conditions. Since June 2023, the BL12SW has been in trial operation. It is expected to officially open to users by early 2024.

Bladder cancer (BLCA) is the most common malignant tumor of the urinary system and is characterized by high metastatic rates and poor prognosis. The expression of tight junction protein 1 (TJP1) is associated with bladder cancer invasion; however, the mechanism by which TJP1 affects vasculature remodeling remains unknown. In this study, we found that TJP1 expression correlated with tumor angiogenesis and poor overall survival in clinical samples. Furthermore, TJP1 overexpression promoted tumor angiogenesis in BLCA cells and stimulated recruitment of macrophages to tumors by upregulating CCL2 expression. Mechanistically, TJP1 interacted with TWIST1 and enhanced the transcriptional activity of CCL2. The impairment of tumor angiogenesis caused by knockdown of TJP1 was dramatically rescued by overexpression of TWIST1. Furthermore, TJP1 recruited USP2, which deubiquitinated TWIST1, thereby protecting TWIST1 from proteasome-mediated protein degradation. In conclusion, our results suggest that TJP1 controls angiogenesis in BLCA via TWIST1-dependent regulation of CCL2. We demonstrate that TJP1 functions as a scaffold for the interaction between USP2 and TWIST1 and this may provide potential therapeutic targets in bladder cancer.

Perfect channel state information (CSI) is usually required when considering relay selection and power allocation in cooperative communication. However, it is difficult to get an accurate CSI in practical situations. In this letter, we study the outage probability minimizing problem based on optimizing relay selection and transmission power. We propose a prioritized experience replay aided deep deterministic policy gradient learning framework, which can find an optimal solution by dealing with continuous action space, without any prior knowledge of CSI. Simulation results reveal that our approach outperforms reinforcement learning based methods in existing literatures, and improves the communication success rate by about 5%.

Drainage dysfunction of stenotic internal jugular veins (IJVs) is considered an etiology of cough headache as a result of fluctuation and imbalance of intracranial pressure.

Clinical Practice Guideline on Acupuncture and Moxibustion: Migraine (WFAS 007.9-2023)is a clinical practice guideline officially released by the World Federation of Acupuncture-Moxibustion Societies (WFAS) on October 9, 2023, and is the first international guideline on the treatment of migraine with acupuncture. This international standard was developed under the guidance of rigorous evidence-based methodology, and it contains guideline purpose, scope, applicable population, applicable settings, overview of acupuncture for migraine, guideline development process and recommendations. For promoting the understanding and application of this guideline, this article summarizes a total of 18 recommendations in order to assist clinical decisions for migraine with acupuncture.

In this paper, a safety-guaranteed game-theoretical velocity planning framework in a hierarchical manner is proposed to generate safe, ride comfort, and travel efficiency-balanced velocity for autonomous vehicles (AVs). In the upper layer, a bang-bang decision-making method is utilized to determine which planning mode to be implemented based on acceleration and jerk constraints, including a comfort mode, an efficiency mode, and a game mode. In the lower layer, asymmetric jerk limits based on comfort characteristics sensibility analysis and safe velocity simultaneously considering longitudinal and lateral stability are firstly developed to maintain ride comfort and driving safety, respectively on curve roads, especially sharp curves where vehicle stability may be not fully considered in most researches. Based on these, a non-cooperative game-theoretical velocity planning method is presented to solve the conflict between comfort mode and efficiency mode by optimizing his own objective based on the other’s action. Finally, for the sake of solving efficiency and accuracy, a chaos optimization-based algorithm (COA) is designed to solve for the Stackelberg equilibrium solution of the bilevel game optimization problem. Three experimental tests are carried out to comprehensively demonstrate the effectiveness, robustness, and real time of the proposed framework. The results show that the proposed method can provide the great performance of ride comfort, travel efficiency, and longitudinal-lateral stability in real time in the velocity planning process.