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The increasing complexity of smart grid (SG) systems necessitates advanced methodologies to ensure their stability and reliability. In this work, we propose a novel online learning framework that leverages the Bee Algorithm for Ensemble Learning (BAEL) with dynamic perturbations to enhance the adaptability and performance of ML models in SG stability prediction. The key contributions of our approach are twofold. First, we introduce a dynamic perturbations mechanism that systematically adjusts variations within the Bee Algorithm, effectively balancing global exploration speed and local convergence accuracy throughout the learning process. Second, we integrate the BAEL strategy, where model selection and evolution are guided by performance-driven ensemble learning, allowing continuous adaptation to evolving data patterns. Through iterative learning cycles augmented by incremental perturbation adjustments, our method significantly improves predictive accuracy. To evaluate the effectiveness of our approach, we conduct extensive experimental assessments, demonstrating that our online learning process achieves an F1-score close to 100 percent. Additionally, we perform a comparative analysis between the Bee Algorithm and a benchmark fusion model incorporating Random Forest (RF), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGB) classifiers, under identical conditions, including the presence of dynamic perturbations. The results confirm that our BAEL-based approach consistently outperforms both the fusion of these classifiers and each of them operating independently across all evaluation metrics, highlighting its robustness in predicting SG stability under dynamic perturbations.

Cyber attacks can cause cascading failures and blackouts in smart grids. Therefore, it is highly necessary to identify the types, impacts and solutions of cyber attacks to ensure the secure operation of power systems. As a well-known practice, steady-state analysis is commonly used to identify cyber attacks and provide effective solutions. However, it cannot fully cover non-linear behaviours and cascaded blackouts of the system caused by dynamic perturbations, as well as provide a post-disturbance operating point. This study presents a novel approach based on dynamic analysis that excludes the limitations of the steady-state analysis and can be used in the events of various cyber attacks. Four types of common attacks are reviewed, and their dynamic impacts are shown on the IEEE benchmark model of the Western System Coordinating Council system implemented in MATLAB Simulink. Then, recommendations are provided to enhance the security of the future smart power grids from the possible cyber attacks.

Background Ligand–protein interactions play a key role in defining protein function, and detecting natural ligands for a given protein is thus a very important bioengineering task. In particular, with the rapid development of AI-based structure prediction algorithms, batch structural models with high reliability and accuracy can be obtained at low cost, giving rise to the urgent requirement for the prediction of natural ligands based on protein structures. In recent years, although several structure-based methods have been developed to predict ligand-binding pockets and ligand-binding sites, accurate and rapid methods are still lacking, especially for the prediction of ligand-binding regions and the spatial extension of ligands in the pockets. Results In this paper, we proposed a multilayer dynamics perturbation analysis (MDPA) method for predicting ligand-binding regions based solely on protein structure, which is an extended version of our previously developed fast dynamic perturbation analysis (FDPA) method. In MDPA/FDPA, ligand binding tends to occur in regions that cause large changes in protein conformational dynamics. MDPA, examined using a standard validation dataset of ligand-protein complexes, yielded an averaged ligand-binding site prediction Matthews coefficient of 0.40, with a prediction precision of at least 50% for 71% of the cases. In particular, for 80% of the cases, the predicted ligand-binding region overlaps the natural ligand by at least 50%. The method was also compared with other state-of-the-art structure-based methods. Conclusions MDPA is a structure-based method to detect ligand-binding regions on protein surface. Our calculations suggested that a range of spaces inside the protein pockets has subtle interactions with the protein, which can significantly impact on the overall dynamics of the protein. This work provides a valuable tool as a starting point upon which further docking and analysis methods can be used for natural ligand detection in protein functional annotation. The source code of MDPA method is freely available at: https://github.com/mingdengming/mdpa .

Query optimization is one of the key factors affecting the performance of database systems that aim to enact the query execution plan with minimum cost. Particularly in distributed database systems, due to the multiple copies of the data that are stored in different data nodes, resulting in the dramatic increase in the feasible query execution plans for a query statement. Because of the increasing volume of stored data, the cluster size of distributed databases also increases, resulting in poor performance of current query optimization algorithms. In this case, a dynamic perturbation-based artificial bee colony algorithm is proposed to solve the query optimization problem in distributed database systems. The improved artificial bee colony algorithm improves the global search capability by combining the selection, crossover, and mutation operators of the genetic algorithm to overcome the problem of falling into the local optimal solution easily. At the same time, the dynamic perturbation factor is introduced so that the algorithm parameters can be dynamically varied along with the process of iteration as well as the convergence degree of the whole population to improve the convergence efficiency of the algorithm. Finally, comparative experiments conducted to assess the average execution cost of Top-k query plans generated by the algorithms and the convergence speed of algorithms under the conditions of query statements in six different dimension sets. The results demonstrate that the Top-k query plans generated by the proposed method have a lower execution cost and a faster convergence speed, which can effectively improve the query efficiency. However, this method requires more execution time.

An interpretation is proposed for the dependence of the melting temperatures of an entire subclass of alkali metal halides on the chemical composition, based on an analysis of changes in different contributions to the internal energy of salts in the molten and crystalline phases with variation in the sum of the radii of their cations and anions. The expression for calculating the energy of liquid salt melts includes the contribution from charge–dipole interactions between ions, which is considered in a work based on thermodynamic perturbation theory with a basis in the form of a model of charged hard spheres. The Born–Mayer formula is used for the energy of the crystalline phase in the electrostatic part, while Debye’s formula is employed to consider the contribution from vibrations. An explanation is given for the lower values of the reduced melting temperatures of lithium and sodium halides, relative to other salts. It is shown that deviations of the reduced melting temperatures of lithium and sodium halides depending on the sum of ionic radii can be explained by Coulomb and translational contributions to the energy in the molten state, along with Madelung and Born contributions in the crystalline phase.

Most of chaos-based cryptosystems utilize stationary dynamics of chaos for the permutation and diffusion, and many of those are successfully attacked. In this paper, novel models of the image permutation and diffusion are proposed, in which chaotic map is perturbed at bit level on state variables, on control parameters or on both. Amounts of perturbation are initially the coordinate of pixels in the permutation, the value of ciphered word in the diffusion, and then a value extracted from state variables in every iteration. Under the persistent perturbation, dynamics of chaotic map is nonstationary and dependent on the image content. The simulation results and analyses demonstrate the effectiveness of the proposed models by means of the good statistical properties of transformed image obtained after just only a single round.

This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against dynamic graph perturbations using robust spatial-temporal self-attentions to learn dynamic graph embeddings. In the RSTGNN model training, a selective spatial self-attention technique is employed to aggregate neighboring information based on projected node similarity, which reduces attention weights of edges with less similarity, enabling better information aggregation and preventing the model from ignoring spatial-temporal information. The temporal self-attention layer in the RSTGNN model intensifies temporal patterns using time-span-limited temporal attention weights. Additionally, the model uses a spatial-temporal loss function that penalizes nodes and edges most likely perturbed to alleviate the influence of dynamic graph perturbation. Specifically, the spatial loss focuses on attention weights associated with high-degree and potentially-attacked nodes, while the temporal loss targets attention weights of high centrality-varied nodes to prevent nodes from experiencing excessive centrality changes. To verify the effectiveness of our approach, we evaluate RSTGNN compared with other graph-based models under different node-based or edge-based perturbation rates. Results demonstrate that RSTGNN maintains high effectiveness in dynamic node classification and link prediction for five real dynamic graph datasets.

A model is proposed to describe liquid–crystal phase equilibria in order to calculate the melting temperatures of ionic compounds. The dependence of the melting temperatures of alkali metal halides on the cation–anion composition of a salt can be described in terms of ionic radii and polarizabilities when the thermodynamic perturbation theory is used for a molten phase. For the chemical potential of a crystalline phase, the Born–Mayer formulas for electrostatic energy and the Debye formula for taking into account the contribution of vibrations are used. The complete system of equations describing the liquid–solid equilibrium includes not only the equality of chemical potentials, but also self-consistency using an equation of state for calculating the equilibrium melt density at the solidification point. Another equation of the system is derived using the mean spherical model of an ion mixture for self-consistent finding of characteristic Blum’s screening parameter. On this basis, the melting temperatures of fluorides, chlorides, bromides, and iodides of lithium, sodium, potassium, rubidium, and cesium are calculated. A combination of the model of charged hard spheres of different diameters, which is taken as a reference in the mean spherical approximation, and the first correction due to the ion dipoles induced by the point charge of another ion to the chemical potential of a liquid salt is shown to be a good basis for quantitative agreement with the experimental data on the melting temperatures within a few percent. In addition, we also discusses the laws of changing the melting temperature reduced to the Coulomb energy at the minimum cation–anion distance and its dependence on the difference in the ionic radii of salts.

An equality has recently been shown relating the free energy difference between two equilibrium ensembles of a system and an ensemble average of the work required to switch between these two configurations. In the present paper it is shown that this result can be derived under the assumption that the system's dynamics is Markovian and microscopically reversible.

Identification of cracks in beam-type components is significant to ensure the safety of structures. Among the approaches relying on mode shapes, the concept of transverse pseudo-force (TPF) has been well proved for single and multiple crack identification in beams made of isotropic materials; however, there is a noticeable gap between the concept of TPF and its applications in composite laminated beams. To fill this gap, an enhanced TPF approach that relies on perturbation to dynamic equilibrium is proposed for the identification of multiple cracks in composite laminated beams. Starting from the transverse equation of motion, this study formulates the TPF in a composite laminated beam for the identification of multiple cracks. The capability of the approach is numerically verified using the FE method. The applicability of the approach is experimentally validated on a carbon fiber-reinforced polymer laminated beam with three cracks, the mode shapes of which are acquired through non-contact vibration measurement using a scanning laser vibrometer. In particular, a statistic manner is utilized to enable the approach to be feasible to real scenarios in the absence of material and structural information; besides, an integrating scheme is utilized to enable the approach to be capable of identifying cracks even in the vicinity of nodes of mode shapes.