Search Results (3,004)

The implementation of community power generation technology not only increases the flexibility of electricity use but also improves the power system’s load distribution, increases the overall system efficiency, and optimizes energy allocation. This article first outlines the operational context of the system and analyzes the roles and missions of the various participants. Subsequently, optimization models are developed for microgrid operators, community power storage facility service providers and load aggregators. Next, the paper explores the game relationship between microgrid operators and load aggregators, proposing a model based on the Stackelberg game theory and proving the presence and singularity of the Stackelberg equilibrium solution. Finally, simulations are conducted using Yalmip tools and the CPLEX solution on the MATLAB R2023a software platform. A combination of heuristic algorithms and solver methods is employed to optimize the strategies of microgrid operators and load aggregators. The research findings show that the proposed framework is not only able to achieve an effective balance of interests between microgrid operators and load aggregators but also creates a win–win situation between load aggregators and shared energy storage operators. Additionally, the solution algorithms used ensure the protection of data privacy. Full article

The brain is a biological system comprising nerve cells and orchestrates its embodied agent’s perception, behavior, and learning in dynamic environments. The free-energy principle (FEP) advocated by Karl Friston explicates the local, recurrent, and self-supervised cognitive dynamics of the brain’s higher-order functions. In this study, we continue to refine the FEP through a physics-guided formulation; specifically, we apply our theory to synaptic learning by considering it an inference problem under the FEP and derive the governing equations, called Bayesian mechanics. Our study uncovers how the brain infers weight changes and postsynaptic activity, conditioned on the presynaptic input, by deploying generative models of the likelihood and prior belief. Consequently, we exemplify the synaptic efficacy in the brain with a simple model; in particular, we illustrate that the brain organizes an optimal trajectory in neural phase space during synaptic learning in continuous time, which variationally minimizes synaptic surprisal. Full article

More than 50 years ago, Rufus Bowen noticed a natural relation between the ergodic theory and the dimension theory of dynamical systems. He proved a formula, known today as the Bowen’s formula, that relates the Hausdorff dimension of a conformal repeller to the zero of a pressure function defined by a single conformal map. In this paper, we extend the result of Bowen to a sequence of conformal maps. We present a dynamical solenoid, i.e., a generalized dynamical system obtained by backward compositions of a sequence of continuous surjections ${(f_n:X\to X)}_{n\in \mathbb{N}}$ defined on a compact metric space $(X,d).$ Under mild assumptions, we provide a self-contained proof that Bowen’s formula holds for dynamical conformal solenoids. As a corollary, we obtain that the Bowen’s formula holds for a conformal surjection $f:X\to X$ of a compact Full article

The widespread use of machine and deep learning algorithms for anomaly detection has created a critical need for robust explanations that can identify the features contributing to anomalies. However, effective evaluation methodologies for anomaly explanations are currently lacking, especially those that compare the explanations against the true underlying causes, or ground truth. This paper aims to address this gap by introducing a rigorous, ground-truth-based framework for evaluating anomaly explanation methods, which enables the assessment of explanation correctness and robustness—key factors for actionable insights in anomaly detection. To achieve this, we present an innovative benchmark dataset of digital circuit truth tables with model-based anomalies, accompanied by local ground truth explanations. These explanations were generated using a novel algorithm designed to accurately identify influential features within each anomaly. Additionally, we propose an evaluation methodology based on correctness and robustness metrics, specifically tailored to quantify the reliability of anomaly explanations. This dataset and evaluation framework are publicly available to facilitate further research and standardize evaluation practices. Our experiments demonstrate the utility of this dataset and methodology by evaluating common model-agnostic explanation methods in an anomaly detection context. The results highlight the importance of ground-truth-based evaluation for reliable and interpretable anomaly explanations, advancing both theory and practical applications in explainable AI. This work establishes a foundation for rigorous, evidence-based assessments of anomaly explanations, fostering greater transparency and trust in AI-driven anomaly detection systems. Full article

This study employed chaotic systems as an innovative approach for shellcode obfuscation to evade current antivirus detection methods. Standard AV solutions primarily rely on static signatures and heuristic analysis to identify malicious code. However, chaotic systems employ dynamic and unpredictable encryption methods, significantly obstructing detection efforts. The utilization of various chaotic maps for shellcode encryption facilitates the generation of multiple unique variations from the same functional code, each exhibiting distinct unpredictability due to the inherent nonlinearity and sensitivity of chaotic systems to initial conditions. The unpredictability of these situations poses a considerable challenge for antivirus software in recognizing consistent patterns, resulting in decreased detection rates. The findings from our experiments demonstrate that chaos-driven encryption methods significantly outperform traditional encryption techniques in terms of evading detection. This paper emphasizes the potential of chaos theory to enhance malware evasion strategies, offering a sophisticated approach to bypassing modern antivirus protections while ensuring the effectiveness of malicious payloads. Full article

Nanowire heterostructures offer almost unlimited possibilities for the bandgap engineering and monolithic integration of III–V photonics with Si electronics. The growth and compositional modelling of III–V nanowire heterostructures provides new insight into the formation mechanisms and assists in the suppression of interfacial broadening and optimization of optical properties. Different models have been proposed in the past decade to calculate the interfacial profiles in axial nanowire heterostructures mainly grown by molecular beam epitaxy and metal–organic vapour phase epitaxy. Based on various assumptions, existing models have different sets of parameters and can yield varying results and conclusions. By focusing on deterministic models based on classical nucleation theory and kinetic growth theory of III–V ternary monolayers in nanowires, we summarize recent advancements in the modelling of axial heterostructures in III–V nanowires, describe and classify the existing models, and determine their applicability to predictive modelling and to the fitting of the available experimental data. In particular, we consider the coordinate-dependent generalizations of the equilibrium, nucleation-limited, kinetic, and regular growth models to make interfacial profiles across axial heterostructures in different III–V nanowires. We examine the factors influencing the interfacial abruptness, discuss the governing parameters, limitations, and modelling of particular material systems, and highlight the areas that require further research. Full article

Quantum deformations offer valuable perspectives into quantum mechanics, particularly by advancing our understanding of symmetry and algebraic structures.The Dunkl oscillator, which integrates Dunkl operators into the harmonic oscillator framework, advances the system’s algebraic properties and opens new approaches for exploring quantum phenomena. Supersymmetric quantum mechanics (SSQM) unifies bosonic and fermionic aspects and facilitates the construction of solvable models using generalized Dunkl operators. This paper introduces a new approach to the Dunkl oscillator, employing a complex reflection operator to deepen the understanding of its connection to Hermite polynomials on radial lines. The results offer new perspectives on the Dunkl oscillator’s algebraic structure and its relevance to SSQM and quantum deformation theory, expanding the potential for discovering solvable quantum models. Full article

In this study, a finite-time disturbance observer (FTDOB) with a new structure is originally put forward for the motion tracking problem of a class of nonlinear systems subject to model uncertainties and exogenous disturbances. Compared to existing disturbance estimator designs in the literature, in which the estimation error only converges to the origin asymptotically under assumptions that the first and/or second derivatives are vanishing, the suggested DOB is able to estimate the disturbance exactly in finite time. Firstly, uncertainties (parametric and unstructured uncertainties), unknown dynamics, and external disturbances in system dynamics are lumped into a generalized disturbance term that is subsequently estimated by the proposed DOB. Based on this, a DOB-based backstepping controller is synthesized to ensure high-accuracy tracking performance under various working conditions. The stability analysis of not only the DOB but also the overall closed-loop system is theoretically confirmed by the Lyapunov stability theory. Finally, the advantages of the proposed FTDOB and the FTDOB-based controller over other DOBs and existing DOB-based controllers are explicitly simultaneously demonstrated by a series of numerical simulations on a second-order mechanical system and comparative experiments on an actual DC motor system. Full article

In this article, we present the mathematical foundations of generative machine intelligence and link them with mean-field-type game theory. The key interaction mechanism is self-attention, which exhibits aggregative properties similar to those found in mean-field-type game theory. It is not necessary to have an infinite number of neural units to handle mean-field-type terms. For instance, the variance reduction in error within generative machine intelligence is a mean-field-type problem and does not involve an infinite number of decision-makers. Based on this insight, we construct mean-field-type transformers that operate on data that are not necessarily identically distributed and evolve over several layers using mean-field-type transition kernels. We demonstrate that the outcomes of these mean-field-type transformers correspond exactly to the mean-field-type equilibria of a hierarchical mean-field-type game. Due to the non-convexity of the operators’ composition, gradient-based methods alone are insufficient. To distinguish a global minimum from other extrema—such as local minima, local maxima, global maxima, and saddle points—alternative methods that exploit hidden convexities of anti-derivatives of activation functions are required. We also discuss the integration of blockchain technologies into machine intelligence, facilitating an incentive design loop for all contributors and enabling blockchain token economics for each system participant. This feature is especially relevant to ensuring the integrity of factual data, legislative information, medical records, and scientifically published references that should remain immutable after the application of generative machine intelligence. Full article

In the present study, we introduce the two-dimensional Chlodovsky-type Bernstein operators based on the $(p,q)$-integer. By leveraging the inherent symmetry properties of $(p,q)$-integers, we examine the approximation properties of our new operator with the help of a Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence utilizing the modulus of continuity and the Lipschitz-type maximal function. Additionally, a Voronovskaja-type theorem is provided for these operators. We also investigate the weighted approximation properties and estimate the rate of convergence in the same space. Finally, illustrative graphics generated with Maple demonstrate the convergence rate of these operators to certain functions. The optimization of approximation speeds by these symmetric operators during system control provides significant improvements in stability and performance. Consequently, the control and modeling of dynamic systems become more efficient and effective through these symmetry-oriented innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications within these areas. Full article

Data-driven machine learning approaches with precise predictive capabilities are proposed to address the long-standing challenges in the calculation of complex many-electron atomic systems, including high computational costs and limited accuracy. In this work, we develop a general workflow for machine learning-assisted atomic structure calculations based on the Cowan code’s Hartree–Fock with relativistic corrections (HFR) theory. The workflow incorporates enhanced ElasticNet and XGBoost algorithms, refined using entropy weight methodology to optimize performance. This semi-empirical framework is applied to calculate and analyze the excited state energy levels of the 4f closed-shell Yb I atom, providing insights into the applicability of different algorithms under various conditions. The reliability and advantages of this innovative approach are demonstrated through comprehensive comparisons with ab initio calculations, experimental data, and other theoretical results. Full article

We present a review of the Semi-Symmetric Metric Gravity (SSMG) theory, representing a geometric extension of standard general relativity, based on a connection introduced by Friedmann and Schouten in 1924. The semi-symmetric connection is a connection that generalizes the Levi-Civita one by allowing for the presence of a simple form of the torsion, described in terms of a torsion vector. The Einstein field equations are postulated to have the same form as in standard general relativity, thus relating the Einstein tensor constructed with the help of the semi-symmetric connection, with the energy–momentum tensor tensor. The inclusion of the torsion contributions in the field equations has intriguing cosmological implications, particularly during the late-time evolution of the Universe. Presumably, these effects also dominate under high-energy conditions, and thus SSMG could potentially address unresolved issues in general relativity and cosmology, such as the initial singularity, inflation, or the ⁷Li problem of the Big-Bang Nucleosynthesis. The explicit presence of torsion in the field equations leads to the non-conservation of the energy–momentum tensor tensor, which can be interpreted within the irreversible thermodynamics of open systems as describing particle creation processes. We also review in detail the cosmological applications of the theory, and investigate the statistical tests for several models, by constraining the model parameters via comparison with several observational datasets. Full article

This article presents information about harmonic distortion and resonance in distribution networks. The theory behind harmonics and resonance is presented. Examples from the literature and the results of power quality measurements, as well as the authors’ experiences connected with significant changes in harmonic distortions, are presented. The harmonic resonance phenomenon is explained, and the risk of resonance in a distribution system network is highlighted. Attention is paid to the connection of a new power plant to the network; however, other risks, e.g., those connected to network reconfiguration, are also highlighted. Further simulation case studies are presented to show interactions between volt/VAR control and harmonic distortion. Simulations consider a few scenarios: impact of voltage change on impedance characteristics and resulting harmonics amplitudes, the impact of a capacitor on impedance characteristics, and the impact of network expansion on harmonic distortion. The final part presents alternative, low-cost harmonics mitigation methods. The concept of the utilization of phase-shifting transformers for two twin-type 1 MW plants located next to each other is verified by on-site measurement. The concept of adapting the harmonics spectrum of new devices to impedance characteristics is presented. Finally, an alternative concept for active mitigation of harmonics under resonance conditions is provided. The concept is based on the reactive power correction in order to change the harmonics phase shift. A comparison of harmonic mitigation methods and general recommendations are provided. Further research is outlined. Full article

Humans are well known to have made paintings and engravings on rock surfaces, both geometric motifs with an unclear representation, and representative motifs that refer to their activities and aspects of their environment. This kind of art is widespread across time and space and has throughout history been subjected to various kinds of approaches. Typically, rock art research focuses on its role in the development of the hominin brain and the capability of abstract thinking, as well as on interpreting representative and non-representative motifs. Ethnography and cognitive research have often stressed that rock art is the result of ritual practises and the expression of a shamanic belief system. However, representative motifs may also shed light on a region’s ecological and human prehistory. Here, we give an overview of the general development of rock art study: we highlight the development of artistic behaviour in humans by discussing aesthetic preferences, and the creation of simple geometric motifs and eventually representative motifs, before describing the theories that developed from the earliest study of rock art. These have largely focused on classification and interpretation of the motifs, and often centred on Palaeolithic material from Europe. We then move on to discuss how ethnography among rock art creating communities often suggests important relationships between specific animals in both the realms of spiritual belief systems and within the local environment. Lastly, we highlight how rock art reflects the local penecontemporaneous environment when it comes to depictions of animals, plants, technologies, humans and their activities. We argue that animal depictions are a useful subject to study on a large scale, as it is the most widespread representative motif, and the most appropriate subject to study when the goal is to draw conclusions on environmental changes. Rock art can fill gaps in the local archaeological record and generate new questions of it, but also offer new insights into the history of local human–animal interaction: animal species depicted and/or referred to in rock art are likely to have been a selection of spiritually important animals and a comparison to known information on human interactions with local species may reveal patterns among which animals are selected for local rock art depictions and which are not. Interregional comparison can in turn shed light on whether humans in general tend to ascribe meaning to the same types of animals. We end the review with suggestions for future study, with a special role for computational methods, which are suitable for the analysis of large databases of visual imagery. Full article

The actual hype around machine learning (ML) methods has pushed the old epistemic struggle between data-driven and theory-driven scientific styles well beyond the academic realm. The potential consequences of the widespread adoption of ML in scientific work have fueled a harsh debate between opponents predicting the decay of basic curiosity-driven science and enthusiasts hoping for the advent of a ‘theory-free’ objective science. In this work, I suggest how the system science style of reasoning could drastically de-potentiate this (sometimes deceptive) opposition through the generation of multi-purpose relational theoretical frames stemming from the network paradigm. The recognition of the virtual non-existence of purely ‘theoryfree’ approaches and the need for a careful balancing of theoretical and empirical contributions is the main claim of the present work. Full article