2025 August - 201 articles
Air pollution poses a threat to public health, ecosystem stability, and sustainable development. Accurate air quality prediction is essential for environmental protection and achieving sustainability. This study proposes a symmetry-driven hybrid fram...
As the global manufacturing industry’s transformation accelerates toward being intelligent, “unmanned”, and low-carbon, manufacturing workshops face conflicts between production schedules and transportation tasks, leading to low eff...
In this study, we propose a novel hybrid metaheuristic framework that leverages symmetry awareness to enhance the performance of multi-objective optimization in renewable energy systems. Motivated by the repetitive and structured patterns commonly fo...
With the rapid development of information technology and the deep integration of the Internet platform, the scale and form of financial transactions continue to grow and expand, significantly improving users’ payment experience and life efficie...
Partial shading conditions cause current mismatches between series-connected panels in photovoltaic (PV) arrays, significantly reducing power efficiency. To mitigate this limitation, reconfiguration methods based on dynamically changing the electrica...
This study explores the potential for reducing CO2 emissions in China’s express freight sector by promoting a modal shift from air and road transport to high-speed rail (HSR) through the implementation of a carbon tax policy. A bi-level optimiz...
We present a short review of the methodology and applications of the Simple Equations Method (SEsM) for obtaining exact solutions to nonlinear differential equations. The applications part of the review is focused on the simple equations used, with e...
Traditional deep neural networks (DNNs) often suffer from a time-consuming training process, which is restricted by accumulation of excessive network layers and a large amount of parameters. More neural units are required to be stacked to achieve des...
Spacecraft and satellites are equipped with cryogenic storage systems to maintain instruments and engines at optimal operating temperatures. However, in cryogenic storage tanks, the steep temperature gradient along the pipeline (arising from sections...
In this work, we explore a class of spherically symmetric black hole (BH) solutions within the framework of modified gravity, focusing on a non-ghost-free
Discrete time-varying matrix problems are prevalent in scientific and engineering fields, and their efficient solution remains a key research objective. Existing direct discrete recurrent neural network models exhibit limitations in noise resistance...
In this paper, control parameters are incorporated into the absolute discrete memristor (A-DM) map proposed by Bao, and its dynamic characteristics are analyzed. Subsequently, the A-DM is introduced into the traditional sine map via parallel coupling...
Infrastructure development in permafrost regions continues to face growing challenges from frost heaves and thaw settlement. The traditional frost heave theory considers that soil freezing is caused by the migration of liquid water in the soil; howev...
This study examines the issue of wind power smoothing in renewable-energy-grid integration scenarios. Under the “dual-carbon” policy initiative, large-scale renewable energy integration (particularly wind power) has become a global focus....
In this paper, we present a Mirsky-type unitarily invariant norm inequality for dual quaternion matrices, which can be regarded as a singular value perturbation theorem for dual quaternion matrices. By using this unitarily invariant norm inequality,...
Products with complex structures are structurally intricate and involve multiple professional fields and engineering construction elements, making it difficult for a single contractor to independently develop and manufacture such complex structural p...
Accurate prediction of geological hazard susceptibility forms the foundation of effective risk management, yet small-sample constraints often limit model generalization. In order to address this issue, this study applied an ensemble method based on p...
This study introduces a memristive Hopfield neural network (M-HNN) model to investigate electromagnetic radiation impacts on neural dynamics in complex electromagnetic environments. The proposed framework integrates a magnetic flux-controlled memrist...
Video stabilization is a critical technology for enhancing visual content quality in dynamic shooting scenarios, especially with the widespread adoption of mobile photography devices and Unmanned Aerial Vehicle (UAV) platforms. While traditional digi...
In recent years, fog-based vehicular ad hoc networks (VANETs) have become a hot research topic. Due to the inherent insecurity of open wireless channels between vehicles and fog nodes, establishing session keys through authenticated key agreement (AK...
Olfactory information, crucial for human perception, is often underrepresented compared to visual and textual data. This work explores methods for understanding smell descriptions within a multimodal context, where scent information is conveyed indir...
Symmetry and asymmetry significantly influence running biomechanics, performance, and injury risk. Given the practical, ethical, and methodological constraints inherent in human-subject studies, computational modeling emerges as a valuable alternativ...
The main goal of this study is to create a computational solver that analyzes the effects of magnetohydrodynamics (MHD) on heat radiation in Cu–water-based Prandtl nanofluid flow using artificial neural networks. Copper nanoparticles are utiliz...
The study of symmetry principles has consistently provided excellent guidance in the search to understand the fundamental laws of nature [...]
The Angular Bisector Insertion Constructive Heuristic Algorithm (ABIA), though effective for small-scale TSPs, suffers from reduced solution quality and high computational complexity in larger instances due to the degradation of its geometric propert...
Accurately predicting stock prices is crucial for investment and risk management, but the non-stationarity of the financial market and the complex correlations among stocks pose challenges to traditional models (ARIMA, LSTM, XGBoost), resulting in di...
This paper introduces a novel strategy for the diagnosis and fault-tolerant control (FTC) of inter-turn short-circuit (ITSC) faults in the stator windings of Doubly Fed Induction Generator (DFIG)-based wind turbines. ITSC faults are among the most co...
Time-variant nonlinear problems have always been a kind of complex research object in the field of control. The accuracy and efficiency of settling time-variant nonlinear inequality-equation (NIE) systems are often affected by the nonlinearity degree...
The aircraft power supply system plays a crucial role in maintaining the stability and safety of airborne avionics. With the evolution toward more electric and all-electric aircraft, its architecture increasingly adopts symmetrical configurations, su...
We present a novel derivation of the spacetime metric generated by matter, without invoking Einstein’s field equations. For static sources, the metric arises from a relativistic formulation of D’Alembert’s principle, where the inert...
To address the challenge of low trajectory tracking accuracy for underactuated unmanned underwater vehicles (UUVs) under external disturbances, this study proposes a method integrating backstepping control with improved active disturbance rejection c...
Predicting short-term buy and sell signals in financial markets remains a significant challenge for algorithmic trading. This difficulty stems from the data’s inherent volatility and noise, which often leads to spurious signals and poor trading...
Adaptive Linear Neuron (ADALINE) is a well-known neural network method that has been utilized for generating a reference current intended to regulate the operation of shunt-typed active harmonic filters (SAHFs). These filters are essential for improv...
The short-time behavior of the survival probability of a system governed by a time-dependent non-Hermitian Hamiltonian is derived using to the second-order perturbative approach. The resulting expression allows for the analysis of some situations whi...
Reliable short-term electricity usage prediction is essential for preserving the stability of topologically symmetric power networks and their dynamic supply–demand equilibrium. To tackle this challenge, this paper proposes a novel approach der...
Complex engineering systems—such as ultra-long horizontal wells in energy exploitation and distributed rail transit infrastructure—operate under harsh physical and environmental conditions, where accurate physical modeling and real-time p...
Symmetry and asymmetry are fascinating topics in chemistry [...]
When periodic and other piecewise linear functions are introduced in a chaotic system with two-dimensional offset boosting for extra feedback, more patterns of diagonal distribution from coexisting attractors can be organized. In this study, the peri...
Traditional educational assessment systems suffer from inefficient question selection strategies that fail to optimally probe student knowledge while requiring extensive testing time. We present a novel hierarchical probabilistic neural framework tha...
This work investigates the intricate dynamics of the Carreau hybrid nanofluid’s inclined magnetohydrodynamic (MHD) flow, exploring both active and passive control modes. The study incorporates critical factors, including Arrhenius activation en...
In recent years, the generation of circularly polarized attosecond pulses has garnered significant attention due to their potential applications in ultrafast spectroscopy and, notably, in chiral-sensitive molecular detection. The traditional methods...
Understanding asymmetric interactions among heterogeneous traffic participants is essential for managing congestion and enhancing safety at urban signalized intersections. This study proposes a cellular automaton modeling framework that captures the...
This study introduces an inverted version of the three-parameter Hjorth lifespan model, characterized by one scale parameter and two shape parameters, referred to as the inverted Hjorth (IH) distribution. This asymmetric distribution can fit various...
The imbalanced classification problem is a key research in machine learning as the relevant algorithms tend to focus on the features and patterns of the majority class instead of insufficient learning of the minority class, resulting in unsatisfactor...
Code generation is a critical task in software engineering, enabling the automation of transforming natural language descriptions into executable code. Recent advancements in large language models (LLMs) have demonstrated their potential to significa...
We review our recent findings on the structure and properties of exotic heavy hadrons, focusing on two main topics. First, we examine the role of correlations driven by the short-range Coulomb-like color interaction in hidden heavy-flavor pentaquarks...
This Special Issue of Symmetry is devoted to recent advances in the analysis and applications of fluid mechanics based on Symmetry/Asymmetry [...]
Accurate prediction of effluent quality indicators is essential for ensuring stable operation and regulatory compliance in wastewater treatment plants. However, the inherent spatial distribution and temporal fluctuations of wastewater processes prese...
With the deep integration of Internet of Vehicles (IoV) and edge computing technologies, the spatiotemporal dynamics, burstiness, and load fluctuations of user requests pose severe challenges to microservices auto-scaling. Existing static or periodic...
The recommendation system based on graphs aims to infer the symmetrical relationship between unconnected users and items nodes. Graph convolutional neural networks (GCNs) are powerful deep learning models widely used in recommender systems, showcasin...
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