Search Results (1,896)

This study has established and resolved a new mathematical model of a homogeneous, generalized, magnetothermoelastic half-space with a thermally loaded bounding surface, subjected to ramp-type heating and supported by a solid foundation where these types of mathematical models have been widely used in many sciences, such as geophysics and aerospace. The governing equations are formulated according to the Green–Lindsay theory of generalized thermoelasticity. This work’s uniqueness lies in the examination of Maxwell’s time-fractional equations via the definition of Caputo’s fractional derivative. The Laplace transform method has been used to obtain the solutions promptly. Inversions of the Laplace transform have been computed via Tzou’s iterative approach. The numerical findings are shown in graphs representing the distributions of the temperature increment, stress, strain, displacement, induced electric field, and induced magnetic field. The time-fractional parameter derived from Maxwell’s equations significantly influences all examined functions; however, it does not impact the temperature increase. The time-fractional parameter of Maxwell’s equations functions as a resistor to material deformation, particle motion, and the resulting magnetic field strength. Conversely, it acts as a catalyst for the stress and electric field intensity inside the material. The strength of the main magnetic field considerably influences the mechanical and electromagnetic functions; however, it has a lesser effect on the thermal function. Full article

Road traffic regulations usually require that a vehicle can only move one lane during one lane change and must turn on the turn signal before changing lanes. Under such constraints, if automated vehicles can plan multiple lane-change maneuvers at one time, then not only adjacent lanes but also farther lanes can be selected as target lanes when making decisions. This would help improve the driving performance in multi-lane scenarios. Many current lane-selection or lane-change methods focus on the surrounding region of the ego vehicle, usually only considering adjacent lanes as potential target lanes. This paper proposes a new tactical functional model that attempts to perform lane-level driving task planning and decision-making over a road area far beyond the surrounding region of the ego vehicle. We refer to this road area as the “outlook region”. In this functional model, the decision-making of lane-level driving tasks will take the overall performance within the outlook region as the goal, rather than pursuing the optimal single lane-change maneuver. The proposed method is implemented using a directed graph-based approach and simulation tests are conducted. The results show that the proposed method helps improve the driving performance of automated vehicles in multi-lane scenarios. Full article

Objectives: This study aimed to investigate changes in brain functional connectivity (FC) and topological structure in girls with idiopathic central precocious puberty (ICPP) using complex network theory analysis. Methods: Resting-state fMRI data from 53 ICPP girls (ages 6–8) and 51 controls were analysed. Graph theory was used to construct whole-brain functional networks, identify topological differences, and assess the relationship between sex hormone levels and network properties in regions with group differences. Results: RS-FC analysis revealed reduced connectivity in cognitive and emotional regulation regions in the ICPP group (p < 0.05), but enhanced connectivity in emotional perception and self-regulation areas, such as the amygdala and insula (p < 0.05), suggesting a compensatory mechanism. Graph theory showed that ICPP girls’ brain networks maintained small-world properties (γ > 1, λ ≈ 1, σ > 1). Local topological changes included decreased clustering and node efficiency in cognitive and emotional regulation regions, like the superior frontal gyrus and praecuneus (p < 0.05), while emotional regulation regions (amygdala, insula) showed increased clustering and node efficiency (p < 0.05), indicating compensation. Conclusions: This study highlights compensatory mechanisms in emotional regulation that may offset impairments in cognitive regions, offering new insights into ICPP’s neural mechanisms. Full article

In this paper, we investigate closed-form meromorphic solutions of the fifth-order Sawada-Kotera (fSK) equation and (3+1)-dimensional generalized shallow water (gSW) equation. The study of high-order and high-dimensional differential equations is pivotal for modeling complex nonlinear phenomena in physics and engineering, where higher-order dispersion, dissipation, and multidimensional dynamics govern system behavior. Constructing explicit solutions is of great significance for the study of these equations. The elliptic, hyperbolic, rational, and exponential function solutions for these high-order and high-dimensional differential equations are achieved by proposing the extended complex method. The planar dynamics behavior of the (3+1)-dimensional gSW equation and its phase portraits are analyzed. Using computational simulation, the chaos behaviors of the high-dimensional differential equation under noise perturbations are examined. The dynamic structures of some obtained solutions are revealed via some 2D and 3D graphs. The results show that the extended complex method is an efficient and straightforward approach to solving diverse differential equations in mathematical physics. Full article

The objective of this paper is to determine how the Bucharest metro network has developed from a topological and functional perspective. The research methodology consisted of conducting a topological analysis of the graph representing the metro network, along with a functional analysis. The topological analysis was carried out at two different moments in time and aimed to determine the connectivity indices of the graph associated with the network. The results showed a decrease in connectivity indices, indicating that the network expanded by extending its structure rather than increasing the number of connections between nodes. The functional analysis consisted in determining nodal accessibility using two models: the generalized nodal accessibility model and the Shimbel matrix and vector model. The results of this analysis led to the establishment of a hierarchy of the network’s nodes. The functional analysis also included the evaluation of accessibility for the zones into which the city was divided. Accessibility was determined using an original model based on the number of metro stations (poles) that can be reached within a certain time interval. The functional analyses, as conducted, aimed to assess the evolution of various network parameters and of accessibility. The accessibility of the metro network was correlated with the population density in the analyzed zones, showing that in many cases, the development of the network did not align with the density of the served areas, which may lead to inefficiencies in metro transportation. The discussions and conclusions focused on the research results and provided directions for future development of the network, aiming to increase the use of metro transportation. Full article

Modern computational models tend to become more and more complex, especially in fields like computational biology, physical modeling, social simulation, and others. With the increasing complexity of simulations, modern computational architectures demand efficient parallel execution strategies. This paper proposes a novel approach leveraging the reactive stream paradigm as a general-purpose synchronization protocol for parallel simulation. We introduce a method to construct simulation graphs from predefined transition functions, ensuring modularity and reusability. Additionally, we outline strategies for graph optimization and interactive simulation through push and pull patterns. The resulting computational graph, implemented using reactive streams, offers a scalable framework for parallel computation. Through theoretical analysis and practical implementation, we demonstrate the feasibility of this approach, highlighting its advantages over traditional parallel simulation methods. Finally, we discuss future challenges, including automatic graph construction, fault tolerance, and optimization strategies, as key areas for further research. Full article

Currently, protein–protein interaction (PPI) networks have become an essential data source for protein function prediction. However, methods utilizing graph neural networks (GNNs) face significant challenges in modeling PPI networks. A primary issue is over-smoothing, which occurs when multiple GNN layers are stacked to capture global information. This architectural limitation inherently impairs the integration of local and global information within PPI networks, thereby limiting the accuracy of protein function prediction. To effectively utilize information within PPI networks, we propose GTPLM-GO, a protein function prediction method based on a dual-branch Graph Transformer and protein language model. The dual-branch Graph Transformer achieves the collaborative modeling of local and global information in PPI networks through two branches: a graph neural network and a linear attention-based Transformer encoder. GTPLM-GO integrates local–global PPI information with the functional semantic encoding constructed by the protein language model, overcoming the issue of inadequate information extraction in existing methods. Experimental results demonstrate that GTPLM-GO outperforms advanced network-based and sequence-based methods on PPI network datasets of varying scales. Full article

In the advancement of wireless communication, multiple-input, multiple-output (MIMO) detection has emerged as a promising technique to meet the high throughput requirements of 6G networks. Traditionally, MIMO detection relies on conventional algorithms, such as zero forcing and minimum mean square error, to mitigate interference and enhance the desired signal. Mathematically, these algorithms operate as linear transformations or functions of received signals. To further enhance MIMO detection performance, researchers have explored the use of nonlinear transformations and functions by leveraging deep learning structures and models. In this paper, we propose a novel model that integrates the Viterbi algorithm with a graph neural network (GNN) to improve signal detection in MIMO systems. Our approach begins by detecting the received signal using the VA, whose output serves as the initial input for the GNN model. Within the GNN framework, the initial signal and the received signal are represented as nodes, while the MIMO channel structure defines the edges. Through an iterative message-passing mechanism, the GNN progressively refines the initial signal, enhancing its accuracy to better approximate the originally transmitted signal. Experimental results demonstrate that the proposed model outperforms conventional and existing approaches, leading to superior detection performance. Full article

In 5G and beyond 5G networks, function placement is a crucial strategy for enhancing the flexibility and efficiency of the Radio Access Network (RAN). However, demonstrating optimal function splitting and placement to meet diverse user demands remains a significant challenge. The function placement problem is known to be NP-hard, and previous studies have attempted to address it using Deep Reinforcement Learning (DRL) approaches. Nevertheless, many existing methods fail to capture the network state in RANs with specific topologies, leading to suboptimal decision-making and resource allocation. In this paper, we propose a method referred to as GDRL, which is a deep reinforcement learning approach that utilizes graph neural networks to address the functional placement problem. To ensure policy stability, we design a policy gradient algorithm called Graph Proximal Policy Optimization (GPPO), which integrates GNNs into both the actor and critic networks. By incorporating both node and edge features, the GDRL enhances feature extraction from the RAN’s nodes and links, providing richer observational data for decision-making and evaluation. This, in turn, enables more accurate and effective decision outcomes. In addition, we formulate the problem as a mixed-integer nonlinear programming model aimed at minimizing the number of active computational nodes while maximizing the centralization level of the virtualized RAN (vRAN). We evaluate the GDRL across different RAN scenarios with varying node configurations. The results demonstrate that our approach achieves superior network centralization and outperforms several existing methods in overall performance. Full article

Reliable fault diagnosis in railway operational equipment is critical to ensuring system safety, operational efficiency, and predictive maintenance. Existing methods struggle to capture the intricate interdependencies among fault causes, failure modes, and corrective actions, limiting their ability to model fault propagation effectively. To address this, we propose TH-RotatE, a novel knowledge graph (KG) embedding framework that integrates TransH’s hierarchical modeling with RotatE’s complex space transformations, while incorporating a hybrid scoring function and self-adversarial negative sampling to enhance embedding quality and fault relationship differentiation. This approach effectively captures hierarchical dependencies, cyclic patterns, and asymmetric transitions inherent in railway faults, enabling a more expressive representation of fault propagation. Furthermore, we construct the Chinese railway operational equipment fault knowledge graph (CROEFKG), a structured multi-relational repository encoding fault descriptions, causal chains, and mitigation strategies. Extensive experiments on real-world railway fault data demonstrate that TH-RotatE outperforms both traditional and advanced KG embedding models, achieving superior fault diagnosis accuracy and link prediction effectiveness. In practical applications, TH-RotatE enables timely fault diagnosis and detection of cascading failures, providing interpretable fault propagation pathways through the CROEFKG’s structured representation. These capabilities offer a scalable, knowledge-driven solution for railway systems, improving diagnostic accuracy while reducing safety risks and unplanned downtime. This work advances domain-specific KG embeddings, bridging the gap between theoretical innovation and industrial reliability. Full article

(1) Background: Brain–computer interface (BCI) technology represents a cutting-edge field that integrates brain intelligence with machine intelligence. Unlike BCIs that rely on external stimuli, motor imagery-based BCIs (MI-BCIs) generate usable brain signals based on an individual’s imagination of specific motor actions. Due to the highly individualized nature of these signals, identifying individuals who are better suited for MI-BCI applications and improving its efficiency is critical. (2) Methods: This study collected four motor imagery tasks (left hand, right hand, foot, and tongue) from 50 healthy subjects and evaluated MI-BCI adaptability through classification accuracy. Functional networks were constructed using the weighted phase lag index (WPLI), and relevant graph theory parameters were calculated to explore the relationship between motor imagery adaptability and functional networks. (3) Results: Research has demonstrated a strong correlation between the network characteristics of tongue imagination and MI-BCI adaptability. Specifically, the nodal degree and characteristic path length in the right hemisphere were found to be significantly correlated with classification accuracy (p < 0.05). (4) Conclusions: The findings of this study offer new insights into the functional network mechanisms of motor imagery, suggesting that tongue imagination holds potential as a predictor of MI-BCI adaptability. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article

Anomalies refer to data inconsistent with the overall trend of the dataset and may indicate an error or an unusual event. Time series prediction can detect anomalies that happen unexpectedly in critical situations during the usage of a system or a network. Detecting or predicting anomalies in the traditional way is time-consuming and error-prone. Accordingly, the automatic recognition of anomalies is applicable to reduce the cost of defects and will pave the way for companies to optimize their performance. This unsupervised technique is an efficient way of detecting abnormal samples during the fluctuations of time series. In this paper, an unsupervised deep network is proposed to predict temporal information. The correlations between the neighboring samples are acquired to construct a graph of neighboring fluctuations. The extricated features related to the temporal distribution of the time samples in the constructed graph representation are used to impose the Chebyshev graph convolution layers. The output is used to train an adversarial network for anomaly detection. A modification is performed for the generative adversarial network’s cost function to perfectly match our purpose. Thus, the proposed method is based on combining generative adversarial networks (GANs) and a Chebyshev graph, which has shown good results in various domains. Accordingly, the performance of the proposed fusion approach of a Chebyshev graph-based modified adversarial network (Cheb-MA) is evaluated on the Numenta dataset. The proposed model was evaluated based on various evaluation indices, including the average F1-score, and was able to reach a value of 82.09%, which is very promising compared to recent research. Full article

This study proposes a design method for the evaluation and redesign of product–service systems (PSSs) from the perspective of social sustainability, one that applies Max-Neef’s framework of fundamental human needs. The proposed method systematically connects PSS functions and requirements—identified through service blueprints and value graphs—to “satisfiers” and “barriers” extracted via needs-based workshops. This connection enables the identification of functions that either contribute to or hinder the fulfillment of fundamental human needs and guide the generation of redesign proposals aimed at sufficiency-oriented outcomes. A case study involving a smart-cart system in Osaka, Japan, was conducted to demonstrate the applicability of the method. Through an online workshop, satisfiers and barriers related to both physical and online shopping experiences were identified. The analysis revealed that existing functions such as promotional information and automated checkout processes negatively impacted needs such as understanding and affection due to information overload and reduced human interaction. In response, redesign concepts were developed, including filtering options for information, product background storytelling, and optional slower checkout lanes with human assistants. The redesigned functions contribute to the fulfillment of fundamental human needs, indicating that the proposed method can enhance social sustainability in PSS design. This study offers a novel framework that extends beyond traditional customer requirement-based approaches by explicitly incorporating human needs into function-level redesign. Full article

This study investigated the characteristics and functionalities of China’s High-Speed Railway (HSR) network based on Complex Network Theory (CNT) and Graph Convolutional Networks (GCN). First, complex network analysis was applied to provide insights into the network’s fundamental characteristics, such as small-world properties, efficiency, and robustness. Then, this research developed three novel GCN models to identify key nodes, detect community structures, and predict new links. Findings from the complex network analysis revealed that China’s HSR network exhibits a typical small-world property, with a degree distribution that follows a log-normal pattern rather than a power law. The global efficiency indicator suggested that stations are typically connected through direct routes, while the local efficiency indicator showed that the network performs effectively within local areas. The robustness study indicated that the network can quickly lose connectivity if key nodes fail, though it showed an ability initially to self-regulate and has partially restored its structure after disruption. The GCN model for key node identification revealed that the key nodes in the network were predominantly located in economically significant and densely populated cities, positively contributing to the network’s overall efficiency and robustness. The community structures identified by the integrated GCN model highlight the economic and social connections between official urban clusters and the communities. Results from the link prediction model suggest the necessity of improving the long-distance connectivity across regions. Future work will explore the network’s socio-economic dynamics and refine and generalise the GCN models. Full article