Search Results (152)

The static topology of surface characteristics and active sites in catalysis overlooks a crucial element: the dynamic processes of optimal pattern formation over time and the creation of intermediate structures that enhance reactions. Nature’s principle of coupling reaction and motion in catalytic processes by enzymes or higher organisms offers a new perspective. This work explores a novel theoretical approach by adding the time dimension to optimise topological variations using the Maximum Entropy Production (MEP) assumption. This approach recognises that the catalyst surface is not an unchanging energy landscape but can change dynamically. The time-dependent transport problem of molecules is here interpreted by a non-equilibrium model used for modelling and predicting dynamic pattern formation in excitable media, a class of active matter requiring an activation threshold. We present a nonlocal reaction–cross-diffusion (RXD) formulation of catalytic reactions that can capture the catalyst’s interaction with the target molecule in space and time. The approach provides a theoretical basis for future deep learning models and multiphysics upscaling of catalysts and their support structures across multiphysics fields. The particular advantage of the RXD approach is that it allows each scale to investigate dynamic pattern-forming processes using linear and nonlinear stability analysis, thus establishing a rule base for developing new catalysts. Full article

Change points caused by extreme events in global economic markets have been widely studied in the literature. However, existing techniques to identify change points rely on subjective judgments and lack robust methodologies. The objective of this paper is to generalize a novel approach that leverages topological data analysis (TDA) to extract topological features from time series data using persistent homology. In this approach, we use Taken’s embedding and sliding window techniques to transform the initial time series data into a high-dimensional topological space. Then, in this topological space, persistent homology is used to extract topological features which can give important information related to change points. As a case study, we analyzed 26 stocks over the last 12 years by using this method and found that there were two financial market volatility indicators derived from our method, denoted as $L_1$ and $L_2$. They serve as effective indicators of long-term and short-term financial market fluctuations, respectively. Moreover, significant differences are observed across markets in different regions and sectors by using these indicators. By setting a significance threshold of 98 % for the two indicators, we found that the detected change points correspond exactly to four major financial extreme events in the past twelve years: the intensification of the European debt crisis in 2011, Brexit in 2016, the outbreak of the COVID-19 pandemic in 2020, and the energy crisis triggered by the Russia–Ukraine war in 2022. Furthermore, benchmark comparisons with established univariate and multivariate CPD methods confirm that the TDA-based indicators consistently achieve superior F1 scores across different tolerance windows, particularly in capturing widely recognized consensus events. Full article

When a city decides to undertake a certain urban project, is it modifying just the physical environment or the social fabric that dwells within? This work investigates the relationship between the geometric configuration of urban space (geometry–city) and the topology of the networks of encounters of its inhabitants (network–city) that form through daily interactions. The research departs from the hypothesis that changes in geometry–city would not significantly alter the topology of the network–city, testing this proposition conceptually through abstract computational simulations developed specifically for this study. In this simulator, abstract maps with buildings distributed over different primary geometries are generated and have activities (use: home or work) and a population assigned. Encounters of the “inhabitants” are registered while daily commute routines, enough to achieve differentiation and stability, are run. The initial results revealed that the geometry description was not enough, and definitions regarding activity attribution were also necessary. Thus, we could not confirm nor reject the original hypothesis exactly, but it had to be complemented, including the idea of an activity–city dimension. We found that despite the geometry–city per se not determining the structure of the network–city, the spatial (geometric) distribution of activities directly impacts the resulting topology. Urban geometry influences networks–city only insofar as it conforms to activity–city, defining areas for activities or restricting routing between them. But it is the geometry of localization of the activities that has a direct impact on the topology of the network–city. This conceptual discovery can have significant implications for urban planning if corroborated in real-world situations. It could suggest that land use policies may be more effective for intervening in network-based characteristics, like social cohesion and resilience, than purely morphological interventions. Full article

Power converters play an important role in incorporating renewable energy sources into power systems. Among different converter designs, Buck and Boost converters are popular, as they use fewer components and deliver cost savings and high efficiency. However, Boost converters are known as non–minimum phase systems, imposing harder constraints for designing a robust converter. Developing an efficient controller for these topologies can be difficult since they exhibit nonlinearity and distortion in high frequency modes. The Lyapunov-based Adaptive Backstepping Control (ABSC) technology is used to regulate suitable outputs for these structures. This approach is an updated version of the technique that uses the stability Lyapunov function to produce increased stability and resistance to fluctuations in real-world circumstances. However, in real-time situations, disturbances with larger ranges such as supply voltage changes, parameter variations, and noise may have a negative impact on the operation of this strategy. To increase the controller’s flexibility under more difficult working settings, the most appropriate first gains must be established. To solve these concerns, the ABSC’s performance is optimized using the Reinforcement Learning (RL) adaptive technique. RL has several advantages, including lower susceptibility to error, more trustworthy findings obtained from data gathering from the environment, perfect model behavior within a certain context, and better frequency matching in real-time applications. Random exploration, on the other hand, can have disastrous effects and produce unexpected results in real-world situations. As a result, we choose the Deep Deterministic Policy Gradient (DDPG) approach, which uses a deterministic action function rather than a stochastic one. Its key advantages include effective handling of continuous action spaces, improved sample efficiency through off-policy learning, and faster convergence via its actor–critic architecture that balances value estimation and policy optimization. Furthermore, this technique uses the Grey Wolf Optimization (GWO) algorithm to improve the initial set of gains, resulting in more reliable outcomes and quicker dynamics. The GWO technique is notable for its disciplined and nature-inspired approach, which leads to faster decision-making and greater accuracy than other optimization methods. This method considers the system as a black box without its exact mathematical modeling, leading to lower complexity and computational burden. The effectiveness of this strategy is tested in both modeling and experimental scenarios utilizing the Hardware-In-Loop (HIL) framework, with considerable results and decreased error sensitivity. Full article

The decline and revitalization of vitality in historic districts of small- and medium-sized cities undergoing rapid urbanization is a frontier issue in global heritage conservation and urban regeneration. Using the East Street Historic District in Mengzi, Yunnan, as a case study, this study proposes a “space–function–time” coupling framework. Topological accessibility is quantified through space syntax metrics—Integration Value (2021) and Integration Value (2025), as well as Choice Value (2021) and Choice Value (2025)—while functional aggregation is represented by POI kernel density analysis. A “Deviation Degree–Change in Deviation Degree” model is developed to track the dynamic evolution before and after the implementation of the conservation plan (2021–2025). The findings indicate that (1) the linear correlation between Integration Value and POI density decreases from a moderate level (r = 0.42) in 2021 to a weak correlation (r = 0.32) in 2025, revealing that the spatial–functional coordination mechanism in small- and medium-sized city historic districts is considerably more fragile than in large cities; (2) Identifying streets with abnormal deviations: The primary street, Renmin Middle Road, exhibits a deviation degree as high as 4.160 due to excessive commercial aggregation, resulting in a “high accessibility–high load” imbalance. The secondary street, Dashu Street, although demonstrating a relatively high Integration Value (0.663), shows a “high accessibility–low vitality” condition due to insufficient functional facilities; (3) the Deviation Degree–Change in Deviation Degree model accurately identifies High Deviation Streets, Medium Deviation Streets, and Low Deviation Streets, and provides quantitative thresholds for planning feedback. This study introduces the Deviation Degree–Change in Deviation Degree model for the first time into the evaluation of historic district renewal in small- and medium-sized cities, establishing a closed-loop “diagnosis–intervention–reassessment” tool. The proposed framework offers both a methodological and operational paradigm for precision-oriented urban regeneration in historic districts. Full article

A four-level atomic medium is used to manipulate the diabolic points of the Hermitian Hamiltonian using driving fields of structured light. The diabolic points of the fourth, third, and second orders are observed by the real and imaginary parts of the eigenvalues of the Hermitian Hamiltonian. The diabolic points and degeneracy regions are studied with variation in Rabi frequencies, detuning, and topological charges. The structured light has a key impact on diabolic points. By changing the topological charges, the number of diabolic points and the degeneracy regions are changing. The imaginary part of eigenvalues shows fourth-order diabolic points. At topological charge $\mathsf{\ell }=even$, the real part of eigenvalues does not show higher-order diabolic points. The obtained results of the diabolic point are helpful in the fields of deformation space, entanglement physics, optomechanical systems, and crystal optics. Full article

Silicene is a two-dimensional silicon monolayer with a band gap caused by relatively strong spin–orbit coupling. This band gap can be steered using a vertical electric field. In turn, the change in this electric field value leads to a transition from a topological insulator to a bulk insulator regime. This study aims to develop a phase-space approach to detecting the topological phase transitions in silicene induced by the presence of parallel magnetic and electric fields with the aid of the concept of topological quantum number based on the Wigner–Rényi entropy. A reinterpreted definition of the Wigner distribution function is employed to determine this indicator. The topological phase transition in silicene as a function of the electric field in the presence of the magnetic field is confirmed through the use of the topological quantum number determined for the one-half, Shannon and collision entropies. Full article

The advanced determination of the type (foreshock–aftershock–swarm) of an ongoing seismic cluster is quite challenging; only retrospective solutions have thus far been proposed. In the period of January–March 2025, a seismic cluster, recorded between Santorini volcano and Amorgos Island, South Aegean Sea, caused considerable social concern. A rapid increase in both the seismicity rate and the earthquake magnitudes was noted until the mainshock of M_L = 5.3 on 10 February; afterwards, activity gradually diminished. Fault-plane solutions indicated SW-NE normal faulting. The epicenters moved with a mean velocity of ~0.72 km/day from SW to NE up to the mainshock area at a distance of ~25 km. Crucial questions publicly emerged during the cluster. Was it a foreshock–aftershock activity or a swarm of possibly volcanic origin? We performed real-time discrimination of the cluster type based on a daily re-evaluation of the space–time–magnitude changes and their significance relative to background seismicity using earthquake statistics and the topological metric betweenness centrality. Our findings were periodically documented during the ongoing cluster starting from the fourth cluster day (2 February 2025), at which point we determined that it was a foreshock and not a case of seismic swarm. The third day after the M_L = 5.3 mainshock, a typical aftershock decay was detected. The observed foreshock properties favored a cascade mechanism, likely facilitated by non-volcanic material softening and the likely subdiffusion processes in a dense fault network. This mechanism was possibly combined with an aseismic nucleation process if transient geodetic deformation was present. No significant aftershock expansion towards the NE was noted, possibly due to the presence of a geometrical fault barrier east of the Anydros Ridge. The 2025 activity offered an excellent opportunity to investigate deciphering the type of ongoing seismicity cluster for real-time discrimination between foreshocks, aftershocks, and swarms. Full article

We present a formal framework for modeling neural network dynamics using Category Theory, specifically through Markov categories. In this setting, neural states are represented as objects and state transitions as Markov kernels, i.e., morphisms in the category. This categorical perspective offers an algebraic alternative to traditional approaches based on stochastic differential equations, enabling a rigorous and structured approach to studying neural dynamics as a stochastic process with topological insights. By abstracting neural states as submeasurable spaces and transitions as kernels, our framework bridges biological complexity with formal mathematical structure, providing a foundation for analyzing emergent behavior. As part of this approach, we incorporate concepts from Interacting Particle Systems and employ mean-field approximations to construct Markov kernels, which are then used to simulate neural dynamics via the Ising model. Our simulations reveal a shift from unimodal to multimodal transition distributions near critical temperatures, reinforcing the connection between emergent behavior and abrupt changes in system dynamics. Full article

Low Earth orbit (LEO) satellite networks deliver superior real-time performance and responsiveness compared to conventional satellite networks, despite technical and economic challenges such as high deployment costs and operational complexity. Nevertheless, rapid topology changes and severe energy constraints of LEO satellites make real-time routing a persistent challenge. In this paper, we employ field-programmable gate arrays (FPGAs) to overcome the resource limitations of on-board computers (OBCs) and to manage energy consumption effectively using the Eclipse-Aware Routing (EAR) algorithm, and we implement the K-Shortest Paths (KSP) algorithm directly on the FPGA. Our method first generates multiple routes from the source to the destination using KSP, then selects the optimal path based on energy consumption rate, eclipse duration, and estimated transmission load as evaluated by EAR. In large-scale LEO networks, the computational burden of KSP grows substantially as connectivity data become more voluminous and complex. To enhance performance, we accelerate complex computations in the programmable logic (PL) via pipelining and design a collaborative architecture between the processing system (PS) and PL, achieving approximately a 3.83× speedup compared to a PS-only implementation. We validate the feasibility of the proposed approach by successfully performing remote routing-table updates on the SpaceWire-based SpaceWire Brick MK4 network system. Full article

Ultraviolet (UV) communication, characterized by non-line-of-sight (NLOS) scattering, holds substantial potential for enabling communication networking in unmanned aerial vehicle (UAV) formations within strong electromagnetic interference environments. This paper proposes a networking protocol for large-scale mobile ultraviolet communication networks (LSM-UVCN). In large-scale networks, the proposed protocol establishes multiple non-interfering transmission paths based on a connection matrix simultaneously, ensuring reliable space division multiplexing (SDM) and optimizing the utilization of network channel resources. To address frequent network topology changes in mobile scenarios, the protocol employs periodic maintenance of the connection matrix, significantly reducing the adverse impacts of node mobility on network performance. Simulation results demonstrate that the proposed protocol achieves superior performance in large-scale mobile UV communication networks. By dynamically adjusting the connection matrix update frequency, it adapts to varying node mobility intensities, effectively minimizing control overhead and data loss rates while enhancing network throughput. This work underscores the protocol’s adaptability to dynamic network environments, providing a robust solution for high-reliability communication requirements in complex electromagnetic scenarios, particularly for UAV swarm applications. The integration of SDM and adaptive matrix maintenance highlights its scalability and efficiency, positioning it as a viable technology for next-generation wireless communication systems in challenging operational conditions. Full article

Indoor location-based services and applications are heavily dependent on the currentness of indoor data. Therefore, it is crucial to update indoor spatial information promptly and efficiently to ensure its relevance and reliability. Maintaining the topological consistency of geometric objects presents a significant challenge in updating indoor data. Consequently, this paper introduces an incremental updating method for 3D indoor data that considers topological linkages. The first step involves categorizing different types of building component changes and their corresponding indoor space alterations, followed by a detailed analysis of the topological linkage types for indoor features. On the basis of these identified changes, a set of updating operators is developed to handle various types of indoor space alterations. The experimental results demonstrate that the proposed updating operations effectively maintain the topological relationships of solids and the topological adjacency relationships of adjacent solids. This method facilitates efficient querying of indoor spatial information and topological adjacencies, thereby providing a robust data foundation for indoor location-based services and applications. Full article

The rapid development of low-Earth-orbit (LEO) satellite constellations has not only provided global users with low-latency and unrestricted high-speed data services but also presented researchers with the challenge of understanding dynamic changes in global network behavior. Unlike geostationary satellites and terrestrial internet infrastructure, LEO satellites move at a relative velocity of 7.6 km/s, leading to frequent alterations in their connectivity status with ground stations. Given the complexity of the space environment, current research on LEO satellite internet primarily focuses on modeling and simulation. However, existing LEO satellite network simulators often overlook the global network characteristics of these systems. We present SatScope, a data-driven simulator for LEO satellite internet. SatScope consists of three main components, space segment modeling, ground segment modeling, and network simulation configuration, providing researchers with an interface to interact with these models. Utilizing both space and ground segment models, SatScope can configure various network topology models, routing algorithms, and load balancing schemes, thereby enabling the evaluation of optimization algorithms for LEO satellite communication systems. We also compare SatScope’s fidelity, lightweight design, scalability, and openness against other simulators. Based on our simulation results using SatScope, we propose two metrics—ground node IP coverage rate and the number of satellite service IPs—to assess the service performance of single-layer satellite networks. Our findings reveal that during each network handover, on average, 38.94% of nodes and 83.66% of links change. Full article

Low-Earth-Orbit (LEO) satellite networks offer a promising avenue for achieving global connectivity, despite certain technical and economic challenges such as high implementation costs and the complexity of network management. Nonetheless, real-time routing remains challenging because of rapid topology changes and strict energy constraints. This paper proposes a GPU-accelerated Eclipse-Aware Routing (EAR) method that simultaneously minimizes hop count and balances energy consumption for real-time routing on an onboard computer (OBC). The approach first employs a Breadth-First Search (BFS)–based K-Shortest Paths (KSP) algorithm to generate candidate routes and then evaluates battery usage to select the most efficient path. In large-scale networks, the computational load of the KSP search increases substantially. Therefore, CUDA-based parallel processing was integrated to enhance performance, resulting in a speedup of approximately 3.081 times over the conventional CPU-based method. The practical applicability of the proposed method is further validated by successfully updating routing tables in a SpaceWire network. Full article

Analyzing how urban ritual spaces transform into everyday living environments is crucial for understanding the spatial structure of contemporary historical districts, particularly in the context of ancestral temples. However, existing research often neglects the integration of both building-level and block-level perspectives when examining such spatial transitions. Grounded in urban morphological principles, we identify the fundamental spatial units of ancestral temples and their surrounding blocks across the early 20th century and the post-1970s era. Using the topological characteristics of an access structure, we construct corresponding network graphs. We then employ embeddedness and conductance metrics to quantify each temple’s changing position within the broader block structure. Moreover, we apply community detection to uncover the structural evolution of clusters in blocks over time. Our findings reveal that, as institutional and cultural factors drive spatial change, ancestral temples exhibit decreased internal cohesion and increased external connectivity. At the block scale, changes in community structure demonstrate how neighborhood clusters transition from a limited number of building-based clusters to everyday living-oriented spatial clusters. These insights highlight the interplay between everyday life demands, land–housing policies, and inherited cultural norms, offering a comprehensive perspective on the secularization of sacred architecture. The framework proposed here not only deepens our understanding of the spatial transformation process but also provides valuable insights for sustainable urban renewal and heritage preservation. Full article